Single-voxel and multi-voxel spectroscopy yield comparable results in the normal juvenile canine brain when using 3 Tesla magnetic resonance imaging.

PubMed

Lee, Alison M; Beasley, Michaela J; Barrett, Emerald D; James, Judy R; Gambino, Jennifer M

2018-06-10

Conventional magnetic resonance imaging (MRI) characteristics of canine brain diseases are often nonspecific. Single- and multi-voxel spectroscopy techniques allow quantification of chemical biomarkers for tissues of interest and may help to improve diagnostic specificity. However, published information is currently lacking for the in vivo performance of these two techniques in dogs. The aim of this prospective, methods comparison study was to compare the performance of single- and multi-voxel spectroscopy in the brains of eight healthy, juvenile dogs using 3 Tesla MRI. Ipsilateral regions of single- and multi-voxel spectroscopy were performed in symmetric regions of interest of each brain in the parietal (n = 3), thalamic (n = 2), and piriform lobes (n = 3). In vivo single-voxel spectroscopy and multi-voxel spectroscopy metabolite ratios from the same size and multi-voxel spectroscopy ratios from different sized regions of interest were compared. No significant difference was seen between single-voxel spectroscopy and multi-voxel spectroscopy metabolite ratios for any lobe when regions of interest were similar in size and shape. Significant lobar single-voxel spectroscopy and multi-voxel spectroscopy differences were seen between the parietal lobe and thalamus (P = 0.047) for the choline to N-acetyl aspartase ratios when large multi-voxel spectroscopy regions of interest were compared to very small multi-voxel spectroscopy regions of interest within the same lobe; and for the N-acetyl aspartase to creatine ratios in all lobes when single-voxel spectroscopy was compared to combined (pooled) multi-voxel spectroscopy datasets. Findings from this preliminary study indicated that single- and multi-voxel spectroscopy techniques using 3T MRI yield comparable results for similar sized regions of interest in the normal canine brain. Findings also supported using the contralateral side as an internal control for dogs with brain lesions. © 2018 American College of Veterinary Radiology.

Multivariate detrending of fMRI signal drifts for real-time multiclass pattern classification.

PubMed

Lee, Dongha; Jang, Changwon; Park, Hae-Jeong

2015-03-01

Signal drift in functional magnetic resonance imaging (fMRI) is an unavoidable artifact that limits classification performance in multi-voxel pattern analysis of fMRI. As conventional methods to reduce signal drift, global demeaning or proportional scaling disregards regional variations of drift, whereas voxel-wise univariate detrending is too sensitive to noisy fluctuations. To overcome these drawbacks, we propose a multivariate real-time detrending method for multiclass classification that involves spatial demeaning at each scan and the recursive detrending of drifts in the classifier outputs driven by a multiclass linear support vector machine. Experiments using binary and multiclass data showed that the linear trend estimation of the classifier output drift for each class (a weighted sum of drifts in the class-specific voxels) was more robust against voxel-wise artifacts that lead to inconsistent spatial patterns and the effect of online processing than voxel-wise detrending. The classification performance of the proposed method was significantly better, especially for multiclass data, than that of voxel-wise linear detrending, global demeaning, and classifier output detrending without demeaning. We concluded that the multivariate approach using classifier output detrending of fMRI signals with spatial demeaning preserves spatial patterns, is less sensitive than conventional methods to sample size, and increases classification performance, which is a useful feature for real-time fMRI classification. Copyright © 2014 Elsevier Inc. All rights reserved.

First Steps in Using Multi-Voxel Pattern Analysis to Disentangle Neural Processes Underlying Generalization of Spider Fear

PubMed Central

Visser, Renée M.; Haver, Pia; Zwitser, Robert J.; Scholte, H. Steven; Kindt, Merel

2016-01-01

A core symptom of anxiety disorders is the tendency to interpret ambiguous information as threatening. Using electroencephalography and blood oxygenation level dependent magnetic resonance imaging (BOLD-MRI), several studies have begun to elucidate brain processes involved in fear-related perceptual biases, but thus far mainly found evidence for general hypervigilance in high fearful individuals. Recently, multi-voxel pattern analysis (MVPA) has become popular for decoding cognitive states from distributed patterns of neural activation. Here, we used this technique to assess whether biased fear generalization, characteristic of clinical fear, is already present during the initial perception and categorization of a stimulus, or emerges during the subsequent interpretation of a stimulus. Individuals with low spider fear (n = 20) and high spider fear (n = 18) underwent functional MRI scanning while viewing series of schematic flowers morphing to spiders. In line with previous studies, individuals with high fear of spiders were behaviorally more likely to classify ambiguous morphs as spiders than individuals with low fear of spiders. Univariate analyses of BOLD-MRI data revealed stronger activation toward spider pictures in high fearful individuals compared to low fearful individuals in numerous areas. Yet, neither average activation, nor support vector machine classification (i.e., a form of MVPA) matched the behavioral results – i.e., a biased response toward ambiguous stimuli – in any of the regions of interest. This may point to limitations of the current design, and to challenges associated with classifying emotional and neutral stimuli in groups that differ in their judgment of emotionality. Improvements for future research are suggested. PMID:27303278

Multi-voxel pattern classification differentiates personally experienced event memories from secondhand event knowledge.

PubMed

Chow, Tiffany E; Westphal, Andrew J; Rissman, Jesse

2018-04-11

Studies of autobiographical memory retrieval often use photographs to probe participants' memories for past events. Recent neuroimaging work has shown that viewing photographs depicting events from one's own life evokes a characteristic pattern of brain activity across a network of frontal, parietal, and medial temporal lobe regions that can be readily distinguished from brain activity associated with viewing photographs from someone else's life (Rissman, Chow, Reggente, and Wagner, 2016). However, it is unclear whether the neural signatures associated with remembering a personally experienced event are distinct from those associated with recognizing previously encountered photographs of an event. The present experiment used a novel functional magnetic resonance imaging (fMRI) paradigm to investigate putative differences in brain activity patterns associated with these distinct expressions of memory retrieval. Eighteen participants wore necklace-mounted digital cameras to capture events from their everyday lives over the course of three weeks. One week later, participants underwent fMRI scanning, where on each trial they viewed a sequence of photographs depicting either an event from their own life or from another participant's life and judged their memory for this event. Importantly, half of the trials featured photographic sequences that had been shown to participants during a laboratory session administered the previous day. Multi-voxel pattern analyses assessed the sensitivity of two brain networks of interest-as identified by a meta-analysis of prior autobiographical and laboratory-based memory retrieval studies-to the original source of the photographs (own life or other's life) and their experiential history as stimuli (previewed or non-previewed). The classification analyses revealed a striking dissociation: activity patterns within the autobiographical memory network were significantly more diagnostic than those within the laboratory-based network as to whether photographs depicted one's own personal experience (regardless of whether they had been previously seen), whereas activity patterns within the laboratory-based memory network were significantly more diagnostic than those within the autobiographical memory network as to whether photographs had been previewed (regardless of whether they were from the participant's own life). These results, also apparent in whole-brain searchlight classifications, provide evidence for dissociable patterns of activation across two putative memory networks as a function of whether real-world photographs trigger the retrieval of firsthand experiences or secondhand event knowledge. Copyright © 2018 Elsevier Inc. All rights reserved.

CA1 and CA3 differentially support spontaneous retrieval of episodic contexts within human hippocampal subfields.

PubMed

Dimsdale-Zucker, Halle R; Ritchey, Maureen; Ekstrom, Arne D; Yonelinas, Andrew P; Ranganath, Charan

2018-01-18

The hippocampus plays a critical role in spatial and episodic memory. Mechanistic models predict that hippocampal subfields have computational specializations that differentially support memory. However, there is little empirical evidence suggesting differences between the subfields, particularly in humans. To clarify how hippocampal subfields support human spatial and episodic memory, we developed a virtual reality paradigm where participants passively navigated through houses (spatial contexts) across a series of videos (episodic contexts). We then used multivariate analyses of high-resolution fMRI data to identify neural representations of contextual information during recollection. Multi-voxel pattern similarity analyses revealed that CA1 represented objects that shared an episodic context as more similar than those from different episodic contexts. CA23DG showed the opposite pattern, differentiating between objects encountered in the same episodic context. The complementary characteristics of these subfields explain how we can parse our experiences into cohesive episodes while retaining the specific details that support vivid recollection.

Biological Parametric Mapping: A Statistical Toolbox for Multi-Modality Brain Image Analysis

PubMed Central

Casanova, Ramon; Ryali, Srikanth; Baer, Aaron; Laurienti, Paul J.; Burdette, Jonathan H.; Hayasaka, Satoru; Flowers, Lynn; Wood, Frank; Maldjian, Joseph A.

2006-01-01

In recent years multiple brain MR imaging modalities have emerged; however, analysis methodologies have mainly remained modality specific. In addition, when comparing across imaging modalities, most researchers have been forced to rely on simple region-of-interest type analyses, which do not allow the voxel-by-voxel comparisons necessary to answer more sophisticated neuroscience questions. To overcome these limitations, we developed a toolbox for multimodal image analysis called biological parametric mapping (BPM), based on a voxel-wise use of the general linear model. The BPM toolbox incorporates information obtained from other modalities as regressors in a voxel-wise analysis, thereby permitting investigation of more sophisticated hypotheses. The BPM toolbox has been developed in MATLAB with a user friendly interface for performing analyses, including voxel-wise multimodal correlation, ANCOVA, and multiple regression. It has a high degree of integration with the SPM (statistical parametric mapping) software relying on it for visualization and statistical inference. Furthermore, statistical inference for a correlation field, rather than a widely-used T-field, has been implemented in the correlation analysis for more accurate results. An example with in-vivo data is presented demonstrating the potential of the BPM methodology as a tool for multimodal image analysis. PMID:17070709

Tracking children's mental states while solving algebra equations.

PubMed

Anderson, John R; Betts, Shawn; Ferris, Jennifer L; Fincham, Jon M

2012-11-01

Behavioral and function magnetic resonance imagery (fMRI) data were combined to infer the mental states of students as they interacted with an intelligent tutoring system. Sixteen children interacted with a computer tutor for solving linear equations over a six-day period (days 0-5), with days 1 and 5 occurring in an fMRI scanner. Hidden Markov model algorithms combined a model of student behavior with multi-voxel imaging pattern data to predict the mental states of students. We separately assessed the algorithms' ability to predict which step in a problem-solving sequence was performed and whether the step was performed correctly. For day 1, the data patterns of other students were used to predict the mental states of a target student. These predictions were improved on day 5 by adding information about the target student's behavioral and imaging data from day 1. Successful tracking of mental states depended on using the combination of a behavioral model and multi-voxel pattern analysis, illustrating the effectiveness of an integrated approach to tracking the cognition of individuals in real time as they perform complex tasks. Copyright © 2011 Wiley Periodicals, Inc.

Neural Categorization of Vibrotactile Frequency in Flutter and Vibration Stimulations: An fMRI Study.

PubMed

Kim, Junsuk; Chung, Yoon Gi; Chung, Soon-Cheol; Bulthoff, Heinrich H; Kim, Sung-Phil

2016-01-01

As the use of wearable haptic devices with vibrating alert features is commonplace, an understanding of the perceptual categorization of vibrotactile frequencies has become important. This understanding can be substantially enhanced by unveiling how neural activity represents vibrotactile frequency information. Using functional magnetic resonance imaging (fMRI), this study investigated categorical clustering patterns of the frequency-dependent neural activity evoked by vibrotactile stimuli with gradually changing frequencies from 20 to 200 Hz. First, a searchlight multi-voxel pattern analysis (MVPA) was used to find brain regions exhibiting neural activities associated with frequency information. We found that the contralateral postcentral gyrus (S1) and the supramarginal gyrus (SMG) carried frequency-dependent information. Next, we applied multidimensional scaling (MDS) to find low-dimensional neural representations of different frequencies obtained from the multi-voxel activity patterns within these regions. The clustering analysis on the MDS results showed that neural activity patterns of 20-100 Hz and 120-200 Hz were divided into two distinct groups. Interestingly, this neural grouping conformed to the perceptual frequency categories found in the previous behavioral studies. Our findings therefore suggest that neural activity patterns in the somatosensory cortical regions may provide a neural basis for the perceptual categorization of vibrotactile frequency.

Correlated displacement-T2 MRI by means of a Pulsed Field Gradient-Multi Spin Echo Method.

PubMed

Windt, Carel W; Vergeldt, Frank J; Van As, Henk

2007-04-01

A method for correlated displacement-T2 imaging is presented. A Pulsed Field Gradient-Multi Spin Echo (PFG-MSE) sequence is used to record T2 resolved propagators on a voxel-by-voxel basis, making it possible to perform single voxel correlated displacement-T2 analyses. In spatially heterogeneous media the method thus gives access to sub-voxel information about displacement and T2 relaxation. The sequence is demonstrated using a number of flow conducting model systems: a tube with flowing water of variable intrinsic T2's, mixing fluids of different T2's in an "X"-shaped connector, and an intact living plant. PFG-MSE can be applied to yield information about the relation between flow, pore size and exchange behavior, and can aid volume flow quantification by making it possible to correct for T2 relaxation during the displacement labeling period Delta in PFG displacement imaging methods. Correlated displacement-T2 imaging can be of special interest for a number of research subjects, such as the flow of liquids and mixtures of liquids or liquids and solids moving through microscopic conduits of different sizes (e.g., plants, porous media, bioreactors, biomats).

Distributed Neural Processing Predictors of Multi-dimensional Properties of Affect

PubMed Central

Bush, Keith A.; Inman, Cory S.; Hamann, Stephan; Kilts, Clinton D.; James, G. Andrew

2017-01-01

Recent evidence suggests that emotions have a distributed neural representation, which has significant implications for our understanding of the mechanisms underlying emotion regulation and dysregulation as well as the potential targets available for neuromodulation-based emotion therapeutics. This work adds to this evidence by testing the distribution of neural representations underlying the affective dimensions of valence and arousal using representational models that vary in both the degree and the nature of their distribution. We used multi-voxel pattern classification (MVPC) to identify whole-brain patterns of functional magnetic resonance imaging (fMRI)-derived neural activations that reliably predicted dimensional properties of affect (valence and arousal) for visual stimuli viewed by a normative sample (n = 32) of demographically diverse, healthy adults. Inter-subject leave-one-out cross-validation showed whole-brain MVPC significantly predicted (p < 0.001) binarized normative ratings of valence (positive vs. negative, 59% accuracy) and arousal (high vs. low, 56% accuracy). We also conducted group-level univariate general linear modeling (GLM) analyses to identify brain regions whose response significantly differed for the contrasts of positive versus negative valence or high versus low arousal. Multivoxel pattern classifiers using voxels drawn from all identified regions of interest (all-ROIs) exhibited mixed performance; arousal was predicted significantly better than chance but worse than the whole-brain classifier, whereas valence was not predicted significantly better than chance. Multivoxel classifiers derived using individual ROIs generally performed no better than chance. Although performance of the all-ROI classifier improved with larger ROIs (generated by relaxing the clustering threshold), performance was still poorer than the whole-brain classifier. These findings support a highly distributed model of neural processing for the affective dimensions of valence and arousal. Finally, joint error analyses of the MVPC hyperplanes encoding valence and arousal identified regions within the dimensional affect space where multivoxel classifiers exhibited the greatest difficulty encoding brain states – specifically, stimuli of moderate arousal and high or low valence. In conclusion, we highlight new directions for characterizing affective processing for mechanistic and therapeutic applications in affective neuroscience. PMID:28959198

Vitality Forms Processing in the Insula during Action Observation: A Multivoxel Pattern Analysis

PubMed Central

Di Cesare, Giuseppe; Valente, Giancarlo; Di Dio, Cinzia; Ruffaldi, Emanuele; Bergamasco, Massimo; Goebel, Rainer; Rizzolatti, Giacomo

2016-01-01

Observing the style of an action done by others allows the observer to understand the cognitive state of the agent. This information has been defined by Stern “vitality forms”. Previous experiments showed that the dorso-central insula is selectively active both during vitality form observation and execution. In the present study, we presented participants with videos showing hand actions performed with different velocities and asked them to judge either their vitality form (gentle, neutral, rude) or their velocity (slow, medium, fast). The aim of the present study was to assess, using multi-voxel pattern analysis, whether vitality forms and velocities of observed goal-directed actions are differentially processed in the insula, and more specifically whether action velocity is encoded per se or it is an element that triggers neural populations of the insula encoding the vitality form. The results showed that, consistently across subjects, in the dorso-central sector of the insula there were voxels selectively tuned to vitality forms, while voxel tuned to velocity were rare. These results indicate that the dorso-central insula, which previous data showed to be involved in the vitality form processing, contains voxels specific for the action style processing. PMID:27375461

Vitality Forms Processing in the Insula during Action Observation: A Multivoxel Pattern Analysis.

PubMed

Di Cesare, Giuseppe; Valente, Giancarlo; Di Dio, Cinzia; Ruffaldi, Emanuele; Bergamasco, Massimo; Goebel, Rainer; Rizzolatti, Giacomo

2016-01-01

Observing the style of an action done by others allows the observer to understand the cognitive state of the agent. This information has been defined by Stern "vitality forms". Previous experiments showed that the dorso-central insula is selectively active both during vitality form observation and execution. In the present study, we presented participants with videos showing hand actions performed with different velocities and asked them to judge either their vitality form (gentle, neutral, rude) or their velocity (slow, medium, fast). The aim of the present study was to assess, using multi-voxel pattern analysis, whether vitality forms and velocities of observed goal-directed actions are differentially processed in the insula, and more specifically whether action velocity is encoded per se or it is an element that triggers neural populations of the insula encoding the vitality form. The results showed that, consistently across subjects, in the dorso-central sector of the insula there were voxels selectively tuned to vitality forms, while voxel tuned to velocity were rare. These results indicate that the dorso-central insula, which previous data showed to be involved in the vitality form processing, contains voxels specific for the action style processing.

Discovering the Sequential Structure of Thought

ERIC Educational Resources Information Center

Anderson, John R.; Fincham, Jon M.

2014-01-01

Multi-voxel pattern recognition techniques combined with Hidden Markov models can be used to discover the mental states that people go through in performing a task. The combined method identifies both the mental states and how their durations vary with experimental conditions. We apply this method to a task where participants solve novel…

Identifying bilingual semantic neural representations across languages

PubMed Central

Buchweitz, Augusto; Shinkareva, Svetlana V.; Mason, Robert A.; Mitchell, Tom M.; Just, Marcel Adam

2015-01-01

The goal of the study was to identify the neural representation of a noun's meaning in one language based on the neural representation of that same noun in another language. Machine learning methods were used to train classifiers to identify which individual noun bilingual participants were thinking about in one language based solely on their brain activation in the other language. The study shows reliable (p < .05) pattern-based classification accuracies for the classification of brain activity for nouns across languages. It also shows that the stable voxels used to classify the brain activation were located in areas associated with encoding information about semantic dimensions of the words in the study. The identification of the semantic trace of individual nouns from the pattern of cortical activity demonstrates the existence of a multi-voxel pattern of activation across the cortex for a single noun common to both languages in bilinguals. PMID:21978845

Mechanisms Supporting Superior Source Memory for Familiar Items: A Multi-Voxel Pattern Analysis Study

ERIC Educational Resources Information Center

Poppenk, Jordan; Norman, Kenneth A.

2012-01-01

Recent cognitive research has revealed better source memory performance for familiar relative to novel stimuli. Here we consider two possible explanations for this finding. The source memory advantage for familiar stimuli could arise because stimulus novelty induces attention to stimulus features at the expense of contextual processing, resulting…

Differential Responses to a Visual Self-Motion Signal in Human Medial Cortical Regions Revealed by Wide-View Stimulation

PubMed Central

Wada, Atsushi; Sakano, Yuichi; Ando, Hiroshi

2016-01-01

Vision is important for estimating self-motion, which is thought to involve optic-flow processing. Here, we investigated the fMRI response profiles in visual area V6, the precuneus motion area (PcM), and the cingulate sulcus visual area (CSv)—three medial brain regions recently shown to be sensitive to optic-flow. We used wide-view stereoscopic stimulation to induce robust self-motion processing. Stimuli included static, randomly moving, and coherently moving dots (simulating forward self-motion). We varied the stimulus size and the presence of stereoscopic information. A combination of univariate and multi-voxel pattern analyses (MVPA) revealed that fMRI responses in the three regions differed from each other. The univariate analysis identified optic-flow selectivity and an effect of stimulus size in V6, PcM, and CSv, among which only CSv showed a significantly lower response to random motion stimuli compared with static conditions. Furthermore, MVPA revealed an optic-flow specific multi-voxel pattern in the PcM and CSv, where the discrimination of coherent motion from both random motion and static conditions showed above-chance prediction accuracy, but that of random motion from static conditions did not. Additionally, while area V6 successfully classified different stimulus sizes regardless of motion pattern, this classification was only partial in PcM and was absent in CSv. This may reflect the known retinotopic representation in V6 and the absence of such clear visuospatial representation in CSv. We also found significant correlations between the strength of subjective self-motion and univariate activation in all examined regions except for primary visual cortex (V1). This neuro-perceptual correlation was significantly higher for V6, PcM, and CSv when compared with V1, and higher for CSv when compared with the visual motion area hMT+. Our convergent results suggest the significant involvement of CSv in self-motion processing, which may give rise to its percept. PMID:26973588

DOE Office of Scientific and Technical Information (OSTI.GOV)

Engstroem, K; Casares-Magaz, O; Muren, L

Purpose: Multi-parametric MRI (mp-MRI) is being introduced in radiotherapy (RT) of prostate cancer, including for tumour delineation in focal boosting strategies. We recently developed an image-based tumour control probability model, based on cell density distributions derived from apparent diffusion coefficient (ADC) maps. Beyond tumour volume and cell densities, tumour hypoxia is also an important determinant of RT response. Since tissue perfusion from mp-MRI has been related to hypoxia we have explored the patterns of ADC and perfusion maps, and the relations between them, inside and outside prostate index lesions. Methods: ADC and perfusion maps from 20 prostate cancer patients weremore » used, with the prostate and index lesion delineated by a dedicated uro-radiologist. To reduce noise, the maps were averaged over a 3×3×3 voxel cube. Associations between different ADC and perfusion histogram parameters within the prostate, inside and outside the index lesion, were evaluated with the Pearson’s correlation coefficient. In the voxel-wise analysis, scatter plots of ADC vs perfusion were analysed for voxels in the prostate, inside and outside of the index lesion, again with the associations quantified with the Pearson’s correlation coefficient. Results: Overall ADC was lower inside the index lesion than in the normal prostate as opposed to ktrans that was higher inside the index lesion than outside. In the histogram analysis, the minimum ktrans was significantly correlated with the maximum ADC (Pearson=0.47; p=0.03). At the voxel level, 15 of the 20 cases had a statistically significant inverse correlation between ADC and perfusion inside the index lesion; ten of the cases had a Pearson < −0.4. Conclusion: The minimum value of ktrans across the tumour was correlated to the maximum ADC. However, on the voxel level, the ‘local’ ktrans in the index lesion is inversely (i.e. negatively) correlated to the ‘local’ ADC in most patients. Research agreement with Varian Medical Systems, not related to the work presented in this abstract.« less

Sparse and Adaptive Diffusion Dictionary (SADD) for recovering intra-voxel white matter structure.

PubMed

Aranda, Ramon; Ramirez-Manzanares, Alonso; Rivera, Mariano

2015-12-01

On the analysis of the Diffusion-Weighted Magnetic Resonance Images, multi-compartment models overcome the limitations of the well-known Diffusion Tensor model for fitting in vivo brain axonal orientations at voxels with fiber crossings, branching, kissing or bifurcations. Some successful multi-compartment methods are based on diffusion dictionaries. The diffusion dictionary-based methods assume that the observed Magnetic Resonance signal at each voxel is a linear combination of the fixed dictionary elements (dictionary atoms). The atoms are fixed along different orientations and diffusivity profiles. In this work, we present a sparse and adaptive diffusion dictionary method based on the Diffusion Basis Functions Model to estimate in vivo brain axonal fiber populations. Our proposal overcomes the following limitations of the diffusion dictionary-based methods: the limited angular resolution and the fixed shapes for the atom set. We propose to iteratively re-estimate the orientations and the diffusivity profile of the atoms independently at each voxel by using a simplified and easier-to-solve mathematical approach. As a result, we improve the fitting of the Diffusion-Weighted Magnetic Resonance signal. The advantages with respect to the former Diffusion Basis Functions method are demonstrated on the synthetic data-set used on the 2012 HARDI Reconstruction Challenge and in vivo human data. We demonstrate that improvements obtained in the intra-voxel fiber structure estimations benefit brain research allowing to obtain better tractography estimations. Hence, these improvements result in an accurate computation of the brain connectivity patterns. Copyright © 2015 Elsevier B.V. All rights reserved.

Brain-based decoding of mentally imagined film clips and sounds reveals experience-based information patterns in film professionals.

PubMed

de Borst, Aline W; Valente, Giancarlo; Jääskeläinen, Iiro P; Tikka, Pia

2016-04-01

In the perceptual domain, it has been shown that the human brain is strongly shaped through experience, leading to expertise in highly-skilled professionals. What has remained unclear is whether specialization also shapes brain networks underlying mental imagery. In our fMRI study, we aimed to uncover modality-specific mental imagery specialization of film experts. Using multi-voxel pattern analysis we decoded from brain activity of professional cinematographers and sound designers whether they were imagining sounds or images of particular film clips. In each expert group distinct multi-voxel patterns, specific for the modality of their expertise, were found during classification of imagery modality. These patterns were mainly localized in the occipito-temporal and parietal cortex for cinematographers and in the auditory cortex for sound designers. We also found generalized patterns across perception and imagery that were distinct for the two expert groups: they involved frontal cortex for the cinematographers and temporal cortex for the sound designers. Notably, the mental representations of film clips and sounds of cinematographers contained information that went beyond modality-specificity. We were able to successfully decode the implicit presence of film genre from brain activity during mental imagery in cinematographers. The results extend existing neuroimaging literature on expertise into the domain of mental imagery and show that experience in visual versus auditory imagery can alter the representation of information in modality-specific association cortices. Copyright © 2016 Elsevier Inc. All rights reserved.

Emotional modulation of body-selective visual areas.

PubMed

Peelen, Marius V; Atkinson, Anthony P; Andersson, Frederic; Vuilleumier, Patrik

2007-12-01

Emotionally expressive faces have been shown to modulate activation in visual cortex, including face-selective regions in ventral temporal lobe. Here, we tested whether emotionally expressive bodies similarly modulate activation in body-selective regions. We show that dynamic displays of bodies with various emotional expressions vs neutral bodies, produce significant activation in two distinct body-selective visual areas, the extrastriate body area and the fusiform body area. Multi-voxel pattern analysis showed that the strength of this emotional modulation was related, on a voxel-by-voxel basis, to the degree of body selectivity, while there was no relation with the degree of selectivity for faces. Across subjects, amygdala responses to emotional bodies positively correlated with the modulation of body-selective areas. Together, these results suggest that emotional cues from body movements produce topographically selective influences on category-specific populations of neurons in visual cortex, and these increases may implicate discrete modulatory projections from the amygdala.

Automatic analysis (aa): efficient neuroimaging workflows and parallel processing using Matlab and XML.

PubMed

Cusack, Rhodri; Vicente-Grabovetsky, Alejandro; Mitchell, Daniel J; Wild, Conor J; Auer, Tibor; Linke, Annika C; Peelle, Jonathan E

2014-01-01

Recent years have seen neuroimaging data sets becoming richer, with larger cohorts of participants, a greater variety of acquisition techniques, and increasingly complex analyses. These advances have made data analysis pipelines complicated to set up and run (increasing the risk of human error) and time consuming to execute (restricting what analyses are attempted). Here we present an open-source framework, automatic analysis (aa), to address these concerns. Human efficiency is increased by making code modular and reusable, and managing its execution with a processing engine that tracks what has been completed and what needs to be (re)done. Analysis is accelerated by optional parallel processing of independent tasks on cluster or cloud computing resources. A pipeline comprises a series of modules that each perform a specific task. The processing engine keeps track of the data, calculating a map of upstream and downstream dependencies for each module. Existing modules are available for many analysis tasks, such as SPM-based fMRI preprocessing, individual and group level statistics, voxel-based morphometry, tractography, and multi-voxel pattern analyses (MVPA). However, aa also allows for full customization, and encourages efficient management of code: new modules may be written with only a small code overhead. aa has been used by more than 50 researchers in hundreds of neuroimaging studies comprising thousands of subjects. It has been found to be robust, fast, and efficient, for simple-single subject studies up to multimodal pipelines on hundreds of subjects. It is attractive to both novice and experienced users. aa can reduce the amount of time neuroimaging laboratories spend performing analyses and reduce errors, expanding the range of scientific questions it is practical to address.

Cortical processing of pitch: Model-based encoding and decoding of auditory fMRI responses to real-life sounds.

PubMed

De Angelis, Vittoria; De Martino, Federico; Moerel, Michelle; Santoro, Roberta; Hausfeld, Lars; Formisano, Elia

2017-11-13

Pitch is a perceptual attribute related to the fundamental frequency (or periodicity) of a sound. So far, the cortical processing of pitch has been investigated mostly using synthetic sounds. However, the complex harmonic structure of natural sounds may require different mechanisms for the extraction and analysis of pitch. This study investigated the neural representation of pitch in human auditory cortex using model-based encoding and decoding analyses of high field (7 T) functional magnetic resonance imaging (fMRI) data collected while participants listened to a wide range of real-life sounds. Specifically, we modeled the fMRI responses as a function of the sounds' perceived pitch height and salience (related to the fundamental frequency and the harmonic structure respectively), which we estimated with a computational algorithm of pitch extraction (de Cheveigné and Kawahara, 2002). First, using single-voxel fMRI encoding, we identified a pitch-coding region in the antero-lateral Heschl's gyrus (HG) and adjacent superior temporal gyrus (STG). In these regions, the pitch representation model combining height and salience predicted the fMRI responses comparatively better than other models of acoustic processing and, in the right hemisphere, better than pitch representations based on height/salience alone. Second, we assessed with model-based decoding that multi-voxel response patterns of the identified regions are more informative of perceived pitch than the remainder of the auditory cortex. Further multivariate analyses showed that complementing a multi-resolution spectro-temporal sound representation with pitch produces a small but significant improvement to the decoding of complex sounds from fMRI response patterns. In sum, this work extends model-based fMRI encoding and decoding methods - previously employed to examine the representation and processing of acoustic sound features in the human auditory system - to the representation and processing of a relevant perceptual attribute such as pitch. Taken together, the results of our model-based encoding and decoding analyses indicated that the pitch of complex real life sounds is extracted and processed in lateral HG/STG regions, at locations consistent with those indicated in several previous fMRI studies using synthetic sounds. Within these regions, pitch-related sound representations reflect the modulatory combination of height and the salience of the pitch percept. Copyright © 2017 Elsevier Inc. All rights reserved.

Abnormal fronto-striatal activation as a marker of threshold and subthreshold Bulimia Nervosa.

PubMed

Cyr, Marilyn; Yang, Xiao; Horga, Guillermo; Marsh, Rachel

2018-04-01

This study aimed to determine whether functional disturbances in fronto-striatal control circuits characterize adolescents with Bulimia Nervosa (BN) spectrum eating disorders regardless of clinical severity. FMRI was used to assess conflict-related brain activations during performance of a Simon task in two samples of adolescents with BN symptoms compared with healthy adolescents. The BN samples differed in the severity of their clinical presentation, illness duration and age. Multi-voxel pattern analyses (MVPAs) based on machine learning were used to determine whether patterns of fronto-striatal activation characterized adolescents with BN spectrum disorders regardless of clinical severity, and whether accurate classification of less symptomatic adolescents (subthreshold BN; SBN) could be achieved based on patterns of activation in adolescents who met DSM5 criteria for BN. MVPA classification analyses revealed that both BN and SBN adolescents could be accurately discriminated from healthy adolescents based on fronto-striatal activation. Notably, the patterns detected in more severely ill BN compared with healthy adolescents accurately discriminated less symptomatic SBN from healthy adolescents. Deficient activation of fronto-striatal circuits can characterize BN early in its course, when clinical presentations are less severe, perhaps pointing to circuit-based disturbances as useful biomarker or risk factor for the disorder, and a tool for understanding its developmental trajectory, as well as the development of early interventions. © 2018 Wiley Periodicals, Inc.

Listening for Recollection: A Multi-Voxel Pattern Analysis of Recognition Memory Retrieval Strategies

PubMed Central

Quamme, Joel R.; Weiss, David J.; Norman, Kenneth A.

2010-01-01

Recent studies of recognition memory indicate that subjects can strategically vary how much they rely on recollection of specific details vs. feelings of familiarity when making recognition judgments. One possible explanation of these results is that subjects can establish an internally directed attentional state (“listening for recollection”) that enhances retrieval of studied details; fluctuations in this attentional state over time should be associated with fluctuations in subjects’ recognition behavior. In this study, we used multi-voxel pattern analysis of fMRI data to identify brain regions that are involved in listening for recollection. We looked for brain regions that met the following criteria: (1) Distinct neural patterns should be present when subjects are instructed to rely on recollection vs. familiarity, and (2) fluctuations in these neural patterns should be related to recognition behavior in the manner predicted by dual-process theories of recognition: Specifically, the presence of the recollection pattern during the pre-stimulus interval (indicating that subjects are “listening for recollection” at that moment) should be associated with a selective decrease in false alarms to related lures. We found that pre-stimulus activity in the right supramarginal gyrus met all of these criteria, suggesting that this region proactively establishes an internally directed attentional state that fosters recollection. We also found other regions (e.g., left middle temporal gyrus) where the pattern of neural activity was related to subjects’ responding to related lures after stimulus onset (but not before), suggesting that these regions implement processes that are engaged in a reactive fashion to boost recollection. PMID:20740073

The orthographic sensitivity to written Chinese in the occipital-temporal cortex.

PubMed

Liu, Haicheng; Jiang, Yi; Zhang, Bo; Ma, Lifei; He, Sheng; Weng, Xuchu

2013-06-01

Previous studies have identified an area in the left lateral fusiform cortex that is highly responsive to written words and has been named the visual word form area (VWFA). However, there is disagreement on the specific functional role of this area in word recognition. Chinese characters, which are dramatically different from Roman alphabets in the visual form and in the form to phonological mapping, provide a unique opportunity to investigate the properties of the VWFA. Specifically, to clarify the orthographic sensitivity in the mid-fusiform cortex, we compared fMRI response amplitudes (Exp. 1) as well as the spatial patterns of response across multiple voxels (Exp. 2) between Chinese characters and stimuli derived from Chinese characters with different orthographic properties. The fMRI response amplitude results suggest the existence of orthographic sensitivity in the VWFA. The results from multi-voxel pattern analysis indicate that spatial distribution of the responses across voxels in the occipitotemporal cortex contained discriminative information between the different types of character-related stimuli. These results together suggest that the orthographic rules are likely represented in a distributed neural network with the VWFA containing the most specific information regarding a stimulus' orthographic regularity.

The neural basis of visual word form processing: a multivariate investigation.

PubMed

Nestor, Adrian; Behrmann, Marlene; Plaut, David C

2013-07-01

Current research on the neurobiological bases of reading points to the privileged role of a ventral cortical network in visual word processing. However, the properties of this network and, in particular, its selectivity for orthographic stimuli such as words and pseudowords remain topics of significant debate. Here, we approached this issue from a novel perspective by applying pattern-based analyses to functional magnetic resonance imaging data. Specifically, we examined whether, where and how, orthographic stimuli elicit distinct patterns of activation in the human cortex. First, at the category level, multivariate mapping found extensive sensitivity throughout the ventral cortex for words relative to false-font strings. Secondly, at the identity level, the multi-voxel pattern classification provided direct evidence that different pseudowords are encoded by distinct neural patterns. Thirdly, a comparison of pseudoword and face identification revealed that both stimulus types exploit common neural resources within the ventral cortical network. These results provide novel evidence regarding the involvement of the left ventral cortex in orthographic stimulus processing and shed light on its selectivity and discriminability profile. In particular, our findings support the existence of sublexical orthographic representations within the left ventral cortex while arguing for the continuity of reading with other visual recognition skills.

Neural Activity Patterns in the Human Brain Reflect Tactile Stickiness Perception.

PubMed

Kim, Junsuk; Yeon, Jiwon; Ryu, Jaekyun; Park, Jang-Yeon; Chung, Soon-Cheol; Kim, Sung-Phil

2017-01-01

Our previous human fMRI study found brain activations correlated with tactile stickiness perception using the uni-variate general linear model (GLM) (Yeon et al., 2017). Here, we conducted an in-depth investigation on neural correlates of sticky sensations by employing a multivoxel pattern analysis (MVPA) on the same dataset. In particular, we statistically compared multi-variate neural activities in response to the three groups of sticky stimuli: A supra-threshold group including a set of sticky stimuli that evoked vivid sticky perception; an infra-threshold group including another set of sticky stimuli that barely evoked sticky perception; and a sham group including acrylic stimuli with no physically sticky property. Searchlight MVPAs were performed to search for local activity patterns carrying neural information of stickiness perception. Similar to the uni-variate GLM results, significant multi-variate neural activity patterns were identified in postcentral gyrus, subcortical (basal ganglia and thalamus), and insula areas (insula and adjacent areas). Moreover, MVPAs revealed that activity patterns in posterior parietal cortex discriminated the perceptual intensities of stickiness, which was not present in the uni-variate analysis. Next, we applied a principal component analysis (PCA) to the voxel response patterns within identified clusters so as to find low-dimensional neural representations of stickiness intensities. Follow-up clustering analyses clearly showed separate neural grouping configurations between the Supra- and Infra-threshold groups. Interestingly, this neural categorization was in line with the perceptual grouping pattern obtained from the psychophysical data. Our findings thus suggest that different stickiness intensities would elicit distinct neural activity patterns in the human brain and may provide a neural basis for the perception and categorization of tactile stickiness.

Neural Activity Patterns in the Human Brain Reflect Tactile Stickiness Perception

PubMed Central

Kim, Junsuk; Yeon, Jiwon; Ryu, Jaekyun; Park, Jang-Yeon; Chung, Soon-Cheol; Kim, Sung-Phil

2017-01-01

Our previous human fMRI study found brain activations correlated with tactile stickiness perception using the uni-variate general linear model (GLM) (Yeon et al., 2017). Here, we conducted an in-depth investigation on neural correlates of sticky sensations by employing a multivoxel pattern analysis (MVPA) on the same dataset. In particular, we statistically compared multi-variate neural activities in response to the three groups of sticky stimuli: A supra-threshold group including a set of sticky stimuli that evoked vivid sticky perception; an infra-threshold group including another set of sticky stimuli that barely evoked sticky perception; and a sham group including acrylic stimuli with no physically sticky property. Searchlight MVPAs were performed to search for local activity patterns carrying neural information of stickiness perception. Similar to the uni-variate GLM results, significant multi-variate neural activity patterns were identified in postcentral gyrus, subcortical (basal ganglia and thalamus), and insula areas (insula and adjacent areas). Moreover, MVPAs revealed that activity patterns in posterior parietal cortex discriminated the perceptual intensities of stickiness, which was not present in the uni-variate analysis. Next, we applied a principal component analysis (PCA) to the voxel response patterns within identified clusters so as to find low-dimensional neural representations of stickiness intensities. Follow-up clustering analyses clearly showed separate neural grouping configurations between the Supra- and Infra-threshold groups. Interestingly, this neural categorization was in line with the perceptual grouping pattern obtained from the psychophysical data. Our findings thus suggest that different stickiness intensities would elicit distinct neural activity patterns in the human brain and may provide a neural basis for the perception and categorization of tactile stickiness. PMID:28936171

Effect of trial-to-trial variability on optimal event-related fMRI design: Implications for Beta-series correlation and multi-voxel pattern analysis

PubMed Central

Abdulrahman, Hunar; Henson, Richard N.

2016-01-01

Functional magnetic resonance imaging (fMRI) studies typically employ rapid, event-related designs for behavioral reasons and for reasons associated with statistical efficiency. Efficiency is calculated from the precision of the parameters (Betas) estimated from a General Linear Model (GLM) in which trial onsets are convolved with a Hemodynamic Response Function (HRF). However, previous calculations of efficiency have ignored likely variability in the neural response from trial to trial, for example due to attentional fluctuations, or different stimuli across trials. Here we compare three GLMs in their efficiency for estimating average and individual Betas across trials as a function of trial variability, scan noise and Stimulus Onset Asynchrony (SOA): “Least Squares All” (LSA), “Least Squares Separate” (LSS) and “Least Squares Unitary” (LSU). Estimation of responses to individual trials in particular is important for both functional connectivity using “Beta-series correlation” and “multi-voxel pattern analysis” (MVPA). Our simulations show that the ratio of trial-to-trial variability to scan noise impacts both the optimal SOA and optimal GLM, especially for short SOAs < 5 s: LSA is better when this ratio is high, whereas LSS and LSU are better when the ratio is low. For MVPA, the consistency across voxels of trial variability and of scan noise is also critical. These findings not only have important implications for design of experiments using Beta-series regression and MVPA, but also statistical parametric mapping studies that seek only efficient estimation of the mean response across trials. PMID:26549299

Neural correlates of own- and other-race face perception: spatial and temporal response differences.

PubMed

Natu, Vaidehi; Raboy, David; O'Toole, Alice J

2011-02-01

Humans show an "other-race effect" for face recognition, with more accurate recognition of own- versus other-race faces. We compared the neural representations of own- and other-race faces using functional magnetic resonance imaging (fMRI) data in combination with a multi-voxel pattern classifier. Neural activity was recorded while Asians and Caucasians viewed Asian and Caucasian faces. A pattern classifier, applied to voxels across a broad range of ventral temporal areas, discriminated the brain activity maps elicited in response to Asian versus Caucasian faces in the brains of both Asians and Caucasians. Classification was most accurate in the first few time points of the block and required the use of own-race faces in the localizer scan to select voxels for classifier input. Next, we examined differences in the time-course of neural responses to own- and other-race faces and found evidence for a temporal "other-race effect." Own-race faces elicited a larger neural response initially that attenuated rapidly. The response to other-race faces was weaker at first, but increased over time, ultimately surpassing the magnitude of the own-race response in the fusiform "face" area (FFA). A similar temporal response pattern held across a broad range of ventral temporal areas. The pattern-classification results indicate the early availability of categorical information about own- versus other-race face status in the spatial pattern of neural activity. The slower, more sustained, brain response to other-race faces may indicate the need to recruit additional neural resources to process other-race faces for identification. Copyright © 2010 Elsevier Inc. All rights reserved.

Multi-voxel Patterns Reveal Functionally Differentiated Networks Underlying Auditory Feedback Processing of Speech

PubMed Central

Zheng, Zane Z.; Vicente-Grabovetsky, Alejandro; MacDonald, Ewen N.; Munhall, Kevin G.; Cusack, Rhodri; Johnsrude, Ingrid S.

2013-01-01

The everyday act of speaking involves the complex processes of speech motor control. An important component of control is monitoring, detection and processing of errors when auditory feedback does not correspond to the intended motor gesture. Here we show, using fMRI and converging operations within a multi-voxel pattern analysis framework, that this sensorimotor process is supported by functionally differentiated brain networks. During scanning, a real-time speech-tracking system was employed to deliver two acoustically different types of distorted auditory feedback or unaltered feedback while human participants were vocalizing monosyllabic words, and to present the same auditory stimuli while participants were passively listening. Whole-brain analysis of neural-pattern similarity revealed three functional networks that were differentially sensitive to distorted auditory feedback during vocalization, compared to during passive listening. One network of regions appears to encode an ‘error signal’ irrespective of acoustic features of the error: this network, including right angular gyrus, right supplementary motor area, and bilateral cerebellum, yielded consistent neural patterns across acoustically different, distorted feedback types, only during articulation (not during passive listening). In contrast, a fronto-temporal network appears sensitive to the speech features of auditory stimuli during passive listening; this preference for speech features was diminished when the same stimuli were presented as auditory concomitants of vocalization. A third network, showing a distinct functional pattern from the other two, appears to capture aspects of both neural response profiles. Taken together, our findings suggest that auditory feedback processing during speech motor control may rely on multiple, interactive, functionally differentiated neural systems. PMID:23467350

Multi-voxel pattern analysis reveals increased memory targeting and reduced use of retrieved details during single-agenda source monitoring

PubMed Central

McDuff, Susan G. R.; Frankel, Hillary C.; Norman, Kenneth A.

2009-01-01

We used multi-voxel pattern analysis (MVPA) of fMRI data to gain insight into how subjects’ retrieval agendas influence source memory judgments (was item X studied using source Y?). In Experiment 1, we used a single-agenda test where subjects judged whether items were studied with the targeted source or not. In Experiment 2, we used a multi-agenda test where subjects judged whether items were studied using the targeted source, studied using a different source, or nonstudied. To evaluate the differences between single- and multi-agenda source monitoring, we trained a classifier to detect source-specific fMRI activity at study, and then we applied the classifier to data from the test phase. We focused on trials where the targeted source and the actual source differed, so we could use MVPA to track neural activity associated with both the targeted source and the actual source. Our results indicate that single-agenda monitoring was associated with increased focus on the targeted source (as evidenced by increased targeted-source activity, relative to baseline) and reduced use of information relating to the actual, non-target source. In the multi-agenda experiment, high-levels of actual-source activity were associated with increased correct rejections, suggesting that subjects were using recollection of actual-source information to avoid source memory errors. In the single-agenda experiment, there were comparable levels of actual-source activity (suggesting that recollection was taking place), but the relationship between actual-source activity and behavior was absent (suggesting that subjects were failing to make proper use of this information). PMID:19144851

Decoding individual episodic memory traces in the human hippocampus.

PubMed

Chadwick, Martin J; Hassabis, Demis; Weiskopf, Nikolaus; Maguire, Eleanor A

2010-03-23

In recent years, multivariate pattern analyses have been performed on functional magnetic resonance imaging (fMRI) data, permitting prediction of mental states from local patterns of blood oxygen-level-dependent (BOLD) signal across voxels. We previously demonstrated that it is possible to predict the position of individuals in a virtual-reality environment from the pattern of activity across voxels in the hippocampus. Although this shows that spatial memories can be decoded, substantially more challenging, and arguably only possible to investigate in humans, is whether it is feasible to predict which complex everyday experience, or episodic memory, a person is recalling. Here we document for the first time that traces of individual rich episodic memories are detectable and distinguishable solely from the pattern of fMRI BOLD signals across voxels in the human hippocampus. In so doing, we uncovered a possible functional topography in the hippocampus, with preferential episodic processing by some hippocampal regions over others. Moreover, our results imply that the neuronal traces of episodic memories are stable (and thus predictable) even over many re-activations. Finally, our data provide further evidence for functional differentiation within the medial temporal lobe, in that we show the hippocampus contains significantly more episodic information than adjacent structures. 2010 Elsevier Ltd. All rights reserved.

Multi-Scale Voxel Segmentation for Terrestrial Lidar Data within Marshes

NASA Astrophysics Data System (ADS)

Nguyen, C. T.; Starek, M. J.; Tissot, P.; Gibeaut, J. C.

2016-12-01

The resilience of marshes to a rising sea is dependent on their elevation response. Terrestrial laser scanning (TLS) is a detailed topographic approach for accurate, dense surface measurement with high potential for monitoring of marsh surface elevation response. The dense point cloud provides a 3D representation of the surface, which includes both terrain and non-terrain objects. Extraction of topographic information requires filtering of the data into like-groups or classes, therefore, methods must be incorporated to identify structure in the data prior to creation of an end product. A voxel representation of three-dimensional space provides quantitative visualization and analysis for pattern recognition. The objectives of this study are threefold: 1) apply a multi-scale voxel approach to effectively extract geometric features from the TLS point cloud data, 2) investigate the utility of K-means and Self Organizing Map (SOM) clustering algorithms for segmentation, and 3) utilize a variety of validity indices to measure the quality of the result. TLS data were collected at a marsh site along the central Texas Gulf Coast using a Riegl VZ 400 TLS. The site consists of both exposed and vegetated surface regions. To characterize structure of the point cloud, octree segmentation is applied to create a tree data structure of voxels containing the points. The flexibility of voxels in size and point density makes this algorithm a promising candidate to locally extract statistical and geometric features of the terrain including surface normal and curvature. The characteristics of the voxel itself such as the volume and point density are also computed and assigned to each point as are laser pulse characteristics. The features extracted from the voxelization are then used as input for clustering of the points using the K-means and SOM clustering algorithms. Optimal number of clusters are then determined based on evaluation of cluster separability criterions. Results for different combinations of the feature space vector and differences between K-means and SOM clustering will be presented. The developed method provides a novel approach for compressing TLS scene complexity in marshes, such as for vegetation biomass studies or erosion monitoring.

A generalized parametric response mapping method for analysis of multi-parametric imaging: A feasibility study with application to glioblastoma.

PubMed

Lausch, Anthony; Yeung, Timothy Pok-Chi; Chen, Jeff; Law, Elton; Wang, Yong; Urbini, Benedetta; Donelli, Filippo; Manco, Luigi; Fainardi, Enrico; Lee, Ting-Yim; Wong, Eugene

2017-11-01

Parametric response map (PRM) analysis of functional imaging has been shown to be an effective tool for early prediction of cancer treatment outcomes and may also be well-suited toward guiding personalized adaptive radiotherapy (RT) strategies such as sub-volume boosting. However, the PRM method was primarily designed for analysis of longitudinally acquired pairs of single-parameter image data. The purpose of this study was to demonstrate the feasibility of a generalized parametric response map analysis framework, which enables analysis of multi-parametric data while maintaining the key advantages of the original PRM method. MRI-derived apparent diffusion coefficient (ADC) and relative cerebral blood volume (rCBV) maps acquired at 1 and 3-months post-RT for 19 patients with high-grade glioma were used to demonstrate the algorithm. Images were first co-registered and then standardized using normal tissue image intensity values. Tumor voxels were then plotted in a four-dimensional Cartesian space with coordinate values equal to a voxel's image intensity in each of the image volumes and an origin defined as the multi-parametric mean of normal tissue image intensity values. Voxel positions were orthogonally projected onto a line defined by the origin and a pre-determined response vector. The voxels are subsequently classified as positive, negative or nil, according to whether projected positions along the response vector exceeded a threshold distance from the origin. The response vector was selected by identifying the direction in which the standard deviation of tumor image intensity values was maximally different between responding and non-responding patients within a training dataset. Voxel classifications were visualized via familiar three-class response maps and then the fraction of tumor voxels associated with each of the classes was investigated for predictive utility analogous to the original PRM method. Independent PRM and MPRM analyses of the contrast-enhancing lesion (CEL) and a 1 cm shell of surrounding peri-tumoral tissue were performed. Prediction using tumor volume metrics was also investigated. Leave-one-out cross validation (LOOCV) was used in combination with permutation testing to assess preliminary predictive efficacy and estimate statistically robust P-values. The predictive endpoint was overall survival (OS) greater than or equal to the median OS of 18.2 months. Single-parameter PRM and multi-parametric response maps (MPRMs) were generated for each patient and used to predict OS via the LOOCV. Tumor volume metrics (P ≥ 0.071 ± 0.01) and single-parameter PRM analyses (P ≥ 0.170 ± 0.01) were not found to be predictive of OS within this study. MPRM analysis of the peri-tumoral region but not the CEL was found to be predictive of OS with a classification sensitivity, specificity and accuracy of 80%, 100%, and 89%, respectively (P = 0.001 ± 0.01). The feasibility of a generalized MPRM analysis framework was demonstrated with improved prediction of overall survival compared to the original single-parameter method when applied to a glioblastoma dataset. The proposed algorithm takes the spatial heterogeneity in multi-parametric response into consideration and enables visualization. MPRM analysis of peri-tumoral regions was shown to have predictive potential supporting further investigation of a larger glioblastoma dataset. © 2017 American Association of Physicists in Medicine.

Physics instruction induces changes in neural knowledge representation during successive stages of learning.

PubMed

Mason, Robert A; Just, Marcel Adam

2015-05-01

Incremental instruction on the workings of a set of mechanical systems induced a progression of changes in the neural representations of the systems. The neural representations of four mechanical systems were assessed before, during, and after three phases of incremental instruction (which first provided information about the system components, then provided partial causal information, and finally provided full functional information). In 14 participants, the neural representations of four systems (a bathroom scale, a fire extinguisher, an automobile braking system, and a trumpet) were assessed using three recently developed techniques: (1) machine learning and classification of multi-voxel patterns; (2) localization of consistently responding voxels; and (3) representational similarity analysis (RSA). The neural representations of the systems progressed through four stages, or states, involving spatially and temporally distinct multi-voxel patterns: (1) initially, the representation was primarily visual (occipital cortex); (2) it subsequently included a large parietal component; (3) it eventually became cortically diverse (frontal, parietal, temporal, and medial frontal regions); and (4) at the end, it demonstrated a strong frontal cortex weighting (frontal and motor regions). At each stage of knowledge, it was possible for a classifier to identify which one of four mechanical systems a participant was thinking about, based on their brain activation patterns. The progression of representational states was suggestive of progressive stages of learning: (1) encoding information from the display; (2) mental animation, possibly involving imagining the components moving; (3) generating causal hypotheses associated with mental animation; and finally (4) determining how a person (probably oneself) would interact with the system. This interpretation yields an initial, cortically-grounded, theory of learning of physical systems that potentially can be related to cognitive learning theories by suggesting links between cortical representations, stages of learning, and the understanding of simple systems. Copyright © 2015 Elsevier Inc. All rights reserved.

Evaluation of Vertical Lacunarity Profiles in Forested Areas Using Airborne Laser Scanning Point Clouds

NASA Astrophysics Data System (ADS)

Székely, B.; Kania, A.; Standovár, T.; Heilmeier, H.

2016-06-01

The horizontal variation and vertical layering of the vegetation are important properties of the canopy structure determining the habitat; three-dimensional (3D) distribution of objects (shrub layers, understory vegetation, etc.) is related to the environmental factors (e.g., illumination, visibility). It has been shown that gaps in forests, mosaic-like structures are essential to biodiversity; various methods have been introduced to quantify this property. As the distribution of gaps in the vegetation is a multi-scale phenomenon, in order to capture it in its entirety, scale-independent methods are preferred; one of these is the calculation of lacunarity. We used Airborne Laser Scanning point clouds measured over a forest plantation situated in a former floodplain. The flat topographic relief ensured that the tree growth is independent of the topographic effects. The tree pattern in the plantation crops provided various quasi-regular and irregular patterns, as well as various ages of the stands. The point clouds were voxelized and layers of voxels were considered as images for two-dimensional input. These images calculated for a certain vicinity of reference points were taken as images for the computation of lacunarity curves, providing a stack of lacunarity curves for each reference points. These sets of curves have been compared to reveal spatial changes of this property. As the dynamic range of the lacunarity values is very large, the natural logarithms of the values were considered. Logarithms of lacunarity functions show canopy-related variations, we analysed these variations along transects. The spatial variation can be related to forest properties and ecology-specific aspects.

Decoding of visual activity patterns from fMRI responses using multivariate pattern analyses and convolutional neural network.

PubMed

Zafar, Raheel; Kamel, Nidal; Naufal, Mohamad; Malik, Aamir Saeed; Dass, Sarat C; Ahmad, Rana Fayyaz; Abdullah, Jafri M; Reza, Faruque

2017-01-01

Decoding of human brain activity has always been a primary goal in neuroscience especially with functional magnetic resonance imaging (fMRI) data. In recent years, Convolutional neural network (CNN) has become a popular method for the extraction of features due to its higher accuracy, however it needs a lot of computation and training data. In this study, an algorithm is developed using Multivariate pattern analysis (MVPA) and modified CNN to decode the behavior of brain for different images with limited data set. Selection of significant features is an important part of fMRI data analysis, since it reduces the computational burden and improves the prediction performance; significant features are selected using t-test. MVPA uses machine learning algorithms to classify different brain states and helps in prediction during the task. General linear model (GLM) is used to find the unknown parameters of every individual voxel and the classification is done using multi-class support vector machine (SVM). MVPA-CNN based proposed algorithm is compared with region of interest (ROI) based method and MVPA based estimated values. The proposed method showed better overall accuracy (68.6%) compared to ROI (61.88%) and estimation values (64.17%).

Computational neuroanatomy using brain deformations: From brain parcellation to multivariate pattern analysis and machine learning.

PubMed

Davatzikos, Christos

2016-10-01

The past 20 years have seen a mushrooming growth of the field of computational neuroanatomy. Much of this work has been enabled by the development and refinement of powerful, high-dimensional image warping methods, which have enabled detailed brain parcellation, voxel-based morphometric analyses, and multivariate pattern analyses using machine learning approaches. The evolution of these 3 types of analyses over the years has overcome many challenges. We present the evolution of our work in these 3 directions, which largely follows the evolution of this field. We discuss the progression from single-atlas, single-registration brain parcellation work to current ensemble-based parcellation; from relatively basic mass-univariate t-tests to optimized regional pattern analyses combining deformations and residuals; and from basic application of support vector machines to generative-discriminative formulations of multivariate pattern analyses, and to methods dealing with heterogeneity of neuroanatomical patterns. We conclude with discussion of some of the future directions and challenges. Copyright © 2016. Published by Elsevier B.V.

Computational neuroanatomy using brain deformations: From brain parcellation to multivariate pattern analysis and machine learning

PubMed Central

Davatzikos, Christos

2017-01-01

The past 20 years have seen a mushrooming growth of the field of computational neuroanatomy. Much of this work has been enabled by the development and refinement of powerful, high-dimensional image warping methods, which have enabled detailed brain parcellation, voxel-based morphometric analyses, and multivariate pattern analyses using machine learning approaches. The evolution of these 3 types of analyses over the years has overcome many challenges. We present the evolution of our work in these 3 directions, which largely follows the evolution of this field. We discuss the progression from single-atlas, single-registration brain parcellation work to current ensemble-based parcellation; from relatively basic mass-univariate t-tests to optimized regional pattern analyses combining deformations and residuals; and from basic application of support vector machines to generative-discriminative formulations of multivariate pattern analyses, and to methods dealing with heterogeneity of neuroanatomical patterns. We conclude with discussion of some of the future directions and challenges. PMID:27514582

An Expanded Multi-scale Monte Carlo Simulation Method for Personalized Radiobiological Effect Estimation in Radiotherapy: a feasibility study

NASA Astrophysics Data System (ADS)

Zhang, Ying; Feng, Yuanming; Wang, Wei; Yang, Chengwen; Wang, Ping

2017-03-01

A novel and versatile “bottom-up” approach is developed to estimate the radiobiological effect of clinic radiotherapy. The model consists of multi-scale Monte Carlo simulations from organ to cell levels. At cellular level, accumulated damages are computed using a spectrum-based accumulation algorithm and predefined cellular damage database. The damage repair mechanism is modeled by an expanded reaction-rate two-lesion kinetic model, which were calibrated through replicating a radiobiological experiment. Multi-scale modeling is then performed on a lung cancer patient under conventional fractionated irradiation. The cell killing effects of two representative voxels (isocenter and peripheral voxel of the tumor) are computed and compared. At microscopic level, the nucleus dose and damage yields vary among all nucleuses within the voxels. Slightly larger percentage of cDSB yield is observed for the peripheral voxel (55.0%) compared to the isocenter one (52.5%). For isocenter voxel, survival fraction increase monotonically at reduced oxygen environment. Under an extreme anoxic condition (0.001%), survival fraction is calculated to be 80% and the hypoxia reduction factor reaches a maximum value of 2.24. In conclusion, with biological-related variations, the proposed multi-scale approach is more versatile than the existing approaches for evaluating personalized radiobiological effects in radiotherapy.

How learning might strengthen existing visual object representations in human object-selective cortex.

PubMed

Brants, Marijke; Bulthé, Jessica; Daniels, Nicky; Wagemans, Johan; Op de Beeck, Hans P

2016-02-15

Visual object perception is an important function in primates which can be fine-tuned by experience, even in adults. Which factors determine the regions and the neurons that are modified by learning is still unclear. Recently, it was proposed that the exact cortical focus and distribution of learning effects might depend upon the pre-learning mapping of relevant functional properties and how this mapping determines the informativeness of neural units for the stimuli and the task to be learned. From this hypothesis we would expect that visual experience would strengthen the pre-learning distributed functional map of the relevant distinctive object properties. Here we present a first test of this prediction in twelve human subjects who were trained in object categorization and differentiation, preceded and followed by a functional magnetic resonance imaging session. Specifically, training increased the distributed multi-voxel pattern information for trained object distinctions in object-selective cortex, resulting in a generalization from pre-training multi-voxel activity patterns to after-training activity patterns. Simulations show that the increased selectivity combined with the inter-session generalization is consistent with a training-induced strengthening of a pre-existing selectivity map. No training-related neural changes were detected in other regions. In sum, training to categorize or individuate objects strengthened pre-existing representations in human object-selective cortex, providing a first indication that the neuroanatomical distribution of learning effects depends upon the pre-learning mapping of visual object properties. Copyright © 2015 Elsevier Inc. All rights reserved.

Mapping neurotransmitter networks with PET: an example on serotonin and opioid systems.

PubMed

Tuominen, Lauri; Nummenmaa, Lauri; Keltikangas-Järvinen, Liisa; Raitakari, Olli; Hietala, Jarmo

2014-05-01

All functions of the human brain are consequences of altered activity of specific neural pathways and neurotransmitter systems. Although the knowledge of "system level" connectivity in the brain is increasing rapidly, we lack "molecular level" information on brain networks and connectivity patterns. We introduce novel voxel-based positron emission tomography (PET) methods for studying internal neurotransmitter network structure and intercorrelations of different neurotransmitter systems in the human brain. We chose serotonin transporter and μ-opioid receptor for this analysis because of their functional interaction at the cellular level and similar regional distribution in the brain. Twenty-one healthy subjects underwent two consecutive PET scans using [(11)C]MADAM, a serotonin transporter tracer, and [(11)C]carfentanil, a μ-opioid receptor tracer. First, voxel-by-voxel "intracorrelations" (hub and seed analyses) were used to study the internal structure of opioid and serotonin systems. Second, voxel-level opioid-serotonin intercorrelations (between neurotransmitters) were computed. Regional μ-opioid receptor binding potentials were uniformly correlated throughout the brain. However, our analyses revealed nonuniformity in the serotonin transporter intracorrelations and identified a highly connected local network (midbrain-striatum-thalamus-amygdala). Regionally specific intercorrelations between the opioid and serotonin tracers were found in anteromedial thalamus, amygdala, anterior cingulate cortex, dorsolateral prefrontal cortex, and left parietal cortex, i.e., in areas relevant for several neuropsychiatric disorders, especially affective disorders. This methodology enables in vivo mapping of connectivity patterns within and between neurotransmitter systems. Quantification of functional neurotransmitter balances may be a useful approach in etiological studies of neuropsychiatric disorders and also in drug development as a biomarker-based rationale for targeted modulation of neurotransmitter networks. Copyright © 2013 Wiley Periodicals, Inc.

Love flows downstream: mothers' and children's neural representation similarity in perceiving distress of self and family.

PubMed

Lee, Tae-Ho; Qu, Yang; Telzer, Eva H

2017-12-01

The current study aimed to capture empathy processing in an interpersonal context. Mother-adolescent dyads (N = 22) each completed an empathy task during fMRI, in which they imagined the target person in distressing scenes as either themselves or their family (i.e. child for the mother, mother for the child). Using multi-voxel pattern approach, we compared neural pattern similarity for the self and family conditions and found that mothers showed greater perceptual similarity between self and child in the fusiform face area (FFA), representing high self-child overlap, whereas adolescents showed significantly less self-mother overlap. Adolescents' pattern similarity was dependent upon family relationship quality, such that they showed greater self-mother overlap with higher relationship quality, whereas mothers' pattern similarity was independent of relationship quality. Furthermore, adolescents' perceptual similarity in the FFA was associated with increased social brain activation (e.g. temporal parietal junction). Mediation analyses indicated that high relationship quality was associated with greater social brain activation, which was mediated by greater self-mother overlap in the FFA. Our findings suggest that adolescents show more distinct neural patterns in perceiving their own vs their mother's distress, and such distinction is sensitive to mother-child relationship quality. In contrast, mothers' perception for their own and child's distress is highly similar and unconditional. © The Author (2017). Published by Oxford University Press.

Love flows downstream: mothers’ and children’s neural representation similarity in perceiving distress of self and family

PubMed Central

Qu, Yang

2017-01-01

Abstract The current study aimed to capture empathy processing in an interpersonal context. Mother–adolescent dyads (N = 22) each completed an empathy task during fMRI, in which they imagined the target person in distressing scenes as either themselves or their family (i.e. child for the mother, mother for the child). Using multi-voxel pattern approach, we compared neural pattern similarity for the self and family conditions and found that mothers showed greater perceptual similarity between self and child in the fusiform face area (FFA), representing high self–child overlap, whereas adolescents showed significantly less self–mother overlap. Adolescents’ pattern similarity was dependent upon family relationship quality, such that they showed greater self–mother overlap with higher relationship quality, whereas mothers’ pattern similarity was independent of relationship quality. Furthermore, adolescents’ perceptual similarity in the FFA was associated with increased social brain activation (e.g. temporal parietal junction). Mediation analyses indicated that high relationship quality was associated with greater social brain activation, which was mediated by greater self–mother overlap in the FFA. Our findings suggest that adolescents show more distinct neural patterns in perceiving their own vs their mother’s distress, and such distinction is sensitive to mother–child relationship quality. In contrast, mothers’ perception for their own and child’s distress is highly similar and unconditional. PMID:29069521

Language-Invariant Verb Processing Regions in Spanish-English Bilinguals

PubMed Central

Willms, Joanna L.; Shapiro, Kevin A.; Peelen, Marius V.; Pajtas, Petra E.; Costa, Albert; Moo, Lauren R.; Caramazza, Alfonso

2011-01-01

Nouns and verbs are fundamental grammatical building blocks of all languages. Studies of brain-damaged patients and healthy individuals have demonstrated that verb processing can be dissociated from noun processing at a neuroanatomical level. In cases where bilingual patients have a noun or verb deficit, the deficit has been observed in both languages. This suggests that the noun-verb distinction may be based on neural components that are common across languages. Here we investigated the cortical organization of grammatical categories in healthy, early Spanish-English bilinguals using functional magnetic resonance imaging (fMRI) in a morphophonological alternation task. Four regions showed greater activity for verbs than for nouns in both languages: left posterior middle temporal gyrus (LMTG), left middle frontal gyrus (LMFG), pre-supplementary motor area (pre-SMA), and right middle occipital gyrus (RMOG); no regions showed greater activation for nouns. Multi-voxel pattern analysis within verb-specific regions showed indistinguishable activity patterns for English and Spanish, indicating language-invariant bilingual processing. In LMTG and LMFG, patterns were more similar within than across grammatical category, both within and across languages, indicating language-invariant grammatical class information. These results suggest that the neural substrates underlying verb-specific processing are largely independent of language in bilinguals, both at the macroscopic neuroanatomical level and at the level of voxel activity patterns. PMID:21515387

TH-EF-BRA-11: Feasibility of Super-Resolution Time-Resolved 4DMRI for Multi-Breath Volumetric Motion Simulation in Radiotherapy Planning

DOE Office of Scientific and Technical Information (OSTI.GOV)

Li, G; Zakian, K; Deasy, J

Purpose: To develop a novel super-resolution time-resolved 4DMRI technique to evaluate multi-breath, irregular and complex organ motion without respiratory surrogate for radiotherapy planning. Methods: The super-resolution time-resolved (TR) 4DMRI approach combines a series of low-resolution 3D cine MRI images acquired during free breathing (FB) with a high-resolution breath-hold (BH) 3DMRI via deformable image registration (DIR). Five volunteers participated in the study under an IRB-approved protocol. The 3D cine images with voxel size of 5×5×5 mm{sup 3} at two volumes per second (2Hz) were acquired coronally using a T1 fast field echo sequence, half-scan (0.8) acceleration, and SENSE (3) parallel imaging.more » Phase-encoding was set in the lateral direction to minimize motion artifacts. The BH image with voxel size of 2×2×2 mm{sup 3} was acquired using the same sequence within 10 seconds. A demons-based DIR program was employed to produce super-resolution 2Hz 4DMRI. Registration quality was visually assessed using difference images between TR 4DMRI and 3D cine and quantitatively assessed using average voxel correlation. The fidelity of the 3D cine images was assessed using a gel phantom and a 1D motion platform by comparing mobile and static images. Results: Owing to voxel intensity similarity using the same MRI scanning sequence, accurate DIR between FB and BH images is achieved. The voxel correlations between 3D cine and TR 4DMRI are greater than 0.92 in all cases and the difference images illustrate minimal residual error with little systematic patterns. The 3D cine images of the mobile gel phantom preserve object geometry with minimal scanning artifacts. Conclusion: The super-resolution time-resolved 4DMRI technique has been achieved via DIR, providing a potential solution for multi-breath motion assessment. Accurate DIR mapping has been achieved to map high-resolution BH images to low-resolution FB images, producing 2Hz volumetric high-resolution 4DMRI. Further validation and improvement are still required prior to clinical applications. This study is in part supported by the NIH (U54CA137788/U54CA132378).« less

What's in a Name: Voxel-Based Morphometric Analyses of MRI and Naming Difficulty in Alzheimer's Disease, Frontotemporal Dementia and Corticobasal Degeneration

ERIC Educational Resources Information Center

Grossman, Murray; McMillan, Corey; Moore, Peachie; Ding, Lijun; Glosser, Guila; Work, Melissa; Gee, James

2004-01-01

Confrontation naming is impaired in neurodegenerative conditions like Alzheimer's disease (AD), frontotemporal dementia (FTD) and corticobasal degeneration (CBD). Some behavioural observations suggest a common source of impaired naming across these patient groups, while others find partially unique patterns of naming difficulty. We hypothesized…

Multiclass fMRI data decoding and visualization using supervised self-organizing maps.

PubMed

Hausfeld, Lars; Valente, Giancarlo; Formisano, Elia

2014-08-01

When multivariate pattern decoding is applied to fMRI studies entailing more than two experimental conditions, a most common approach is to transform the multiclass classification problem into a series of binary problems. Furthermore, for decoding analyses, classification accuracy is often the only outcome reported although the topology of activation patterns in the high-dimensional features space may provide additional insights into underlying brain representations. Here we propose to decode and visualize voxel patterns of fMRI datasets consisting of multiple conditions with a supervised variant of self-organizing maps (SSOMs). Using simulations and real fMRI data, we evaluated the performance of our SSOM-based approach. Specifically, the analysis of simulated fMRI data with varying signal-to-noise and contrast-to-noise ratio suggested that SSOMs perform better than a k-nearest-neighbor classifier for medium and large numbers of features (i.e. 250 to 1000 or more voxels) and similar to support vector machines (SVMs) for small and medium numbers of features (i.e. 100 to 600voxels). However, for a larger number of features (>800voxels), SSOMs performed worse than SVMs. When applied to a challenging 3-class fMRI classification problem with datasets collected to examine the neural representation of three human voices at individual speaker level, the SSOM-based algorithm was able to decode speaker identity from auditory cortical activation patterns. Classification performances were similar between SSOMs and other decoding algorithms; however, the ability to visualize decoding models and underlying data topology of SSOMs promotes a more comprehensive understanding of classification outcomes. We further illustrated this visualization ability of SSOMs with a re-analysis of a dataset examining the representation of visual categories in the ventral visual cortex (Haxby et al., 2001). This analysis showed that SSOMs could retrieve and visualize topography and neighborhood relations of the brain representation of eight visual categories. We conclude that SSOMs are particularly suited for decoding datasets consisting of more than two classes and are optimally combined with approaches that reduce the number of voxels used for classification (e.g. region-of-interest or searchlight approaches). Copyright © 2014. Published by Elsevier Inc.

Whole Brain Functional Connectivity Pattern Homogeneity Mapping.

PubMed

Wang, Lijie; Xu, Jinping; Wang, Chao; Wang, Jiaojian

2018-01-01

Mounting studies have demonstrated that brain functions are determined by its external functional connectivity patterns. However, how to characterize the voxel-wise similarity of whole brain functional connectivity pattern is still largely unknown. In this study, we introduced a new method called functional connectivity homogeneity (FcHo) to delineate the voxel-wise similarity of whole brain functional connectivity patterns. FcHo was defined by measuring the whole brain functional connectivity patterns similarity of a given voxel with its nearest 26 neighbors using Kendall's coefficient concordance (KCC). The robustness of this method was tested in four independent datasets selected from a large repository of MRI. Furthermore, FcHo mapping results were further validated using the nearest 18 and six neighbors and intra-subject reproducibility with each subject scanned two times. We also compared FcHo distribution patterns with local regional homogeneity (ReHo) to identify the similarity and differences of the two methods. Finally, FcHo method was used to identify the differences of whole brain functional connectivity patterns between professional Chinese chess players and novices to test its application. FcHo mapping consistently revealed that the high FcHo was mainly distributed in association cortex including parietal lobe, frontal lobe, occipital lobe and default mode network (DMN) related areas, whereas the low FcHo was mainly found in unimodal cortex including primary visual cortex, sensorimotor cortex, paracentral lobule and supplementary motor area. These results were further supported by analyses of the nearest 18 and six neighbors and intra-subject similarity. Moreover, FcHo showed both similar and different whole brain distribution patterns compared to ReHo. Finally, we demonstrated that FcHo can effectively identify the whole brain functional connectivity pattern differences between professional Chinese chess players and novices. Our findings indicated that FcHo is a reliable method to delineate the whole brain functional connectivity pattern similarity and may provide a new way to study the functional organization and to reveal neuropathological basis for brain disorders.

Disrupted Cerebro-cerebellar Intrinsic Functional Connectivity in Young Adults with High-functioning Autism Spectrum Disorder: A Data-driven, Whole-brain, High Temporal Resolution fMRI Study.

PubMed

Arnold Anteraper, Sheeba; Guell, Xavier; D'Mello, Anila; Joshi, Neha; Whitfield-Gabrieli, Susan; Joshi, Gagan

2018-06-13

To examine the resting-state functional-connectivity (RsFc) in young adults with high-functioning autism spectrum disorder (HF-ASD) using state-of-the-art fMRI data acquisition and analysis techniques. Simultaneous multi-slice, high temporal resolution fMRI acquisition; unbiased whole-brain connectome-wide multivariate pattern analysis (MVPA) techniques for assessing RsFc; and post-hoc whole-brain seed-to-voxel analyses using MVPA results as seeds. MVPA revealed two clusters of abnormal connectivity in the cerebellum. Whole-brain seed-based functional connectivity analyses informed by MVPA-derived clusters showed significant under connectivity between the cerebellum and social, emotional, and language brain regions in the HF-ASD group compared to healthy controls. The results we report are coherent with existing structural, functional, and RsFc literature in autism, extend previous literature reporting cerebellar abnormalities in the neuropathology of autism, and highlight the cerebellum as a potential target for therapeutic, diagnostic, predictive, and prognostic developments in ASD. The description of functional connectivity abnormalities using whole-brain, data-driven analyses as reported in the present study may crucially advance the development of ASD biomarkers, targets for therapeutic interventions, and neural predictors for measuring treatment response.

Multivariate pattern analysis of fMRI data reveals deficits in distributed representations in schizophrenia

PubMed Central

Yoon, Jong H.; Tamir, Diana; Minzenberg, Michael J.; Ragland, J. Daniel; Ursu, Stefan; Carter, Cameron S.

2009-01-01

Background Multivariate pattern analysis is an alternative method of analyzing fMRI data, which is capable of decoding distributed neural representations. We applied this method to test the hypothesis of the impairment in distributed representations in schizophrenia. We also compared the results of this method with traditional GLM-based univariate analysis. Methods 19 schizophrenia and 15 control subjects viewed two runs of stimuli--exemplars of faces, scenes, objects, and scrambled images. To verify engagement with stimuli, subjects completed a 1-back matching task. A multi-voxel pattern classifier was trained to identify category-specific activity patterns on one run of fMRI data. Classification testing was conducted on the remaining run. Correlation of voxel-wise activity across runs evaluated variance over time in activity patterns. Results Patients performed the task less accurately. This group difference was reflected in the pattern analysis results with diminished classification accuracy in patients compared to controls, 59% and 72% respectively. In contrast, there was no group difference in GLM-based univariate measures. In both groups, classification accuracy was significantly correlated with behavioral measures. Both groups showed highly significant correlation between inter-run correlations and classification accuracy. Conclusions Distributed representations of visual objects are impaired in schizophrenia. This impairment is correlated with diminished task performance, suggesting that decreased integrity of cortical activity patterns is reflected in impaired behavior. Comparisons with univariate results suggest greater sensitivity of pattern analysis in detecting group differences in neural activity and reduced likelihood of non-specific factors driving these results. PMID:18822407

Voxel-based statistical analysis of cerebral blood flow using Tc-99m ECD brain SPECT in patients with traumatic brain injury: group and individual analyses.

PubMed

Shin, Yong Beom; Kim, Seong-Jang; Kim, In-Ju; Kim, Yong-Ki; Kim, Dong-Soo; Park, Jae Heung; Yeom, Seok-Ran

2006-06-01

Statistical parametric mapping (SPM) was applied to brain perfusion single photon emission computed tomography (SPECT) images in patients with traumatic brain injury (TBI) to investigate regional cerebral abnormalities compared to age-matched normal controls. Thirteen patients with TBI underwent brain perfusion SPECT were included in this study (10 males, three females, mean age 39.8 +/- 18.2, range 21 - 74). SPM2 software implemented in MATLAB 5.3 was used for spatial pre-processing and analysis and to determine the quantitative differences between TBI patients and age-matched normal controls. Three large voxel clusters of significantly decreased cerebral blood perfusion were found in patients with TBI. The largest clusters were area including medial frontal gyrus (voxel number 3642, peak Z-value = 4.31, 4.27, p = 0.000) in both hemispheres. The second largest clusters were areas including cingulated gyrus and anterior cingulate gyrus of left hemisphere (voxel number 381, peak Z-value = 3.67, 3.62, p = 0.000). Other clusters were parahippocampal gyrus (voxel number 173, peak Z-value = 3.40, p = 0.000) and hippocampus (voxel number 173, peak Z-value = 3.23, p = 0.001) in the left hemisphere. The false discovery rate (FDR) was less than 0.04. From this study, group and individual analyses of SPM2 could clearly identify the perfusion abnormalities of brain SPECT in patients with TBI. Group analysis of SPM2 showed hypoperfusion pattern in the areas including medial frontal gyrus of both hemispheres, cingulate gyrus, anterior cingulate gyrus, parahippocampal gyrus and hippocampus in the left hemisphere compared to age-matched normal controls. Also, left parahippocampal gyrus and left hippocampus were additional hypoperfusion areas. However, these findings deserve further investigation on a larger number of patients to be performed to allow a better validation of objective SPM analysis in patients with TBI.

Language-invariant verb processing regions in Spanish-English bilinguals.

PubMed

Willms, Joanna L; Shapiro, Kevin A; Peelen, Marius V; Pajtas, Petra E; Costa, Albert; Moo, Lauren R; Caramazza, Alfonso

2011-07-01

Nouns and verbs are fundamental grammatical building blocks of all languages. Studies of brain-damaged patients and healthy individuals have demonstrated that verb processing can be dissociated from noun processing at a neuroanatomical level. In cases where bilingual patients have a noun or verb deficit, the deficit has been observed in both languages. This suggests that the noun-verb distinction may be based on neural components that are common across languages. Here we investigated the cortical organization of grammatical categories in healthy, early Spanish-English bilinguals using functional magnetic resonance imaging (fMRI) in a morphophonological alternation task. Four regions showed greater activity for verbs than for nouns in both languages: left posterior middle temporal gyrus (LMTG), left middle frontal gyrus (LMFG), pre-supplementary motor area (pre-SMA), and right middle occipital gyrus (RMOG); no regions showed greater activation for nouns. Multi-voxel pattern analysis within verb-specific regions showed indistinguishable activity patterns for English and Spanish, indicating language-invariant bilingual processing. In LMTG and LMFG, patterns were more similar within than across grammatical category, both within and across languages, indicating language-invariant grammatical class information. These results suggest that the neural substrates underlying verb-specific processing are largely independent of language in bilinguals, both at the macroscopic neuroanatomical level and at the level of voxel activity patterns. Copyright © 2011 Elsevier Inc. All rights reserved.

Real-time 3D human pose recognition from reconstructed volume via voxel classifiers

NASA Astrophysics Data System (ADS)

Yoo, ByungIn; Choi, Changkyu; Han, Jae-Joon; Lee, Changkyo; Kim, Wonjun; Suh, Sungjoo; Park, Dusik; Kim, Junmo

2014-03-01

This paper presents a human pose recognition method which simultaneously reconstructs a human volume based on ensemble of voxel classifiers from a single depth image in real-time. The human pose recognition is a difficult task since a single depth camera can capture only visible surfaces of a human body. In order to recognize invisible (self-occluded) surfaces of a human body, the proposed algorithm employs voxel classifiers trained with multi-layered synthetic voxels. Specifically, ray-casting onto a volumetric human model generates a synthetic voxel, where voxel consists of a 3D position and ID corresponding to the body part. The synthesized volumetric data which contain both visible and invisible body voxels are utilized to train the voxel classifiers. As a result, the voxel classifiers not only identify the visible voxels but also reconstruct the 3D positions and the IDs of the invisible voxels. The experimental results show improved performance on estimating the human poses due to the capability of inferring the invisible human body voxels. It is expected that the proposed algorithm can be applied to many fields such as telepresence, gaming, virtual fitting, wellness business, and real 3D contents control on real 3D displays.

Deep multi-spectral ensemble learning for electronic cleansing in dual-energy CT colonography

NASA Astrophysics Data System (ADS)

Tachibana, Rie; Näppi, Janne J.; Hironaka, Toru; Kim, Se Hyung; Yoshida, Hiroyuki

2017-03-01

We developed a novel electronic cleansing (EC) method for dual-energy CT colonography (DE-CTC) based on an ensemble deep convolution neural network (DCNN) and multi-spectral multi-slice image patches. In the method, an ensemble DCNN is used to classify each voxel of a DE-CTC image volume into five classes: luminal air, soft tissue, tagged fecal materials, and partial-volume boundaries between air and tagging and those between soft tissue and tagging. Each DCNN acts as a voxel classifier, where an input image patch centered at the voxel is generated as input to the DCNNs. An image patch has three channels that are mapped from a region-of-interest containing the image plane of the voxel and the two adjacent image planes. Six different types of spectral input image datasets were derived using two dual-energy CT images, two virtual monochromatic images, and two material images. An ensemble DCNN was constructed by use of a meta-classifier that combines the output of multiple DCNNs, each of which was trained with a different type of multi-spectral image patches. The electronically cleansed CTC images were calculated by removal of regions classified as other than soft tissue, followed by a colon surface reconstruction. For pilot evaluation, 359 volumes of interest (VOIs) representing sources of subtraction artifacts observed in current EC schemes were sampled from 30 clinical CTC cases. Preliminary results showed that the ensemble DCNN can yield high accuracy in labeling of the VOIs, indicating that deep learning of multi-spectral EC with multi-slice imaging could accurately remove residual fecal materials from CTC images without generating major EC artifacts.

Multi-resolution statistical image reconstruction for mitigation of truncation effects: application to cone-beam CT of the head

NASA Astrophysics Data System (ADS)

Dang, Hao; Webster Stayman, J.; Sisniega, Alejandro; Zbijewski, Wojciech; Xu, Jennifer; Wang, Xiaohui; Foos, David H.; Aygun, Nafi; Koliatsos, Vassilis E.; Siewerdsen, Jeffrey H.

2017-01-01

A prototype cone-beam CT (CBCT) head scanner featuring model-based iterative reconstruction (MBIR) has been recently developed and demonstrated the potential for reliable detection of acute intracranial hemorrhage (ICH), which is vital to diagnosis of traumatic brain injury and hemorrhagic stroke. However, data truncation (e.g. due to the head holder) can result in artifacts that reduce image uniformity and challenge ICH detection. We propose a multi-resolution MBIR method with an extended reconstruction field of view (RFOV) to mitigate truncation effects in CBCT of the head. The image volume includes a fine voxel size in the (inner) nontruncated region and a coarse voxel size in the (outer) truncated region. This multi-resolution scheme allows extension of the RFOV to mitigate truncation effects while introducing minimal increase in computational complexity. The multi-resolution method was incorporated in a penalized weighted least-squares (PWLS) reconstruction framework previously developed for CBCT of the head. Experiments involving an anthropomorphic head phantom with truncation due to a carbon-fiber holder were shown to result in severe artifacts in conventional single-resolution PWLS, whereas extending the RFOV within the multi-resolution framework strongly reduced truncation artifacts. For the same extended RFOV, the multi-resolution approach reduced computation time compared to the single-resolution approach (viz. time reduced by 40.7%, 83.0%, and over 95% for an image volume of 6003, 8003, 10003 voxels). Algorithm parameters (e.g. regularization strength, the ratio of the fine and coarse voxel size, and RFOV size) were investigated to guide reliable parameter selection. The findings provide a promising method for truncation artifact reduction in CBCT and may be useful for other MBIR methods and applications for which truncation is a challenge.

Multi-layer cube sampling for liver boundary detection in PET-CT images.

PubMed

Liu, Xinxin; Yang, Jian; Song, Shuang; Song, Hong; Ai, Danni; Zhu, Jianjun; Jiang, Yurong; Wang, Yongtian

2018-06-01

Liver metabolic information is considered as a crucial diagnostic marker for the diagnosis of fever of unknown origin, and liver recognition is the basis of automatic diagnosis of metabolic information extraction. However, the poor quality of PET and CT images is a challenge for information extraction and target recognition in PET-CT images. The existing detection method cannot meet the requirement of liver recognition in PET-CT images, which is the key problem in the big data analysis of PET-CT images. A novel texture feature descriptor called multi-layer cube sampling (MLCS) is developed for liver boundary detection in low-dose CT and PET images. The cube sampling feature is proposed for extracting more texture information, which uses a bi-centric voxel strategy. Neighbour voxels are divided into three regions by the centre voxel and the reference voxel in the histogram, and the voxel distribution information is statistically classified as texture feature. Multi-layer texture features are also used to improve the ability and adaptability of target recognition in volume data. The proposed feature is tested on the PET and CT images for liver boundary detection. For the liver in the volume data, mean detection rate (DR) and mean error rate (ER) reached 95.15 and 7.81% in low-quality PET images, and 83.10 and 21.08% in low-contrast CT images. The experimental results demonstrated that the proposed method is effective and robust for liver boundary detection.

An Eye Model for Computational Dosimetry Using A Multi-Scale Voxel Phantom

NASA Astrophysics Data System (ADS)

Caracappa, Peter F.; Rhodes, Ashley; Fiedler, Derek

2014-06-01

The lens of the eye is a radiosensitive tissue with cataract formation being the major concern. Recently reduced recommended dose limits to the lens of the eye have made understanding the dose to this tissue of increased importance. Due to memory limitations, the voxel resolution of computational phantoms used for radiation dose calculations is too large to accurately represent the dimensions of the eye. A revised eye model is constructed using physiological data for the dimensions of radiosensitive tissues, and is then transformed into a high-resolution voxel model. This eye model is combined with an existing set of whole body models to form a multi-scale voxel phantom, which is used with the MCNPX code to calculate radiation dose from various exposure types. This phantom provides an accurate representation of the radiation transport through the structures of the eye. Two alternate methods of including a high-resolution eye model within an existing whole body model are developed. The accuracy and performance of each method is compared against existing computational phantoms.

Sparsely-distributed organization of face and limb activations in human ventral temporal cortex

PubMed Central

Weiner, Kevin S.; Grill-Spector, Kalanit

2011-01-01

Functional magnetic resonance imaging (fMRI) has identified face- and body part-selective regions, as well as distributed activation patterns for object categories across human ventral temporal cortex (VTC), eliciting a debate regarding functional organization in VTC and neural coding of object categories. Using high-resolution fMRI, we illustrate that face- and limb-selective activations alternate in a series of largely nonoverlapping clusters in lateral VTC along the inferior occipital gyrus (IOG), fusiform gyrus (FG), and occipitotemporal sulcus (OTS). Both general linear model (GLM) and multivoxel pattern (MVP) analyses show that face- and limb-selective activations minimally overlap and that this organization is consistent across experiments and days. We provide a reliable method to separate two face-selective clusters on the middle and posterior FG (mFus and pFus), and another on the IOG using their spatial relation to limb-selective activations and retinotopic areas hV4, VO-1/2, and hMT+. Furthermore, these activations show a gradient of increasing face selectivity and decreasing limb selectivity from the IOG to the mFus. Finally, MVP analyses indicate that there is differential information for faces in lateral VTC (containing weakly- and highly-selective voxels) relative to non-selective voxels in medial VTC. These findings suggest a sparsely-distributed organization where sparseness refers to the presence of several face- and limb-selective clusters in VTC, and distributed refers to the presence of different amounts of information in highly-, weakly-, and non-selective voxels. Consequently, theories of object recognition should consider the functional and spatial constraints of neural coding across a series of nonoverlapping category-selective clusters that are themselves distributed. PMID:20457261

Multistep Lattice-Voxel method utilizing lattice function for Monte-Carlo treatment planning with pixel based voxel model.

PubMed

Kumada, H; Saito, K; Nakamura, T; Sakae, T; Sakurai, H; Matsumura, A; Ono, K

2011-12-01

Treatment planning for boron neutron capture therapy generally utilizes Monte-Carlo methods for calculation of the dose distribution. The new treatment planning system JCDS-FX employs the multi-purpose Monte-Carlo code PHITS to calculate the dose distribution. JCDS-FX allows to build a precise voxel model consisting of pixel based voxel cells in the scale of 0.4×0.4×2.0 mm(3) voxel in order to perform high-accuracy dose estimation, e.g. for the purpose of calculating the dose distribution in a human body. However, the miniaturization of the voxel size increases calculation time considerably. The aim of this study is to investigate sophisticated modeling methods which can perform Monte-Carlo calculations for human geometry efficiently. Thus, we devised a new voxel modeling method "Multistep Lattice-Voxel method," which can configure a voxel model that combines different voxel sizes by utilizing the lattice function over and over. To verify the performance of the calculation with the modeling method, several calculations for human geometry were carried out. The results demonstrated that the Multistep Lattice-Voxel method enabled the precise voxel model to reduce calculation time substantially while keeping the high-accuracy of dose estimation. Copyright © 2011 Elsevier Ltd. All rights reserved.

Functional connectivity of task context representations in prefrontal nodes of the multiple demand network.

PubMed

Stiers, Peter; Goulas, Alexandros

2018-06-01

A subset of regions in the lateral and medial prefrontal cortex and the anterior insula increase their activity level whenever a cognitive task becomes more demanding, regardless of the specific nature of this demand. During execution of a task, these areas and the surrounding cortex temporally encode aspects of the task context in spatially distributed patterns of activity. It is not clear whether these patterns reflect underlying anatomical subnetworks that still exist when task execution has finished. We use fMRI in 12 participants performing alternating blocks of three cognitive tasks to address this question. A first data set is used to define multiple demand regions in each participant. A second dataset from the same participants is used to determine multiple demand voxel assemblies with a preference for one task over the others. We then show that these voxels remain functionally coupled during execution of non-preferred tasks and that they exhibit stronger functional connectivity during rest. This indicates that the assemblies of task preference sharing voxels reflect patterns of underlying anatomical connections. Moreover, we show that voxels preferring the same task have more similar whole brain functional connectivity profiles that are consistent across participants. This suggests that voxel assemblies differ in patterns of input-output connections, most likely reflecting task demand-specific information exchange.

A genome-scale map of expression for a mouse brain section obtained using voxelation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chin, Mark H.; Geng, Alex B.; Khan, Arshad H.

Gene expression signatures in the mammalian brain hold the key to understanding neural development and neurological diseases. We have reconstructed 2- dimensional images of gene expression for 20,000 genes in a coronal slice of the mouse brain at the level of the striatum by using microarrays in combination with voxelation at a resolution of 1 mm3. Good reliability of the microarray results were confirmed using multiple replicates, subsequent quantitative RT-PCR voxelation, mass spectrometry voxelation and publicly available in situ hybridization data. Known and novel genes were identified with expression patterns localized to defined substructures within the brain. In addition, genesmore » with unexpected patterns were identified and cluster analysis identified a set of genes with a gradient of dorsal/ventral expression not restricted to known anatomical boundaries. The genome-scale maps of gene expression obtained using voxelation will be a valuable tool for the neuroscience community.« less

Multi-resolution voxel phantom modeling: a high-resolution eye model for computational dosimetry

NASA Astrophysics Data System (ADS)

Caracappa, Peter F.; Rhodes, Ashley; Fiedler, Derek

2014-09-01

Voxel models of the human body are commonly used for simulating radiation dose with a Monte Carlo radiation transport code. Due to memory limitations, the voxel resolution of these computational phantoms is typically too large to accurately represent the dimensions of small features such as the eye. Recently reduced recommended dose limits to the lens of the eye, which is a radiosensitive tissue with a significant concern for cataract formation, has lent increased importance to understanding the dose to this tissue. A high-resolution eye model is constructed using physiological data for the dimensions of radiosensitive tissues, and combined with an existing set of whole-body models to form a multi-resolution voxel phantom, which is used with the MCNPX code to calculate radiation dose from various exposure types. This phantom provides an accurate representation of the radiation transport through the structures of the eye. Two alternate methods of including a high-resolution eye model within an existing whole-body model are developed. The accuracy and performance of each method is compared against existing computational phantoms.

A Local Fast Marching-Based Diffusion Tensor Image Registration Algorithm by Simultaneously Considering Spatial Deformation and Tensor Orientation

PubMed Central

Xue, Zhong; Li, Hai; Guo, Lei; Wong, Stephen T.C.

2010-01-01

It is a key step to spatially align diffusion tensor images (DTI) to quantitatively compare neural images obtained from different subjects or the same subject at different timepoints. Different from traditional scalar or multi-channel image registration methods, tensor orientation should be considered in DTI registration. Recently, several DTI registration methods have been proposed in the literature, but deformation fields are purely dependent on the tensor features not the whole tensor information. Other methods, such as the piece-wise affine transformation and the diffeomorphic non-linear registration algorithms, use analytical gradients of the registration objective functions by simultaneously considering the reorientation and deformation of tensors during the registration. However, only relatively local tensor information such as voxel-wise tensor-similarity, is utilized. This paper proposes a new DTI image registration algorithm, called local fast marching (FM)-based simultaneous registration. The algorithm not only considers the orientation of tensors during registration but also utilizes the neighborhood tensor information of each voxel to drive the deformation, and such neighborhood tensor information is extracted from a local fast marching algorithm around the voxels of interest. These local fast marching-based tensor features efficiently reflect the diffusion patterns around each voxel within a spherical neighborhood and can capture relatively distinctive features of the anatomical structures. Using simulated and real DTI human brain data the experimental results show that the proposed algorithm is more accurate compared with the FA-based registration and is more efficient than its counterpart, the neighborhood tensor similarity-based registration. PMID:20382233

Magnetoencephalography with temporal spread imaging to visualize propagation of epileptic activity.

PubMed

Shibata, Sumiya; Matsuhashi, Masao; Kunieda, Takeharu; Yamao, Yukihiro; Inano, Rika; Kikuchi, Takayuki; Imamura, Hisaji; Takaya, Shigetoshi; Matsumoto, Riki; Ikeda, Akio; Takahashi, Ryosuke; Mima, Tatsuya; Fukuyama, Hidenao; Mikuni, Nobuhiro; Miyamoto, Susumu

2017-05-01

We describe temporal spread imaging (TSI) that can identify the spatiotemporal pattern of epileptic activity using Magnetoencephalography (MEG). A three-dimensional grid of voxels covering the brain is created. The array-gain minimum-variance spatial filter is applied to an interictal spike to estimate the magnitude of the source and the time (Ta) when the magnitude exceeds a predefined threshold at each voxel. This calculation is performed through all spikes. Each voxel has the mean Ta () and spike number (N sp ), which is the number of spikes whose source exceeds the threshold. Then, a random resampling method is used to determine the cutoff value of N sp for the statistically reproducible pattern of the activity. Finally, all the voxels where the source exceeds the threshold reproducibly shown on the MRI with a color scale representing . Four patients with intractable mesial temporal lobe epilepsy (MTLE) were analyzed. In three patients, the common pattern of the overlap between the propagation and the hypometabolism shown by fluorodeoxyglucose-positron emission tomography (FDG-PET) was identified. TSI can visualize statistically reproducible patterns of the temporal and spatial spread of epileptic activity. TSI can assess the statistical significance of the spatiotemporal pattern based on its reproducibility. Copyright © 2017 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.

VoxelStats: A MATLAB Package for Multi-Modal Voxel-Wise Brain Image Analysis.

PubMed

Mathotaarachchi, Sulantha; Wang, Seqian; Shin, Monica; Pascoal, Tharick A; Benedet, Andrea L; Kang, Min Su; Beaudry, Thomas; Fonov, Vladimir S; Gauthier, Serge; Labbe, Aurélie; Rosa-Neto, Pedro

2016-01-01

In healthy individuals, behavioral outcomes are highly associated with the variability on brain regional structure or neurochemical phenotypes. Similarly, in the context of neurodegenerative conditions, neuroimaging reveals that cognitive decline is linked to the magnitude of atrophy, neurochemical declines, or concentrations of abnormal protein aggregates across brain regions. However, modeling the effects of multiple regional abnormalities as determinants of cognitive decline at the voxel level remains largely unexplored by multimodal imaging research, given the high computational cost of estimating regression models for every single voxel from various imaging modalities. VoxelStats is a voxel-wise computational framework to overcome these computational limitations and to perform statistical operations on multiple scalar variables and imaging modalities at the voxel level. VoxelStats package has been developed in Matlab(®) and supports imaging formats such as Nifti-1, ANALYZE, and MINC v2. Prebuilt functions in VoxelStats enable the user to perform voxel-wise general and generalized linear models and mixed effect models with multiple volumetric covariates. Importantly, VoxelStats can recognize scalar values or image volumes as response variables and can accommodate volumetric statistical covariates as well as their interaction effects with other variables. Furthermore, this package includes built-in functionality to perform voxel-wise receiver operating characteristic analysis and paired and unpaired group contrast analysis. Validation of VoxelStats was conducted by comparing the linear regression functionality with existing toolboxes such as glim_image and RMINC. The validation results were identical to existing methods and the additional functionality was demonstrated by generating feature case assessments (t-statistics, odds ratio, and true positive rate maps). In summary, VoxelStats expands the current methods for multimodal imaging analysis by allowing the estimation of advanced regional association metrics at the voxel level.

Generation of Ground Truth Datasets for the Analysis of 3d Point Clouds in Urban Scenes Acquired via Different Sensors

NASA Astrophysics Data System (ADS)

Xu, Y.; Sun, Z.; Boerner, R.; Koch, T.; Hoegner, L.; Stilla, U.

2018-04-01

In this work, we report a novel way of generating ground truth dataset for analyzing point cloud from different sensors and the validation of algorithms. Instead of directly labeling large amount of 3D points requiring time consuming manual work, a multi-resolution 3D voxel grid for the testing site is generated. Then, with the help of a set of basic labeled points from the reference dataset, we can generate a 3D labeled space of the entire testing site with different resolutions. Specifically, an octree-based voxel structure is applied to voxelize the annotated reference point cloud, by which all the points are organized by 3D grids of multi-resolutions. When automatically annotating the new testing point clouds, a voting based approach is adopted to the labeled points within multiple resolution voxels, in order to assign a semantic label to the 3D space represented by the voxel. Lastly, robust line- and plane-based fast registration methods are developed for aligning point clouds obtained via various sensors. Benefiting from the labeled 3D spatial information, we can easily create new annotated 3D point clouds of different sensors of the same scene directly by considering the corresponding labels of 3D space the points located, which would be convenient for the validation and evaluation of algorithms related to point cloud interpretation and semantic segmentation.

Progression of brain atrophy in PSP and CBS over 6 months and 1 year.

PubMed

Dutt, Shubir; Binney, Richard J; Heuer, Hilary W; Luong, Phi; Attygalle, Suneth; Bhatt, Priyanka; Marx, Gabe A; Elofson, Jonathan; Tartaglia, Maria C; Litvan, Irene; McGinnis, Scott M; Dickerson, Bradford C; Kornak, John; Waltzman, Dana; Voltarelli, Lisa; Schuff, Norbert; Rabinovici, Gil D; Kramer, Joel H; Jack, Clifford R; Miller, Bruce L; Rosen, Howard J; Boxer, Adam L

2016-11-08

To examine the utility and reliability of volumetric MRI in measuring disease progression in the 4 repeat tauopathies, progressive supranuclear palsy (PSP) and corticobasal syndrome (CBS), to support clinical development of new tau-directed therapeutic agents. Six- and 12-month changes in regional MRI volumes and PSP Rating Scale scores were examined in 55 patients with PSP and 33 patients with CBS (78% amyloid PET negative) compared to 30 normal controls from a multicenter natural history study. Longitudinal voxel-based morphometric analyses identified patterns of volume loss, and region-of-interest analyses examined rates of volume loss in brainstem (midbrain, pons, superior cerebellar peduncle), cortical, and subcortical regions based on previously validated atlases. Results were compared to those in a replication cohort of 226 patients with PSP with MRI data from the AL-108-231 clinical trial. Patients with CBS exhibited greater baseline atrophy and greater longitudinal atrophy rates in cortical and basal ganglia regions than patients with PSP; however, midbrain and pontine atrophy rates were similar. Voxel-wise analyses showed distinct patterns of regional longitudinal atrophy in each group as compared to normal controls. The midbrain/pons volumetric ratio differed between diagnoses but remained stable over time. In both patient groups, brainstem atrophy rates were correlated with disease progression measured using the PSP Rating Scale. Volume loss is quantifiable over a period of 6 months in CBS and PSP. Future clinical trials may be able to combine CBS and PSP to measure therapeutic effects. © 2016 American Academy of Neurology.

Multi-Atlas Based Segmentation of Brainstem Nuclei from MR Images by Deep Hyper-Graph Learning.

PubMed

Dong, Pei; Guo, Yangrong; Gao, Yue; Liang, Peipeng; Shi, Yonghong; Wang, Qian; Shen, Dinggang; Wu, Guorong

2016-10-01

Accurate segmentation of brainstem nuclei (red nucleus and substantia nigra) is very important in various neuroimaging applications such as deep brain stimulation and the investigation of imaging biomarkers for Parkinson's disease (PD). Due to iron deposition during aging, image contrast in the brainstem is very low in Magnetic Resonance (MR) images. Hence, the ambiguity of patch-wise similarity makes the recently successful multi-atlas patch-based label fusion methods have difficulty to perform as competitive as segmenting cortical and sub-cortical regions from MR images. To address this challenge, we propose a novel multi-atlas brainstem nuclei segmentation method using deep hyper-graph learning. Specifically, we achieve this goal in three-fold. First , we employ hyper-graph to combine the advantage of maintaining spatial coherence from graph-based segmentation approaches and the benefit of harnessing population priors from multi-atlas based framework. Second , besides using low-level image appearance, we also extract high-level context features to measure the complex patch-wise relationship. Since the context features are calculated on a tentatively estimated label probability map, we eventually turn our hyper-graph learning based label propagation into a deep and self-refining model. Third , since anatomical labels on some voxels (usually located in uniform regions) can be identified much more reliably than other voxels (usually located at the boundary between two regions), we allow these reliable voxels to propagate their labels to the nearby difficult-to-label voxels. Such hierarchical strategy makes our proposed label fusion method deep and dynamic. We evaluate our proposed label fusion method in segmenting substantia nigra (SN) and red nucleus (RN) from 3.0 T MR images, where our proposed method achieves significant improvement over the state-of-the-art label fusion methods.

Detecting subject-specific activations using fuzzy clustering

PubMed Central

Seghier, Mohamed L.; Friston, Karl J.; Price, Cathy J.

2007-01-01

Inter-subject variability in evoked brain responses is attracting attention because it may reflect important variability in structure–function relationships over subjects. This variability could be a signature of degenerate (many-to-one) structure–function mappings in normal subjects or reflect changes that are disclosed by brain damage. In this paper, we describe a non-iterative fuzzy clustering algorithm (FCP: fuzzy clustering with fixed prototypes) for characterizing inter-subject variability in between-subject or second-level analyses of fMRI data. The approach identifies the contribution of each subject to response profiles in voxels surviving a classical F-statistic criterion. The output identifies subjects who drive activation in specific cortical regions (local effects) or in voxels distributed across neural systems (global effects). The sensitivity of the approach was assessed in 38 normal subjects performing an overt naming task. FCP revealed that several subjects had either abnormally high or abnormally low responses. FCP may be particularly useful for characterizing outlier responses in rare patients or heterogeneous populations. In these cases, atypical activations may not be detected by standard tests, under parametric assumptions. The advantage of using FCP is that it searches all voxels systematically and can identify atypical activation patterns in a quantitative and unsupervised manner. PMID:17478103

Modality-independent representations of small quantities based on brain activation patterns.

PubMed

Damarla, Saudamini Roy; Cherkassky, Vladimir L; Just, Marcel Adam

2016-04-01

Machine learning or MVPA (Multi Voxel Pattern Analysis) studies have shown that the neural representation of quantities of objects can be decoded from fMRI patterns, in cases where the quantities were visually displayed. Here we apply these techniques to investigate whether neural representations of quantities depicted in one modality (say, visual) can be decoded from brain activation patterns evoked by quantities depicted in the other modality (say, auditory). The main finding demonstrated, for the first time, that quantities of dots were decodable by a classifier that was trained on the neural patterns evoked by quantities of auditory tones, and vice-versa. The representations that were common across modalities were mainly right-lateralized in frontal and parietal regions. A second finding was that the neural patterns in parietal cortex that represent quantities were common across participants. These findings demonstrate a common neuronal foundation for the representation of quantities across sensory modalities and participants and provide insight into the role of parietal cortex in the representation of quantity information. © 2016 Wiley Periodicals, Inc.

Characterizing Heterogeneity within Head and Neck Lesions Using Cluster Analysis of Multi-Parametric MRI Data.

PubMed

Borri, Marco; Schmidt, Maria A; Powell, Ceri; Koh, Dow-Mu; Riddell, Angela M; Partridge, Mike; Bhide, Shreerang A; Nutting, Christopher M; Harrington, Kevin J; Newbold, Katie L; Leach, Martin O

2015-01-01

To describe a methodology, based on cluster analysis, to partition multi-parametric functional imaging data into groups (or clusters) of similar functional characteristics, with the aim of characterizing functional heterogeneity within head and neck tumour volumes. To evaluate the performance of the proposed approach on a set of longitudinal MRI data, analysing the evolution of the obtained sub-sets with treatment. The cluster analysis workflow was applied to a combination of dynamic contrast-enhanced and diffusion-weighted imaging MRI data from a cohort of squamous cell carcinoma of the head and neck patients. Cumulative distributions of voxels, containing pre and post-treatment data and including both primary tumours and lymph nodes, were partitioned into k clusters (k = 2, 3 or 4). Principal component analysis and cluster validation were employed to investigate data composition and to independently determine the optimal number of clusters. The evolution of the resulting sub-regions with induction chemotherapy treatment was assessed relative to the number of clusters. The clustering algorithm was able to separate clusters which significantly reduced in voxel number following induction chemotherapy from clusters with a non-significant reduction. Partitioning with the optimal number of clusters (k = 4), determined with cluster validation, produced the best separation between reducing and non-reducing clusters. The proposed methodology was able to identify tumour sub-regions with distinct functional properties, independently separating clusters which were affected differently by treatment. This work demonstrates that unsupervised cluster analysis, with no prior knowledge of the data, can be employed to provide a multi-parametric characterization of functional heterogeneity within tumour volumes.

Laser-induced forward transfer (LIFT) of congruent voxels

NASA Astrophysics Data System (ADS)

Piqué, Alberto; Kim, Heungsoo; Auyeung, Raymond C. Y.; Beniam, Iyoel; Breckenfeld, Eric

2016-06-01

Laser-induced forward transfer (LIFT) of functional materials offers unique advantages and capabilities for the rapid prototyping of electronic, optical and sensor elements. The use of LIFT for printing high viscosity metallic nano-inks and nano-pastes can be optimized for the transfer of voxels congruent with the shape of the laser pulse, forming thin film-like structures non-lithographically. These processes are capable of printing patterns with excellent lateral resolution and thickness uniformity typically found in 3-dimensional stacked assemblies, MEMS-like structures and free-standing interconnects. However, in order to achieve congruent voxel transfer with LIFT, the particle size and viscosity of the ink or paste suspensions must be adjusted to minimize variations due to wetting and drying effects. When LIFT is carried out with high-viscosity nano-suspensions, the printed voxel size and shape become controllable parameters, allowing the printing of thin-film like structures whose shape is determined by the spatial distribution of the laser pulse. The result is a new level of parallelization beyond current serial direct-write processes whereby the geometry of each printed voxel can be optimized according to the pattern design. This work shows how LIFT of congruent voxels can be applied to the fabrication of 2D and 3D microstructures by adjusting the viscosity of the nano-suspension and laser transfer parameters.

Non-parametric representation and prediction of single- and multi-shell diffusion-weighted MRI data using Gaussian processes

PubMed Central

Andersson, Jesper L.R.; Sotiropoulos, Stamatios N.

2015-01-01

Diffusion MRI offers great potential in studying the human brain microstructure and connectivity. However, diffusion images are marred by technical problems, such as image distortions and spurious signal loss. Correcting for these problems is non-trivial and relies on having a mechanism that predicts what to expect. In this paper we describe a novel way to represent and make predictions about diffusion MRI data. It is based on a Gaussian process on one or several spheres similar to the Geostatistical method of “Kriging”. We present a choice of covariance function that allows us to accurately predict the signal even from voxels with complex fibre patterns. For multi-shell data (multiple non-zero b-values) the covariance function extends across the shells which means that data from one shell is used when making predictions for another shell. PMID:26236030

Altered voxel-wise gray matter structural brain networks in schizophrenia: Association with brain genetic expression pattern.

PubMed

Liu, Feng; Tian, Hongjun; Li, Jie; Li, Shen; Zhuo, Chuanjun

2018-05-04

Previous seed- and atlas-based structural covariance/connectivity analyses have demonstrated that patients with schizophrenia is accompanied by aberrant structural connection and abnormal topological organization. However, it remains unclear whether this disruption is present in unbiased whole-brain voxel-wise structural covariance networks (SCNs) and whether brain genetic expression variations are linked with network alterations. In this study, ninety-five patients with schizophrenia and 95 matched healthy controls were recruited and gray matter volumes were extracted from high-resolution structural magnetic resonance imaging scans. Whole-brain voxel-wise gray matter SCNs were constructed at the group level and were further analyzed by using graph theory method. Nonparametric permutation tests were employed for group comparisons. In addition, regression modes along with random effect analysis were utilized to explore the associations between structural network changes and gene expression from the Allen Human Brain Atlas. Compared with healthy controls, the patients with schizophrenia showed significantly increased structural covariance strength (SCS) in the right orbital part of superior frontal gyrus and bilateral middle frontal gyrus, while decreased SCS in the bilateral superior temporal gyrus and precuneus. The altered SCS showed reproducible correlations with the expression profiles of the gene classes involved in therapeutic targets and neurodevelopment. Overall, our findings not only demonstrate that the topological architecture of whole-brain voxel-wise SCNs is impaired in schizophrenia, but also provide evidence for the possible role of therapeutic targets and neurodevelopment-related genes in gray matter structural brain networks in schizophrenia.

Creativity and borderline personality disorder: evidence from a voxel-based morphometry study.

PubMed

Leutgeb, Verena; Ille, Rottraut; Wabnegger, Albert; Schienle, Anne; Schöggl, Helmut; Weber, Bernhard; Papousek, Ilona; Weiss, Elisabeth M; Fink, Andreas

2016-05-01

Throughout the history, various examples of eminent creative people suffering from mental disorders along with some empirical research reports strengthened the idea of a potential link between creativity and psychopathology. This study investigated different facets of psychometrically determined creativity in 20 females diagnosed with borderline personality disorder (BPD) relative to 19 healthy female controls. In addition, group differences in grey matter (GM) were examined. Behavioural findings revealed no significant differences between the BPD group and healthy controls with respect to verbal and figural-graphic creative task performance and creativity-related personality characteristics. Whole-brain voxel-based morphometry analyses revealed a distinct pattern of GM reductions in the BPD group (relative to controls) in a network of brain regions closely associated with various cognitive and emotional functions (including the bilateral orbital inferior frontal gyri and the left superior temporal gyrus), partly overlapping with creativity-related brain regions. Correlation analyses moreover revealed that in the BPD group GM reductions in the orbital parts of the inferior and middle frontal gyri were associated with lower levels of creativity. This study provides no indications in favour of the putative link between creativity and psychopathology, as sometimes reported in the literature.

Sparse network-based models for patient classification using fMRI

PubMed Central

Rosa, Maria J.; Portugal, Liana; Hahn, Tim; Fallgatter, Andreas J.; Garrido, Marta I.; Shawe-Taylor, John; Mourao-Miranda, Janaina

2015-01-01

Pattern recognition applied to whole-brain neuroimaging data, such as functional Magnetic Resonance Imaging (fMRI), has proved successful at discriminating psychiatric patients from healthy participants. However, predictive patterns obtained from whole-brain voxel-based features are difficult to interpret in terms of the underlying neurobiology. Many psychiatric disorders, such as depression and schizophrenia, are thought to be brain connectivity disorders. Therefore, pattern recognition based on network models might provide deeper insights and potentially more powerful predictions than whole-brain voxel-based approaches. Here, we build a novel sparse network-based discriminative modeling framework, based on Gaussian graphical models and L1-norm regularized linear Support Vector Machines (SVM). In addition, the proposed framework is optimized in terms of both predictive power and reproducibility/stability of the patterns. Our approach aims to provide better pattern interpretation than voxel-based whole-brain approaches by yielding stable brain connectivity patterns that underlie discriminative changes in brain function between the groups. We illustrate our technique by classifying patients with major depressive disorder (MDD) and healthy participants, in two (event- and block-related) fMRI datasets acquired while participants performed a gender discrimination and emotional task, respectively, during the visualization of emotional valent faces. PMID:25463459

Nicotine deprivation elevates neural representation of smoking-related cues in object-sensitive visual cortex: a proof of concept study.

PubMed

Havermans, Anne; van Schayck, Onno C P; Vuurman, Eric F P M; Riedel, Wim J; van den Hurk, Job

2017-08-01

In the current study, we use functional magnetic resonance imaging (fMRI) and multi-voxel pattern analysis (MVPA) to investigate whether tobacco addiction biases basic visual processing in favour of smoking-related images. We hypothesize that the neural representation of smoking-related stimuli in the lateral occipital complex (LOC) is elevated after a period of nicotine deprivation compared to a satiated state, but that this is not the case for object categories unrelated to smoking. Current smokers (≥10 cigarettes a day) underwent two fMRI scanning sessions: one after 10 h of nicotine abstinence and the other one after smoking ad libitum. Regional blood oxygenated level-dependent (BOLD) response was measured while participants were presented with 24 blocks of 8 colour-matched pictures of cigarettes, pencils or chairs. The functional data of 10 participants were analysed through a pattern classification approach. In bilateral LOC clusters, the classifier was able to discriminate between patterns of activity elicited by visually similar smoking-related (cigarettes) and neutral objects (pencils) above empirically estimated chance levels only during deprivation (mean = 61.0%, chance (permutations) = 50.0%, p = .01) but not during satiation (mean = 53.5%, chance (permutations) = 49.9%, ns.). For all other stimulus contrasts, there was no difference in discriminability between the deprived and satiated conditions. The discriminability between smoking and non-smoking visual objects was elevated in object-selective brain region LOC after a period of nicotine abstinence. This indicates that attention bias likely affects basic visual object processing.

Multi-Voxel Decoding and the Topography of Maintained Information During Visual Working Memory

PubMed Central

Lee, Sue-Hyun; Baker, Chris I.

2016-01-01

The ability to maintain representations in the absence of external sensory stimulation, such as in working memory, is critical for guiding human behavior. Human functional brain imaging studies suggest that visual working memory can recruit a network of brain regions from visual to parietal to prefrontal cortex. In this review, we focus on the maintenance of representations during visual working memory and discuss factors determining the topography of those representations. In particular, we review recent studies employing multi-voxel pattern analysis (MVPA) that demonstrate decoding of the maintained content in visual cortex, providing support for a “sensory recruitment” model of visual working memory. However, there is some evidence that maintained content can also be decoded in areas outside of visual cortex, including parietal and frontal cortex. We suggest that the ability to maintain representations during working memory is a general property of cortex, not restricted to specific areas, and argue that it is important to consider the nature of the information that must be maintained. Such information-content is critically determined by the task and the recruitment of specific regions during visual working memory will be both task- and stimulus-dependent. Thus, the common finding of maintained information in visual, but not parietal or prefrontal, cortex may be more of a reflection of the need to maintain specific types of visual information and not of a privileged role of visual cortex in maintenance. PMID:26912997

Patterns of Gray Matter Abnormalities in Schizophrenia Based on an International Mega-analysis.

PubMed

Gupta, Cota Navin; Calhoun, Vince D; Rachakonda, Srinivas; Chen, Jiayu; Patel, Veena; Liu, Jingyu; Segall, Judith; Franke, Barbara; Zwiers, Marcel P; Arias-Vasquez, Alejandro; Buitelaar, Jan; Fisher, Simon E; Fernandez, Guillen; van Erp, Theo G M; Potkin, Steven; Ford, Judith; Mathalon, Daniel; McEwen, Sarah; Lee, Hyo Jong; Mueller, Bryon A; Greve, Douglas N; Andreassen, Ole; Agartz, Ingrid; Gollub, Randy L; Sponheim, Scott R; Ehrlich, Stefan; Wang, Lei; Pearlson, Godfrey; Glahn, David C; Sprooten, Emma; Mayer, Andrew R; Stephen, Julia; Jung, Rex E; Canive, Jose; Bustillo, Juan; Turner, Jessica A

2015-09-01

Analyses of gray matter concentration (GMC) deficits in patients with schizophrenia (Sz) have identified robust changes throughout the cortex. We assessed the relationships between diagnosis, overall symptom severity, and patterns of gray matter in the largest aggregated structural imaging dataset to date. We performed both source-based morphometry (SBM) and voxel-based morphometry (VBM) analyses on GMC images from 784 Sz and 936 controls (Ct) across 23 scanning sites in Europe and the United States. After correcting for age, gender, site, and diagnosis by site interactions, SBM analyses showed 9 patterns of diagnostic differences. They comprised separate cortical, subcortical, and cerebellar regions. Seven patterns showed greater GMC in Ct than Sz, while 2 (brainstem and cerebellum) showed greater GMC for Sz. The greatest GMC deficit was in a single pattern comprising regions in the superior temporal gyrus, inferior frontal gyrus, and medial frontal cortex, which replicated over analyses of data subsets. VBM analyses identified overall cortical GMC loss and one small cluster of increased GMC in Sz, which overlapped with the SBM brainstem component. We found no significant association between the component loadings and symptom severity in either analysis. This mega-analysis confirms that the commonly found GMC loss in Sz in the anterior temporal lobe, insula, and medial frontal lobe form a single, consistent spatial pattern even in such a diverse dataset. The separation of GMC loss into robust, repeatable spatial patterns across multiple datasets paves the way for the application of these methods to identify subtle genetic and clinical cohort effects. © The Author 2014. Published by Oxford University Press on behalf of the Maryland Psychiatric Research Center. All rights reserved. For permissions, please email: journals.permissions@oup.com.

Semi-automated brain tumor segmentation on multi-parametric MRI using regularized non-negative matrix factorization.

PubMed

Sauwen, Nicolas; Acou, Marjan; Sima, Diana M; Veraart, Jelle; Maes, Frederik; Himmelreich, Uwe; Achten, Eric; Huffel, Sabine Van

2017-05-04

Segmentation of gliomas in multi-parametric (MP-)MR images is challenging due to their heterogeneous nature in terms of size, appearance and location. Manual tumor segmentation is a time-consuming task and clinical practice would benefit from (semi-) automated segmentation of the different tumor compartments. We present a semi-automated framework for brain tumor segmentation based on non-negative matrix factorization (NMF) that does not require prior training of the method. L1-regularization is incorporated into the NMF objective function to promote spatial consistency and sparseness of the tissue abundance maps. The pathological sources are initialized through user-defined voxel selection. Knowledge about the spatial location of the selected voxels is combined with tissue adjacency constraints in a post-processing step to enhance segmentation quality. The method is applied to an MP-MRI dataset of 21 high-grade glioma patients, including conventional, perfusion-weighted and diffusion-weighted MRI. To assess the effect of using MP-MRI data and the L1-regularization term, analyses are also run using only conventional MRI and without L1-regularization. Robustness against user input variability is verified by considering the statistical distribution of the segmentation results when repeatedly analyzing each patient's dataset with a different set of random seeding points. Using L1-regularized semi-automated NMF segmentation, mean Dice-scores of 65%, 74 and 80% are found for active tumor, the tumor core and the whole tumor region. Mean Hausdorff distances of 6.1 mm, 7.4 mm and 8.2 mm are found for active tumor, the tumor core and the whole tumor region. Lower Dice-scores and higher Hausdorff distances are found without L1-regularization and when only considering conventional MRI data. Based on the mean Dice-scores and Hausdorff distances, segmentation results are competitive with state-of-the-art in literature. Robust results were found for most patients, although careful voxel selection is mandatory to avoid sub-optimal segmentation.

Quantitative analysis of hepatic iron in patients suspected of coexisting iron overload and steatosis using multi-echo single-voxel magnetic resonance spectroscopy: Comparison with fat-saturated multi-echo gradient echo sequence.

PubMed

Lin, Huimin; Fu, Caixia; Kannengiesser, Stephan; Cheng, Shu; Shen, Jun; Dong, Haipeng; Yan, Fuhua

2018-03-07

The coexistence of hepatic iron and fat is common in patients with hyperferritinemia, which plays an interactive and aggressive role in the progression of diseases (fibrosis, cirrhosis, and hepatocellular carcinomas). To evaluate a modified high-speed T 2 -corrected multi-echo, single voxel spectroscopy sequence (HISTOV) for liver iron concentration (LIC) quantification in patients with hyperferritinemia, with simultaneous fat fraction (FF) estimation. Retrospective cohort study. Thirty-eight patients with hyperferritinemia were enrolled. HISTOV, a fat-saturated multi-echo gradient echo (GRE) sequence, and a spin echo sequence (FerriScan) were performed at 1.5T. R 2 of the water signal and FF were calculated with HISTOV, and R2* values were derived from the GRE sequence, with R 2 and LIC from FerriScan serving as the references. Linear regression, correlation analyses, receiver operating characteristic analyses, and Bland-Altman analyses were conducted. Abnormal hepatic iron load was detected in 32/38 patients, of whom 10/32 had coexisting steatosis. Strong correlation was found between R2* and FerriScan-LIC (R 2 = 0.861), and between HISTOV-R 2_ water and FerriScan-R 2 (R 2  = 0.889). Furthermore, HISTOV-R 2_ water was not correlated with HISTOV-FF. The area under the curve (AUC) for HISTOV-R 2_ water was 0.974, 0.971, and 1, corresponding to clinical FerriScan-LIC thresholds of 1.8, 3.2, and 7.0 mg/g dw, respectively. No significant difference in the AUC was found between HISTOV-R 2_ water and R2* at any of the LIC thresholds, with P-values of 0.42, 0.37, and 1, respectively. HISTOV-LIC showed excellent agreement with FerriScan-LIC, with a mean bias of 0.00 ± 1.18 mg/g dw, whereas the mean bias between GRE-LIC and FerriScan-LIC was 0.53 ± 1.49 mg/g dw. HISTOV is useful for the quantification and grading of liver iron overload in patients with hyperferritinemia, particularly in cases with coexisting steatosis. HISTOV-LIC showed no systematic bias compared with FerriScan-LIC, making it a promising alternative for iron quantification. 3 Technical Efficacy Stage 2 J. Magn. Reson. Imaging 2018. © 2018 International Society for Magnetic Resonance in Medicine.

Characterizing Heterogeneity within Head and Neck Lesions Using Cluster Analysis of Multi-Parametric MRI Data

PubMed Central

Borri, Marco; Schmidt, Maria A.; Powell, Ceri; Koh, Dow-Mu; Riddell, Angela M.; Partridge, Mike; Bhide, Shreerang A.; Nutting, Christopher M.; Harrington, Kevin J.; Newbold, Katie L.; Leach, Martin O.

2015-01-01

Purpose To describe a methodology, based on cluster analysis, to partition multi-parametric functional imaging data into groups (or clusters) of similar functional characteristics, with the aim of characterizing functional heterogeneity within head and neck tumour volumes. To evaluate the performance of the proposed approach on a set of longitudinal MRI data, analysing the evolution of the obtained sub-sets with treatment. Material and Methods The cluster analysis workflow was applied to a combination of dynamic contrast-enhanced and diffusion-weighted imaging MRI data from a cohort of squamous cell carcinoma of the head and neck patients. Cumulative distributions of voxels, containing pre and post-treatment data and including both primary tumours and lymph nodes, were partitioned into k clusters (k = 2, 3 or 4). Principal component analysis and cluster validation were employed to investigate data composition and to independently determine the optimal number of clusters. The evolution of the resulting sub-regions with induction chemotherapy treatment was assessed relative to the number of clusters. Results The clustering algorithm was able to separate clusters which significantly reduced in voxel number following induction chemotherapy from clusters with a non-significant reduction. Partitioning with the optimal number of clusters (k = 4), determined with cluster validation, produced the best separation between reducing and non-reducing clusters. Conclusion The proposed methodology was able to identify tumour sub-regions with distinct functional properties, independently separating clusters which were affected differently by treatment. This work demonstrates that unsupervised cluster analysis, with no prior knowledge of the data, can be employed to provide a multi-parametric characterization of functional heterogeneity within tumour volumes. PMID:26398888

Edge-Related Activity Is Not Necessary to Explain Orientation Decoding in Human Visual Cortex.

PubMed

Wardle, Susan G; Ritchie, J Brendan; Seymour, Kiley; Carlson, Thomas A

2017-02-01

Multivariate pattern analysis is a powerful technique; however, a significant theoretical limitation in neuroscience is the ambiguity in interpreting the source of decodable information used by classifiers. This is exemplified by the continued controversy over the source of orientation decoding from fMRI responses in human V1. Recently Carlson (2014) identified a potential source of decodable information by modeling voxel responses based on the Hubel and Wiesel (1972) ice-cube model of visual cortex. The model revealed that activity associated with the edges of gratings covaries with orientation and could potentially be used to discriminate orientation. Here we empirically evaluate whether "edge-related activity" underlies orientation decoding from patterns of BOLD response in human V1. First, we systematically mapped classifier performance as a function of stimulus location using population receptive field modeling to isolate each voxel's overlap with a large annular grating stimulus. Orientation was decodable across the stimulus; however, peak decoding performance occurred for voxels with receptive fields closer to the fovea and overlapping with the inner edge. Critically, we did not observe the expected second peak in decoding performance at the outer stimulus edge as predicted by the edge account. Second, we evaluated whether voxels that contribute most to classifier performance have receptive fields that cluster in cortical regions corresponding to the retinotopic location of the stimulus edge. Instead, we find the distribution of highly weighted voxels to be approximately random, with a modest bias toward more foveal voxels. Our results demonstrate that edge-related activity is likely not necessary for orientation decoding. A significant theoretical limitation of multivariate pattern analysis in neuroscience is the ambiguity in interpreting the source of decodable information used by classifiers. For example, orientation can be decoded from BOLD activation patterns in human V1, even though orientation columns are at a finer spatial scale than 3T fMRI. Consequently, the source of decodable information remains controversial. Here we test the proposal that information related to the stimulus edges underlies orientation decoding. We map voxel population receptive fields in V1 and evaluate orientation decoding performance as a function of stimulus location in retinotopic cortex. We find orientation is decodable from voxels whose receptive fields do not overlap with the stimulus edges, suggesting edge-related activity does not substantially drive orientation decoding. Copyright © 2017 the authors 0270-6474/17/371187-10$15.00/0.

Development, validation, and implementation of a patient-specific Monte Carlo 3D internal dosimetry platform

NASA Astrophysics Data System (ADS)

Besemer, Abigail E.

Targeted radionuclide therapy is emerging as an attractive treatment option for a broad spectrum of tumor types because it has the potential to simultaneously eradicate both the primary tumor site as well as the metastatic disease throughout the body. Patient-specific absorbed dose calculations for radionuclide therapies are important for reducing the risk of normal tissue complications and optimizing tumor response. However, the only FDA approved software for internal dosimetry calculates doses based on the MIRD methodology which estimates mean organ doses using activity-to-dose scaling factors tabulated from standard phantom geometries. Despite the improved dosimetric accuracy afforded by direct Monte Carlo dosimetry methods these methods are not widely used in routine clinical practice because of the complexity of implementation, lack of relevant standard protocols, and longer dose calculation times. The main goal of this work was to develop a Monte Carlo internal dosimetry platform in order to (1) calculate patient-specific voxelized dose distributions in a clinically feasible time frame, (2) examine and quantify the dosimetric impact of various parameters and methodologies used in 3D internal dosimetry methods, and (3) develop a multi-criteria treatment planning optimization framework for multi-radiopharmaceutical combination therapies. This platform utilizes serial PET/CT or SPECT/CT images to calculate voxelized 3D internal dose distributions with the Monte Carlo code Geant4. Dosimetry can be computed for any diagnostic or therapeutic radiopharmaceutical and for both pre-clinical and clinical applications. In this work, the platform's dosimetry calculations were successfully validated against previously published reference doses values calculated in standard phantoms for a variety of radionuclides, over a wide range of photon and electron energies, and for many different organs and tumor sizes. Retrospective dosimetry was also calculated for various pre-clinical and clinical patients and large dosimetric differences resulted when using conventional organ-level methods and the patient-specific voxelized methods described in this work. The dosimetric impact of various steps in the 3D voxelized dosimetry process were evaluated including quantitative imaging acquisition, image coregistration, voxel resampling, ROI contouring, CT-based material segmentation, and pharmacokinetic fitting. Finally, a multi-objective treatment planning optimization framework was developed for multi-radiopharmaceutical combination therapies.

Volumetric and Voxel-Based Morphometry Findings in Autism Subjects With and Without Macrocephaly

PubMed Central

Bigler, Erin D.; Abildskov, Tracy J.; Petrie, Jo Ann; Johnson, Michael; Lange, Nicholas; Chipman, Jonathan; Lu, Jeffrey; McMahon, William; Lainhart, Janet E.

2015-01-01

This study sought to replicate Herbert et al. (2003a), which found increased overall white matter (WM) volume in subjects with autism, even after controlling for head size differences. To avoid the possibility that greater WM volume in autism is merely an epiphenomena of macrocephaly over-representation associated with the disorder, the current study included control subjects with benign macrocephaly. The control group also included subjects with a reading disability to insure cognitive heterogeneity. WM volume in autism was significantly larger, even when controlling for brain volume, rate of macrocephaly, and other demographic variables. Autism and controls differed little on whole-brain WM voxel-based morphometry (VBM) analyses suggesting that the overall increase in WM volume was non-localized. Autism subjects exhibited a differential pattern of IQ relationships with brain volumetry findings from controls. Current theories of brain overgrowth and their importance in the development of autism are discussed in the context of these findings. PMID:20446133

Micro-computed tomography pore-scale study of flow in porous media: Effect of voxel resolution

NASA Astrophysics Data System (ADS)

Shah, S. M.; Gray, F.; Crawshaw, J. P.; Boek, E. S.

2016-09-01

A fundamental understanding of flow in porous media at the pore-scale is necessary to be able to upscale average displacement processes from core to reservoir scale. The study of fluid flow in porous media at the pore-scale consists of two key procedures: Imaging - reconstruction of three-dimensional (3D) pore space images; and modelling such as with single and two-phase flow simulations with Lattice-Boltzmann (LB) or Pore-Network (PN) Modelling. Here we analyse pore-scale results to predict petrophysical properties such as porosity, single-phase permeability and multi-phase properties at different length scales. The fundamental issue is to understand the image resolution dependency of transport properties, in order to up-scale the flow physics from pore to core scale. In this work, we use a high resolution micro-computed tomography (micro-CT) scanner to image and reconstruct three dimensional pore-scale images of five sandstones (Bentheimer, Berea, Clashach, Doddington and Stainton) and five complex carbonates (Ketton, Estaillades, Middle Eastern sample 3, Middle Eastern sample 5 and Indiana Limestone 1) at four different voxel resolutions (4.4 μm, 6.2 μm, 8.3 μm and 10.2 μm), scanning the same physical field of view. Implementing three phase segmentation (macro-pore phase, intermediate phase and grain phase) on pore-scale images helps to understand the importance of connected macro-porosity in the fluid flow for the samples studied. We then compute the petrophysical properties for all the samples using PN and LB simulations in order to study the influence of voxel resolution on petrophysical properties. We then introduce a numerical coarsening scheme which is used to coarsen a high voxel resolution image (4.4 μm) to lower resolutions (6.2 μm, 8.3 μm and 10.2 μm) and study the impact of coarsening data on macroscopic and multi-phase properties. Numerical coarsening of high resolution data is found to be superior to using a lower resolution scan because it avoids the problem of partial volume effects and reduces the scaling effect by preserving the pore-space properties influencing the transport properties. This is evidently compared in this study by predicting several pore network properties such as number of pores and throats, average pore and throat radius and coordination number for both scan based analysis and numerical coarsened data.

Patch forest: a hybrid framework of random forest and patch-based segmentation

NASA Astrophysics Data System (ADS)

Xie, Zhongliu; Gillies, Duncan

2016-03-01

The development of an accurate, robust and fast segmentation algorithm has long been a research focus in medical computer vision. State-of-the-art practices often involve non-rigidly registering a target image with a set of training atlases for label propagation over the target space to perform segmentation, a.k.a. multi-atlas label propagation (MALP). In recent years, the patch-based segmentation (PBS) framework has gained wide attention due to its advantage of relaxing the strict voxel-to-voxel correspondence to a series of pair-wise patch comparisons for contextual pattern matching. Despite a high accuracy reported in many scenarios, computational efficiency has consistently been a major obstacle for both approaches. Inspired by recent work on random forest, in this paper we propose a patch forest approach, which by equipping the conventional PBS with a fast patch search engine, is able to boost segmentation speed significantly while retaining an equal level of accuracy. In addition, a fast forest training mechanism is also proposed, with the use of a dynamic grid framework to efficiently approximate data compactness computation and a 3D integral image technique for fast box feature retrieval.

Functional magnetic resonance imaging of working memory in Huntington's disease: cross-sectional data from the IMAGE-HD study.

PubMed

Georgiou-Karistianis, Nellie; Stout, Julie C; Domínguez D, Juan F; Carron, Sarah P; Ando, Ayaka; Churchyard, Andrew; Chua, Phyllis; Bohanna, India; Dymowski, Alicia R; Poudel, Govinda; Egan, Gary F

2014-05-01

We used functional magnetic resonance imaging (fMRI) to investigate spatial working memory (WM) in an N-BACK task (0, 1, and 2-BACK) in premanifest Huntington's disease (pre-HD, n = 35), early symptomatic Huntington's disease (symp-HD, n = 23), and control (n = 32) individuals. Overall, both WM conditions (1-BACK and 2-BACK) activated a large network of regions throughout the brain, common to all groups. However, voxel-wise and time-course analyses revealed significant functional group differences, despite no significant behavioral performance differences. During 1-BACK, voxel-wise blood-oxygen-level-dependent (BOLD) signal activity was significantly reduced in a number of regions from the WM network (inferior frontal gyrus, anterior insula, caudate, putamen, and cerebellum) in pre-HD and symp-HD groups, compared with controls; however, time-course analysis of the BOLD response in the dorsolateral prefrontal cortex (DLPFC) showed increased activation in symp-HD, compared with pre-HD and controls. The pattern of reduced voxel-wise BOLD activity in pre-HD and symp-HD, relative to controls, became more pervasive during 2-BACK affecting the same structures as in 1-BACK, but also incorporated further WM regions (anterior cingulate gyrus, parietal lobe and thalamus). The DLPFC BOLD time-course for 2-BACK showed a reversed pattern to that observed in 1-BACK, with a significantly diminished signal in symp-HD, relative to pre-HD and controls. Our findings provide support for functional brain reorganisation in cortical and subcortical regions in both pre-HD and symp-HD, which are modulated by task difficulty. Moreover, the lack of a robust striatal BOLD signal in pre-HD may represent a very early signature of change observed up to 15 years prior to clinical diagnosis. Copyright © 2013 Wiley Periodicals, Inc.

a Voxel-Based Filtering Algorithm for Mobile LIDAR Data

NASA Astrophysics Data System (ADS)

Qin, H.; Guan, G.; Yu, Y.; Zhong, L.

2018-04-01

This paper presents a stepwise voxel-based filtering algorithm for mobile LiDAR data. In the first step, to improve computational efficiency, mobile LiDAR points, in xy-plane, are first partitioned into a set of two-dimensional (2-D) blocks with a given block size, in each of which all laser points are further organized into an octree partition structure with a set of three-dimensional (3-D) voxels. Then, a voxel-based upward growing processing is performed to roughly separate terrain from non-terrain points with global and local terrain thresholds. In the second step, the extracted terrain points are refined by computing voxel curvatures. This voxel-based filtering algorithm is comprehensively discussed in the analyses of parameter sensitivity and overall performance. An experimental study performed on multiple point cloud samples, collected by different commercial mobile LiDAR systems, showed that the proposed algorithm provides a promising solution to terrain point extraction from mobile point clouds.

Is there more valuable information in PWI datasets for a voxel-wise acute ischemic stroke tissue outcome prediction than what is represented by typical perfusion maps?

NASA Astrophysics Data System (ADS)

Forkert, Nils Daniel; Siemonsen, Susanne; Dalski, Michael; Verleger, Tobias; Kemmling, Andre; Fiehler, Jens

2014-03-01

The acute ischemic stroke is a leading cause for death and disability in the industry nations. In case of a present acute ischemic stroke, the prediction of the future tissue outcome is of high interest for the clinicians as it can be used to support therapy decision making. Within this context, it has already been shown that the voxel-wise multi-parametric tissue outcome prediction leads to more promising results compared to single channel perfusion map thresholding. Most previously published multi-parametric predictions employ information from perfusion maps derived from perfusion-weighted MRI together with other image sequences such as diffusion-weighted MRI. However, it remains unclear if the typically calculated perfusion maps used for this purpose really include all valuable information from the PWI dataset for an optimal tissue outcome prediction. To investigate this problem in more detail, two different methods to predict tissue outcome using a k-nearest-neighbor approach were developed in this work and evaluated based on 18 datasets of acute stroke patients with known tissue outcome. The first method integrates apparent diffusion coefficient and perfusion parameter (Tmax, MTT, CBV, CBF) information for the voxel-wise prediction, while the second method employs also apparent diffusion coefficient information but the complete perfusion information in terms of the voxel-wise residue functions instead of the perfusion parameter maps for the voxel-wise prediction. Overall, the comparison of the results of the two prediction methods for the 18 patients using a leave-one-out cross validation revealed no considerable differences. Quantitatively, the parameter-based prediction of tissue outcome led to a mean Dice coefficient of 0.474, while the prediction using the residue functions led to a mean Dice coefficient of 0.461. Thus, it may be concluded from the results of this study that the perfusion parameter maps typically derived from PWI datasets include all valuable perfusion information required for a voxel-based tissue outcome prediction, while the complete analysis of the residue functions does not add further benefits for the voxel-wise tissue outcome prediction and is also computationally more expensive.

Common and distinct patterns of grey-matter volume alteration in major depression and bipolar disorder: evidence from voxel-based meta-analysis.

PubMed

Wise, T; Radua, J; Via, E; Cardoner, N; Abe, O; Adams, T M; Amico, F; Cheng, Y; Cole, J H; de Azevedo Marques Périco, C; Dickstein, D P; Farrow, T F D; Frodl, T; Wagner, G; Gotlib, I H; Gruber, O; Ham, B J; Job, D E; Kempton, M J; Kim, M J; Koolschijn, P C M P; Malhi, G S; Mataix-Cols, D; McIntosh, A M; Nugent, A C; O'Brien, J T; Pezzoli, S; Phillips, M L; Sachdev, P S; Salvadore, G; Selvaraj, S; Stanfield, A C; Thomas, A J; van Tol, M J; van der Wee, N J A; Veltman, D J; Young, A H; Fu, C H; Cleare, A J; Arnone, D

2017-10-01

Finding robust brain substrates of mood disorders is an important target for research. The degree to which major depression (MDD) and bipolar disorder (BD) are associated with common and/or distinct patterns of volumetric changes is nevertheless unclear. Furthermore, the extant literature is heterogeneous with respect to the nature of these changes. We report a meta-analysis of voxel-based morphometry (VBM) studies in MDD and BD. We identified studies published up to January 2015 that compared grey matter in MDD (50 data sets including 4101 individuals) and BD (36 data sets including 2407 individuals) using whole-brain VBM. We used statistical maps from the studies included where available and reported peak coordinates otherwise. Group comparisons and conjunction analyses identified regions in which the disorders showed common and distinct patterns of volumetric alteration. Both disorders were associated with lower grey-matter volume relative to healthy individuals in a number of areas. Conjunction analysis showed smaller volumes in both disorders in clusters in the dorsomedial and ventromedial prefrontal cortex, including the anterior cingulate cortex and bilateral insula. Group comparisons indicated that findings of smaller grey-matter volumes relative to controls in the right dorsolateral prefrontal cortex and left hippocampus, along with cerebellar, temporal and parietal regions were more substantial in major depression. These results suggest that MDD and BD are characterised by both common and distinct patterns of grey-matter volume changes. This combination of differences and similarities has the potential to inform the development of diagnostic biomarkers for these conditions.

Lower Parietal Encoding Activation Is Associated with Sharper Information and Better Memory.

PubMed

Lee, Hongmi; Chun, Marvin M; Kuhl, Brice A

2017-04-01

Mean fMRI activation in ventral posterior parietal cortex (vPPC) during memory encoding often negatively predicts successful remembering. A popular interpretation of this phenomenon is that vPPC reflects "off-task" processing. However, recent fMRI studies considering distributed patterns of activity suggest that vPPC actively represents encoded material. Here, we assessed the relationships between pattern-based content representations in vPPC, mean activation in vPPC, and subsequent remembering. We analyzed data from two fMRI experiments where subjects studied then recalled word-face or word-scene associations. For each encoding trial, we measured 1) mean univariate activation within vPPC and 2) the strength of face/scene information as indexed by pattern analysis. Mean activation in vPPC negatively predicted subsequent remembering, but the strength of pattern-based information in the same vPPC voxels positively predicted later memory. Indeed, univariate amplitude averaged across vPPC voxels negatively correlated with pattern-based information strength. This dissociation reflected a tendency for univariate reductions to maximally occur in voxels that were not strongly tuned for the category of encoded stimuli. These results indicate that vPPC activity patterns reflect the content and quality of memory encoding and constitute a striking example of lower univariate activity corresponding to stronger pattern-based information. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Estimating the functional dimensionality of neural representations.

PubMed

Ahlheim, Christiane; Love, Bradley C

2018-06-07

Recent advances in multivariate fMRI analysis stress the importance of information inherent to voxel patterns. Key to interpreting these patterns is estimating the underlying dimensionality of neural representations. Dimensions may correspond to psychological dimensions, such as length and orientation, or involve other coding schemes. Unfortunately, the noise structure of fMRI data inflates dimensionality estimates and thus makes it difficult to assess the true underlying dimensionality of a pattern. To address this challenge, we developed a novel approach to identify brain regions that carry reliable task-modulated signal and to derive an estimate of the signal's functional dimensionality. We combined singular value decomposition with cross-validation to find the best low-dimensional projection of a pattern of voxel-responses at a single-subject level. Goodness of the low-dimensional reconstruction is measured as Pearson correlation with a test set, which allows to test for significance of the low-dimensional reconstruction across participants. Using hierarchical Bayesian modeling, we derive the best estimate and associated uncertainty of underlying dimensionality across participants. We validated our method on simulated data of varying underlying dimensionality, showing that recovered dimensionalities match closely true dimensionalities. We then applied our method to three published fMRI data sets all involving processing of visual stimuli. The results highlight three possible applications of estimating the functional dimensionality of neural data. Firstly, it can aid evaluation of model-based analyses by revealing which areas express reliable, task-modulated signal that could be missed by specific models. Secondly, it can reveal functional differences across brain regions. Thirdly, knowing the functional dimensionality allows assessing task-related differences in the complexity of neural patterns. Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.

Supercomputer description of human lung morphology for imaging analysis.

PubMed

Martonen, T B; Hwang, D; Guan, X; Fleming, J S

1998-04-01

A supercomputer code that describes the three-dimensional branching structure of the human lung has been developed. The algorithm was written for the Cray C94. In our simulations, the human lung was divided into a matrix containing discrete volumes (voxels) so as to be compatible with analyses of SPECT images. The matrix has 3840 voxels. The matrix can be segmented into transverse, sagittal and coronal layers analogous to human subject examinations. The compositions of individual voxels were identified by the type and respective number of airways present. The code provides a mapping of the spatial positions of the almost 17 million airways in human lungs and unambiguously assigns each airway to a voxel. Thus, the clinician and research scientist in the medical arena have a powerful new tool to be used in imaging analyses. The code was designed to be integrated into diverse applications, including the interpretation of SPECT images, the design of inhalation exposure experiments and the targeted delivery of inhaled pharmacologic drugs.

Association and Host Selectivity in Multi-Host Pathogens

PubMed Central

Malpica, José M.; Sacristán, Soledad; Fraile, Aurora; García-Arenal, Fernando

2006-01-01

The distribution of multi-host pathogens over their host range conditions their population dynamics and structure. Also, host co-infection by different pathogens may have important consequences for the evolution of hosts and pathogens, and host-pathogen co-evolution. Hence it is of interest to know if the distribution of pathogens over their host range is random, or if there are associations between hosts and pathogens, or between pathogens sharing a host. To analyse these issues we propose indices for the observed patterns of host infection by pathogens, and for the observed patterns of co-infection, and tests to analyse if these patterns conform to randomness or reflect associations. Applying these tests to the prevalence of five plant viruses on 21 wild plant species evidenced host-virus associations: most hosts and viruses were selective for viruses and hosts, respectively. Interestingly, the more host-selective viruses were the more prevalent ones, suggesting that host specialisation is a successful strategy for multi-host pathogens. Analyses also showed that viruses tended to associate positively in co-infected hosts. The developed indices and tests provide the tools to analyse how strong and common are these associations among different groups of pathogens, which will help to understand and model the population biology of multi-host pathogens. PMID:17183670

Pairwise mixture model for unmixing partial volume effect in multi-voxel MR spectroscopy of brain tumour patients

NASA Astrophysics Data System (ADS)

Olliverre, Nathan; Asad, Muhammad; Yang, Guang; Howe, Franklyn; Slabaugh, Gregory

2017-03-01

Multi-Voxel Magnetic Resonance Spectroscopy (MV-MRS) provides an important and insightful technique for the examination of the chemical composition of brain tissue, making it an attractive medical imaging modality for the examination of brain tumours. MRS, however, is affected by the issue of the Partial Volume Effect (PVE), where the signals of multiple tissue types can be found within a single voxel and provides an obstacle to the interpretation of the data. The PVE results from the low resolution achieved in MV-MRS images relating to the signal to noise ratio (SNR). To counteract PVE, this paper proposes a novel Pairwise Mixture Model (PMM), that extends a recently reported Signal Mixture Model (SMM) for representing the MV-MRS signal as normal, low or high grade tissue types. Inspired by Conditional Random Field (CRF) and its continuous variant the PMM incorporates the surrounding voxel neighbourhood into an optimisation problem, the solution of which provides an estimation to a set of coefficients. The values of the estimated coefficients represents the amount of each tissue type (normal, low or high) found within a voxel. These coefficients can then be visualised as a nosological rendering using a coloured grid representing the MV-MRS image overlaid on top of a structural image, such as a Magnetic Resonance Image (MRI). Experimental results show an accuracy of 92.69% in classifying patient tumours as either low or high grade compared against the histopathology for each patient. Compared to 91.96% achieved by the SMM, the proposed PMM method demonstrates the importance of incorporating spatial coherence into the estimation as well as its potential clinical usage.

The impact of sample size on the reproducibility of voxel-based lesion-deficit mappings.

PubMed

Lorca-Puls, Diego L; Gajardo-Vidal, Andrea; White, Jitrachote; Seghier, Mohamed L; Leff, Alexander P; Green, David W; Crinion, Jenny T; Ludersdorfer, Philipp; Hope, Thomas M H; Bowman, Howard; Price, Cathy J

2018-07-01

This study investigated how sample size affects the reproducibility of findings from univariate voxel-based lesion-deficit analyses (e.g., voxel-based lesion-symptom mapping and voxel-based morphometry). Our effect of interest was the strength of the mapping between brain damage and speech articulation difficulties, as measured in terms of the proportion of variance explained. First, we identified a region of interest by searching on a voxel-by-voxel basis for brain areas where greater lesion load was associated with poorer speech articulation using a large sample of 360 right-handed English-speaking stroke survivors. We then randomly drew thousands of bootstrap samples from this data set that included either 30, 60, 90, 120, 180, or 360 patients. For each resample, we recorded effect size estimates and p values after conducting exactly the same lesion-deficit analysis within the previously identified region of interest and holding all procedures constant. The results show (1) how often small effect sizes in a heterogeneous population fail to be detected; (2) how effect size and its statistical significance varies with sample size; (3) how low-powered studies (due to small sample sizes) can greatly over-estimate as well as under-estimate effect sizes; and (4) how large sample sizes (N ≥ 90) can yield highly significant p values even when effect sizes are so small that they become trivial in practical terms. The implications of these findings for interpreting the results from univariate voxel-based lesion-deficit analyses are discussed. Copyright © 2018 The Author(s). Published by Elsevier Ltd.. All rights reserved.

The effect of brain lesions on sound localization in complex acoustic environments.

PubMed

Zündorf, Ida C; Karnath, Hans-Otto; Lewald, Jörg

2014-05-01

Localizing sound sources of interest in cluttered acoustic environments--as in the 'cocktail-party' situation--is one of the most demanding challenges to the human auditory system in everyday life. In this study, stroke patients' ability to localize acoustic targets in a single-source and in a multi-source setup in the free sound field were directly compared. Subsequent voxel-based lesion-behaviour mapping analyses were computed to uncover the brain areas associated with a deficit in localization in the presence of multiple distracter sound sources rather than localization of individually presented sound sources. Analyses revealed a fundamental role of the right planum temporale in this task. The results from the left hemisphere were less straightforward, but suggested an involvement of inferior frontal and pre- and postcentral areas. These areas appear to be particularly involved in the spectrotemporal analyses crucial for effective segregation of multiple sound streams from various locations, beyond the currently known network for localization of isolated sound sources in otherwise silent surroundings.

Cholinergic and perfusion brain networks in Parkinson disease dementia.

PubMed

Colloby, Sean J; McKeith, Ian G; Burn, David J; Wyper, David J; O'Brien, John T; Taylor, John-Paul

2016-07-12

To investigate muscarinic M1/M4 cholinergic networks in Parkinson disease dementia (PDD) and their association with changes in Mini-Mental State Examination (MMSE) after 12 weeks of treatment with donepezil. Forty-nine participants (25 PDD and 24 elderly controls) underwent (123)I-QNB and (99m)Tc-exametazime SPECT scanning. We implemented voxel principal components (PC) analysis, producing a series of PC images of patterns of interrelated voxels across individuals. Linear regression analyses derived specific M1/M4 and perfusion spatial covariance patterns (SCPs). We found an M1/M4 SCP of relative decreased binding in basal forebrain, temporal, striatum, insula, and anterior cingulate (F1,47 = 31.9, p < 0.001) in cholinesterase inhibitor-naive patients with PDD, implicating limbic-paralimbic and salience cholinergic networks. The corresponding regional cerebral blood flow SCP showed relative decreased uptake in temporoparietal and prefrontal areas (F1,47 = 177.5, p < 0.001) and nodes of the frontoparietal and default mode networks (DMN). The M1/M4 pattern that correlated with an improvement in MMSE (r = 0.58, p = 0.005) revealed relatively preserved/increased pre/medial/orbitofrontal, parietal, and posterior cingulate areas coinciding with the DMN and frontoparietal networks. Dysfunctional limbic-paralimbic and salience cholinergic networks were associated with PDD. Established cholinergic maintenance of the DMN and frontoparietal networks may be prerequisite for cognitive remediation following cholinergic treatment in this condition. © 2016 American Academy of Neurology.

Cholinergic and perfusion brain networks in Parkinson disease dementia

PubMed Central

McKeith, Ian G.; Burn, David J.; Wyper, David J.; O'Brien, John T.; Taylor, John-Paul

2016-01-01

Objective: To investigate muscarinic M1/M4 cholinergic networks in Parkinson disease dementia (PDD) and their association with changes in Mini-Mental State Examination (MMSE) after 12 weeks of treatment with donepezil. Methods: Forty-nine participants (25 PDD and 24 elderly controls) underwent 123I-QNB and 99mTc-exametazime SPECT scanning. We implemented voxel principal components (PC) analysis, producing a series of PC images of patterns of interrelated voxels across individuals. Linear regression analyses derived specific M1/M4 and perfusion spatial covariance patterns (SCPs). Results: We found an M1/M4 SCP of relative decreased binding in basal forebrain, temporal, striatum, insula, and anterior cingulate (F1,47 = 31.9, p < 0.001) in cholinesterase inhibitor–naive patients with PDD, implicating limbic-paralimbic and salience cholinergic networks. The corresponding regional cerebral blood flow SCP showed relative decreased uptake in temporoparietal and prefrontal areas (F1,47 = 177.5, p < 0.001) and nodes of the frontoparietal and default mode networks (DMN). The M1/M4 pattern that correlated with an improvement in MMSE (r = 0.58, p = 0.005) revealed relatively preserved/increased pre/medial/orbitofrontal, parietal, and posterior cingulate areas coinciding with the DMN and frontoparietal networks. Conclusion: Dysfunctional limbic-paralimbic and salience cholinergic networks were associated with PDD. Established cholinergic maintenance of the DMN and frontoparietal networks may be prerequisite for cognitive remediation following cholinergic treatment in this condition. PMID:27306636

Monitoring of human brain functions in risk decision-making task by diffuse optical tomography using voxel-wise general linear model

NASA Astrophysics Data System (ADS)

Lin, Zi-Jing; Li, Lin; Cazzell, Marry; Liu, Hanli

2013-03-01

Functional near-infrared spectroscopy (fNIRS) is a non-invasive imaging technique which measures the hemodynamic changes that reflect the brain activity. Diffuse optical tomography (DOT), a variant of fNIRS with multi-channel NIRS measurements, has demonstrated capability of three dimensional (3D) reconstructions of hemodynamic changes due to the brain activity. Conventional method of DOT image analysis to define the brain activation is based upon the paired t-test between two different states, such as resting-state versus task-state. However, it has limitation because the selection of activation and post-activation period is relatively subjective. General linear model (GLM) based analysis can overcome this limitation. In this study, we combine the 3D DOT image reconstruction with GLM-based analysis (i.e., voxel-wise GLM analysis) to investigate the brain activity that is associated with the risk-decision making process. Risk decision-making is an important cognitive process and thus is an essential topic in the field of neuroscience. The balloon analogue risk task (BART) is a valid experimental model and has been commonly used in behavioral measures to assess human risk taking action and tendency while facing risks. We have utilized the BART paradigm with a blocked design to investigate brain activations in the prefrontal and frontal cortical areas during decision-making. Voxel-wise GLM analysis was performed on 18human participants (10 males and 8females).In this work, we wish to demonstrate the feasibility of using voxel-wise GLM analysis to image and study cognitive functions in response to risk decision making by DOT. Results have shown significant changes in the dorsal lateral prefrontal cortex (DLPFC) during the active choice mode and a different hemodynamic pattern between genders, which are in good agreements with published literatures in functional magnetic resonance imaging (fMRI) and fNIRS studies.

Decoding the content of recollection within the core recollection network and beyond.

PubMed

Thakral, Preston P; Wang, Tracy H; Rugg, Michael D

2017-06-01

Recollection - retrieval of qualitative information about a past event - is associated with enhanced neural activity in a consistent set of neural regions (the 'core recollection network') seemingly regardless of the nature of the recollected content. Here, we employed multi-voxel pattern analysis (MVPA) to assess whether retrieval-related functional magnetic resonance imaging (fMRI) activity in core recollection regions - including the hippocampus, angular gyrus, medial prefrontal cortex, retrosplenial/posterior cingulate cortex, and middle temporal gyrus - contain information about studied content and thus demonstrate retrieval-related 'reinstatement' effects. During study, participants viewed objects and concrete words that were subjected to different encoding tasks. Test items included studied words, the names of studied objects, or unstudied words. Participants judged whether the items were recollected, familiar, or new by making 'remember', 'know', and 'new' responses, respectively. The study history of remembered test items could be reliably decoded using MVPA in most regions, as well as from the dorsolateral prefrontal cortex, a region where univariate recollection effects could not be detected. The findings add to evidence that members of the core recollection network, as well as at least one neural region where mean signal is insensitive to recollection success, carry information about recollected content. Importantly, the study history of recognized items endorsed with a 'know' response could be decoded with equal accuracy. The results thus demonstrate a striking dissociation between mean signal and multi-voxel indices of recollection. Moreover, they converge with prior findings in suggesting that, as it is operationalized by classification-based MVPA, reinstatement is not uniquely a signature of recollection. Copyright © 2016 Elsevier Ltd. All rights reserved.

Separability of Abstract-Category and Specific-Exemplar Visual Object Subsystems: Evidence from fMRI Pattern Analysis

PubMed Central

McMenamin, Brenton W.; Deason, Rebecca G.; Steele, Vaughn R.; Koutstaal, Wilma; Marsolek, Chad J.

2014-01-01

Previous research indicates that dissociable neural subsystems underlie abstract-category (AC) recognition and priming of objects (e.g., cat, piano) and specific-exemplar (SE) recognition and priming of objects (e.g., a calico cat, a different calico cat, a grand piano, etc.). However, the degree of separability between these subsystems is not known, despite the importance of this issue for assessing relevant theories. Visual object representations are widely distributed in visual cortex, thus a multivariate pattern analysis (MVPA) approach to analyzing functional magnetic resonance imaging (fMRI) data may be critical for assessing the separability of different kinds of visual object processing. Here we examined the neural representations of visual object categories and visual object exemplars using multi-voxel pattern analyses of brain activity elicited in visual object processing areas during a repetition-priming task. In the encoding phase, participants viewed visual objects and the printed names of other objects. In the subsequent test phase, participants identified objects that were either same-exemplar primed, different-exemplar primed, word-primed, or unprimed. In visual object processing areas, classifiers were trained to distinguish same-exemplar primed objects from word-primed objects. Then, the abilities of these classifiers to discriminate different-exemplar primed objects and word-primed objects (reflecting AC priming) and to discriminate same-exemplar primed objects and different-exemplar primed objects (reflecting SE priming) was assessed. Results indicated that (a) repetition priming in occipital-temporal regions is organized asymmetrically, such that AC priming is more prevalent in the left hemisphere and SE priming is more prevalent in the right hemisphere, and (b) AC and SE subsystems are weakly modular, not strongly modular or unified. PMID:25528436

Separability of abstract-category and specific-exemplar visual object subsystems: evidence from fMRI pattern analysis.

PubMed

McMenamin, Brenton W; Deason, Rebecca G; Steele, Vaughn R; Koutstaal, Wilma; Marsolek, Chad J

2015-02-01

Previous research indicates that dissociable neural subsystems underlie abstract-category (AC) recognition and priming of objects (e.g., cat, piano) and specific-exemplar (SE) recognition and priming of objects (e.g., a calico cat, a different calico cat, a grand piano, etc.). However, the degree of separability between these subsystems is not known, despite the importance of this issue for assessing relevant theories. Visual object representations are widely distributed in visual cortex, thus a multivariate pattern analysis (MVPA) approach to analyzing functional magnetic resonance imaging (fMRI) data may be critical for assessing the separability of different kinds of visual object processing. Here we examined the neural representations of visual object categories and visual object exemplars using multi-voxel pattern analyses of brain activity elicited in visual object processing areas during a repetition-priming task. In the encoding phase, participants viewed visual objects and the printed names of other objects. In the subsequent test phase, participants identified objects that were either same-exemplar primed, different-exemplar primed, word-primed, or unprimed. In visual object processing areas, classifiers were trained to distinguish same-exemplar primed objects from word-primed objects. Then, the abilities of these classifiers to discriminate different-exemplar primed objects and word-primed objects (reflecting AC priming) and to discriminate same-exemplar primed objects and different-exemplar primed objects (reflecting SE priming) was assessed. Results indicated that (a) repetition priming in occipital-temporal regions is organized asymmetrically, such that AC priming is more prevalent in the left hemisphere and SE priming is more prevalent in the right hemisphere, and (b) AC and SE subsystems are weakly modular, not strongly modular or unified. Copyright © 2014 Elsevier Inc. All rights reserved.

How distributed processing produces false negatives in voxel-based lesion-deficit analyses.

PubMed

Gajardo-Vidal, Andrea; Lorca-Puls, Diego L; Crinion, Jennifer T; White, Jitrachote; Seghier, Mohamed L; Leff, Alex P; Hope, Thomas M H; Ludersdorfer, Philipp; Green, David W; Bowman, Howard; Price, Cathy J

2018-07-01

In this study, we hypothesized that if the same deficit can be caused by damage to one or another part of a distributed neural system, then voxel-based analyses might miss critical lesion sites because preservation of each site will not be consistently associated with preserved function. The first part of our investigation used voxel-based multiple regression analyses of data from 359 right-handed stroke survivors to identify brain regions where lesion load is associated with picture naming abilities after factoring out variance related to object recognition, semantics and speech articulation so as to focus on deficits arising at the word retrieval level. A highly significant lesion-deficit relationship was identified in left temporal and frontal/premotor regions. Post-hoc analyses showed that damage to either of these sites caused the deficit of interest in less than half the affected patients (76/162 = 47%). After excluding all patients with damage to one or both of the identified regions, our second analysis revealed a new region, in the anterior part of the left putamen, which had not been previously detected because many patients had the deficit of interest after temporal or frontal damage that preserved the left putamen. The results illustrate how (i) false negative results arise when the same deficit can be caused by different lesion sites; (ii) some of the missed effects can be unveiled by adopting an iterative approach that systematically excludes patients with lesions to the areas identified in previous analyses, (iii) statistically significant voxel-based lesion-deficit mappings can be driven by a subset of patients; (iv) focal lesions to the identified regions are needed to determine whether the deficit of interest is the consequence of focal damage or much more extensive damage that includes the identified region; and, finally, (v) univariate voxel-based lesion-deficit mappings cannot, in isolation, be used to predict outcome in other patients. Copyright © 2018 The Authors. Published by Elsevier Ltd.. All rights reserved.

18F-FDG PET brain images as features for Alzheimer classification

NASA Astrophysics Data System (ADS)

Azmi, M. H.; Saripan, M. I.; Nordin, A. J.; Ahmad Saad, F. F.; Abdul Aziz, S. A.; Wan Adnan, W. A.

2017-08-01

2-Deoxy-2-[fluorine-18] fluoro-D-glucose (18F-FDG) Positron Emission Tomography (PET) imaging offers meaningful information for various types of diseases diagnosis. In Alzheimer's disease (AD), the hypometabolism of glucose which observed on the low intensity voxel in PET image may relate to the onset of the disease. The importance of early detection of AD is inevitable because the resultant brain damage is irreversible. Several statistical analysis and machine learning algorithm have been proposed to investigate the rate and the pattern of the hypometabolism. This study focus on the same aim with further investigation was performed on several hypometabolism pattern. Some pre-processing steps were implemented to standardize the data in order to minimize the effect of resolution and anatomical differences. The features used are the mean voxel intensity within the AD pattern mask, which derived from several z-score and FDR threshold values. The global mean voxel (GMV) and slice-based mean voxel (SbMV) intensity were observed and used as input to the neural network. Several neural network architectures were tested and compared to the nearest neighbour method. The highest accuracy equals to 0.9 and recorded at z-score ≤-1.3 with 1 node neural network architecture (sensitivity=0.81 and specificity=0.95) and at z-score ≤-0.7 with 10 nodes neural network (sensitivity=0.83 and specificity=0.94).

Sources of Phoneme Errors in Repetition: Perseverative, Neologistic, and Lesion Patterns in Jargon Aphasia

PubMed Central

Pilkington, Emma; Keidel, James; Kendrick, Luke T.; Saddy, James D.; Sage, Karen; Robson, Holly

2017-01-01

This study examined patterns of neologistic and perseverative errors during word repetition in fluent Jargon aphasia. The principal hypotheses accounting for Jargon production indicate that poor activation of a target stimulus leads to weakly activated target phoneme segments, which are outcompeted at the phonological encoding level. Voxel-lesion symptom mapping studies of word repetition errors suggest a breakdown in the translation from auditory-phonological analysis to motor activation. Behavioral analyses of repetition data were used to analyse the target relatedness (Phonological Overlap Index: POI) of neologistic errors and patterns of perseveration in 25 individuals with Jargon aphasia. Lesion-symptom analyses explored the relationship between neurological damage and jargon repetition in a group of 38 aphasia participants. Behavioral results showed that neologisms produced by 23 jargon individuals contained greater degrees of target lexico-phonological information than predicted by chance and that neologistic and perseverative production were closely associated. A significant relationship between jargon production and lesions to temporoparietal regions was identified. Region of interest regression analyses suggested that damage to the posterior superior temporal gyrus and superior temporal sulcus in combination was best predictive of a Jargon aphasia profile. Taken together, these results suggest that poor phonological encoding, secondary to impairment in sensory-motor integration, alongside impairments in self-monitoring result in jargon repetition. Insights for clinical management and future directions are discussed. PMID:28522967

Automated diagnosis of Alzheimer's disease with multi-atlas based whole brain segmentations

NASA Astrophysics Data System (ADS)

Luo, Yuan; Tang, Xiaoying

2017-03-01

Voxel-based analysis is widely used in quantitative analysis of structural brain magnetic resonance imaging (MRI) and automated disease detection, such as Alzheimer's disease (AD). However, noise at the voxel level may cause low sensitivity to AD-induced structural abnormalities. This can be addressed with the use of a whole brain structural segmentation approach which greatly reduces the dimension of features (the number of voxels). In this paper, we propose an automatic AD diagnosis system that combines such whole brain segmen- tations with advanced machine learning methods. We used a multi-atlas segmentation technique to parcellate T1-weighted images into 54 distinct brain regions and extract their structural volumes to serve as the features for principal-component-analysis-based dimension reduction and support-vector-machine-based classification. The relationship between the number of retained principal components (PCs) and the diagnosis accuracy was systematically evaluated, in a leave-one-out fashion, based on 28 AD subjects and 23 age-matched healthy subjects. Our approach yielded pretty good classification results with 96.08% overall accuracy being achieved using the three foremost PCs. In addition, our approach yielded 96.43% specificity, 100% sensitivity, and 0.9891 area under the receiver operating characteristic curve.

The evaluation of multi-structure, multi-atlas pelvic anatomy features in a prostate MR lymphography CAD system

NASA Astrophysics Data System (ADS)

Meijs, M.; Debats, O.; Huisman, H.

2015-03-01

In prostate cancer, the detection of metastatic lymph nodes indicates progression from localized disease to metastasized cancer. The detection of positive lymph nodes is, however, a complex and time consuming task for experienced radiologists. Assistance of a two-stage Computer-Aided Detection (CAD) system in MR Lymphography (MRL) is not yet feasible due to the large number of false positives in the first stage of the system. By introducing a multi-structure, multi-atlas segmentation, using an affine transformation followed by a B-spline transformation for registration, the organ location is given by a mean density probability map. The atlas segmentation is semi-automatically drawn with ITK-SNAP, using Active Contour Segmentation. Each anatomic structure is identified by a label number. Registration is performed using Elastix, using Mutual Information and an Adaptive Stochastic Gradient optimization. The dataset consists of the MRL scans of ten patients, with lymph nodes manually annotated in consensus by two expert readers. The feature map of the CAD system consists of the Multi-Atlas and various other features (e.g. Normalized Intensity and multi-scale Blobness). The voxel-based Gentleboost classifier is evaluated using ROC analysis with cross validation. We show in a set of 10 studies that adding multi-structure, multi-atlas anatomical structure likelihood features improves the quality of the lymph node voxel likelihood map. Multiple structure anatomy maps may thus make MRL CAD more feasible.

Measure Projection Analysis: A Probabilistic Approach to EEG Source Comparison and Multi-Subject Inference

PubMed Central

Bigdely-Shamlo, Nima; Mullen, Tim; Kreutz-Delgado, Kenneth; Makeig, Scott

2013-01-01

A crucial question for the analysis of multi-subject and/or multi-session electroencephalographic (EEG) data is how to combine information across multiple recordings from different subjects and/or sessions, each associated with its own set of source processes and scalp projections. Here we introduce a novel statistical method for characterizing the spatial consistency of EEG dynamics across a set of data records. Measure Projection Analysis (MPA) first finds voxels in a common template brain space at which a given dynamic measure is consistent across nearby source locations, then computes local-mean EEG measure values for this voxel subspace using a statistical model of source localization error and between-subject anatomical variation. Finally, clustering the mean measure voxel values in this locally consistent brain subspace finds brain spatial domains exhibiting distinguishable measure features and provides 3-D maps plus statistical significance estimates for each EEG measure of interest. Applied to sufficient high-quality data, the scalp projections of many maximally independent component (IC) processes contributing to recorded high-density EEG data closely match the projection of a single equivalent dipole located in or near brain cortex. We demonstrate the application of MPA to a multi-subject EEG study decomposed using independent component analysis (ICA), compare the results to k-means IC clustering in EEGLAB (sccn.ucsd.edu/eeglab), and use surrogate data to test MPA robustness. A Measure Projection Toolbox (MPT) plug-in for EEGLAB is available for download (sccn.ucsd.edu/wiki/MPT). Together, MPA and ICA allow use of EEG as a 3-D cortical imaging modality with near-cm scale spatial resolution. PMID:23370059

Comparative Study of SVM Methods Combined with Voxel Selection for Object Category Classification on fMRI Data

PubMed Central

Song, Sutao; Zhan, Zhichao; Long, Zhiying; Zhang, Jiacai; Yao, Li

2011-01-01

Background Support vector machine (SVM) has been widely used as accurate and reliable method to decipher brain patterns from functional MRI (fMRI) data. Previous studies have not found a clear benefit for non-linear (polynomial kernel) SVM versus linear one. Here, a more effective non-linear SVM using radial basis function (RBF) kernel is compared with linear SVM. Different from traditional studies which focused either merely on the evaluation of different types of SVM or the voxel selection methods, we aimed to investigate the overall performance of linear and RBF SVM for fMRI classification together with voxel selection schemes on classification accuracy and time-consuming. Methodology/Principal Findings Six different voxel selection methods were employed to decide which voxels of fMRI data would be included in SVM classifiers with linear and RBF kernels in classifying 4-category objects. Then the overall performances of voxel selection and classification methods were compared. Results showed that: (1) Voxel selection had an important impact on the classification accuracy of the classifiers: in a relative low dimensional feature space, RBF SVM outperformed linear SVM significantly; in a relative high dimensional space, linear SVM performed better than its counterpart; (2) Considering the classification accuracy and time-consuming holistically, linear SVM with relative more voxels as features and RBF SVM with small set of voxels (after PCA) could achieve the better accuracy and cost shorter time. Conclusions/Significance The present work provides the first empirical result of linear and RBF SVM in classification of fMRI data, combined with voxel selection methods. Based on the findings, if only classification accuracy was concerned, RBF SVM with appropriate small voxels and linear SVM with relative more voxels were two suggested solutions; if users concerned more about the computational time, RBF SVM with relative small set of voxels when part of the principal components were kept as features was a better choice. PMID:21359184

Comparative study of SVM methods combined with voxel selection for object category classification on fMRI data.

PubMed

Song, Sutao; Zhan, Zhichao; Long, Zhiying; Zhang, Jiacai; Yao, Li

2011-02-16

Support vector machine (SVM) has been widely used as accurate and reliable method to decipher brain patterns from functional MRI (fMRI) data. Previous studies have not found a clear benefit for non-linear (polynomial kernel) SVM versus linear one. Here, a more effective non-linear SVM using radial basis function (RBF) kernel is compared with linear SVM. Different from traditional studies which focused either merely on the evaluation of different types of SVM or the voxel selection methods, we aimed to investigate the overall performance of linear and RBF SVM for fMRI classification together with voxel selection schemes on classification accuracy and time-consuming. Six different voxel selection methods were employed to decide which voxels of fMRI data would be included in SVM classifiers with linear and RBF kernels in classifying 4-category objects. Then the overall performances of voxel selection and classification methods were compared. Results showed that: (1) Voxel selection had an important impact on the classification accuracy of the classifiers: in a relative low dimensional feature space, RBF SVM outperformed linear SVM significantly; in a relative high dimensional space, linear SVM performed better than its counterpart; (2) Considering the classification accuracy and time-consuming holistically, linear SVM with relative more voxels as features and RBF SVM with small set of voxels (after PCA) could achieve the better accuracy and cost shorter time. The present work provides the first empirical result of linear and RBF SVM in classification of fMRI data, combined with voxel selection methods. Based on the findings, if only classification accuracy was concerned, RBF SVM with appropriate small voxels and linear SVM with relative more voxels were two suggested solutions; if users concerned more about the computational time, RBF SVM with relative small set of voxels when part of the principal components were kept as features was a better choice.

A 4D biomechanical lung phantom for joint segmentation/registration evaluation

NASA Astrophysics Data System (ADS)

Markel, Daniel; Levesque, Ives; Larkin, Joe; Léger, Pierre; El Naqa, Issam

2016-10-01

At present, there exists few openly available methods for evaluation of simultaneous segmentation and registration algorithms. These methods allow for a combination of both techniques to track the tumor in complex settings such as adaptive radiotherapy. We have produced a quality assurance platform for evaluating this specific subset of algorithms using a preserved porcine lung in such that it is multi-modality compatible: positron emission tomography (PET), computer tomography (CT) and magnetic resonance imaging (MRI). A computer controlled respirator was constructed to pneumatically manipulate the lungs in order to replicate human breathing traces. A registration ground truth was provided using an in-house bifurcation tracking pipeline. Segmentation ground truth was provided by synthetic multi-compartment lesions to simulate biologically active tumor, background tissue and a necrotic core. The bifurcation tracking pipeline results were compared to digital deformations and used to evaluate three registration algorithms, Diffeomorphic demons, fast-symmetric forces demons and MiMVista’s deformable registration tool. Three segmentation algorithms the Chan Vese level sets method, a Hybrid technique and the multi-valued level sets algorithm. The respirator was able to replicate three seperate breathing traces with a mean accuracy of 2-2.2%. Bifurcation tracking error was found to be sub-voxel when using human CT data for displacements up to 6.5 cm and approximately 1.5 voxel widths for displacements up to 3.5 cm for the porcine lungs. For the fast-symmetric, diffeomorphic and MiMvista registration algorithms, mean geometric errors were found to be 0.430+/- 0.001 , 0.416+/- 0.001 and 0.605+/- 0.002 voxels widths respectively using the vector field differences and 0.4+/- 0.2 , 0.4+/- 0.2 and 0.6+/- 0.2 voxel widths using the bifurcation tracking pipeline. The proposed phantom was found sufficient for accurate evaluation of registration and segmentation algorithms. The use of automatically generated anatomical landmarks proposed can eliminate the time and potential innacuracy of manual landmark selection using expert observers.

Cerebral glucose metabolic prediction from amnestic mild cognitive impairment to Alzheimer's dementia: a meta-analysis.

PubMed

Ma, Hai Rong; Sheng, Li Qin; Pan, Ping Lei; Wang, Gen Di; Luo, Rong; Shi, Hai Cun; Dai, Zhen Yu; Zhong, Jian Guo

2018-01-01

Brain 18 F-fluorodeoxyglucose positron emission tomography (FDG-PET) has been utilized to monitor disease conversion from amnestic mild cognitive impairment (aMCI) to Alzheimer's dementia (AD). However, the conversion patterns of FDG-PET metabolism across studies are not conclusive. We conducted a voxel-wise meta-analysis using Seed-based d Mapping that included 10 baseline voxel-wise FDG-PET comparisons between 93 aMCI converters and 129 aMCI non-converters from nine longitudinal studies. The most robust and reliable metabolic alterations that predicted conversion from aMCI to AD were localized in the left posterior cingulate cortex (PCC)/precuneus. Furthermore, meta-regression analyses indicated that baseline mean age and severity of cognitive impairment, and follow-up duration were significant moderators for metabolic alterations in aMCI converters. Our study revealed hypometabolism in the left PCC/precuneus as an early feature in the development of AD. This finding has important implications in understanding the neural substrates for AD conversion and could serve as a potential imaging biomarker for early detection of AD as well as for tracking disease progression at the predementia stage.

Direct single-layered fabrication of 3D concavo convex patterns in nano-stereolithography

NASA Astrophysics Data System (ADS)

Lim, T. W.; Park, S. H.; Yang, D. Y.; Kong, H. J.; Lee, K. S.

2006-09-01

A nano-surfacing process (NSP) is proposed to directly fabricate three-dimensional (3D) concavo convex-shaped microstructures such as micro-lens arrays using two-photon polymerization (TPP), a promising technique for fabricating arbitrary 3D highly functional micro-devices. In TPP, commonly utilized methods for fabricating complex 3D microstructures to date are based on a layer-by-layer accumulating technique employing two-dimensional sliced data derived from 3D computer-aided design data. As such, this approach requires much time and effort for precise fabrication. In this work, a novel single-layer exposure method is proposed in order to improve the fabricating efficiency for 3D concavo convex-shaped microstructures. In the NSP, 3D microstructures are divided into 13 sub-regions horizontally with consideration of the heights. Those sub-regions are then expressed as 13 characteristic colors, after which a multi-voxel matrix (MVM) is composed with the characteristic colors. Voxels with various heights and diameters are generated to construct 3D structures using a MVM scanning method. Some 3D concavo convex-shaped microstructures were fabricated to estimate the usefulness of the NSP, and the results show that it readily enables the fabrication of single-layered 3D microstructures.

Lingering representations of stimuli influence recall organization

PubMed Central

Chan, Stephanie C.Y.; Applegate, Marissa C.; Morton, Neal W; Polyn, Sean M.; Norman, Kenneth A.

2017-01-01

Several prominent theories posit that information about recent experiences lingers in the brain and organizes memories for current experiences, by forming a temporal context that is linked to those memories at encoding. According to these theories, if the thoughts preceding an experience X resemble the thoughts preceding an experience Y, then X and Y should show an elevated probability of being recalled together. We tested this prediction by using multi-voxel pattern analysis (MVPA) of fMRI data to measure neural evidence for lingering processing of preceding stimuli. As predicted, memories encoded with similar lingering thoughts about the category of preceding stimuli were more likely to be recalled together. Our results demonstrate that the “fading embers” of previous stimuli help to organize recall, confirming a key prediction of computational models of episodic memory. PMID:28132858

Abnormal regional cerebral blood flow in childhood autism.

PubMed

Ohnishi, T; Matsuda, H; Hashimoto, T; Kunihiro, T; Nishikawa, M; Uema, T; Sasaki, M

2000-09-01

Neuroimaging studies of autism have shown abnormalities in the limbic system and cerebellar circuits and additional sites. These findings are not, however, specific or consistent enough to build up a coherent theory of the origin and nature of the brain abnormality in autistic patients. Twenty-three children with infantile autism and 26 non-autistic controls matched for IQ and age were examined using brain-perfusion single photon emission computed tomography with technetium-99m ethyl cysteinate dimer. In autistic subjects, we assessed the relationship between regional cerebral blood flow (rCBF) and symptom profiles. Images were anatomically normalized, and voxel-by-voxel analyses were performed. Decreases in rCBF in autistic patients compared with the control group were identified in the bilateral insula, superior temporal gyri and left prefrontal cortices. Analysis of the correlations between syndrome scores and rCBF revealed that each syndrome was associated with a specific pattern of perfusion in the limbic system and the medial prefrontal cortex. The results confirmed the associations of (i) impairments in communication and social interaction that are thought to be related to deficits in the theory of mind (ToM) with altered perfusion in the medial prefrontal cortex and anterior cingulate gyrus, and (ii) the obsessive desire for sameness with altered perfusion in the right medial temporal lobe. The perfusion abnormalities seem to be related to the cognitive dysfunction observed in autism, such as deficits in ToM, abnormal responses to sensory stimuli, and the obsessive desire for sameness. The perfusion patterns suggest possible locations of abnormalities of brain function underlying abnormal behaviour patterns in autistic individuals.

Shared and distinct contributions of rostrolateral prefrontal cortex to analogical reasoning and episodic memory retrieval.

PubMed

Westphal, Andrew J; Reggente, Nicco; Ito, Kaori L; Rissman, Jesse

2016-03-01

Rostrolateral prefrontal cortex (RLPFC) is widely appreciated to support higher cognitive functions, including analogical reasoning and episodic memory retrieval. However, these tasks have typically been studied in isolation, and thus it is unclear whether they involve common or distinct RLPFC mechanisms. Here, we introduce a novel functional magnetic resonance imaging (fMRI) task paradigm to compare brain activity during reasoning and memory tasks while holding bottom-up perceptual stimulation and response demands constant. Univariate analyses on fMRI data from twenty participants identified a large swath of left lateral prefrontal cortex, including RLPFC, that showed common engagement on reasoning trials with valid analogies and memory trials with accurately retrieved source details. Despite broadly overlapping recruitment, multi-voxel activity patterns within left RLPFC reliably differentiated these two trial types, highlighting the presence of at least partially distinct information processing modes. Functional connectivity analyses demonstrated that while left RLPFC showed consistent coupling with the fronto-parietal control network across tasks, its coupling with other cortical areas varied in a task-dependent manner. During the memory task, this region strengthened its connectivity with the default mode and memory retrieval networks, whereas during the reasoning task it coupled more strongly with a nearby left prefrontal region (BA 45) associated with semantic processing, as well as with a superior parietal region associated with visuospatial processing. Taken together, these data suggest a domain-general role for left RLPFC in monitoring and/or integrating task-relevant knowledge representations and showcase how its function cannot solely be attributed to episodic memory or analogical reasoning computations. © 2015 Wiley Periodicals, Inc.

Mechanisms supporting superior source memory for familiar items: A multi-voxel pattern analysis study

PubMed Central

Poppenk, Jordan; Norman, Kenneth A.

2012-01-01

Recent cognitive research has revealed better source memory performance for familiar relative to novel stimuli. Here we consider two possible explanations for this finding. The source memory advantage for familiar stimuli could arise because stimulus novelty induces attention to stimulus features at the expense of contextual processing, resulting in diminished overall levels of contextual processing at study for novel (vs. familiar) stimuli. Another possibility is that stimulus information retrieved from long-term memory (LTM) provides scaffolding that facilitates the formation of item-context associations. If contextual features are indeed more effectively bound to familiar (vs. novel) items, the relationship between contextual processing at study and subsequent source memory should be stronger for familiar items. We tested these possibilities by applying multi-voxel pattern analysis (MVPA) to a recently collected functional magnetic resonance imaging (fMRI) dataset, with the goal of measuring contextual processing at study and relating it to subsequent source memory performance. Participants were scanned with fMRI while viewing novel proverbs, repeated proverbs (previously novel proverbs that were shown in a pre-study phase), and previously known proverbs in the context of one of two experimental tasks. After scanning was complete, we evaluated participants’ source memory for the task associated with each proverb. Drawing upon fMRI data from the study phase, we trained a classifier to detect on-task processing (i.e., how strongly was the correct task set activated). On-task processing was greater for previously known than novel proverbs and similar for repeated and novel proverbs. However, both within- and across participants, the relationship between on-task processing and subsequent source memory was stronger for repeated than novel proverbs and similar for previously known and novel proverbs. Finally, focusing on the repeated condition, we found that higher levels of hippocampal activity during the pre-study phase, which we used as an index of episodic encoding, led to a stronger relationship between on-task processing at study and subsequent memory. Together, these findings suggest different mechanisms may be primarily responsible for superior source memory for repeated and previously known stimuli. Specifically, they suggest that prior stimulus knowledge enhances memory by boosting the overall level of contextual processing, whereas stimulus repetition enhances the probability that contextual features will be successfully bound to item features. Several possible theoretical explanations for this pattern are discussed. PMID:22820636

Gray matter abnormalities associated with fibromyalgia: A meta-analysis of voxel-based morphometric studies.

PubMed

Shi, HaiCun; Yuan, CongHu; Dai, ZhenYu; Ma, HaiRong; Sheng, LiQin

2016-12-01

Studies employing voxel-based morphometry (VBM) have reported inconsistent findings on the association of gray matter (GM) abnormalities with fibromyalgia. The aim of the present study is to identify the most prominent and replicable GM areas that involved in fibromyalgia. A systematic search of the PubMed database from January 2000 to September 2015 was performed to identify eligible whole-brain VBM studies. Comprehensive meta-analyses to investigate regional GM abnormalities in fibromyalgia were conducted with the Seed-based d Mapping software package. Seven studies, reporting nine comparisons and including a grand total of 180 fibromyalgia patients and 126 healthy controls, were included in the meta-analyses. In fibromyalgia patients compared with healthy controls, regional GM decreases were consistently found in the bilateral anterior cingulate/paracingulate cortex/medial prefrontal cortex, the bilateral posterior cingulate/paracingulate cortex, the left parahippocampal gyrus/fusiform cortex, and the right parahippocampal gyrus/hippocampus. Regional GM increases were consistently found in the left cerebellum. Meta-regression demonstrated that age was correlated with GM anomalies in fibromyalgia patients. The current meta-analysis identified a characteristic pattern of GM alterations within the medial pain system, default mode network, and cerebro-cerebellar circuits, which further supports the concept that fibromyalgia is a symptom complex involving brain areas beyond those implicated in chronic pain. Copyright © 2016 Elsevier Inc. All rights reserved.

Framing effects reveal discrete lexical-semantic and sublexical procedures in reading: an fMRI study

PubMed Central

Danelli, Laura; Marelli, Marco; Berlingeri, Manuela; Tettamanti, Marco; Sberna, Maurizio; Paulesu, Eraldo; Luzzatti, Claudio

2015-01-01

According to the dual-route model, a printed string of letters can be processed by either a grapheme-to-phoneme conversion (GPC) route or a lexical-semantic route. Although meta-analyses of the imaging literature support the existence of distinct but interacting reading procedures, individual neuroimaging studies that explored neural correlates of reading yielded inconclusive results. We used a list-manipulation paradigm to provide a fresh empirical look at this issue and to isolate specific areas that underlie the two reading procedures. In a lexical condition, we embedded disyllabic Italian words (target stimuli) in lists of either loanwords or trisyllabic Italian words with unpredictable stress position. In a GPC condition, similar target stimuli were included within lists of pseudowords. The procedure was designed to induce participants to emphasize either the lexical-semantic or the GPC reading procedure, while controlling for possible linguistic confounds and keeping the reading task requirements stable across the two conditions. Thirty-three adults participated in the behavioral study, and 20 further adult participants were included in the fMRI study. At the behavioral level, we found sizeable effects of the framing manipulations that included slower voice onset times for stimuli in the pseudoword frames. At the functional anatomical level, the occipital and temporal regions, and the intraparietal sulcus were specifically activated when subjects were reading target words in a lexical frame. The inferior parietal and anterior fusiform cortex were specifically activated in the GPC condition. These patterns of activation represented a valid classifying model of fMRI images associated with target reading in both frames in the multi-voxel pattern analyses. Further activations were shared by the two procedures in the occipital and inferior parietal areas, in the premotor cortex, in the frontal regions and the left supplementary motor area. These regions are most likely involved in either early input or late output processes. PMID:26441712

Framing effects reveal discrete lexical-semantic and sublexical procedures in reading: an fMRI study.

PubMed

Danelli, Laura; Marelli, Marco; Berlingeri, Manuela; Tettamanti, Marco; Sberna, Maurizio; Paulesu, Eraldo; Luzzatti, Claudio

2015-01-01

According to the dual-route model, a printed string of letters can be processed by either a grapheme-to-phoneme conversion (GPC) route or a lexical-semantic route. Although meta-analyses of the imaging literature support the existence of distinct but interacting reading procedures, individual neuroimaging studies that explored neural correlates of reading yielded inconclusive results. We used a list-manipulation paradigm to provide a fresh empirical look at this issue and to isolate specific areas that underlie the two reading procedures. In a lexical condition, we embedded disyllabic Italian words (target stimuli) in lists of either loanwords or trisyllabic Italian words with unpredictable stress position. In a GPC condition, similar target stimuli were included within lists of pseudowords. The procedure was designed to induce participants to emphasize either the lexical-semantic or the GPC reading procedure, while controlling for possible linguistic confounds and keeping the reading task requirements stable across the two conditions. Thirty-three adults participated in the behavioral study, and 20 further adult participants were included in the fMRI study. At the behavioral level, we found sizeable effects of the framing manipulations that included slower voice onset times for stimuli in the pseudoword frames. At the functional anatomical level, the occipital and temporal regions, and the intraparietal sulcus were specifically activated when subjects were reading target words in a lexical frame. The inferior parietal and anterior fusiform cortex were specifically activated in the GPC condition. These patterns of activation represented a valid classifying model of fMRI images associated with target reading in both frames in the multi-voxel pattern analyses. Further activations were shared by the two procedures in the occipital and inferior parietal areas, in the premotor cortex, in the frontal regions and the left supplementary motor area. These regions are most likely involved in either early input or late output processes.

High performance volume-of-intersection projectors for 3D-PET image reconstruction based on polar symmetries and SIMD vectorisation.

PubMed

Scheins, J J; Vahedipour, K; Pietrzyk, U; Shah, N J

2015-12-21

For high-resolution, iterative 3D PET image reconstruction the efficient implementation of forward-backward projectors is essential to minimise the calculation time. Mathematically, the projectors are summarised as a system response matrix (SRM) whose elements define the contribution of image voxels to lines-of-response (LORs). In fact, the SRM easily comprises billions of non-zero matrix elements to evaluate the tremendous number of LORs as provided by state-of-the-art PET scanners. Hence, the performance of iterative algorithms, e.g. maximum-likelihood-expectation-maximisation (MLEM), suffers from severe computational problems due to the intensive memory access and huge number of floating point operations. Here, symmetries occupy a key role in terms of efficient implementation. They reduce the amount of independent SRM elements, thus allowing for a significant matrix compression according to the number of exploitable symmetries. With our previous work, the PET REconstruction Software TOolkit (PRESTO), very high compression factors (>300) are demonstrated by using specific non-Cartesian voxel patterns involving discrete polar symmetries. In this way, a pre-calculated memory-resident SRM using complex volume-of-intersection calculations can be achieved. However, our original ray-driven implementation suffers from addressing voxels, projection data and SRM elements in disfavoured memory access patterns. As a consequence, a rather limited numerical throughput is observed due to the massive waste of memory bandwidth and inefficient usage of cache respectively. In this work, an advantageous symmetry-driven evaluation of the forward-backward projectors is proposed to overcome these inefficiencies. The polar symmetries applied in PRESTO suggest a novel organisation of image data and LOR projection data in memory to enable an efficient single instruction multiple data vectorisation, i.e. simultaneous use of any SRM element for symmetric LORs. In addition, the calculation time is further reduced by using simultaneous multi-threading (SMT). A global speedup factor of 11 without SMT and above 100 with SMT has been achieved for the improved CPU-based implementation while obtaining equivalent numerical results.

High performance volume-of-intersection projectors for 3D-PET image reconstruction based on polar symmetries and SIMD vectorisation

NASA Astrophysics Data System (ADS)

Scheins, J. J.; Vahedipour, K.; Pietrzyk, U.; Shah, N. J.

2015-12-01

For high-resolution, iterative 3D PET image reconstruction the efficient implementation of forward-backward projectors is essential to minimise the calculation time. Mathematically, the projectors are summarised as a system response matrix (SRM) whose elements define the contribution of image voxels to lines-of-response (LORs). In fact, the SRM easily comprises billions of non-zero matrix elements to evaluate the tremendous number of LORs as provided by state-of-the-art PET scanners. Hence, the performance of iterative algorithms, e.g. maximum-likelihood-expectation-maximisation (MLEM), suffers from severe computational problems due to the intensive memory access and huge number of floating point operations. Here, symmetries occupy a key role in terms of efficient implementation. They reduce the amount of independent SRM elements, thus allowing for a significant matrix compression according to the number of exploitable symmetries. With our previous work, the PET REconstruction Software TOolkit (PRESTO), very high compression factors (>300) are demonstrated by using specific non-Cartesian voxel patterns involving discrete polar symmetries. In this way, a pre-calculated memory-resident SRM using complex volume-of-intersection calculations can be achieved. However, our original ray-driven implementation suffers from addressing voxels, projection data and SRM elements in disfavoured memory access patterns. As a consequence, a rather limited numerical throughput is observed due to the massive waste of memory bandwidth and inefficient usage of cache respectively. In this work, an advantageous symmetry-driven evaluation of the forward-backward projectors is proposed to overcome these inefficiencies. The polar symmetries applied in PRESTO suggest a novel organisation of image data and LOR projection data in memory to enable an efficient single instruction multiple data vectorisation, i.e. simultaneous use of any SRM element for symmetric LORs. In addition, the calculation time is further reduced by using simultaneous multi-threading (SMT). A global speedup factor of 11 without SMT and above 100 with SMT has been achieved for the improved CPU-based implementation while obtaining equivalent numerical results.

A voxel-based technique to estimate the volume of trees from terrestrial laser scanner data

NASA Astrophysics Data System (ADS)

Bienert, A.; Hess, C.; Maas, H.-G.; von Oheimb, G.

2014-06-01

The precise determination of the volume of standing trees is very important for ecological and economical considerations in forestry. If terrestrial laser scanner data are available, a simple approach for volume determination is given by allocating points into a voxel structure and subsequently counting the filled voxels. Generally, this method will overestimate the volume. The paper presents an improved algorithm to estimate the wood volume of trees using a voxel-based method which will correct for the overestimation. After voxel space transformation, each voxel which contains points is reduced to the volume of its surrounding bounding box. In a next step, occluded (inner stem) voxels are identified by a neighbourhood analysis sweeping in the X and Y direction of each filled voxel. Finally, the wood volume of the tree is composed by the sum of the bounding box volumes of the outer voxels and the volume of all occluded inner voxels. Scan data sets from several young Norway maple trees (Acer platanoides) were used to analyse the algorithm. Therefore, the scanned trees as well as their representing point clouds were separated in different components (stem, branches) to make a meaningful comparison. Two reference measurements were performed for validation: A direct wood volume measurement by placing the tree components into a water tank, and a frustum calculation of small trunk segments by measuring the radii along the trunk. Overall, the results show slightly underestimated volumes (-0.3% for a probe of 13 trees) with a RMSE of 11.6% for the individual tree volume calculated with the new approach.

Use of spectral analysis with iterative filter for voxelwise determination of regional rates of cerebral protein synthesis with L-[1-11C]leucine PET.

PubMed

Veronese, Mattia; Schmidt, Kathleen C; Smith, Carolyn Beebe; Bertoldo, Alessandra

2012-06-01

A spectral analysis approach was used to estimate kinetic parameters of the L-[1-(11)C]leucine positron emission tomography (PET) method and regional rates of cerebral protein synthesis (rCPS) on a voxel-by-voxel basis. Spectral analysis applies to both heterogeneous and homogeneous tissues; it does not require prior assumptions concerning number of tissue compartments. Parameters estimated with spectral analysis can be strongly affected by noise, but numerical filters improve estimation performance. Spectral analysis with iterative filter (SAIF) was originally developed to improve estimation of leucine kinetic parameters and rCPS in region-of-interest (ROI) data analyses. In the present study, we optimized SAIF for application at the voxel level. In measured L-[1-(11)C]leucine PET data, voxel-level SAIF parameter estimates averaged over all voxels within a ROI (mean voxel-SAIF) generally agreed well with corresponding estimates derived by applying the originally developed SAIF to ROI time-activity curves (ROI-SAIF). Region-of-interest-SAIF and mean voxel-SAIF estimates of rCPS were highly correlated. Simulations showed that mean voxel-SAIF rCPS estimates were less biased and less variable than ROI-SAIF estimates in the whole brain and cortex; biases were similar in white matter. We conclude that estimation of rCPS with SAIF is improved when the method is applied at voxel level than in ROI analysis.

Multi-resolution Gabor wavelet feature extraction for needle detection in 3D ultrasound

NASA Astrophysics Data System (ADS)

Pourtaherian, Arash; Zinger, Svitlana; Mihajlovic, Nenad; de With, Peter H. N.; Huang, Jinfeng; Ng, Gary C.; Korsten, Hendrikus H. M.

2015-12-01

Ultrasound imaging is employed for needle guidance in various minimally invasive procedures such as biopsy guidance, regional anesthesia and brachytherapy. Unfortunately, a needle guidance using 2D ultrasound is very challenging, due to a poor needle visibility and a limited field of view. Nowadays, 3D ultrasound systems are available and more widely used. Consequently, with an appropriate 3D image-based needle detection technique, needle guidance and interventions may significantly be improved and simplified. In this paper, we present a multi-resolution Gabor transformation for an automated and reliable extraction of the needle-like structures in a 3D ultrasound volume. We study and identify the best combination of the Gabor wavelet frequencies. High precision in detecting the needle voxels leads to a robust and accurate localization of the needle for the intervention support. Evaluation in several ex-vivo cases shows that the multi-resolution analysis significantly improves the precision of the needle voxel detection from 0.23 to 0.32 at a high recall rate of 0.75 (gain 40%), where a better robustness and confidence were confirmed in the practical experiments.

Brain Tumor Segmentation Using Deep Belief Networks and Pathological Knowledge.

PubMed

Zhan, Tianming; Chen, Yi; Hong, Xunning; Lu, Zhenyu; Chen, Yunjie

2017-01-01

In this paper, we propose an automatic brain tumor segmentation method based on Deep Belief Networks (DBNs) and pathological knowledge. The proposed method is targeted against gliomas (both low and high grade) obtained in multi-sequence magnetic resonance images (MRIs). Firstly, a novel deep architecture is proposed to combine the multi-sequences intensities feature extraction with classification to get the classification probabilities of each voxel. Then, graph cut based optimization is executed on the classification probabilities to strengthen the spatial relationships of voxels. At last, pathological knowledge of gliomas is applied to remove some false positives. Our method was validated in the Brain Tumor Segmentation Challenge 2012 and 2013 databases (BRATS 2012, 2013). The performance of segmentation results demonstrates our proposal providing a competitive solution with stateof- the-art methods. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

An investigation of voxel geometries for MCNP-based radiation dose calculations.

PubMed

Zhang, Juying; Bednarz, Bryan; Xu, X George

2006-11-01

Voxelized geometry such as those obtained from medical images is increasingly used in Monte Carlo calculations of absorbed doses. One useful application of calculated absorbed dose is the determination of fluence-to-dose conversion factors for different organs. However, confusion still exists about how such a geometry is defined and how the energy deposition is best computed, especially involving a popular code, MCNP5. This study investigated two different types of geometries in the MCNP5 code, cell and lattice definitions. A 10 cm x 10 cm x 10 cm test phantom, which contained an embedded 2 cm x 2 cm x 2 cm target at its center, was considered. A planar source emitting parallel photons was also considered in the study. The results revealed that MCNP5 does not calculate total target volume for multi-voxel geometries. Therefore, tallies which involve total target volume must be divided by the user by the total number of voxels to obtain a correct dose result. Also, using planar source areas greater than the phantom size results in the same fluence-to-dose conversion factor.

Brainstem Involvement as a Cause of Central Sleep Apnea: Pattern of Microstructural Cerebral Damage in Patients with Cerebral Microangiopathy

PubMed Central

Duning, Thomas; Deppe, Michael; Brand, Eva; Stypmann, Jörg; Becht, Charlotte; Heidbreder, Anna; Young, Peter

2013-01-01

Background The exact underlying pathomechanism of central sleep apnea with Cheyne-Stokes respiration (CSA-CSR) is still unclear. Recent studies have demonstrated an association between cerebral white matter changes and CSA. A dysfunction of central respiratory control centers in the brainstem was suggested by some authors. Novel MR-imaging analysis tools now allow far more subtle assessment of microstructural cerebral changes. The aim of this study was to investigate whether and what severity of subtle structural cerebral changes could lead to CSA-CSR, and whether there is a specific pattern of neurodegenerative changes that cause CSR. Therefore, we examined patients with Fabry disease (FD), an inherited, lysosomal storage disease. White matter lesions are early and frequent findings in FD. Thus, FD can serve as a "model disease" of cerebral microangiopathy to study in more detail the impact of cerebral lesions on central sleep apnea. Patients and Methods Genetically proven FD patients (n = 23) and age-matched healthy controls (n = 44) underwent a cardio-respiratory polysomnography and brain MRI at 3.0 Tesla. We applied different MR-imaging techniques, ranging from semiquantitative measurement of white matter lesion (WML) volumes and automated calculation of brain tissue volumes to VBM of gray matter and voxel-based diffusion tensor imaging (DTI) analysis. Results In 5 of 23 Fabry patients (22%) CSA-CSR was detected. Voxel-based DTI analysis revealed widespread structural changes in FD patients when compared to the healthy controls. When calculated as a separate group, DTI changes of CSA-CSR patients were most prominent in the brainstem. Voxel-based regression analysis revealed a significant association between CSR severity and microstructural DTI changes within the brainstem. Conclusion Subtle microstructural changes in the brainstem might be a neuroanatomical correlate of CSA-CSR in patients at risk of WML. DTI is more sensitive and specific than conventional structural MRI and other advanced MR analyses tools in demonstrating these abnormalities. PMID:23637744

Attention enhances multi-voxel representation of novel objects in frontal, parietal and visual cortices.

PubMed

Woolgar, Alexandra; Williams, Mark A; Rich, Anina N

2015-04-01

Selective attention is fundamental for human activity, but the details of its neural implementation remain elusive. One influential theory, the adaptive coding hypothesis (Duncan, 2001, An adaptive coding model of neural function in prefrontal cortex, Nature Reviews Neuroscience 2:820-829), proposes that single neurons in certain frontal and parietal regions dynamically adjust their responses to selectively encode relevant information. This selective representation may in turn support selective processing in more specialized brain regions such as the visual cortices. Here, we use multi-voxel decoding of functional magnetic resonance images to demonstrate selective representation of attended--and not distractor--objects in frontal, parietal, and visual cortices. In addition, we highlight a critical role for task demands in determining which brain regions exhibit selective coding. Strikingly, representation of attended objects in frontoparietal cortex was highest under conditions of high perceptual demand, when stimuli were hard to perceive and coding in early visual cortex was weak. Coding in early visual cortex varied as a function of attention and perceptual demand, while coding in higher visual areas was sensitive to the allocation of attention but robust to changes in perceptual difficulty. Consistent with high-profile reports, peripherally presented objects could also be decoded from activity at the occipital pole, a region which corresponds to the fovea. Our results emphasize the flexibility of frontoparietal and visual systems. They support the hypothesis that attention enhances the multi-voxel representation of information in the brain, and suggest that the engagement of this attentional mechanism depends critically on current task demands. Copyright © 2015 Elsevier Inc. All rights reserved.

Using multi-level Bayesian lesion-symptom mapping to probe the body-part-specificity of gesture imitation skills.

PubMed

Achilles, Elisabeth I S; Weiss, Peter H; Fink, Gereon R; Binder, Ellen; Price, Cathy J; Hope, Thomas M H

2017-11-01

Past attempts to identify the neural substrates of hand and finger imitation skills in the left hemisphere of the brain have yielded inconsistent results. Here, we analyse those associations in a large sample of 257 left hemisphere stroke patients. By introducing novel Bayesian methods, we characterise lesion symptom associations at three levels: the voxel-level, the single-region level (using anatomically defined regions), and the region-pair level. The results are inconsistent across those three levels and we argue that each level of analysis makes assumptions which constrain the results it can produce. Regardless of the inconsistencies across levels, and contrary to past studies which implicated differential neural substrates for hand and finger imitation, we find no consistent voxels or regions, where damage affects one imitation skill and not the other, at any of the three analysis levels. Our novel Bayesian approach indicates that any apparent differences appear to be driven by an increased sensitivity of hand imitation skills to lesions that also impair finger imitation. In our analyses, the results of the highest level of analysis (region-pairs) emphasise a role of the primary somatosensory and motor cortices, and the occipital lobe in imitation. We argue that this emphasis supports an account of both imitation tasks based on direct sensor-motor connections, which throws doubt on past accounts which imply the need for an intermediate (e.g. body-part-coding) system of representation. Copyright © 2017 The Author(s). Published by Elsevier Inc. All rights reserved.

Voxel modelling of sands and gravels of Pleistocene Rhine and Meuse deposits in Flanders (Belgium)

NASA Astrophysics Data System (ADS)

van Haren, Tom; Dirix, Katrijn; De Koninck, Roel

2017-04-01

Voxel modelling or 3D volume modelling of Quaternary raw materials is VITO's next step in the geological layer modelling of the Flanders and Brussels Capital Region in Belgium (G3D - Matthijs et al., 2013). The aim is to schematise deposits as voxels ('volumetric pixels') that represent lithological information on a grid in three-dimensional space (25 x 25 x 0.5 m). A new voxel model on Pleistocene Meuse and Rhine sands and gravels will be illustrated succeeding a voxel model on loess resources (van Haren et al., 2016). The model methodology is based on a geological 'skeleton' extracted from the regional geological layer model of Flanders. This framework holds the 3D interpolated lithological information of 5.000 boreholes. First a check on quality and spatial location filtered out significant and usable lithological information. Subsequently a manual geological interpretation was performed to analyse stratigraphical arrangement and identify the raw materials of interest. Finally, a workflow was developed that automatically encodes and classifies the borehole descriptions in a standardized manner. This workflow was implemented by combining Microsoft Access® and ArcMap® and is able to convert borehole descriptions into specific geological parameters. An analysis of the conversed lithological data prior to interpolation improves the understanding of the spatial distribution, to fine tune the modelling process and to know the limitations of the data. The converted lithological data were 3D interpolated in Voxler using IDW and resulted in a model containing 52 million voxels. It gives an overview on the regional distribution and thickness variation of interesting Pleistocene aggregates of Meuse and Rhine. Much effort has been put in setting up a database structure in Microsoft Access® and Microsoft SQL Server® in order to arrange and analyse the lithological information, link the voxel model with the geological layer model and handle and analyse the resulting voxelmodel data. The database structure allows to analyse and set certain preconditions (minimal thickness or maximum depth of aggregates, maximum thickness of intercalating clays) on the model in order to calculate and view distributions of deposits which meet these preconditions. These results are interesting for pre-prospective purposes, illustrating the distribution of lithological information and making the end user more aware of the potential economic value of the subsurface. References van Haren T. et al (2016) - An interactive voxel model for mineral resources: loess deposits in Flanders (Belgium). Zeitschrift der Deutschen Gesellschaft für Geowissenschaften, Volume 167, Number 4, pp. 363-376(14). Matthijs J. et al. (2013) - Geological 3D layer model of the Flanders Region and Brussels-Capital Region - 2nd version. Study performed in order of the Ministery of the Flemish Community. VITO report 2013/R/ETE/43, 24p. (in Dutch)

Dense image matching of terrestrial imagery for deriving high-resolution topographic properties of vegetation locations in alpine terrain

NASA Astrophysics Data System (ADS)

Niederheiser, R.; Rutzinger, M.; Bremer, M.; Wichmann, V.

2018-04-01

The investigation of changes in spatial patterns of vegetation and identification of potential micro-refugia requires detailed topographic and terrain information. However, mapping alpine topography at very detailed scales is challenging due to limited accessibility of sites. Close-range sensing by photogrammetric dense matching approaches based on terrestrial images captured with hand-held cameras offers a light-weight and low-cost solution to retrieve high-resolution measurements even in steep terrain and at locations, which are difficult to access. We propose a novel approach for rapid capturing of terrestrial images and a highly automated processing chain for retrieving detailed dense point clouds for topographic modelling. For this study, we modelled 249 plot locations. For the analysis of vegetation distribution and location properties, topographic parameters, such as slope, aspect, and potential solar irradiation were derived by applying a multi-scale approach utilizing voxel grids and spherical neighbourhoods. The result is a micro-topography archive of 249 alpine locations that includes topographic parameters at multiple scales ready for biogeomorphological analysis. Compared with regional elevation models at larger scales and traditional 2D gridding approaches to create elevation models, we employ analyses in a fully 3D environment that yield much more detailed insights into interrelations between topographic parameters, such as potential solar irradiation, surface area, aspect and roughness.

Effects of RF pulse profile and intra-voxel phase dispersion on MR fingerprinting with balanced SSFP readout.

PubMed

Chiu, Su-Chin; Lin, Te-Ming; Lin, Jyh-Miin; Chung, Hsiao-Wen; Ko, Cheng-Wen; Büchert, Martin; Bock, Michael

2017-09-01

To investigate possible errors in T1 and T2 quantification via MR fingerprinting with balanced steady-state free precession readout in the presence of intra-voxel phase dispersion and RF pulse profile imperfections, using computer simulations based on Bloch equations. A pulse sequence with TR changing in a Perlin noise pattern and a nearly sinusoidal pattern of flip angle following an initial 180-degree inversion pulse was employed. Gaussian distributions of off-resonance frequency were assumed for intra-voxel phase dispersion effects. Slice profiles of sinc-shaped RF pulses were computed to investigate flip angle profile influences. Following identification of the best fit between the acquisition signals and those established in the dictionary based on known parameters, estimation errors were reported. In vivo experiments were performed at 3T to examine the results. Slight intra-voxel phase dispersion with standard deviations from 1 to 3Hz resulted in prominent T2 under-estimations, particularly at large T2 values. T1 and off-resonance frequencies were relatively unaffected. Slice profile imperfections led to under-estimations of T1, which became greater as regional off-resonance frequencies increased, but could be corrected by including slice profile effects in the dictionary. Results from brain imaging experiments in vivo agreed with the simulation results qualitatively. MR fingerprinting using balanced SSFP readout in the presence of intra-voxel phase dispersion and imperfect slice profile leads to inaccuracies in quantitative estimations of the relaxation times. Copyright © 2017 Elsevier Inc. All rights reserved.

Decoded fMRI neurofeedback can induce bidirectional confidence changes within single participants.

PubMed

Cortese, Aurelio; Amano, Kaoru; Koizumi, Ai; Lau, Hakwan; Kawato, Mitsuo

2017-04-01

Neurofeedback studies using real-time functional magnetic resonance imaging (rt-fMRI) have recently incorporated the multi-voxel pattern decoding approach, allowing for fMRI to serve as a tool to manipulate fine-grained neural activity embedded in voxel patterns. Because of its tremendous potential for clinical applications, certain questions regarding decoded neurofeedback (DecNef) must be addressed. Specifically, can the same participants learn to induce neural patterns in opposite directions in different sessions? If so, how does previous learning affect subsequent induction effectiveness? These questions are critical because neurofeedback effects can last for months, but the short- to mid-term dynamics of such effects are unknown. Here we employed a within-subjects design, where participants underwent two DecNef training sessions to induce behavioural changes of opposing directionality (up or down regulation of perceptual confidence in a visual discrimination task), with the order of training counterbalanced across participants. Behavioral results indicated that the manipulation was strongly influenced by the order and the directionality of neurofeedback training. We applied nonlinear mathematical modeling to parametrize four main consequences of DecNef: main effect of change in confidence, strength of down-regulation of confidence relative to up-regulation, maintenance of learning effects, and anterograde learning interference. Modeling results revealed that DecNef successfully induced bidirectional confidence changes in different sessions within single participants. Furthermore, the effect of up- compared to down-regulation was more prominent, and confidence changes (regardless of the direction) were largely preserved even after a week-long interval. Lastly, the effect of the second session was markedly diminished as compared to the effect of the first session, indicating strong anterograde learning interference. These results are interpreted in the framework of reinforcement learning and provide important implications for its application to basic neuroscience, to occupational and sports training, and to therapy. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

Decoded fMRI neurofeedback can induce bidirectional confidence changes within single participants

PubMed Central

Cortese, Aurelio; Amano, Kaoru; Koizumi, Ai; Lau, Hakwan; Kawato, Mitsuo

2017-01-01

Neurofeedback studies using real-time functional magnetic resonance imaging (rt-fMRI) have recently incorporated the multi-voxel pattern decoding approach, allowing for fMRI to serve as a tool to manipulate fine-grained neural activity embedded in voxel patterns. Because of its tremendous potential for clinical applications, certain questions regarding decoded neurofeedback (DecNef) must be addressed. Specifically, can the same participants learn to induce neural patterns in opposite directions in different sessions? If so, how does previous learning affect subsequent induction effectiveness? These questions are critical because neurofeedback effects can last for months, but the short- to mid-term dynamics of such effects are unknown. Here we employed a within-subjects design, where participants underwent two DecNef training sessions to induce behavioural changes of opposing directionality (up or down regulation of perceptual confidence in a visual discrimination task), with the order of training counterbalanced across participants. Behavioral results indicated that the manipulation was strongly influenced by the order and the directionality of neurofeedback training. We applied nonlinear mathematical modeling to parametrize four main consequences of DecNef: main effect of change in confidence, strength of down-regulation of confidence relative to up-regulation, maintenance of learning effects, and anterograde learning interference. Modeling results revealed that DecNef successfully induced bidirectional confidence changes in different sessions within single participants. Furthermore, the effect of up- compared to down-regulation was more prominent, and confidence changes (regardless of the direction) were largely preserved even after a week-long interval. Lastly, the effect of the second session was markedly diminished as compared to the effect of the first session, indicating strong anterograde learning interference. These results are interpreted in the framework of reinforcement learning and provide important implications for its application to basic neuroscience, to occupational and sports training, and to therapy. PMID:28163140

Multiple imputation of missing fMRI data in whole brain analysis

PubMed Central

Vaden, Kenneth I.; Gebregziabher, Mulugeta; Kuchinsky, Stefanie E.; Eckert, Mark A.

2012-01-01

Whole brain fMRI analyses rarely include the entire brain because of missing data that result from data acquisition limits and susceptibility artifact, in particular. This missing data problem is typically addressed by omitting voxels from analysis, which may exclude brain regions that are of theoretical interest and increase the potential for Type II error at cortical boundaries or Type I error when spatial thresholds are used to establish significance. Imputation could significantly expand statistical map coverage, increase power, and enhance interpretations of fMRI results. We examined multiple imputation for group level analyses of missing fMRI data using methods that leverage the spatial information in fMRI datasets for both real and simulated data. Available case analysis, neighbor replacement, and regression based imputation approaches were compared in a general linear model framework to determine the extent to which these methods quantitatively (effect size) and qualitatively (spatial coverage) increased the sensitivity of group analyses. In both real and simulated data analysis, multiple imputation provided 1) variance that was most similar to estimates for voxels with no missing data, 2) fewer false positive errors in comparison to mean replacement, and 3) fewer false negative errors in comparison to available case analysis. Compared to the standard analysis approach of omitting voxels with missing data, imputation methods increased brain coverage in this study by 35% (from 33,323 to 45,071 voxels). In addition, multiple imputation increased the size of significant clusters by 58% and number of significant clusters across statistical thresholds, compared to the standard voxel omission approach. While neighbor replacement produced similar results, we recommend multiple imputation because it uses an informed sampling distribution to deal with missing data across subjects that can include neighbor values and other predictors. Multiple imputation is anticipated to be particularly useful for 1) large fMRI data sets with inconsistent missing voxels across subjects and 2) addressing the problem of increased artifact at ultra-high field, which significantly limit the extent of whole brain coverage and interpretations of results. PMID:22500925

Brain regions that retain the spatial layout of tactile stimuli during working memory - A 'tactospatial sketchpad'?

PubMed

Schmidt, Timo Torsten; Blankenburg, Felix

2018-05-31

Working memory (WM) studies have been essential for ascertaining how the brain flexibly handles mentally represented information in the absence of sensory stimulation. Most studies on the memory of sensory stimulus features have focused, however, on the visual domain. Here, we report a human WM study in the tactile modality where participants had to memorize the spatial layout of patterned Braille-like stimuli presented to the index finger. We used a whole-brain searchlight approach in combination with multi-voxel pattern analysis (MVPA) to investigate tactile WM representations without a priori assumptions about which brain regions code tactospatial information. Our analysis revealed that posterior and parietal cortices, as well as premotor regions, retained information across the twelve-second delay phase. Interestingly, parts of this brain network were previously shown to also contain information of visuospatial WM. Also, by specifically testing somatosensory regions for WM representations, we observed content-specific activation patterns in primary somatosensory cortex (SI). Our findings demonstrate that tactile WM depends on a distributed network of brain regions in analogy to the representation of visuospatial information. Copyright © 2018. Published by Elsevier Inc.

Lingering representations of stimuli influence recall organization.

PubMed

Chan, Stephanie C Y; Applegate, Marissa C; Morton, Neal W; Polyn, Sean M; Norman, Kenneth A

2017-03-01

Several prominent theories posit that information about recent experiences lingers in the brain and organizes memories for current experiences, by forming a temporal context that is linked to those memories at encoding. According to these theories, if the thoughts preceding an experience X resemble the thoughts preceding an experience Y, then X and Y should show an elevated probability of being recalled together. We tested this prediction by using multi-voxel pattern analysis (MVPA) of fMRI data to measure neural evidence for lingering processing of preceding stimuli. As predicted, memories encoded with similar lingering thoughts about the category of preceding stimuli were more likely to be recalled together. Our results demonstrate that the "fading embers" of previous stimuli help to organize recall, confirming a key prediction of computational models of episodic memory. Copyright © 2017 Elsevier Ltd. All rights reserved.

Awake, Offline Processing during Associative Learning

PubMed Central

Nestor, Adrian; Tarr, Michael J.; Creswell, J. David

2016-01-01

Offline processing has been shown to strengthen memory traces and enhance learning in the absence of conscious rehearsal or awareness. Here we evaluate whether a brief, two-minute offline processing period can boost associative learning and test a memory reactivation account for these offline processing effects. After encoding paired associates, subjects either completed a distractor task for two minutes or were immediately tested for memory of the pairs in a counterbalanced, within-subjects functional magnetic resonance imaging study. Results showed that brief, awake, offline processing improves memory for associate pairs. Moreover, multi-voxel pattern analysis of the neuroimaging data suggested reactivation of encoded memory representations in dorsolateral prefrontal cortex during offline processing. These results signify the first demonstration of awake, active, offline enhancement of associative memory and suggest that such enhancement is accompanied by the offline reactivation of encoded memory representations. PMID:27119345

Awake, Offline Processing during Associative Learning.

PubMed

Bursley, James K; Nestor, Adrian; Tarr, Michael J; Creswell, J David

2016-01-01

Offline processing has been shown to strengthen memory traces and enhance learning in the absence of conscious rehearsal or awareness. Here we evaluate whether a brief, two-minute offline processing period can boost associative learning and test a memory reactivation account for these offline processing effects. After encoding paired associates, subjects either completed a distractor task for two minutes or were immediately tested for memory of the pairs in a counterbalanced, within-subjects functional magnetic resonance imaging study. Results showed that brief, awake, offline processing improves memory for associate pairs. Moreover, multi-voxel pattern analysis of the neuroimaging data suggested reactivation of encoded memory representations in dorsolateral prefrontal cortex during offline processing. These results signify the first demonstration of awake, active, offline enhancement of associative memory and suggest that such enhancement is accompanied by the offline reactivation of encoded memory representations.

Automated segmentation of thyroid gland on CT images with multi-atlas label fusion and random classification forest

NASA Astrophysics Data System (ADS)

Liu, Jiamin; Chang, Kevin; Kim, Lauren; Turkbey, Evrim; Lu, Le; Yao, Jianhua; Summers, Ronald

2015-03-01

The thyroid gland plays an important role in clinical practice, especially for radiation therapy treatment planning. For patients with head and neck cancer, radiation therapy requires a precise delineation of the thyroid gland to be spared on the pre-treatment planning CT images to avoid thyroid dysfunction. In the current clinical workflow, the thyroid gland is normally manually delineated by radiologists or radiation oncologists, which is time consuming and error prone. Therefore, a system for automated segmentation of the thyroid is desirable. However, automated segmentation of the thyroid is challenging because the thyroid is inhomogeneous and surrounded by structures that have similar intensities. In this work, the thyroid gland segmentation is initially estimated by multi-atlas label fusion algorithm. The segmentation is refined by supervised statistical learning based voxel labeling with a random forest algorithm. Multiatlas label fusion (MALF) transfers expert-labeled thyroids from atlases to a target image using deformable registration. Errors produced by label transfer are reduced by label fusion that combines the results produced by all atlases into a consensus solution. Then, random forest (RF) employs an ensemble of decision trees that are trained on labeled thyroids to recognize features. The trained forest classifier is then applied to the thyroid estimated from the MALF by voxel scanning to assign the class-conditional probability. Voxels from the expert-labeled thyroids in CT volumes are treated as positive classes; background non-thyroid voxels as negatives. We applied this automated thyroid segmentation system to CT scans of 20 patients. The results showed that the MALF achieved an overall 0.75 Dice Similarity Coefficient (DSC) and the RF classification further improved the DSC to 0.81.

Modality-specific spectral dynamics in response to visual and tactile sequential shape information processing tasks: An MEG study using multivariate pattern classification analysis.

PubMed

Gohel, Bakul; Lee, Peter; Jeong, Yong

2016-08-01

Brain regions that respond to more than one sensory modality are characterized as multisensory regions. Studies on the processing of shape or object information have revealed recruitment of the lateral occipital cortex, posterior parietal cortex, and other regions regardless of input sensory modalities. However, it remains unknown whether such regions show similar (modality-invariant) or different (modality-specific) neural oscillatory dynamics, as recorded using magnetoencephalography (MEG), in response to identical shape information processing tasks delivered to different sensory modalities. Modality-invariant or modality-specific neural oscillatory dynamics indirectly suggest modality-independent or modality-dependent participation of particular brain regions, respectively. Therefore, this study investigated the modality-specificity of neural oscillatory dynamics in the form of spectral power modulation patterns in response to visual and tactile sequential shape-processing tasks that are well-matched in terms of speed and content between the sensory modalities. Task-related changes in spectral power modulation and differences in spectral power modulation between sensory modalities were investigated at source-space (voxel) level, using a multivariate pattern classification (MVPC) approach. Additionally, whole analyses were extended from the voxel level to the independent-component level to take account of signal leakage effects caused by inverse solution. The modality-specific spectral dynamics in multisensory and higher-order brain regions, such as the lateral occipital cortex, posterior parietal cortex, inferior temporal cortex, and other brain regions, showed task-related modulation in response to both sensory modalities. This suggests modality-dependency of such brain regions on the input sensory modality for sequential shape-information processing. Copyright © 2016 Elsevier B.V. All rights reserved.

Quantification of ultrasonic texture intra-heterogeneity via volumetric stochastic modeling for tissue characterization.

PubMed

Al-Kadi, Omar S; Chung, Daniel Y F; Carlisle, Robert C; Coussios, Constantin C; Noble, J Alison

2015-04-01

Intensity variations in image texture can provide powerful quantitative information about physical properties of biological tissue. However, tissue patterns can vary according to the utilized imaging system and are intrinsically correlated to the scale of analysis. In the case of ultrasound, the Nakagami distribution is a general model of the ultrasonic backscattering envelope under various scattering conditions and densities where it can be employed for characterizing image texture, but the subtle intra-heterogeneities within a given mass are difficult to capture via this model as it works at a single spatial scale. This paper proposes a locally adaptive 3D multi-resolution Nakagami-based fractal feature descriptor that extends Nakagami-based texture analysis to accommodate subtle speckle spatial frequency tissue intensity variability in volumetric scans. Local textural fractal descriptors - which are invariant to affine intensity changes - are extracted from volumetric patches at different spatial resolutions from voxel lattice-based generated shape and scale Nakagami parameters. Using ultrasound radio-frequency datasets we found that after applying an adaptive fractal decomposition label transfer approach on top of the generated Nakagami voxels, tissue characterization results were superior to the state of art. Experimental results on real 3D ultrasonic pre-clinical and clinical datasets suggest that describing tumor intra-heterogeneity via this descriptor may facilitate improved prediction of therapy response and disease characterization. Copyright © 2014 The Authors. Published by Elsevier B.V. All rights reserved.

Multi-Source Fusion for Explosive Hazard Detection in Forward Looking Sensors

DTIC Science & Technology

2016-12-01

include; (1) Investigating (a) thermal, (b) synthetic aperture acoustics ( SAA ) and (c) voxel space Radar for buried and side threat attacks. (2...detection. (3) With respect to SAA , we developed new approaches in the time and frequency domains for analyzing signature of concealed targets (called...Fraz). We also developed a method to extract a multi-spectral signature from SAA and deep learning was used on limited training and class imbalance

What do results from coordinate-based meta-analyses tell us?

PubMed

Albajes-Eizagirre, Anton; Radua, Joaquim

2018-08-01

Coordinate-based meta-analyses (CBMA) methods, such as Activation Likelihood Estimation (ALE) and Seed-based d Mapping (SDM), have become an invaluable tool for summarizing the findings of voxel-based neuroimaging studies. However, the progressive sophistication of these methods may have concealed two particularities of their statistical tests. Common univariate voxelwise tests (such as the t/z-tests used in SPM and FSL) detect voxels that activate, or voxels that show differences between groups. Conversely, the tests conducted in CBMA test for "spatial convergence" of findings, i.e., they detect regions where studies report "more peaks than in most regions", regions that activate "more than most regions do", or regions that show "larger differences between groups than most regions do". The first particularity is that these tests rely on two spatial assumptions (voxels are independent and have the same probability to have a "false" peak), whose violation may make their results either conservative or liberal, though fortunately current versions of ALE, SDM and some other methods consider these assumptions. The second particularity is that the use of these tests involves an important paradox: the statistical power to detect a given effect is higher if there are no other effects in the brain, whereas lower in presence of multiple effects. Copyright © 2018 Elsevier Inc. All rights reserved.

Sci—Thur PM: Imaging — 01: Position-sensitive noise characteristics in multi-pinhole cardiac SPECT imaging

DOE Office of Scientific and Technical Information (OSTI.GOV)

Cuddy-Walsh, SG; University of Ottawa Heart Institute; Wells, RG

2014-08-15

Myocardial perfusion imaging (MPI) with Single Photon Emission Computed Tomography (SPECT) is invaluable in the diagnosis and management of heart disease. It provides essential information on myocardial blood flow and ischemia. Multi-pinhole dedicated cardiac-SPECT cameras offer improved count sensitivity, and spatial and energy resolutions over parallel-hole camera designs however variable sensitivity across the field-of-view (FOV) can lead to position-dependent noise variations. Since MPI evaluates differences in the signal-to-noise ratio, noise variations in the camera could significantly impact the sensitivity of the test for ischemia. We evaluated the noise characteristics of GE Healthcare's Discovery NM530c camera with a goal of optimizingmore » the accuracy of our patient assessment and thereby improving outcomes. Theoretical sensitivity maps of the camera FOV, including attenuation effects, were estimated analytically based on the distance and angle between the spatial position of a given voxel and each pinhole. The standard deviation in counts, σ was inferred for each voxel position from the square root of the sensitivity mapped at that position. Noise was measured experimentally from repeated (N=16) acquisitions of a uniform spherical Tc-99m-water phantom. The mean (μ) and standard deviation (σ) were calculated for each voxel position in the reconstructed FOV. Noise increased ∼2.1× across a 12 cm sphere. A correlation of 0.53 is seen when experimental noise is compared with theory suggesting that ∼53% of the noise is attributed to the combined effects of attenuation and the multi-pinhole geometry. Further investigations are warranted to determine the clinical impact of the position-dependent noise variation.« less

Multi-GPU Acceleration of Branchless Distance Driven Projection and Backprojection for Clinical Helical CT.

PubMed

Mitra, Ayan; Politte, David G; Whiting, Bruce R; Williamson, Jeffrey F; O'Sullivan, Joseph A

2017-01-01

Model-based image reconstruction (MBIR) techniques have the potential to generate high quality images from noisy measurements and a small number of projections which can reduce the x-ray dose in patients. These MBIR techniques rely on projection and backprojection to refine an image estimate. One of the widely used projectors for these modern MBIR based technique is called branchless distance driven (DD) projection and backprojection. While this method produces superior quality images, the computational cost of iterative updates keeps it from being ubiquitous in clinical applications. In this paper, we provide several new parallelization ideas for concurrent execution of the DD projectors in multi-GPU systems using CUDA programming tools. We have introduced some novel schemes for dividing the projection data and image voxels over multiple GPUs to avoid runtime overhead and inter-device synchronization issues. We have also reduced the complexity of overlap calculation of the algorithm by eliminating the common projection plane and directly projecting the detector boundaries onto image voxel boundaries. To reduce the time required for calculating the overlap between the detector edges and image voxel boundaries, we have proposed a pre-accumulation technique to accumulate image intensities in perpendicular 2D image slabs (from a 3D image) before projection and after backprojection to ensure our DD kernels run faster in parallel GPU threads. For the implementation of our iterative MBIR technique we use a parallel multi-GPU version of the alternating minimization (AM) algorithm with penalized likelihood update. The time performance using our proposed reconstruction method with Siemens Sensation 16 patient scan data shows an average of 24 times speedup using a single TITAN X GPU and 74 times speedup using 3 TITAN X GPUs in parallel for combined projection and backprojection.

Brain tumor classification and segmentation using sparse coding and dictionary learning.

PubMed

Salman Al-Shaikhli, Saif Dawood; Yang, Michael Ying; Rosenhahn, Bodo

2016-08-01

This paper presents a novel fully automatic framework for multi-class brain tumor classification and segmentation using a sparse coding and dictionary learning method. The proposed framework consists of two steps: classification and segmentation. The classification of the brain tumors is based on brain topology and texture. The segmentation is based on voxel values of the image data. Using K-SVD, two types of dictionaries are learned from the training data and their associated ground truth segmentation: feature dictionary and voxel-wise coupled dictionaries. The feature dictionary consists of global image features (topological and texture features). The coupled dictionaries consist of coupled information: gray scale voxel values of the training image data and their associated label voxel values of the ground truth segmentation of the training data. For quantitative evaluation, the proposed framework is evaluated using different metrics. The segmentation results of the brain tumor segmentation (MICCAI-BraTS-2013) database are evaluated using five different metric scores, which are computed using the online evaluation tool provided by the BraTS-2013 challenge organizers. Experimental results demonstrate that the proposed approach achieves an accurate brain tumor classification and segmentation and outperforms the state-of-the-art methods.

a Super Voxel-Based Riemannian Graph for Multi Scale Segmentation of LIDAR Point Clouds

NASA Astrophysics Data System (ADS)

Li, Minglei

2018-04-01

Automatically segmenting LiDAR points into respective independent partitions has become a topic of great importance in photogrammetry, remote sensing and computer vision. In this paper, we cast the problem of point cloud segmentation as a graph optimization problem by constructing a Riemannian graph. The scale space of the observed scene is explored by an octree-based over-segmentation with different depths. The over-segmentation produces many super voxels which restrict the structure of the scene and will be used as nodes of the graph. The Kruskal coordinates are used to compute edge weights that are proportional to the geodesic distance between nodes. Then we compute the edge-weight matrix in which the elements reflect the sectional curvatures associated with the geodesic paths between super voxel nodes on the scene surface. The final segmentation results are generated by clustering similar super voxels and cutting off the weak edges in the graph. The performance of this method was evaluated on LiDAR point clouds for both indoor and outdoor scenes. Additionally, extensive comparisons to state of the art techniques show that our algorithm outperforms on many metrics.

The use of error-category mapping in pharmacokinetic model analysis of dynamic contrast-enhanced MRI data.

PubMed

Gill, Andrew B; Anandappa, Gayathri; Patterson, Andrew J; Priest, Andrew N; Graves, Martin J; Janowitz, Tobias; Jodrell, Duncan I; Eisen, Tim; Lomas, David J

2015-02-01

This study introduces the use of 'error-category mapping' in the interpretation of pharmacokinetic (PK) model parameter results derived from dynamic contrast-enhanced (DCE-) MRI data. Eleven patients with metastatic renal cell carcinoma were enrolled in a multiparametric study of the treatment effects of bevacizumab. For the purposes of the present analysis, DCE-MRI data from two identical pre-treatment examinations were analysed by application of the extended Tofts model (eTM), using in turn a model arterial input function (AIF), an individually-measured AIF and a sample-average AIF. PK model parameter maps were calculated. Errors in the signal-to-gadolinium concentration ([Gd]) conversion process and the model-fitting process itself were assigned to category codes on a voxel-by-voxel basis, thereby forming a colour-coded 'error-category map' for each imaged slice. These maps were found to be repeatable between patient visits and showed that the eTM converged adequately in the majority of voxels in all the tumours studied. However, the maps also clearly indicated sub-regions of low Gd uptake and of non-convergence of the model in nearly all tumours. The non-physical condition ve ≥ 1 was the most frequently indicated error category and appeared sensitive to the form of AIF used. This simple method for visualisation of errors in DCE-MRI could be used as a routine quality-control technique and also has the potential to reveal otherwise hidden patterns of failure in PK model applications. Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.

Multi-institutional MicroCT image comparison of image-guided small animal irradiators

NASA Astrophysics Data System (ADS)

Johnstone, Chris D.; Lindsay, Patricia; E Graves, Edward; Wong, Eugene; Perez, Jessica R.; Poirier, Yannick; Ben-Bouchta, Youssef; Kanesalingam, Thilakshan; Chen, Haijian; E Rubinstein, Ashley; Sheng, Ke; Bazalova-Carter, Magdalena

2017-07-01

To recommend imaging protocols and establish tolerance levels for microCT image quality assurance (QA) performed on conformal image-guided small animal irradiators. A fully automated QA software SAPA (small animal phantom analyzer) for image analysis of the commercial Shelley micro-CT MCTP 610 phantom was developed, in which quantitative analyses of CT number linearity, signal-to-noise ratio (SNR), uniformity and noise, geometric accuracy, spatial resolution by means of modulation transfer function (MTF), and CT contrast were performed. Phantom microCT scans from eleven institutions acquired with four image-guided small animal irradiator units (including the commercial PXi X-RAD SmART and Xstrahl SARRP systems) with varying parameters used for routine small animal imaging were analyzed. Multi-institutional data sets were compared using SAPA, based on which tolerance levels for each QA test were established and imaging protocols for QA were recommended. By analyzing microCT data from 11 institutions, we established image QA tolerance levels for all image quality tests. CT number linearity set to R 2  >  0.990 was acceptable in microCT data acquired at all but three institutions. Acceptable SNR  >  36 and noise levels  <55 HU were obtained at five of the eleven institutions, where failing scans were acquired with current-exposure time of less than 120 mAs. Acceptable spatial resolution (>1.5 lp mm-1 for MTF  =  0.2) was obtained at all but four institutions due to their large image voxel size used (>0.275 mm). Ten of the eleven institutions passed the set QA tolerance for geometric accuracy (<1.5%) and nine of the eleven institutions passed the QA tolerance for contrast (>2000 HU for 30 mgI ml-1). We recommend performing imaging QA with 70 kVp, 1.5 mA, 120 s imaging time, 0.20 mm voxel size, and a frame rate of 5 fps for the PXi X-RAD SmART. For the Xstrahl SARRP, we recommend using 60 kVp, 1.0 mA, 240 s imaging time, 0.20 mm voxel size, and 6 fps. These imaging protocols should result in high quality images that pass the set tolerance levels on all systems. Average SAPA computation time for complete QA analysis for a 0.20 mm voxel, 400 slice Shelley phantom microCT data set was less than 20 s. We present image quality assurance recommendations for image-guided small animal radiotherapy systems that can aid researchers in maintaining high image quality, allowing for spatially precise conformal dose delivery to small animals.

Grid-cell representations in mental simulation

PubMed Central

Bellmund, Jacob LS; Deuker, Lorena; Navarro Schröder, Tobias; Doeller, Christian F

2016-01-01

Anticipating the future is a key motif of the brain, possibly supported by mental simulation of upcoming events. Rodent single-cell recordings suggest the ability of spatially tuned cells to represent subsequent locations. Grid-like representations have been observed in the human entorhinal cortex during virtual and imagined navigation. However, hitherto it remains unknown if grid-like representations contribute to mental simulation in the absence of imagined movement. Participants imagined directions between building locations in a large-scale virtual-reality city while undergoing fMRI without re-exposure to the environment. Using multi-voxel pattern analysis, we provide evidence for representations of absolute imagined direction at a resolution of 30° in the parahippocampal gyrus, consistent with the head-direction system. Furthermore, we capitalize on the six-fold rotational symmetry of grid-cell firing to demonstrate a 60° periodic pattern-similarity structure in the entorhinal cortex. Our findings imply a role of the entorhinal grid-system in mental simulation and future thinking beyond spatial navigation. DOI: http://dx.doi.org/10.7554/eLife.17089.001 PMID:27572056

Novel Super-Resolution Approach to Time-Resolved Volumetric 4-Dimensional Magnetic Resonance Imaging With High Spatiotemporal Resolution for Multi-Breathing Cycle Motion Assessment

DOE Office of Scientific and Technical Information (OSTI.GOV)

Li, Guang, E-mail: lig2@mskcc.org; Wei, Jie; Kadbi, Mo

Purpose: To develop and evaluate a super-resolution approach to reconstruct time-resolved 4-dimensional magnetic resonance imaging (TR-4DMRI) with a high spatiotemporal resolution for multi-breathing cycle motion assessment. Methods and Materials: A super-resolution approach was developed to combine fast 3-dimensional (3D) cine MRI with low resolution during free breathing (FB) and high-resolution 3D static MRI during breath hold (BH) using deformable image registration. A T1-weighted, turbo field echo sequence, coronal 3D cine acquisition, partial Fourier approximation, and SENSitivity Encoding parallel acceleration were used. The same MRI pulse sequence, field of view, and acceleration techniques were applied in both FB and BH acquisitions;more » the intensity-based Demons deformable image registration method was used. Under an institutional review board–approved protocol, 7 volunteers were studied with 3D cine FB scan (voxel size: 5 × 5 × 5 mm{sup 3}) at 2 Hz for 40 seconds and a 3D static BH scan (2 × 2 × 2 mm{sup 3}). To examine the image fidelity of 3D cine and super-resolution TR-4DMRI, a mobile gel phantom with multi-internal targets was scanned at 3 speeds and compared with the 3D static image. Image similarity among 3D cine, 4DMRI, and 3D static was evaluated visually using difference image and quantitatively using voxel intensity correlation and Dice index (phantom only). Multi-breathing-cycle waveforms were extracted and compared in both phantom and volunteer images using the 3D cine as the references. Results: Mild imaging artifacts were found in the 3D cine and TR-4DMRI of the mobile gel phantom with a Dice index of >0.95. Among 7 volunteers, the super-resolution TR-4DMRI yielded high voxel-intensity correlation (0.92 ± 0.05) and low voxel-intensity difference (<0.05). The detected motion differences between TR-4DMRI and 3D cine were −0.2 ± 0.5 mm (phantom) and −0.2 ± 1.9 mm (diaphragms). Conclusion: Super-resolution TR-4DMRI has been reconstructed with adequate temporal (2 Hz) and spatial (2 × 2 × 2 mm{sup 3}) resolutions. Further TR-4DMRI characterization and improvement are necessary before clinical applications. Multi-breathing cycles can be examined, providing patient-specific breathing irregularities and motion statistics for future 4D radiation therapy.« less

Classifying social anxiety disorder using multivoxel pattern analyses of brain function and structure☆

PubMed Central

Frick, Andreas; Gingnell, Malin; Marquand, Andre F.; Howner, Katarina; Fischer, Håkan; Kristiansson, Marianne; Williams, Steven C.R.; Fredrikson, Mats; Furmark, Tomas

2014-01-01

Functional neuroimaging of social anxiety disorder (SAD) support altered neural activation to threat-provoking stimuli focally in the fear network, while structural differences are distributed over the temporal and frontal cortices as well as limbic structures. Previous neuroimaging studies have investigated the brain at the voxel level using mass-univariate methods which do not enable detection of more complex patterns of activity and structural alterations that may separate SAD from healthy individuals. Support vector machine (SVM) is a supervised machine learning method that capitalizes on brain activation and structural patterns to classify individuals. The aim of this study was to investigate if it is possible to discriminate SAD patients (n = 14) from healthy controls (n = 12) using SVM based on (1) functional magnetic resonance imaging during fearful face processing and (2) regional gray matter volume. Whole brain and region of interest (fear network) SVM analyses were performed for both modalities. For functional scans, significant classifications were obtained both at whole brain level and when restricting the analysis to the fear network while gray matter SVM analyses correctly classified participants only when using the whole brain search volume. These results support that SAD is characterized by aberrant neural activation to affective stimuli in the fear network, while disorder-related alterations in regional gray matter volume are more diffusely distributed over the whole brain. SVM may thus be useful for identifying imaging biomarkers of SAD. PMID:24239689

Voxel-based morphometry of auditory and speech-related cortex in stutterers.

PubMed

Beal, Deryk S; Gracco, Vincent L; Lafaille, Sophie J; De Nil, Luc F

2007-08-06

Stutterers demonstrate unique functional neural activation patterns during speech production, including reduced auditory activation, relative to nonstutterers. The extent to which these functional differences are accompanied by abnormal morphology of the brain in stutterers is unclear. This study examined the neuroanatomical differences in speech-related cortex between stutterers and nonstutterers using voxel-based morphometry. Results revealed significant differences in localized grey matter and white matter densities of left and right hemisphere regions involved in auditory processing and speech production.

Mechanisms supporting superior source memory for familiar items: a multi-voxel pattern analysis study.

PubMed

Poppenk, Jordan; Norman, Kenneth A

2012-11-01

Recent cognitive research has revealed better source memory performance for familiar relative to novel stimuli. Here we consider two possible explanations for this finding. The source memory advantage for familiar stimuli could arise because stimulus novelty induces attention to stimulus features at the expense of contextual processing, resulting in diminished overall levels of contextual processing at study for novel (vs. familiar) stimuli. Another possibility is that stimulus information retrieved from long-term memory (LTM) provides scaffolding that facilitates the formation of item-context associations. If contextual features are indeed more effectively bound to familiar (vs. novel) items, the relationship between contextual processing at study and subsequent source memory should be stronger for familiar items. We tested these possibilities by applying multi-voxel pattern analysis (MVPA) to a recently collected functional magnetic resonance imaging (fMRI) dataset, with the goal of measuring contextual processing at study and relating it to subsequent source memory performance. Participants were scanned with fMRI while viewing novel proverbs, repeated proverbs (previously novel proverbs that were shown in a pre-study phase), and previously known proverbs in the context of one of two experimental tasks. After scanning was complete, we evaluated participants' source memory for the task associated with each proverb. Drawing upon fMRI data from the study phase, we trained a classifier to detect on-task processing (i.e., how strongly was the correct task set activated). On-task processing was greater for previously known than novel proverbs and similar for repeated and novel proverbs. However, both within and across participants, the relationship between on-task processing and subsequent source memory was stronger for repeated than novel proverbs and similar for previously known and novel proverbs. Finally, focusing on the repeated condition, we found that higher levels of hippocampal activity during the pre-study phase, which we used as an index of episodic encoding, led to a stronger relationship between on-task processing at study and subsequent memory. Together, these findings suggest different mechanisms may be primarily responsible for superior source memory for repeated and previously known stimuli. Specifically, they suggest that prior stimulus knowledge enhances memory by boosting the overall level of contextual processing, whereas stimulus repetition enhances the probability that contextual features will be successfully bound to item features. Several possible theoretical explanations for this pattern are discussed. Copyright © 2012 Elsevier Ltd. All rights reserved.

A Fully GPU-Based Ray-Driven Backprojector via a Ray-Culling Scheme with Voxel-Level Parallelization for Cone-Beam CT Reconstruction.

PubMed

Park, Hyeong-Gyu; Shin, Yeong-Gil; Lee, Ho

2015-12-01

A ray-driven backprojector is based on ray-tracing, which computes the length of the intersection between the ray paths and each voxel to be reconstructed. To reduce the computational burden caused by these exhaustive intersection tests, we propose a fully graphics processing unit (GPU)-based ray-driven backprojector in conjunction with a ray-culling scheme that enables straightforward parallelization without compromising the high computing performance of a GPU. The purpose of the ray-culling scheme is to reduce the number of ray-voxel intersection tests by excluding rays irrelevant to a specific voxel computation. This rejection step is based on an axis-aligned bounding box (AABB) enclosing a region of voxel projection, where eight vertices of each voxel are projected onto the detector plane. The range of the rectangular-shaped AABB is determined by min/max operations on the coordinates in the region. Using the indices of pixels inside the AABB, the rays passing through the voxel can be identified and the voxel is weighted as the length of intersection between the voxel and the ray. This procedure makes it possible to reflect voxel-level parallelization, allowing an independent calculation at each voxel, which is feasible for a GPU implementation. To eliminate redundant calculations during ray-culling, a shared-memory optimization is applied to exploit the GPU memory hierarchy. In experimental results using real measurement data with phantoms, the proposed GPU-based ray-culling scheme reconstructed a volume of resolution 28032803176 in 77 seconds from 680 projections of resolution 10243768 , which is 26 times and 7.5 times faster than standard CPU-based and GPU-based ray-driven backprojectors, respectively. Qualitative and quantitative analyses showed that the ray-driven backprojector provides high-quality reconstruction images when compared with those generated by the Feldkamp-Davis-Kress algorithm using a pixel-driven backprojector, with an average of 2.5 times higher contrast-to-noise ratio, 1.04 times higher universal quality index, and 1.39 times higher normalized mutual information. © The Author(s) 2014.

Parietal lobe critically supports successful paired immediate and single-item delayed memory for targets.

PubMed

Krumm, Sabine; Kivisaari, Sasa L; Monsch, Andreas U; Reinhardt, Julia; Ulmer, Stephan; Stippich, Christoph; Kressig, Reto W; Taylor, Kirsten I

2017-05-01

The parietal lobe is important for successful recognition memory, but its role is not yet fully understood. We investigated the parietal lobes' contribution to immediate paired-associate memory and delayed item-recognition memory separately for hits (targets) and correct rejections (distractors). We compared the behavioral performance of 56 patients with known parietal and medial temporal lobe dysfunction (i.e. early Alzheimer's Disease) to 56 healthy control participants in an immediate paired and delayed single item object memory task. Additionally, we performed voxel-based morphometry analyses to investigate the functional-neuroanatomic relationships between performance and voxel-based estimates of atrophy in whole-brain analyses. Behaviorally, all participants performed better identifying targets than rejecting distractors. The voxel-based morphometry analyses associated atrophy in the right ventral parietal cortex with fewer correct responses to familiar items (i.e. hits) in the immediate and delayed conditions. Additionally, medial temporal lobe integrity correlated with better performance in rejecting distractors, but not in identifying targets, in the immediate paired-associate task. Our findings suggest that the parietal lobe critically supports successful immediate and delayed target recognition memory, and that the ventral aspect of the parietal cortex and the medial temporal lobe may have complementary preferences for identifying targets and rejecting distractors, respectively, during recognition memory. Copyright © 2017. Published by Elsevier Inc.

Subsurface data visualization in Virtual Reality

NASA Astrophysics Data System (ADS)

Krijnen, Robbert; Smelik, Ruben; Appleton, Rick; van Maanen, Peter-Paul

2017-04-01

Due to their increasing complexity and size, visualization of geological data is becoming more and more important. It enables detailed examining and reviewing of large volumes of geological data and it is often used as a communication tool for reporting and education to demonstrate the importance of the geology to policy makers. In the Netherlands two types of nation-wide geological models are available: 1) Layer-based models in which the subsurface is represented by a series of tops and bases of geological or hydrogeological units, and 2) Voxel models in which the subsurface is subdivided in a regular grid of voxels that can contain different properties per voxel. The Geological Survey of the Netherlands (GSN) provides an interactive web portal that delivers maps and vertical cross-sections of such layer-based and voxel models. From this portal you can download a 3D subsurface viewer that can visualize the voxel model data of an area of 20 × 25 km with 100 × 100 × 5 meter voxel resolution on a desktop computer. Virtual Reality (VR) technology enables us to enhance the visualization of this volumetric data in a more natural way as compared to a standard desktop, keyboard mouse setup. The use of VR for data visualization is not new but recent developments has made expensive hardware and complex setups unnecessary. The availability of consumer of-the-shelf VR hardware enabled us to create an new intuitive and low visualization tool. A VR viewer has been implemented using the HTC Vive head set and allows visualization and analysis of the GSN voxel model data with geological or hydrogeological units. The user can navigate freely around the voxel data (20 × 25 km) which is presented in a virtual room at a scale of 2 × 2 or 3 × 3 meters. To enable analysis, e.g. hydraulic conductivity, the user can select filters to remove specific hydrogeological units. The user can also use slicing to cut-off specific sections of the voxel data to get a closer look. This slicing can be done in any direction using a 'virtual knife'. Future plans are to further improve performance from 30 up to 90 Hz update rate to reduce possible motion sickness, add more advanced filtering capabilities as well as a multi user setup, annotation capabilities and visualizing of historical data.

Cerebral morphology and dopamine D2/D3receptor distribution in humans: A combined [18F]fallypride and voxel-based morphometry study

PubMed Central

Woodward, Neil D.; Zald, David H.; Ding, Zhaohua; Riccardi, Patrizia; Ansari, M. Sib; Baldwin, Ronald M.; Cowan, Ronald L.; Li, Rui; Kessler, Robert M.

2009-01-01

The relationship between cerebral morphology and the expression of dopamine receptors has not been extensively studied in humans. Elucidation of such relationships may have important methodological implications for clinical studies of dopamine receptor ligand binding differences between control and patient groups. The association between cerebral morphology and dopamine receptor distribution was examined in 45 healthy subjects who completed T1-weighted structural MRI and PET scanning with the D2/D3 ligand [18F]fallypride. Optimized voxel-based morphometry was used to create grey matter volume and density images. Grey matter volume and density images were correlated with binding potential (BPND) images on a voxel-by-voxel basis using the Biological Parametric Mapping toolbox. Associations between cerebral morphology and BPND were also examined for selected regions-of-interest (ROIs) after spatial normalization. Voxel-wise analyses indicated that grey matter volume and density positively correlated with BPND throughout the midbrain, including the substantia nigra. Positive correlations were observed in medial cortical areas, including anterior cingulate and medial prefrontal cortex, and circumscribed regions of the temporal, frontal, and parietal lobes. ROI analyses revealed significant positive correlations between BPND and cerebral morphology in the caudate, thalamus, and amygdala. Few negative correlations between morphology and BPND were observed. Overall, grey matter density appeared more strongly correlated with BPND than grey matter volume. Cerebral morphology, particularly grey matter density, correlates with [18F]fallypride BPND in a regionally specific manner. Clinical studies comparing dopamine receptor availability between clinical and control groups may benefit by accounting for potential differences in cerebral morphology that exist even after spatial normalization. PMID:19457373

Cerebral morphology and dopamine D2/D3 receptor distribution in humans: a combined [18F]fallypride and voxel-based morphometry study.

PubMed

Woodward, Neil D; Zald, David H; Ding, Zhaohua; Riccardi, Patrizia; Ansari, M Sib; Baldwin, Ronald M; Cowan, Ronald L; Li, Rui; Kessler, Robert M

2009-05-15

The relationship between cerebral morphology and the expression of dopamine receptors has not been extensively studied in humans. Elucidation of such relationships may have important methodological implications for clinical studies of dopamine receptor ligand binding differences between control and patient groups. The association between cerebral morphology and dopamine receptor distribution was examined in 45 healthy subjects who completed T1-weighted structural MRI and PET scanning with the D(2)/D(3) ligand [(18)F]fallypride. Optimized voxel-based morphometry was used to create grey matter volume and density images. Grey matter volume and density images were correlated with binding potential (BP(ND)) images on a voxel-by-voxel basis using the Biological Parametric Mapping toolbox. Associations between cerebral morphology and BP(ND) were also examined for selected regions-of-interest (ROIs) after spatial normalization. Voxel-wise analyses indicated that grey matter volume and density positively correlated with BP(ND) throughout the midbrain, including the substantia nigra. Positive correlations were observed in medial cortical areas, including anterior cingulate and medial prefrontal cortex, and circumscribed regions of the temporal, frontal, and parietal lobes. ROI analyses revealed significant positive correlations between BP(ND) and cerebral morphology in the caudate, thalamus, and amygdala. Few negative correlations between morphology and BP(ND) were observed. Overall, grey matter density appeared more strongly correlated with BP(ND) than grey matter volume. Cerebral morphology, particularly grey matter density, correlates with [(18)F]fallypride BP(ND) in a regionally specific manner. Clinical studies comparing dopamine receptor availability between clinical and control groups may benefit by accounting for potential differences in cerebral morphology that exist even after spatial normalization.

MIDAS: Regionally linear multivariate discriminative statistical mapping.

PubMed

Varol, Erdem; Sotiras, Aristeidis; Davatzikos, Christos

2018-07-01

Statistical parametric maps formed via voxel-wise mass-univariate tests, such as the general linear model, are commonly used to test hypotheses about regionally specific effects in neuroimaging cross-sectional studies where each subject is represented by a single image. Despite being informative, these techniques remain limited as they ignore multivariate relationships in the data. Most importantly, the commonly employed local Gaussian smoothing, which is important for accounting for registration errors and making the data follow Gaussian distributions, is usually chosen in an ad hoc fashion. Thus, it is often suboptimal for the task of detecting group differences and correlations with non-imaging variables. Information mapping techniques, such as searchlight, which use pattern classifiers to exploit multivariate information and obtain more powerful statistical maps, have become increasingly popular in recent years. However, existing methods may lead to important interpretation errors in practice (i.e., misidentifying a cluster as informative, or failing to detect truly informative voxels), while often being computationally expensive. To address these issues, we introduce a novel efficient multivariate statistical framework for cross-sectional studies, termed MIDAS, seeking highly sensitive and specific voxel-wise brain maps, while leveraging the power of regional discriminant analysis. In MIDAS, locally linear discriminative learning is applied to estimate the pattern that best discriminates between two groups, or predicts a variable of interest. This pattern is equivalent to local filtering by an optimal kernel whose coefficients are the weights of the linear discriminant. By composing information from all neighborhoods that contain a given voxel, MIDAS produces a statistic that collectively reflects the contribution of the voxel to the regional classifiers as well as the discriminative power of the classifiers. Critically, MIDAS efficiently assesses the statistical significance of the derived statistic by analytically approximating its null distribution without the need for computationally expensive permutation tests. The proposed framework was extensively validated using simulated atrophy in structural magnetic resonance imaging (MRI) and further tested using data from a task-based functional MRI study as well as a structural MRI study of cognitive performance. The performance of the proposed framework was evaluated against standard voxel-wise general linear models and other information mapping methods. The experimental results showed that MIDAS achieves relatively higher sensitivity and specificity in detecting group differences. Together, our results demonstrate the potential of the proposed approach to efficiently map effects of interest in both structural and functional data. Copyright © 2018. Published by Elsevier Inc.

Multi-scale X-ray Microtomography Imaging of Immiscible Fluids After Imbibition

NASA Astrophysics Data System (ADS)

Garing, C.; de Chalendar, J.; Voltolini, M.; Ajo Franklin, J. B.; Benson, S. M.

2015-12-01

A major issue for CO2 storage security is the efficiency and long-term reliability of the trapping mechanisms occurring in the reservoir where CO2 is injected. Residual trapping is one of the key processes for storage security beyond the primary stratigraphic seal. Although classical conceptual models of residual fluid trapping assume that disconnected ganglia are permanently immobilized, multiple mechanisms exist which could allow the remobilization of residually trapped CO2. The aim of this study is to quantify fluid phases saturation, connectivity and morphology after imbibition using x-ray microtomography in order to evaluate potential changes in droplets organization due to differences in capillary pressure between disconnected ganglia. Particular emphasis is placed on the effect of image resolution. Synchrotron-based x-ray microtomographic datasets of air-water spontaneous imbibition were acquired in sintered glass beads and sandstone samples with voxel sizes varying from 0.64 to 4.44 μm. The results show that for both sandstones the residual air phase is homogeneously distributed within the entire pore space and consists of disconnected clusters of multiple sizes and morphologies. The multi-scale analysis of subsamples of few pores and throats imaged at the same location of the sample reveals significant variations in the estimation of connectivity, size and shape of the fluid phases. This is particularly noticeable when comparing the results from the images with voxel sizes above 1 μm with the results from the images acquired with voxel sizes below 1 μm.

Gray matter volume covariance patterns associated with gait speed in older adults: a multi-cohort MRI study.

PubMed

Blumen, Helena M; Brown, Lucy L; Habeck, Christian; Allali, Gilles; Ayers, Emmeline; Beauchet, Olivier; Callisaya, Michele; Lipton, Richard B; Mathuranath, P S; Phan, Thanh G; Pradeep Kumar, V G; Srikanth, Velandai; Verghese, Joe

2018-04-09

Accelerated gait decline in aging is associated with many adverse outcomes, including an increased risk for falls, cognitive decline, and dementia. Yet, the brain structures associated with gait speed, and how they relate to specific cognitive domains, are not well-understood. We examined structural brain correlates of gait speed, and how they relate to processing speed, executive function, and episodic memory in three non-demented and community-dwelling older adult cohorts (Overall N = 352), using voxel-based morphometry and multivariate covariance-based statistics. In all three cohorts, we identified gray matter volume covariance patterns associated with gait speed that included brain stem, precuneus, fusiform, motor, supplementary motor, and prefrontal (particularly ventrolateral prefrontal) cortex regions. Greater expression of these gray matter volume covariance patterns linked to gait speed were associated with better processing speed in all three cohorts, and with better executive function in one cohort. These gray matter covariance patterns linked to gait speed were not associated with episodic memory in any of the cohorts. These findings suggest that gait speed, processing speed (and to some extent executive functions) rely on shared neural systems that are subject to age-related and dementia-related change. The implications of these findings are discussed within the context of the development of interventions to compensate for age-related gait and cognitive decline.

A voxel-based approach to gray matter asymmetries.

PubMed

Luders, E; Gaser, C; Jancke, L; Schlaug, G

2004-06-01

Voxel-based morphometry (VBM) was used to analyze gray matter (GM) asymmetries in a large sample (n = 60) of male and female professional musicians with and without absolute pitch (AP). We chose to examine these particular groups because previous studies using traditional region-of-interest (ROI) analyses have shown differences in hemispheric asymmetry related to AP and gender. Voxel-based methods may have advantages over traditional ROI-based methods since the analysis can be performed across the whole brain with minimal user bias. After determining that the VBM method was sufficiently sensitive for the detection of differences in GM asymmetries between groups, we found that male AP musicians were more leftward lateralized in the anterior region of the planum temporale (PT) than male non-AP musicians. This confirmed the results of previous studies using ROI-based methods that showed an association between PT asymmetry and the AP phenotype. We further observed that male non-AP musicians revealed an increased leftward GM asymmetry in the postcentral gyrus compared to female non-AP musicians, again corroborating results of a previously published study using ROI-based methods. By analyzing hemispheric GM differences across our entire sample, we were able to partially confirm findings of previous studies using traditional morphometric techniques, as well as more recent, voxel-based analyses. In addition, we found some unusually pronounced GM asymmetries in our musician sample not previously detected in subjects unselected for musical training. Since we were able to validate gender- and AP-related brain asymmetries previously described using traditional ROI-based morphometric techniques, the results of our analyses support the use of VBM for examinations of GM asymmetries.

Neural Representations of Belief Concepts: A Representational Similarity Approach to Social Semantics

PubMed Central

Leshinskaya, Anna; Contreras, Juan Manuel; Caramazza, Alfonso; Mitchell, Jason P.

2017-01-01

Abstract The present experiment identified neural regions that represent a class of concepts that are independent of perceptual or sensory attributes. During functional magnetic resonance imaging scanning, participants viewed names of social groups (e.g. Atheists, Evangelicals, and Economists) and performed a one-back similarity judgment according to 1 of 2 dimensions of belief attributes: political orientation (Liberal to Conservative) or spiritualism (Spiritualist to Materialist). By generalizing across a wide variety of social groups that possess these beliefs, these attribute concepts did not coincide with any specific sensory quality, allowing us to target conceptual, rather than perceptual, representations. Multi-voxel pattern searchlight analysis was used to identify regions in which activation patterns distinguished the 2 ends of both dimensions: Conservative from Liberal social groups when participants focused on the political orientation dimension, and spiritual from Materialist groups when participants focused on the spiritualism dimension. A cluster in right precuneus exhibited such a pattern, indicating that it carries information about belief-attribute concepts and forms part of semantic memory—perhaps a component particularly concerned with psychological traits. This region did not overlap with the theory of mind network, which engaged nearby, but distinct, parts of precuneus. These findings have implications for the neural organization of conceptual knowledge, especially the understanding of social groups. PMID:28108495

Relationship between grey matter integrity and executive abilities in aging.

PubMed

Manard, Marine; Bahri, Mohamed Ali; Salmon, Eric; Collette, Fabienne

2016-07-01

This cross-sectional study was designed to investigate grey matter changes that occur in healthy aging and the relationship between grey matter characteristics and executive functioning. Thirty-six young adults (18-30 years old) and 43 seniors (60-75 years old) were included. A general executive score was derived from a large battery of neuropsychological tests assessing three major aspects of executive functioning (inhibition, updating and shifting). Age-related grey matter changes were investigated by comparing young and older adults using voxel-based morphometry and voxel-based cortical thickness methods. A widespread difference in grey matter volume was found across many brain regions, whereas cortical thinning was mainly restricted to central areas. Multivariate analyses showed age-related changes in relatively similar brain regions to the respective univariate analyses but appeared more limited. Finally, in the older adult sample, a significant relationship between global executive performance and decreased grey matter volume in anterior (i.e. frontal, insular and cingulate cortex) but also some posterior brain areas (i.e. temporal and parietal cortices) as well as subcortical structures was observed. Results of this study highlight the distribution of age-related effects on grey matter volume and show that cortical atrophy does not appear primarily in "frontal" brain regions. From a cognitive viewpoint, age-related executive functioning seems to be related to grey matter volume but not to cortical thickness. Therefore, our results also highlight the influence of methodological aspects (from preprocessing to statistical analysis) on the pattern of results, which could explain the lack of consensus in literature. Copyright © 2016 Elsevier B.V. All rights reserved.

SU-D-207A-02: Possible Characterization of the Brain Tumor Vascular Environment by a Novel Strategy of Quantitative Analysis in Dynamic Contrast Enhanced MR Imaging: A Combination of Both Patlak and Logan Analyses

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yee, S; Chinnaiyan, P; Wloch, J

Purpose: The majority of quantitative analyses involving dynamic contrast enhanced (DCE) MRI have been performed to obtain kinetic parameters such as Ktrans and ve. Such analyses are generally performed assuming a “reversible” tissue compartment, where the tracer is assumed to be rapidly equilibrated between the plasma and tissue compartments. However, some tumor vascular environments may be more suited for a “non-reversible” tissue compartment, where, as with FDG PET imaging, the tracer is continuously deposited into the tissue compartment (or the return back to the plasma compartment is very slow in the imaging time scale). Therefore, Patlak and Logan analyses, whichmore » represent tools for the “non-reversible” and “reversible” modeling, respectively, were performed to better characterize the brain tumor vascular environment. Methods: A voxel-by-voxel analysis was performed to generate both Patlak and Logan plots in two brain tumor patients, one with grade III astrocytoma and the other with grade IV astrocytoma or glioblastoma. The slopes of plots and the r-square were then obtained by linear fitting and compared for each voxel. Results: The 2-dimensional scatter plots of Logan (Y-axis) vs. Patlak slopes (X-axis) clearly showed increased Logan slopes for glioblastoma (Figure 3A). The scatter plots of goodness-of-fit (Figure 3B) also suggested glioblastoma, relative to grade III astrocytoma, might consist of more voxels that are kinetically Logan-like (i.e. rapidly equilibrated extravascular space and active vascular environment). Therefore, the enhanced Logan-like behavior (and the Logan slope) in glioblastoma may imply an increased fraction of active vascular environment, while the enhanced Patlak-like behavior implies the vascular environment permitting a relatively slower washout of the tracer. Conclusion: Although further verification is required, the combination of Patlak and Logan analyses in DCE MRI may be useful in characterizing the tumor vascular environment, and thus, may have implications in tumor grading and monitoring response to anti-vascular therapy.« less

Long-term ground deformation patterns of Bucharest using multi-temporal InSAR and multivariate dynamic analyses: a possible transpressional system?

PubMed Central

Armaş, Iuliana; Mendes, Diana A.; Popa, Răzvan-Gabriel; Gheorghe, Mihaela; Popovici, Diana

2017-01-01

The aim of this exploratory research is to capture spatial evolution patterns in the Bucharest metropolitan area using sets of single polarised synthetic aperture radar (SAR) satellite data and multi-temporal radar interferometry. Three sets of SAR data acquired during the years 1992–2010 from ERS-1/-2 and ENVISAT, and 2011–2014 from TerraSAR-X satellites were used in conjunction with the Small Baseline Subset (SBAS) and persistent scatterers (PS) high-resolution multi-temporal interferometry (InSAR) techniques to provide maps of line-of-sight displacements. The satellite-based remote sensing results were combined with results derived from classical methodologies (i.e., diachronic cartography) and field research to study possible trends in developments over former clay pits, landfill excavation sites, and industrial parks. The ground displacement trend patterns were analysed using several linear and nonlinear models, and techniques. Trends based on the estimated ground displacement are characterised by long-term memory, indicated by low noise Hurst exponents, which in the long-term form interesting attractors. We hypothesize these attractors to be tectonic stress fields generated by transpressional movements. PMID:28252103

Long-term ground deformation patterns of Bucharest using multi-temporal InSAR and multivariate dynamic analyses: a possible transpressional system?

PubMed

Armaş, Iuliana; Mendes, Diana A; Popa, Răzvan-Gabriel; Gheorghe, Mihaela; Popovici, Diana

2017-03-02

The aim of this exploratory research is to capture spatial evolution patterns in the Bucharest metropolitan area using sets of single polarised synthetic aperture radar (SAR) satellite data and multi-temporal radar interferometry. Three sets of SAR data acquired during the years 1992-2010 from ERS-1/-2 and ENVISAT, and 2011-2014 from TerraSAR-X satellites were used in conjunction with the Small Baseline Subset (SBAS) and persistent scatterers (PS) high-resolution multi-temporal interferometry (InSAR) techniques to provide maps of line-of-sight displacements. The satellite-based remote sensing results were combined with results derived from classical methodologies (i.e., diachronic cartography) and field research to study possible trends in developments over former clay pits, landfill excavation sites, and industrial parks. The ground displacement trend patterns were analysed using several linear and nonlinear models, and techniques. Trends based on the estimated ground displacement are characterised by long-term memory, indicated by low noise Hurst exponents, which in the long-term form interesting attractors. We hypothesize these attractors to be tectonic stress fields generated by transpressional movements.

SU-F-T-342: Dosimetric Constraint Prediction Guided Automatic Mulit-Objective Optimization for Intensity Modulated Radiotherapy

DOE Office of Scientific and Technical Information (OSTI.GOV)

Song, T; Zhou, L; Li, Y

Purpose: For intensity modulated radiotherapy, the plan optimization is time consuming with difficulties of selecting objectives and constraints, and their relative weights. A fast and automatic multi-objective optimization algorithm with abilities to predict optimal constraints and manager their trade-offs can help to solve this problem. Our purpose is to develop such a framework and algorithm for a general inverse planning. Methods: There are three main components contained in this proposed multi-objective optimization framework: prediction of initial dosimetric constraints, further adjustment of constraints and plan optimization. We firstly use our previously developed in-house geometry-dosimetry correlation model to predict the optimal patient-specificmore » dosimetric endpoints, and treat them as initial dosimetric constraints. Secondly, we build an endpoint(organ) priority list and a constraint adjustment rule to repeatedly tune these constraints from their initial values, until every single endpoint has no room for further improvement. Lastly, we implement a voxel-independent based FMO algorithm for optimization. During the optimization, a model for tuning these voxel weighting factors respecting to constraints is created. For framework and algorithm evaluation, we randomly selected 20 IMRT prostate cases from the clinic and compared them with our automatic generated plans, in both the efficiency and plan quality. Results: For each evaluated plan, the proposed multi-objective framework could run fluently and automatically. The voxel weighting factor iteration time varied from 10 to 30 under an updated constraint, and the constraint tuning time varied from 20 to 30 for every case until no more stricter constraint is allowed. The average total costing time for the whole optimization procedure is ∼30mins. By comparing the DVHs, better OAR dose sparing could be observed in automatic generated plan, for 13 out of the 20 cases, while others are with competitive results. Conclusion: We have successfully developed a fast and automatic multi-objective optimization for intensity modulated radiotherapy. This work is supported by the National Natural Science Foundation of China (No: 81571771)« less

GPU-based multi-volume ray casting within VTK for medical applications.

PubMed

Bozorgi, Mohammadmehdi; Lindseth, Frank

2015-03-01

Multi-volume visualization is important for displaying relevant information in multimodal or multitemporal medical imaging studies. The main objective with the current study was to develop an efficient GPU-based multi-volume ray caster (MVRC) and validate the proposed visualization system in the context of image-guided surgical navigation. Ray casting can produce high-quality 2D images from 3D volume data but the method is computationally demanding, especially when multiple volumes are involved, so a parallel GPU version has been implemented. In the proposed MVRC, imaginary rays are sent through the volumes (one ray for each pixel in the view), and at equal and short intervals along the rays, samples are collected from each volume. Samples from all the volumes are composited using front to back α-blending. Since all the rays can be processed simultaneously, the MVRC was implemented in parallel on the GPU to achieve acceptable interactive frame rates. The method is fully integrated within the visualization toolkit (VTK) pipeline with the ability to apply different operations (e.g., transformations, clipping, and cropping) on each volume separately. The implemented method is cross-platform (Windows, Linux and Mac OSX) and runs on different graphics card (NVidia and AMD). The speed of the MVRC was tested with one to five volumes of varying sizes: 128(3), 256(3), and 512(3). A Tesla C2070 GPU was used, and the output image size was 600 × 600 pixels. The original VTK single-volume ray caster and the MVRC were compared when rendering only one volume. The multi-volume rendering system achieved an interactive frame rate (> 15 fps) when rendering five small volumes (128 (3) voxels), four medium-sized volumes (256(3) voxels), and two large volumes (512(3) voxels). When rendering single volumes, the frame rate of the MVRC was comparable to the original VTK ray caster for small and medium-sized datasets but was approximately 3 frames per second slower for large datasets. The MVRC was successfully integrated in an existing surgical navigation system and was shown to be clinically useful during an ultrasound-guided neurosurgical tumor resection. A GPU-based MVRC for VTK is a useful tool in medical visualization. The proposed multi-volume GPU-based ray caster for VTK provided high-quality images at reasonable frame rates. The MVRC was effective when used in a neurosurgical navigation application.

Using 3D spatial correlations to improve the noise robustness of multi component analysis of 3D multi echo quantitative T2 relaxometry data.

PubMed

Kumar, Dushyant; Hariharan, Hari; Faizy, Tobias D; Borchert, Patrick; Siemonsen, Susanne; Fiehler, Jens; Reddy, Ravinder; Sedlacik, Jan

2018-05-12

We present a computationally feasible and iterative multi-voxel spatially regularized algorithm for myelin water fraction (MWF) reconstruction. This method utilizes 3D spatial correlations present in anatomical/pathological tissues and underlying B1 + -inhomogeneity or flip angle inhomogeneity to enhance the noise robustness of the reconstruction while intrinsically accounting for stimulated echo contributions using T2-distribution data alone. Simulated data and in vivo data acquired using 3D non-selective multi-echo spin echo (3DNS-MESE) were used to compare the reconstruction quality of the proposed approach against those of the popular algorithm (the method by Prasloski et al.) and our previously proposed 2D multi-slice spatial regularization spatial regularization approach. We also investigated whether the inter-sequence correlations and agreements improved as a result of the proposed approach. MWF-quantifications from two sequences, 3DNS-MESE vs 3DNS-gradient and spin echo (3DNS-GRASE), were compared for both reconstruction approaches to assess correlations and agreements between inter-sequence MWF-value pairs. MWF values from whole-brain data of six volunteers and two multiple sclerosis patients are being reported as well. In comparison with competing approaches such as Prasloski's method or our previously proposed 2D multi-slice spatial regularization method, the proposed method showed better agreements with simulated truths using regression analyses and Bland-Altman analyses. For 3DNS-MESE data, MWF-maps reconstructed using the proposed algorithm provided better depictions of white matter structures in subcortical areas adjoining gray matter which agreed more closely with corresponding contrasts on T2-weighted images than MWF-maps reconstructed with the method by Prasloski et al. We also achieved a higher level of correlations and agreements between inter-sequence (3DNS-MESE vs 3DNS-GRASE) MWF-value pairs. The proposed algorithm provides more noise-robust fits to T2-decay data and improves MWF-quantifications in white matter structures especially in the sub-cortical white matter and major white matter tract regions. Copyright © 2018 Elsevier Inc. All rights reserved.

Patterns of fMRI activity dissociate overlapping functional brain areas that respond to biological motion.

PubMed

Peelen, Marius V; Wiggett, Alison J; Downing, Paul E

2006-03-16

Accurate perception of the actions and intentions of other people is essential for successful interactions in a social environment. Several cortical areas that support this process respond selectively in fMRI to static and dynamic displays of human bodies and faces. Here we apply pattern-analysis techniques to arrive at a new understanding of the neural response to biological motion. Functionally defined body-, face-, and motion-selective visual areas all responded significantly to "point-light" human motion. Strikingly, however, only body selectivity was correlated, on a voxel-by-voxel basis, with biological motion selectivity. We conclude that (1) biological motion, through the process of structure-from-motion, engages areas involved in the analysis of the static human form; (2) body-selective regions in posterior fusiform gyrus and posterior inferior temporal sulcus overlap with, but are distinct from, face- and motion-selective regions; (3) the interpretation of region-of-interest findings may be substantially altered when multiple patterns of selectivity are considered.

GenePattern | Informatics Technology for Cancer Research (ITCR)

Cancer.gov

GenePattern is a genomic analysis platform that provides access to hundreds of tools for the analysis and visualization of multiple data types. A web-based interface provides easy access to these tools and allows the creation of multi-step analysis pipelines that enable reproducible in silico research. A new GenePattern Notebook environment allows users to combine GenePattern analyses with text, graphics, and code to create complete reproducible research narratives.

Comparison of multi-fiber reproducibility of PAS-MRI and Q-ball with empirical multiple b-value HARDI

NASA Astrophysics Data System (ADS)

Nath, Vishwesh; Schilling, Kurt G.; Blaber, Justin A.; Ding, Zhaohua; Anderson, Adam W.; Landman, Bennett A.

2017-02-01

Crossing fibers are prevalent in human brains and a subject of intense interest for neuroscience. Diffusion tensor imaging (DTI) can resolve tissue orientation but is blind to crossing fibers. Many advanced diffusion-weighted magnetic resolution imaging (MRI) approaches have been presented to extract crossing-fibers from high angular resolution diffusion imaging (HARDI), but the relative sensitivity and specificity of approaches remains unclear. Here, we examine two leading approaches (PAS and q-ball) in the context of a large-scale, single subject reproducibility study. A single healthy individual was scanned 11 times with 96 diffusion weighted directions and 10 reference volumes for each of five b-values (1000, 1500, 2000, 2500, 3000 s/mm2) for a total of 5830 volumes (over the course of three sessions). We examined the reproducibility of the number of fibers per voxel, volume fraction, and crossing-fiber angles. For each method, we determined the minimum resolvable angle for each acquisition. Reproducibility of fiber counts per voxel was generally high ( 80% consensus for PAS and 70% for q-ball), but there was substantial bias between individual repetitions and model estimated with all data ( 10% lower consensus for PAS and 15% lower for q-ball). Both PAS and q-ball predominantly discovered fibers crossing at near 90 degrees, but reproducibility was higher for PAS across most measures. Within voxels with low anisotropy, q-ball finds more intra-voxel structure; meanwhile, PAS resolves multiple fibers at greater than 75 degrees for more voxels. These results can inform researchers when deciding between HARDI approaches or interpreting findings across studies.

High resolution MRI imaging at 9.4 Tesla of the osteochondral unit in a translational model of articular cartilage repair.

PubMed

Goebel, Lars; Müller, Andreas; Bücker, Arno; Madry, Henning

2015-04-16

Non-destructive structural evaluation of the osteochondral unit is challenging. Here, the capability of high-field magnetic resonance imaging (μMRI) at 9.4 Tesla (T) was explored to examine osteochondral repair ex vivo in a preclinical large animal model. A specific aim of this study was to detect recently described alterations of the subchondral bone associated with cartilage repair. Osteochondral samples of medial femoral condyles from adult ewes containing full-thickness articular cartilage defects treated with marrow stimulation were obtained after 6 month in vivo and scanned in a 9.4 T μMRI. Ex vivo imaging of small osteochondral samples (typical volume: 1-2 cm(3)) at μMRI was optimised by variation of repetition time (TR), time echo (TE), flip angle (FA), spatial resolution and number of excitations (NEX) from standard MultiSliceMultiEcho (MSME) and three-dimensional (3D) spoiled GradientEcho (SGE) sequences. A 3D SGE sequence with the parameters: TR = 10 ms, TE = 3 ms, FA = 10°, voxel size = 120 × 120 × 120 μm(3) and NEX = 10 resulted in the best fitting for sample size, image quality, scanning time and artifacts. An isovolumetric voxel shape allowed for multiplanar reconstructions. Within the osteochondral unit articular cartilage, cartilaginous repair tissue and bone marrow could clearly be distinguished from the subchondral bone plate and subarticular spongiosa. Specific alterations of the osteochondral unit associated with cartilage repair such as persistent drill holes, subchondral bone cysts, sclerosis of the subchondral bone plate and of the subarticular spongiosa and intralesional osteophytes were precisely detected. High resolution, non-destructive ex vivo analysis of the entire osteochondral unit in a preclinical large animal model that is sufficient for further analyses is possible using μMRI at 9.4 T. In particular, 9.4 T is capable of accurately depicting alterations of the subchondral bone that are associated with osteochondral repair.

Multi-country health surveys: are the analyses misleading?

PubMed

Masood, Mohd; Reidpath, Daniel D

2014-05-01

The aim of this paper was to review the types of approaches currently utilized in the analysis of multi-country survey data, specifically focusing on design and modeling issues with a focus on analyses of significant multi-country surveys published in 2010. A systematic search strategy was used to identify the 10 multi-country surveys and the articles published from them in 2010. The surveys were selected to reflect diverse topics and foci; and provide an insight into analytic approaches across research themes. The search identified 159 articles appropriate for full text review and data extraction. The analyses adopted in the multi-country surveys can be broadly classified as: univariate/bivariate analyses, and multivariate/multivariable analyses. Multivariate/multivariable analyses may be further divided into design- and model-based analyses. Of the 159 articles reviewed, 129 articles used model-based analysis, 30 articles used design-based analyses. Similar patterns could be seen in all the individual surveys. While there is general agreement among survey statisticians that complex surveys are most appropriately analyzed using design-based analyses, most researchers continued to use the more common model-based approaches. Recent developments in design-based multi-level analysis may be one approach to include all the survey design characteristics. This is a relatively new area, however, and there remains statistical, as well as applied analytic research required. An important limitation of this study relates to the selection of the surveys used and the choice of year for the analysis, i.e., year 2010 only. There is, however, no strong reason to believe that analytic strategies have changed radically in the past few years, and 2010 provides a credible snapshot of current practice.

Radiomics-based differentiation of lung disease models generated by polluted air based on X-ray computed tomography data.

PubMed

Szigeti, Krisztián; Szabó, Tibor; Korom, Csaba; Czibak, Ilona; Horváth, Ildikó; Veres, Dániel S; Gyöngyi, Zoltán; Karlinger, Kinga; Bergmann, Ralf; Pócsik, Márta; Budán, Ferenc; Máthé, Domokos

2016-02-11

Lung diseases (resulting from air pollution) require a widely accessible method for risk estimation and early diagnosis to ensure proper and responsive treatment. Radiomics-based fractal dimension analysis of X-ray computed tomography attenuation patterns in chest voxels of mice exposed to different air polluting agents was performed to model early stages of disease and establish differential diagnosis. To model different types of air pollution, BALBc/ByJ mouse groups were exposed to cigarette smoke combined with ozone, sulphur dioxide gas and a control group was established. Two weeks after exposure, the frequency distributions of image voxel attenuation data were evaluated. Specific cut-off ranges were defined to group voxels by attenuation. Cut-off ranges were binarized and their spatial pattern was associated with calculated fractal dimension, then abstracted by the fractal dimension -- cut-off range mathematical function. Nonparametric Kruskal-Wallis (KW) and Mann-Whitney post hoc (MWph) tests were used. Each cut-off range versus fractal dimension function plot was found to contain two distinctive Gaussian curves. The ratios of the Gaussian curve parameters are considerably significant and are statistically distinguishable within the three exposure groups. A new radiomics evaluation method was established based on analysis of the fractal dimension of chest X-ray computed tomography data segments. The specific attenuation patterns calculated utilizing our method may diagnose and monitor certain lung diseases, such as chronic obstructive pulmonary disease (COPD), asthma, tuberculosis or lung carcinomas.

Decoding rule search domain in the left inferior frontal gyrus

PubMed Central

Babcock, Laura; Vallesi, Antonino

2018-01-01

Traditionally, the left hemisphere has been thought to extract mainly verbal patterns of information, but recent evidence has shown that the left Inferior Frontal Gyrus (IFG) is active during inductive reasoning in both the verbal and spatial domains. We aimed to understand whether the left IFG supports inductive reasoning in a domain-specific or domain-general fashion. To do this we used Multi-Voxel Pattern Analysis to decode the representation of domain during a rule search task. Thirteen participants were asked to extract the rule underlying streams of letters presented in different spatial locations. Each rule was either verbal (letters forming words) or spatial (positions forming geometric figures). Our results show that domain was decodable in the left prefrontal cortex, suggesting that this region represents domain-specific information, rather than processes common to the two domains. A replication study with the same participants tested two years later confirmed these findings, though the individual representations changed, providing evidence for the flexible nature of representations. This study extends our knowledge on the neural basis of goal-directed behaviors and on how information relevant for rule extraction is flexibly mapped in the prefrontal cortex. PMID:29547623

Repetition Suppression and Multi-Voxel Pattern Similarity Differentially Track Implicit and Explicit Visual Memory

PubMed Central

Chun, Marvin M.; Kuhl, Brice A.

2013-01-01

Repeated exposure to a visual stimulus is associated with corresponding reductions in neural activity, particularly within visual cortical areas. It has been argued that this phenomenon of repetition suppression is related to increases in processing fluency or implicit memory. However, repetition of a visual stimulus can also be considered in terms of the similarity of the pattern of neural activity elicited at each exposure—a measure that has recently been linked to explicit memory. Despite the popularity of each of these measures, direct comparisons between the two have been limited, and the extent to which they differentially (or similarly) relate to behavioral measures of memory has not been clearly established. In the present study, we compared repetition suppression and pattern similarity as predictors of both implicit and explicit memory. Using functional magnetic resonance imaging, we scanned 20 participants while they viewed and categorized repeated presentations of scenes. Repetition priming (facilitated categorization across repetitions) was used as a measure of implicit memory, and subsequent scene recognition was used as a measure of explicit memory. We found that repetition priming was predicted by repetition suppression in prefrontal, parietal, and occipitotemporal regions; however, repetition priming was not predicted by pattern similarity. In contrast, subsequent explicit memory was predicted by pattern similarity (across repetitions) in some of the same occipitotemporal regions that exhibited a relationship between priming and repetition suppression; however, explicit memory was not related to repetition suppression. This striking double dissociation indicates that repetition suppression and pattern similarity differentially track implicit and explicit learning. PMID:24027275

Allozyme markers in breeding zone designation

Treesearch

R. D. Westfall; M. T. Conkle

1992-01-01

Early studies of allozyme variation in plant populations suggested that allelic frequencies in some loci vary by geography. Since then, the expectation that allozymes might be useful in describing geographic patterns has generally not been borne out by single locus analyses, except on the broadest scale. Multi-locus analyses reveal the converse: canonical correlation...

SU-F-J-207: Non-Small Cell Lung Cancer Patient Survival Prediction with Quantitative Tumor Textures Analysis in Baseline CT

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wu, Y; Zou, J; Murillo, P

Purpose: Chemo-radiation therapy (CRT) is widely used in treating patients with locally advanced non-small cell lung cancer (NSCLC). Determination of the likelihood of patient response to treatment and optimization of treatment regime is of clinical significance. Up to date, no imaging biomarker has reliably correlated to NSCLC patient survival rate. This pilot study is to extract CT texture information from tumor regions for patient survival prediction. Methods: Thirteen patients with stage II-III NSCLC were treated using CRT with a median dose of 6210 cGy. Non-contrast-enhanced CT images were acquired for treatment planning and retrospectively collected for this study. Texture analysismore » was applied in segmented tumor regions using the Local Binary Pattern method (LBP). By comparing its HU with neighboring voxels, the LBPs of a voxel were measured in multiple scales with different group radiuses and numbers of neighbors. The LBP histograms formed a multi-dimensional texture vector for each patient, which was then used to establish and test a Support Vector Machine (SVM) model to predict patients’ one year survival. The leave-one-out cross validation strategy was used recursively to enlarge the training set and derive a reliable predictor. The predictions were compared with the true clinical outcomes. Results: A 10-dimensional LBP histogram was extracted from 3D segmented tumor region for each of the 13 patients. Using the SVM model with the leave-one-out strategy, only 1 out of 13 patients was misclassified. The experiments showed an accuracy of 93%, sensitivity of 100%, and specificity of 86%. Conclusion: Within the framework of a Support Vector Machine based model, the Local Binary Pattern method is able to extract a quantitative imaging biomarker in the prediction of NSCLC patient survival. More patients are to be included in the study.« less

Large-scale automated image analysis for computational profiling of brain tissue surrounding implanted neuroprosthetic devices using Python.

PubMed

Rey-Villamizar, Nicolas; Somasundar, Vinay; Megjhani, Murad; Xu, Yan; Lu, Yanbin; Padmanabhan, Raghav; Trett, Kristen; Shain, William; Roysam, Badri

2014-01-01

In this article, we describe the use of Python for large-scale automated server-based bio-image analysis in FARSIGHT, a free and open-source toolkit of image analysis methods for quantitative studies of complex and dynamic tissue microenvironments imaged by modern optical microscopes, including confocal, multi-spectral, multi-photon, and time-lapse systems. The core FARSIGHT modules for image segmentation, feature extraction, tracking, and machine learning are written in C++, leveraging widely used libraries including ITK, VTK, Boost, and Qt. For solving complex image analysis tasks, these modules must be combined into scripts using Python. As a concrete example, we consider the problem of analyzing 3-D multi-spectral images of brain tissue surrounding implanted neuroprosthetic devices, acquired using high-throughput multi-spectral spinning disk step-and-repeat confocal microscopy. The resulting images typically contain 5 fluorescent channels. Each channel consists of 6000 × 10,000 × 500 voxels with 16 bits/voxel, implying image sizes exceeding 250 GB. These images must be mosaicked, pre-processed to overcome imaging artifacts, and segmented to enable cellular-scale feature extraction. The features are used to identify cell types, and perform large-scale analysis for identifying spatial distributions of specific cell types relative to the device. Python was used to build a server-based script (Dell 910 PowerEdge servers with 4 sockets/server with 10 cores each, 2 threads per core and 1TB of RAM running on Red Hat Enterprise Linux linked to a RAID 5 SAN) capable of routinely handling image datasets at this scale and performing all these processing steps in a collaborative multi-user multi-platform environment. Our Python script enables efficient data storage and movement between computers and storage servers, logs all the processing steps, and performs full multi-threaded execution of all codes, including open and closed-source third party libraries.

Voxel-Based LIDAR Analysis and Applications

NASA Astrophysics Data System (ADS)

Hagstrom, Shea T.

One of the greatest recent changes in the field of remote sensing is the addition of high-quality Light Detection and Ranging (LIDAR) instruments. In particular, the past few decades have been greatly beneficial to these systems because of increases in data collection speed and accuracy, as well as a reduction in the costs of components. These improvements allow modern airborne instruments to resolve sub-meter details, making them ideal for a wide variety of applications. Because LIDAR uses active illumination to capture 3D information, its output is fundamentally different from other modalities. Despite this difference, LIDAR datasets are often processed using methods appropriate for 2D images and that do not take advantage of its primary virtue of 3-dimensional data. It is this problem we explore by using volumetric voxel modeling. Voxel-based analysis has been used in many applications, especially medical imaging, but rarely in traditional remote sensing. In part this is because the memory requirements are substantial when handling large areas, but with modern computing and storage this is no longer a significant impediment. Our reason for using voxels to model scenes from LIDAR data is that there are several advantages over standard triangle-based models, including better handling of overlapping surfaces and complex shapes. We show how incorporating system position information from early in the LIDAR point cloud generation process allows radiometrically-correct transmission and other novel voxel properties to be recovered. This voxelization technique is validated on simulated data using the Digital Imaging and Remote Sensing Image Generation (DIRSIG) software, a first-principles based ray-tracer developed at the Rochester Institute of Technology. Voxel-based modeling of LIDAR can be useful on its own, but we believe its primary advantage is when applied to problems where simpler surface-based 3D models conflict with the requirement of realistic geometry. To show the voxel model's advantage, we apply it to several outstanding problems in remote sensing: LIDAR quality metrics, line-of-sight mapping, and multi-model fusion. Each of these applications is derived, validated, and examined in detail, and our results compared with other state-of-the-art methods. In most cases the voxel-based methods demonstrate superior results and are able to derive information not available to existing methods. Realizing these improvements requires only a shift away from traditional 3D model generation, and our results give a small indicator of what is possible. Many examples of possible areas for future improvement and expansion of algorithms beyond the scope of our work are also noted.

Automatic falx cerebri and tentorium cerebelli segmentation from magnetic resonance images

NASA Astrophysics Data System (ADS)

Glaister, Jeffrey; Carass, Aaron; Pham, Dzung L.; Butman, John A.; Prince, Jerry L.

2017-03-01

The falx cerebri and tentorium cerebelli are dural structures found in the brain. Due to the roles both structures play in constraining brain motion, the falx and tentorium must be identified and included in finite element models of the head to accurately predict brain dynamics during injury events. To date there has been very little research work on automatically segmenting these two structures, which is understandable given that their 1) thin structure challenges the resolution limits of in vivo 3D imaging, and 2) contrast with respect to surrounding tissue is low in standard magnetic resonance imaging. An automatic segmentation algorithm to find the falx and tentorium which uses the results of a multi-atlas segmentation and cortical reconstruction algorithm is proposed. Gray matter labels are used to find the location of the falx and tentorium. The proposed algorithm is applied to five datasets with manual delineations. 3D visualizations of the final results are provided, and Hausdorff distance (HD) and mean surface distance (MSD) is calculated to quantify the accuracy of the proposed method. For the falx, the mean HD is 43.84 voxels and the mean MSD is 2.78 voxels, with the largest errors occurring at the frontal inferior falx boundary. For the tentorium, the mean HD is 14.50 voxels and mean MSD is 1.38 voxels.

Automatic falx cerebri and tentorium cerebelli segmentation from Magnetic Resonance Images.

PubMed

Glaister, Jeffrey; Carass, Aaron; Pham, Dzung L; Butman, John A; Prince, Jerry L

2017-02-01

The falx cerebri and tentorium cerebelli are dural structures found in the brain. Due to the roles both structures play in constraining brain motion, the falx and tentorium must be identified and included in finite element models of the head to accurately predict brain dynamics during injury events. To date there has been very little research work on automatically segmenting these two structures, which is understandable given that their 1) thin structure challenges the resolution limits of in vivo 3D imaging, and 2) contrast with respect to surrounding tissue is low in standard magnetic resonance imaging. An automatic segmentation algorithm to find the falx and tentorium which uses the results of a multi-atlas segmentation and cortical reconstruction algorithm is proposed. Gray matter labels are used to find the location of the falx and tentorium. The proposed algorithm is applied to five datasets with manual delineations. 3D visualizations of the final results are provided, and Hausdorff distance (HD) and mean surface distance (MSD) is calculated to quantify the accuracy of the proposed method. For the falx, the mean HD is 43.84 voxels and the mean MSD is 2.78 voxels, with the largest errors occurring at the frontal inferior falx boundary. For the tentorium, the mean HD is 14.50 voxels and mean MSD is 1.38 voxels.

Changes in NAA and lactate following ischemic stroke: a serial MR spectroscopic imaging study.

PubMed

Muñoz Maniega, S; Cvoro, V; Chappell, F M; Armitage, P A; Marshall, I; Bastin, M E; Wardlaw, J M

2008-12-09

Although much tissue damage may occur within the first few hours of ischemic stroke, the duration of tissue injury is not well defined. We assessed the temporal pattern of neuronal loss and ischemia after ischemic stroke using magnetic resonance spectroscopic imaging (MRSI) and diffusion-weighted imaging (DWI). We measured N-acetylaspartate (NAA) and lactate in 51 patients with acute ischemic stroke at five time points, from admission to 3 months, in voxels classified as normal, possibly or definitely abnormal (ischemic) according to the appearance of the stroke lesion on the admission DWI. We compared changes in NAA and lactate in different voxel classes using linear mixed models. NAA was significantly reduced from admission in definitely and possibly abnormal (p < 0.01) compared to contralateral normal voxels, reaching a nadir by 2 weeks and remaining reduced at 3 months. Lactate was significantly increased in definitely and possibly abnormal voxels (p < 0.01) during the first 5 days, falling to normal at 2 weeks, rising again later in these voxels. The progressive fall in N-acetylaspartate suggests that some additional neuronal death may continue beyond the first few hours for up to 2 weeks or longer. The mechanism is unclear but, if correct, then it is possible that interventions to limit this ongoing subacute tissue damage might add to the benefit of hyperacute treatment, making further improvements in outcome possible.

Voxel-Based Neighborhood for Spatial Shape Pattern Classification of Lidar Point Clouds with Supervised Learning.

PubMed

Plaza-Leiva, Victoria; Gomez-Ruiz, Jose Antonio; Mandow, Anthony; García-Cerezo, Alfonso

2017-03-15

Improving the effectiveness of spatial shape features classification from 3D lidar data is very relevant because it is largely used as a fundamental step towards higher level scene understanding challenges of autonomous vehicles and terrestrial robots. In this sense, computing neighborhood for points in dense scans becomes a costly process for both training and classification. This paper proposes a new general framework for implementing and comparing different supervised learning classifiers with a simple voxel-based neighborhood computation where points in each non-overlapping voxel in a regular grid are assigned to the same class by considering features within a support region defined by the voxel itself. The contribution provides offline training and online classification procedures as well as five alternative feature vector definitions based on principal component analysis for scatter, tubular and planar shapes. Moreover, the feasibility of this approach is evaluated by implementing a neural network (NN) method previously proposed by the authors as well as three other supervised learning classifiers found in scene processing methods: support vector machines (SVM), Gaussian processes (GP), and Gaussian mixture models (GMM). A comparative performance analysis is presented using real point clouds from both natural and urban environments and two different 3D rangefinders (a tilting Hokuyo UTM-30LX and a Riegl). Classification performance metrics and processing time measurements confirm the benefits of the NN classifier and the feasibility of voxel-based neighborhood.

Distinct [18F]THK5351 binding patterns in primary progressive aphasia variants.

PubMed

Schaeverbeke, Jolien; Evenepoel, Charlotte; Declercq, Lieven; Gabel, Silvy; Meersmans, Karen; Bruffaerts, Rose; Adamczuk, Kate; Dries, Eva; Van Bouwel, Karen; Sieben, Anne; Pijnenburg, Yolande; Peeters, Ronald; Bormans, Guy; Van Laere, Koen; Koole, Michel; Dupont, Patrick; Vandenberghe, Rik

2018-06-26

To assess the binding of the PET tracer [ 18 F]THK5351 in patients with different primary progressive aphasia (PPA) variants and its correlation with clinical deficits. The majority of patients with nonfluent variant (NFV) and logopenic variant (LV) PPA have underlying tauopathy of the frontotemporal lobar or Alzheimer disease type, respectively, while patients with the semantic variant (SV) have predominantly transactive response DNA binding protein 43-kDa pathology. The study included 20 PPA patients consecutively recruited through a memory clinic (12 NFV, 5 SV, 3 LV), and 20 healthy controls. All participants received an extensive neurolinguistic assessment, magnetic resonance imaging and amyloid biomarker tests. [ 18 F]THK5351 binding patterns were assessed on standardized uptake value ratio (SUVR) images with the cerebellar grey matter as the reference using statistical parametric mapping. Whole-brain voxel-wise regression analysis was performed to evaluate the association between [ 18 F]THK5351 SUVR images and neurolinguistic scores. Analyses were performed with and without partial volume correction. Patients with NFV showed increased binding in the supplementary motor area, left premotor cortex, thalamus, basal ganglia and midbrain compared with controls and patients with SV. Patients with SV had increased binding in the temporal lobes bilaterally and in the right ventromedial frontal cortex compared with controls and patients with NFV. The whole-brain voxel-wise regression analysis revealed a correlation between agrammatism and motor speech impairment, and [ 18 F]THK5351 binding in the left supplementary motor area and left postcentral gyrus. Analysis of [ 18 F]THK5351 scans without partial volume correction revealed similar results. [ 18 F]THK5351 imaging shows a topography closely matching the anatomical distribution of predicted underlying pathology characteristic of NFV and SV PPA. [ 18 F]THK5351 binding correlates with the severity of clinical impairment.

Non-invasive MRI detection of individual pellets in the human stomach.

PubMed

Knörgen, Manfred; Spielmann, Rolf Peter; Abdalla, Ahmed; Metz, Hendrik; Mäder, Karsten

2010-01-01

MRI is a powerful and non-invasive method to follow the fate of oral drug delivery systems in humans. Until now, most MRI studies focused on monolithic dosage forms (tablets and capsules). Small-sized multi-particulate drug delivery systems are very difficult to detect due to the poor differentiation between the delivery system and the food. A new approach was developed to overcome the described difficulties and permit the selective imaging of small multi-particulate dosage forms within the stomach. We took advantage of the different sensitivities to susceptibility artefacts of T(2)-weighted spin-echo sequences and T(2)-weighted gradient echo pulse sequences. Using a combination of both methods within a breath hold followed by a specific mathematical image analysis involving co-registration, motion correction, voxel-by-voxel comparison of the maps from different pulse sequences and graphic 2D-/3D-presentation, we were able to obtain pictures with a high sensitivity due to susceptibility effects caused by a 1% magnetite load. By means of the new imaging sequence, single pellets as small as 1mm can be detected with high selectivity within surrounding heterogeneous food in the human stomach. The developed method greatly expands the use of MRI to study the fate of oral multi-particulate drug delivery systems and their food dependency in men. Copyright 2009 Elsevier B.V. All rights reserved.

3D multi-scale FCN with random modality voxel dropout learning for Intervertebral Disc Localization and Segmentation from Multi-modality MR Images.

PubMed

Li, Xiaomeng; Dou, Qi; Chen, Hao; Fu, Chi-Wing; Qi, Xiaojuan; Belavý, Daniel L; Armbrecht, Gabriele; Felsenberg, Dieter; Zheng, Guoyan; Heng, Pheng-Ann

2018-04-01

Intervertebral discs (IVDs) are small joints that lie between adjacent vertebrae. The localization and segmentation of IVDs are important for spine disease diagnosis and measurement quantification. However, manual annotation is time-consuming and error-prone with limited reproducibility, particularly for volumetric data. In this work, our goal is to develop an automatic and accurate method based on fully convolutional networks (FCN) for the localization and segmentation of IVDs from multi-modality 3D MR data. Compared with single modality data, multi-modality MR images provide complementary contextual information, which contributes to better recognition performance. However, how to effectively integrate such multi-modality information to generate accurate segmentation results remains to be further explored. In this paper, we present a novel multi-scale and modality dropout learning framework to locate and segment IVDs from four-modality MR images. First, we design a 3D multi-scale context fully convolutional network, which processes the input data in multiple scales of context and then merges the high-level features to enhance the representation capability of the network for handling the scale variation of anatomical structures. Second, to harness the complementary information from different modalities, we present a random modality voxel dropout strategy which alleviates the co-adaption issue and increases the discriminative capability of the network. Our method achieved the 1st place in the MICCAI challenge on automatic localization and segmentation of IVDs from multi-modality MR images, with a mean segmentation Dice coefficient of 91.2% and a mean localization error of 0.62 mm. We further conduct extensive experiments on the extended dataset to validate our method. We demonstrate that the proposed modality dropout strategy with multi-modality images as contextual information improved the segmentation accuracy significantly. Furthermore, experiments conducted on extended data collected from two different time points demonstrate the efficacy of our method on tracking the morphological changes in a longitudinal study. Copyright © 2018 Elsevier B.V. All rights reserved.

Enhanced multi-protocol analysis via intelligent supervised embedding (EMPrAvISE): detecting prostate cancer on multi-parametric MRI

NASA Astrophysics Data System (ADS)

Viswanath, Satish; Bloch, B. Nicholas; Chappelow, Jonathan; Patel, Pratik; Rofsky, Neil; Lenkinski, Robert; Genega, Elizabeth; Madabhushi, Anant

2011-03-01

Currently, there is significant interest in developing methods for quantitative integration of multi-parametric (structural, functional) imaging data with the objective of building automated meta-classifiers to improve disease detection, diagnosis, and prognosis. Such techniques are required to address the differences in dimensionalities and scales of individual protocols, while deriving an integrated multi-parametric data representation which best captures all disease-pertinent information available. In this paper, we present a scheme called Enhanced Multi-Protocol Analysis via Intelligent Supervised Embedding (EMPrAvISE); a powerful, generalizable framework applicable to a variety of domains for multi-parametric data representation and fusion. Our scheme utilizes an ensemble of embeddings (via dimensionality reduction, DR); thereby exploiting the variance amongst multiple uncorrelated embeddings in a manner similar to ensemble classifier schemes (e.g. Bagging, Boosting). We apply this framework to the problem of prostate cancer (CaP) detection on 12 3 Tesla pre-operative in vivo multi-parametric (T2-weighted, Dynamic Contrast Enhanced, and Diffusion-weighted) magnetic resonance imaging (MRI) studies, in turn comprising a total of 39 2D planar MR images. We first align the different imaging protocols via automated image registration, followed by quantification of image attributes from individual protocols. Multiple embeddings are generated from the resultant high-dimensional feature space which are then combined intelligently to yield a single stable solution. Our scheme is employed in conjunction with graph embedding (for DR) and probabilistic boosting trees (PBTs) to detect CaP on multi-parametric MRI. Finally, a probabilistic pairwise Markov Random Field algorithm is used to apply spatial constraints to the result of the PBT classifier, yielding a per-voxel classification of CaP presence. Per-voxel evaluation of detection results against ground truth for CaP extent on MRI (obtained by spatially registering pre-operative MRI with available whole-mount histological specimens) reveals that EMPrAvISE yields a statistically significant improvement (AUC=0.77) over classifiers constructed from individual protocols (AUC=0.62, 0.62, 0.65, for T2w, DCE, DWI respectively) as well as one trained using multi-parametric feature concatenation (AUC=0.67).

Acute caffeine administration effect on brain activation patterns in mild cognitive impairment.

PubMed

Haller, Sven; Montandon, Marie-Louise; Rodriguez, Cristelle; Moser, Dominik; Toma, Simona; Hofmeister, Jeremy; Sinanaj, Indrit; Lovblad, Karl-Olof; Giannakopoulos, Panteleimon

2014-01-01

Previous studies showed that acute caffeine administration enhances task-related brain activation in elderly individuals with preserved cognition. To explore the effects of this widely used agent on cognition and brain activation in early phases of cognitive decline, we performed a double-blinded, placebo-controlled functional magnetic resonance imaging (fMRI) study during an n-back working memory task in 17 individuals with mild cognitive impairment (MCI) compared to 17 age-matched healthy controls (HC). All individuals were regular caffeine consumers with an overnight abstinence and given 200 mg caffeine versus placebo tablets 30 minutes before testing. Analyses included assessment of task-related activation (general linear model), functional connectivity (tensorial-independent component analysis, TICA), baseline perfusion (arterial spin labeling, ASL), grey matter density (voxel-based morphometry, VBM), and white matter microstructure (tract-based spatial statistics, TBSS). Acute caffeine administration induced a focal activation of the prefrontal areas in HC with a more diffuse and posteromedial activation pattern in MCI individuals. In MCI, TICA documented a significant caffeine-related enhancement in the prefrontal cortex, supplementary motor area, ventral premotor and parietal cortex as well as the basal ganglia and cerebellum. The absence of significant group differences in baseline ASL perfusion patterns supports a neuronal rather than a purely vascular origin of these differences. The VBM and TBSS analyses excluded potentially confounding differences in grey matter density and white matter microstructure between MCI and HC. The present findings suggest a posterior displacement of working memory-related brain activation patterns after caffeine administration in MCI that may represent a compensatory mechanism to counterbalance a frontal lobe dysfunction.

Intersubject synchronization of cortical activity during natural vision.

PubMed

Hasson, Uri; Nir, Yuval; Levy, Ifat; Fuhrmann, Galit; Malach, Rafael

2004-03-12

To what extent do all brains work alike during natural conditions? We explored this question by letting five subjects freely view half an hour of a popular movie while undergoing functional brain imaging. Applying an unbiased analysis in which spatiotemporal activity patterns in one brain were used to "model" activity in another brain, we found a striking level of voxel-by-voxel synchronization between individuals, not only in primary and secondary visual and auditory areas but also in association cortices. The results reveal a surprising tendency of individual brains to "tick collectively" during natural vision. The intersubject synchronization consisted of a widespread cortical activation pattern correlated with emotionally arousing scenes and regionally selective components. The characteristics of these activations were revealed with the use of an open-ended "reverse-correlation" approach, which inverts the conventional analysis by letting the brain signals themselves "pick up" the optimal stimuli for each specialized cortical area.

Assessing the Regional Frequency, Intensity, and Spatial Extent of Tropical Cyclone Rainfall

NASA Astrophysics Data System (ADS)

Bosma, C.; Wright, D.; Nguyen, P.

2017-12-01

While the strength of a hurricane is generally classified based on its wind speed, the unprecedented rainfall-driven flooding experienced in southeastern Texas during Hurricane Harvey clearly highlights the need for better understanding of the hazards associated with extreme rainfall from hurricanes and other tropical systems. In this study, we seek to develop a framework for describing the joint probabilistic and spatio-temporal properties of extreme rainfall from hurricanes and other tropical systems. Furthermore, we argue that commonly-used terminology - such as the "500-year storm" - fail to convey the true properties of tropical cyclone rainfall occurrences in the United States. To quantify the magnitude and spatial extent of these storms, a database consisting of hundreds of unique rainfall volumetric shapes (or "voxels") was created. Each voxel is a four-dimensional object, created by connecting, in both space and time, gridded rainfall observations from the daily, gauge-based NOAA CPC-Unified precipitation dataset. Individual voxels were then associated with concurrent tropical cyclone tracks from NOAA's HURDAT-2 archive, to create distinct representations of the rainfall associated with every Atlantic tropical system making landfall over (or passing near) the United States since 1948. Using these voxels, a series of threshold-excess extreme value models were created to estimate the recurrence intervals of extreme tropical cyclone rainfall, both nationally and locally, for single and multi-day timescales. This voxel database also allows for the "indexing" of past events, placing recent extremes - such as the 50+ inches of rain observed during Hurricane Harvey - into a national context and emphasizing how rainfall totals that are rare at the point scale may be more frequent from a regional perspective.

Cognitive and affective theory of mind share the same local patterns of activity in posterior temporal but not medial prefrontal cortex

PubMed Central

Hofstetter, Christoph; Vuilleumier, Patrik

2014-01-01

Understanding emotions in others engages specific brain regions in temporal and medial prefrontal cortices. These activations are often attributed to more general cognitive ‘mentalizing’ functions, associated with theory of mind and also necessary to represent people’s non-emotional mental states, such as beliefs or intentions. Here, we directly investigated whether understanding emotional feelings recruit similar or specific brain systems, relative to other non-emotional mental states. We used functional magnetic resonance imaging with multivoxel pattern analysis in 46 volunteers to compare activation patterns in theory-of-mind tasks for emotions, relative to beliefs or somatic states accompanied with pain. We found a striking dissociation between the temporoparietal cortex, that exhibited a remarkable voxel-by-voxel pattern overlap between emotions and beliefs (but not pain), and the dorsomedial prefrontal cortex, that exhibited distinct (and yet nearby) patterns of activity during the judgment of beliefs and emotions in others. Pain judgment was instead associated with activity in the supramarginal gyrus, middle cingulate cortex and middle insular cortex. Our data reveal for the first time a functional dissociation within brain networks sub-serving theory of mind for different mental contents, with a common recruitment for cognitive and affective states in temporal regions, and distinct recruitment in prefrontal areas. PMID:23770622

Influence of magnetic field strength and image registration strategy on voxel-based morphometry in a study of Alzheimer's disease.

PubMed

Marchewka, Artur; Kherif, Ferath; Krueger, Gunnar; Grabowska, Anna; Frackowiak, Richard; Draganski, Bogdan

2014-05-01

Multi-centre data repositories like the Alzheimer's Disease Neuroimaging Initiative (ADNI) offer a unique research platform, but pose questions concerning comparability of results when using a range of imaging protocols and data processing algorithms. The variability is mainly due to the non-quantitative character of the widely used structural T1-weighted magnetic resonance (MR) images. Although the stability of the main effect of Alzheimer's disease (AD) on brain structure across platforms and field strength has been addressed in previous studies using multi-site MR images, there are only sparse empirically-based recommendations for processing and analysis of pooled multi-centre structural MR data acquired at different magnetic field strengths (MFS). Aiming to minimise potential systematic bias when using ADNI data we investigate the specific contributions of spatial registration strategies and the impact of MFS on voxel-based morphometry in AD. We perform a whole-brain analysis within the framework of Statistical Parametric Mapping, testing for main effects of various diffeomorphic spatial registration strategies, of MFS and their interaction with disease status. Beyond the confirmation of medial temporal lobe volume loss in AD, we detect a significant impact of spatial registration strategy on estimation of AD related atrophy. Additionally, we report a significant effect of MFS on the assessment of brain anatomy (i) in the cerebellum, (ii) the precentral gyrus and (iii) the thalamus bilaterally, showing no interaction with the disease status. We provide empirical evidence in support of pooling data in multi-centre VBM studies irrespective of disease status or MFS. Copyright © 2013 Wiley Periodicals, Inc.

Studying Axon-Astrocyte Functional Interactions by 3D Two-Photon Ca2+ Imaging: A Practical Guide to Experiments and "Big Data" Analysis.

PubMed

Savtchouk, Iaroslav; Carriero, Giovanni; Volterra, Andrea

2018-01-01

Recent advances in fast volumetric imaging have enabled rapid generation of large amounts of multi-dimensional functional data. While many computer frameworks exist for data storage and analysis of the multi-gigabyte Ca 2+ imaging experiments in neurons, they are less useful for analyzing Ca 2+ dynamics in astrocytes, where transients do not follow a predictable spatio-temporal distribution pattern. In this manuscript, we provide a detailed protocol and commentary for recording and analyzing three-dimensional (3D) Ca 2+ transients through time in GCaMP6f-expressing astrocytes of adult brain slices in response to axonal stimulation, using our recently developed tools to perform interactive exploration, filtering, and time-correlation analysis of the transients. In addition to the protocol, we release our in-house software tools and discuss parameters pertinent to conducting axonal stimulation/response experiments across various brain regions and conditions. Our software tools are available from the Volterra Lab webpage at https://wwwfbm.unil.ch/dnf/group/glia-an-active-synaptic-partner/member/volterra-andrea-volterra in the form of software plugins for Image J (NIH)-a de facto standard in scientific image analysis. Three programs are available: MultiROI_TZ_profiler for interactive graphing of several movable ROIs simultaneously, Gaussian_Filter5D for Gaussian filtering in several dimensions, and Correlation_Calculator for computing various cross-correlation parameters on voxel collections through time.

Characterization of groups using composite kernels and multi-source fMRI analysis data: application to schizophrenia

PubMed Central

Castro, Eduardo; Martínez-Ramón, Manel; Pearlson, Godfrey; Sui, Jing; Calhoun, Vince D.

2011-01-01

Pattern classification of brain imaging data can enable the automatic detection of differences in cognitive processes of specific groups of interest. Furthermore, it can also give neuroanatomical information related to the regions of the brain that are most relevant to detect these differences by means of feature selection procedures, which are also well-suited to deal with the high dimensionality of brain imaging data. This work proposes the application of recursive feature elimination using a machine learning algorithm based on composite kernels to the classification of healthy controls and patients with schizophrenia. This framework, which evaluates nonlinear relationships between voxels, analyzes whole-brain fMRI data from an auditory task experiment that is segmented into anatomical regions and recursively eliminates the uninformative ones based on their relevance estimates, thus yielding the set of most discriminative brain areas for group classification. The collected data was processed using two analysis methods: the general linear model (GLM) and independent component analysis (ICA). GLM spatial maps as well as ICA temporal lobe and default mode component maps were then input to the classifier. A mean classification accuracy of up to 95% estimated with a leave-two-out cross-validation procedure was achieved by doing multi-source data classification. In addition, it is shown that the classification accuracy rate obtained by using multi-source data surpasses that reached by using single-source data, hence showing that this algorithm takes advantage of the complimentary nature of GLM and ICA. PMID:21723948

The sequential structure of brain activation predicts skill.

PubMed

Anderson, John R; Bothell, Daniel; Fincham, Jon M; Moon, Jungaa

2016-01-29

In an fMRI study, participants were trained to play a complex video game. They were scanned early and then again after substantial practice. While better players showed greater activation in one region (right dorsal striatum) their relative skill was better diagnosed by considering the sequential structure of whole brain activation. Using a cognitive model that played this game, we extracted a characterization of the mental states that are involved in playing a game and the statistical structure of the transitions among these states. There was a strong correspondence between this measure of sequential structure and the skill of different players. Using multi-voxel pattern analysis, it was possible to recognize, with relatively high accuracy, the cognitive states participants were in during particular scans. We used the sequential structure of these activation-recognized states to predict the skill of individual players. These findings indicate that important features about information-processing strategies can be identified from a model-based analysis of the sequential structure of brain activation. Copyright © 2015 Elsevier Ltd. All rights reserved.

Training Humans to Categorize Monkey Calls: Auditory Feature- and Category-Selective Neural Tuning Changes.

PubMed

Jiang, Xiong; Chevillet, Mark A; Rauschecker, Josef P; Riesenhuber, Maximilian

2018-04-18

Grouping auditory stimuli into common categories is essential for a variety of auditory tasks, including speech recognition. We trained human participants to categorize auditory stimuli from a large novel set of morphed monkey vocalizations. Using fMRI-rapid adaptation (fMRI-RA) and multi-voxel pattern analysis (MVPA) techniques, we gained evidence that categorization training results in two distinct sets of changes: sharpened tuning to monkey call features (without explicit category representation) in left auditory cortex and category selectivity for different types of calls in lateral prefrontal cortex. In addition, the sharpness of neural selectivity in left auditory cortex, as estimated with both fMRI-RA and MVPA, predicted the steepness of the categorical boundary, whereas categorical judgment correlated with release from adaptation in the left inferior frontal gyrus. These results support the theory that auditory category learning follows a two-stage model analogous to the visual domain, suggesting general principles of perceptual category learning in the human brain. Copyright © 2018 Elsevier Inc. All rights reserved.

Mapping the cortical representation of speech sounds in a syllable repetition task.

PubMed

Markiewicz, Christopher J; Bohland, Jason W

2016-11-01

Speech repetition relies on a series of distributed cortical representations and functional pathways. A speaker must map auditory representations of incoming sounds onto learned speech items, maintain an accurate representation of those items in short-term memory, interface that representation with the motor output system, and fluently articulate the target sequence. A "dorsal stream" consisting of posterior temporal, inferior parietal and premotor regions is thought to mediate auditory-motor representations and transformations, but the nature and activation of these representations for different portions of speech repetition tasks remains unclear. Here we mapped the correlates of phonetic and/or phonological information related to the specific phonemes and syllables that were heard, remembered, and produced using a series of cortical searchlight multi-voxel pattern analyses trained on estimates of BOLD responses from individual trials. Based on responses linked to input events (auditory syllable presentation), predictive vowel-level information was found in the left inferior frontal sulcus, while syllable prediction revealed significant clusters in the left ventral premotor cortex and central sulcus and the left mid superior temporal sulcus. Responses linked to output events (the GO signal cueing overt production) revealed strong clusters of vowel-related information bilaterally in the mid to posterior superior temporal sulcus. For the prediction of onset and coda consonants, input-linked responses yielded distributed clusters in the superior temporal cortices, which were further informative for classifiers trained on output-linked responses. Output-linked responses in the Rolandic cortex made strong predictions for the syllables and consonants produced, but their predictive power was reduced for vowels. The results of this study provide a systematic survey of how cortical response patterns covary with the identity of speech sounds, which will help to constrain and guide theoretical models of speech perception, speech production, and phonological working memory. Copyright © 2016 Elsevier Inc. All rights reserved.

Voxel-based morphometry in autopsy proven PSP and CBD.

PubMed

Josephs, Keith A; Whitwell, Jennifer L; Dickson, Dennis W; Boeve, Bradley F; Knopman, David S; Petersen, Ronald C; Parisi, Joseph E; Jack, Clifford R

2008-02-01

The aim of this study was to compare the patterns of grey and white matter atrophy on MRI in autopsy confirmed progressive supranuclear palsy (PSP) and corticobasal degeneration (CBD), and to determine whether the patterns vary depending on the clinical syndrome. Voxel-based morphometry was used to compare patterns of atrophy in 13 PSP and 11 CBD subjects and 24 controls. PSP and CBD subjects were also subdivided into those with a dominant dementia or extrapyramidal syndrome. PSP subjects showed brainstem atrophy with involvement of the cortex and underlying white matter. Frontoparietal grey and subcortical grey matter atrophy occurred in CBD. When subdivided, PSP subjects with an extrapyramidal syndrome had more brainstem atrophy and less cortical atrophy than CBD subjects with an extrapyramidal syndrome. PSP subjects with a dementia syndrome had more subcortical white matter atrophy than CBD subjects with a dementia syndrome. These results show regional differences between PSP and CBD that are useful in predicting the underlying pathology, and help to shed light on the in vivo distribution of regional atrophy in PSP and CBD.

TU-AB-202-11: Tumor Segmentation by Fusion of Multi-Tracer PET Images Using Copula Based Statistical Methods

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lapuyade-Lahorgue, J; Ruan, S; Li, H

Purpose: Multi-tracer PET imaging is getting more attention in radiotherapy by providing additional tumor volume information such as glucose and oxygenation. However, automatic PET-based tumor segmentation is still a very challenging problem. We propose a statistical fusion approach to joint segment the sub-area of tumors from the two tracers FDG and FMISO PET images. Methods: Non-standardized Gamma distributions are convenient to model intensity distributions in PET. As a serious correlation exists in multi-tracer PET images, we proposed a new fusion method based on copula which is capable to represent dependency between different tracers. The Hidden Markov Field (HMF) model ismore » used to represent spatial relationship between PET image voxels and statistical dynamics of intensities for each modality. Real PET images of five patients with FDG and FMISO are used to evaluate quantitatively and qualitatively our method. A comparison between individual and multi-tracer segmentations was conducted to show advantages of the proposed fusion method. Results: The segmentation results show that fusion with Gaussian copula can receive high Dice coefficient of 0.84 compared to that of 0.54 and 0.3 of monomodal segmentation results based on individual segmentation of FDG and FMISO PET images. In addition, high correlation coefficients (0.75 to 0.91) for the Gaussian copula for all five testing patients indicates the dependency between tumor regions in the multi-tracer PET images. Conclusion: This study shows that using multi-tracer PET imaging can efficiently improve the segmentation of tumor region where hypoxia and glucidic consumption are present at the same time. Introduction of copulas for modeling the dependency between two tracers can simultaneously take into account information from both tracers and deal with two pathological phenomena. Future work will be to consider other families of copula such as spherical and archimedian copulas, and to eliminate partial volume effect by considering dependency between neighboring voxels.« less

[Voxel-Based Morphometry in Autism Spectrum Disorder].

PubMed

Yamasue, Hidenori

2017-05-01

Autism spectrum disorder shows deficits in social communication and interaction including nonverbal communicative behaviors (e.g., eye contact, gestures, voice prosody, and facial expressions) and restricted and repetitive behaviors as its core symptoms. These core symptoms are emerged as an atypical behavioral development in toddlers with the disorder. Atypical neural development is considered to be a neural underpinning of such behaviorally atypical development. A number of studies using voxel-based morphometry have already been conducted to compare regional brain volumes between individuals with autism spectrum disorder and those with typical development. Furthermore, more than ten papers employing meta-analyses of the comparisons using voxel based morphometry between individuals with autism spectrum disorder and those with typical development have already been published. The current review paper adds some brief discussions about potential factors contributing to the inconsistency observed in the previous findings such as difficulty in controlling the confounding effects of different developmental phases among study participants.

Multi-atlas label fusion using hybrid of discriminative and generative classifiers for segmentation of cardiac MR images.

PubMed

Sedai, Suman; Garnavi, Rahil; Roy, Pallab; Xi Liang

2015-08-01

Multi-atlas segmentation first registers each atlas image to the target image and transfers the label of atlas image to the coordinate system of the target image. The transferred labels are then combined, using a label fusion algorithm. In this paper, we propose a novel label fusion method which aggregates discriminative learning and generative modeling for segmentation of cardiac MR images. First, a probabilistic Random Forest classifier is trained as a discriminative model to obtain the prior probability of a label at the given voxel of the target image. Then, a probability distribution of image patches is modeled using Gaussian Mixture Model for each label, providing the likelihood of the voxel belonging to the label. The final label posterior is obtained by combining the classification score and the likelihood score under Bayesian rule. Comparative study performed on MICCAI 2013 SATA Segmentation Challenge demonstrates that our proposed hybrid label fusion algorithm is accurate than other five state-of-the-art label fusion methods. The proposed method obtains dice similarity coefficient of 0.94 and 0.92 in segmenting epicardium and endocardium respectively. Moreover, our label fusion method achieves more accurate segmentation results compared to four other label fusion methods.

Voxel-Based Neighborhood for Spatial Shape Pattern Classification of Lidar Point Clouds with Supervised Learning

PubMed Central

Plaza-Leiva, Victoria; Gomez-Ruiz, Jose Antonio; Mandow, Anthony; García-Cerezo, Alfonso

2017-01-01

Improving the effectiveness of spatial shape features classification from 3D lidar data is very relevant because it is largely used as a fundamental step towards higher level scene understanding challenges of autonomous vehicles and terrestrial robots. In this sense, computing neighborhood for points in dense scans becomes a costly process for both training and classification. This paper proposes a new general framework for implementing and comparing different supervised learning classifiers with a simple voxel-based neighborhood computation where points in each non-overlapping voxel in a regular grid are assigned to the same class by considering features within a support region defined by the voxel itself. The contribution provides offline training and online classification procedures as well as five alternative feature vector definitions based on principal component analysis for scatter, tubular and planar shapes. Moreover, the feasibility of this approach is evaluated by implementing a neural network (NN) method previously proposed by the authors as well as three other supervised learning classifiers found in scene processing methods: support vector machines (SVM), Gaussian processes (GP), and Gaussian mixture models (GMM). A comparative performance analysis is presented using real point clouds from both natural and urban environments and two different 3D rangefinders (a tilting Hokuyo UTM-30LX and a Riegl). Classification performance metrics and processing time measurements confirm the benefits of the NN classifier and the feasibility of voxel-based neighborhood. PMID:28294963

Learning discriminative functional network features of schizophrenia

NASA Astrophysics Data System (ADS)

Gheiratmand, Mina; Rish, Irina; Cecchi, Guillermo; Brown, Matthew; Greiner, Russell; Bashivan, Pouya; Polosecki, Pablo; Dursun, Serdar

2017-03-01

Associating schizophrenia with disrupted functional connectivity is a central idea in schizophrenia research. However, identifying neuroimaging-based features that can serve as reliable "statistical biomarkers" of the disease remains a challenging open problem. We argue that generalization accuracy and stability of candidate features ("biomarkers") must be used as additional criteria on top of standard significance tests in order to discover more robust biomarkers. Generalization accuracy refers to the utility of biomarkers for making predictions about individuals, for example discriminating between patients and controls, in novel datasets. Feature stability refers to the reproducibility of the candidate features across different datasets. Here, we extracted functional connectivity network features from fMRI data at both high-resolution (voxel-level) and a spatially down-sampled lower-resolution ("supervoxel" level). At the supervoxel level, we used whole-brain network links, while at the voxel level, due to the intractably large number of features, we sampled a subset of them. We compared statistical significance, stability and discriminative utility of both feature types in a multi-site fMRI dataset, composed of schizophrenia patients and healthy controls. For both feature types, a considerable fraction of features showed significant differences between the two groups. Also, both feature types were similarly stable across multiple data subsets. However, the whole-brain supervoxel functional connectivity features showed a higher cross-validation classification accuracy of 78.7% vs. 72.4% for the voxel-level features. Cross-site variability and heterogeneity in the patient samples in the multi-site FBIRN dataset made the task more challenging compared to single-site studies. The use of the above methodology in combination with the fully data-driven approach using the whole brain information have the potential to shed light on "biomarker discovery" in schizophrenia.

Impact of Strategically Located White Matter Hyperintensities on Cognition in Memory Clinic Patients with Small Vessel Disease.

PubMed

Biesbroek, J Matthijs; Weaver, Nick A; Hilal, Saima; Kuijf, Hugo J; Ikram, Mohammad Kamran; Xu, Xin; Tan, Boon Yeow; Venketasubramanian, Narayanaswamy; Postma, Albert; Biessels, Geert Jan; Chen, Christopher P L H

2016-01-01

Studies on the impact of small vessel disease (SVD) on cognition generally focus on white matter hyperintensity (WMH) volume. The extent to which WMH location relates to cognitive performance has received less attention, but is likely to be functionally important. We examined the relation between WMH location and cognition in a memory clinic cohort of patients with sporadic SVD. A total of 167 patients with SVD were recruited from memory clinics. Assumption-free region of interest-based analyses based on major white matter tracts and voxel-wise analyses were used to determine the association between WMH location and executive functioning, visuomotor speed and memory. Region of interest-based analyses showed that WMHs located particularly within the anterior thalamic radiation and forceps minor were inversely associated with both executive functioning and visuomotor speed, independent of total WMH volume. Memory was significantly associated with WMH volume in the forceps minor, independent of total WMH volume. An independent assumption-free voxel-wise analysis identified strategic voxels in these same tracts. Region of interest-based analyses showed that WMH volume within the anterior thalamic radiation explained 6.8% of variance in executive functioning, compared to 3.9% for total WMH volume; WMH volume within the forceps minor explained 4.6% of variance in visuomotor speed and 4.2% of variance in memory, compared to 1.8% and 1.3% respectively for total WMH volume. Our findings identify the anterior thalamic radiation and forceps minor as strategic white matter tracts in which WMHs are most strongly associated with cognitive impairment in memory clinic patients with SVD. WMH volumes in individual tracts explained more variance in cognition than total WMH burden, emphasizing the importance of lesion location when addressing the functional consequences of WMHs.

Voxel-Based Approach for Estimating Urban Tree Volume from Terrestrial Laser Scanning Data

NASA Astrophysics Data System (ADS)

Vonderach, C.; Voegtle, T.; Adler, P.

2012-07-01

The importance of single trees and the determination of related parameters has been recognized in recent years, e.g. for forest inventories or management. For urban areas an increasing interest in the data acquisition of trees can be observed concerning aspects like urban climate, CO2 balance, and environmental protection. Urban trees differ significantly from natural systems with regard to the site conditions (e.g. technogenic soils, contaminants, lower groundwater level, regular disturbance), climate (increased temperature, reduced humidity) and species composition and arrangement (habitus and health status) and therefore allometric relations cannot be transferred from natural sites to urban areas. To overcome this problem an extended approach was developed for a fast and non-destructive extraction of branch volume, DBH (diameter at breast height) and height of single trees from point clouds of terrestrial laser scanning (TLS). For data acquisition, the trees were scanned with highest scan resolution from several (up to five) positions located around the tree. The resulting point clouds (20 to 60 million points) are analysed with an algorithm based on voxel (volume elements) structure, leading to an appropriate data reduction. In a first step, two kinds of noise reduction are carried out: the elimination of isolated voxels as well as voxels with marginal point density. To obtain correct volume estimates, the voxels inside the stem and branches (interior voxels) where voxels contain no laser points must be regarded. For this filling process, an easy and robust approach was developed based on a layer-wise (horizontal layers of the voxel structure) intersection of four orthogonal viewing directions. However, this procedure also generates several erroneous "phantom" voxels, which have to be eliminated. For this purpose the previous approach was extended by a special region growing algorithm. In a final step the volume is determined layer-wise based on the extracted branch areas Ai of this horizontal cross-section multiplied by the thickness of the voxel layer. A significant improvement of this method could be obtained by a reasonable determination of the threshold for excluding sparsely filled voxels for noise reduction which can be defined based on the function change of filled voxels. Field measurements were used to validate this method. For a quality assessment nine deciduous trees were selected for control and were scanned before felling and weighing. The results are in good accordance to the control trees within a range of only -5.1% to +14.3%. The determined DBH values show only minor deviations, while the heights of trees are systematically underestimated, mainly due to field measurements. Possible error sources including gaps in surface voxels, influence of thin twigs and others are discussed in detail and several improvements of this approach are suggested. The advantages of the algorithm are the robustness and simple structure as well as the quality of the results obtained. The drawbacks are the high effort both in data acquisition and analysis, even if a remarkable data reduction can be obtained by the voxel structure.

Iterative approach of dual regression with a sparse prior enhances the performance of independent component analysis for group functional magnetic resonance imaging (fMRI) data.

PubMed

Kim, Yong-Hwan; Kim, Junghoe; Lee, Jong-Hwan

2012-12-01

This study proposes an iterative dual-regression (DR) approach with sparse prior regularization to better estimate an individual's neuronal activation using the results of an independent component analysis (ICA) method applied to a temporally concatenated group of functional magnetic resonance imaging (fMRI) data (i.e., Tc-GICA method). An ordinary DR approach estimates the spatial patterns (SPs) of neuronal activation and corresponding time courses (TCs) specific to each individual's fMRI data with two steps involving least-squares (LS) solutions. Our proposed approach employs iterative LS solutions to refine both the individual SPs and TCs with an additional a priori assumption of sparseness in the SPs (i.e., minimally overlapping SPs) based on L(1)-norm minimization. To quantitatively evaluate the performance of this approach, semi-artificial fMRI data were created from resting-state fMRI data with the following considerations: (1) an artificially designed spatial layout of neuronal activation patterns with varying overlap sizes across subjects and (2) a BOLD time series (TS) with variable parameters such as onset time, duration, and maximum BOLD levels. To systematically control the spatial layout variability of neuronal activation patterns across the "subjects" (n=12), the degree of spatial overlap across all subjects was varied from a minimum of 1 voxel (i.e., 0.5-voxel cubic radius) to a maximum of 81 voxels (i.e., 2.5-voxel radius) across the task-related SPs with a size of 100 voxels for both the block-based and event-related task paradigms. In addition, several levels of maximum percentage BOLD intensity (i.e., 0.5, 1.0, 2.0, and 3.0%) were used for each degree of spatial overlap size. From the results, the estimated individual SPs of neuronal activation obtained from the proposed iterative DR approach with a sparse prior showed an enhanced true positive rate and reduced false positive rate compared to the ordinary DR approach. The estimated TCs of the task-related SPs from our proposed approach showed greater temporal correlation coefficients with a reference hemodynamic response function than those of the ordinary DR approach. Moreover, the efficacy of the proposed DR approach was also successfully demonstrated by the results of real fMRI data acquired from left-/right-hand clenching tasks in both block-based and event-related task paradigms. Copyright © 2012 Elsevier Inc. All rights reserved.

Higher Media Multi-Tasking Activity Is Associated with Smaller Gray-Matter Density in the Anterior Cingulate Cortex

PubMed Central

Loh, Kep Kee; Kanai, Ryota

2014-01-01

Media multitasking, or the concurrent consumption of multiple media forms, is increasingly prevalent in today’s society and has been associated with negative psychosocial and cognitive impacts. Individuals who engage in heavier media-multitasking are found to perform worse on cognitive control tasks and exhibit more socio-emotional difficulties. However, the neural processes associated with media multi-tasking remain unexplored. The present study investigated relationships between media multitasking activity and brain structure. Research has demonstrated that brain structure can be altered upon prolonged exposure to novel environments and experience. Thus, we expected differential engagements in media multitasking to correlate with brain structure variability. This was confirmed via Voxel-Based Morphometry (VBM) analyses: Individuals with higher Media Multitasking Index (MMI) scores had smaller gray matter density in the anterior cingulate cortex (ACC). Functional connectivity between this ACC region and the precuneus was negatively associated with MMI. Our findings suggest a possible structural correlate for the observed decreased cognitive control performance and socio-emotional regulation in heavy media-multitaskers. While the cross-sectional nature of our study does not allow us to specify the direction of causality, our results brought to light novel associations between individual media multitasking behaviors and ACC structure differences. PMID:25250778

Structural covariance in the hallucinating brain: a voxel-based morphometry study

PubMed Central

Modinos, Gemma; Vercammen, Ans; Mechelli, Andrea; Knegtering, Henderikus; McGuire, Philip K.; Aleman, André

2009-01-01

Background Neuroimaging studies have indicated that a number of cortical regions express altered patterns of structural covariance in schizophrenia. The relation between these alterations and specific psychotic symptoms is yet to be investigated. We used voxel-based morphometry to examine regional grey matter volumes and structural covariance associated with severity of auditory verbal hallucinations. Methods We applied optimized voxel-based morphometry to volumetric magnetic resonance imaging data from 26 patients with medication-resistant auditory verbal hallucinations (AVHs); statistical inferences were made at p < 0.05 after correction for multiple comparisons. Results Grey matter volume in the left inferior frontal gyrus was positively correlated with severity of AVHs. Hallucination severity influenced the pattern of structural covariance between this region and the left superior/middle temporal gyri, the right inferior frontal gyrus and hippocampus, and the insula bilaterally. Limitations The results are based on self-reported severity of auditory hallucinations. Complementing with a clinician-based instrument could have made the findings more compelling. Future studies would benefit from including a measure to control for other symptoms that may covary with AVHs and for the effects of antipsychotic medication. Conclusion The results revealed that overall severity of AVHs modulated cortical intercorrelations between frontotemporal regions involved in language production and verbal monitoring, supporting the critical role of this network in the pathophysiology of hallucinations. PMID:19949723

Cocaine dependence and thalamic functional connectivity: a multivariate pattern analysis.

PubMed

Zhang, Sheng; Hu, Sien; Sinha, Rajita; Potenza, Marc N; Malison, Robert T; Li, Chiang-Shan R

2016-01-01

Cocaine dependence is associated with deficits in cognitive control. Previous studies demonstrated that chronic cocaine use affects the activity and functional connectivity of the thalamus, a subcortical structure critical for cognitive functioning. However, the thalamus contains nuclei heterogeneous in functions, and it is not known how thalamic subregions contribute to cognitive dysfunctions in cocaine dependence. To address this issue, we used multivariate pattern analysis (MVPA) to examine how functional connectivity of the thalamus distinguishes 100 cocaine-dependent participants (CD) from 100 demographically matched healthy control individuals (HC). We characterized six task-related networks with independent component analysis of fMRI data of a stop signal task and employed MVPA to distinguish CD from HC on the basis of voxel-wise thalamic connectivity to the six independent components. In an unbiased model of distinct training and testing data, the analysis correctly classified 72% of subjects with leave-one-out cross-validation (p < 0.001), superior to comparison brain regions with similar voxel counts (p < 0.004, two-sample t test). Thalamic voxels that form the basis of classification aggregate in distinct subclusters, suggesting that connectivities of thalamic subnuclei distinguish CD from HC. Further, linear regressions provided suggestive evidence for a correlation of the thalamic connectivities with clinical variables and performance measures on the stop signal task. Together, these findings support thalamic circuit dysfunction in cognitive control as an important neural marker of cocaine dependence.

Regional Gray Matter Volumes Are Related to Concern About Falling in Older People: A Voxel-Based Morphometric Study.

PubMed

Tuerk, Carola; Zhang, Haobo; Sachdev, Perminder; Lord, Stephen R; Brodaty, Henry; Wen, Wei; Delbaere, Kim

2016-01-01

Concern about falling is common in older people. Various related psychological constructs as well as poor balance and slow gait have been associated with decreased gray matter (GM) volume in old age. The current study investigates the association between concern about falling and voxel-wise GM volumes. A total of 281 community-dwelling older people aged 70-90 years underwent structural magnetic resonance imaging. Concern about falling was assessed using Falls Efficacy Scale-International (FES-I). For each participant, voxel-wise GM volumes were generated with voxel-based morphometry and regressed on raw FES-I scores (p < .05 family-wise error corrected on cluster level). FES-I scores were negatively correlated with total brain volume (r = -.212; p ≤ .001), GM volume (r = -.210; p ≤ .001), and white matter volume (r = -.155; p ≤ .001). Voxel-based morphometry analysis revealed significant negative associations between FES-I and GM volumes of (i) left cerebellum and bilateral inferior occipital gyrus (voxels-in-cluster = 2,981; p < .001) and (ii) bilateral superior frontal gyrus and left supplementary motor area (voxels-in-cluster = 1,900; p = .004). Additional adjustment for vision and physical fall risk did not alter these associations. After adjustment for anxiety, only left cerebellum and bilateral inferior occipital gyrus remained negatively associated with FES-I scores (voxels-in-cluster = 2,426; p < .001). Adjustment for neuroticism removed all associations between FES-I and GM volumes. Our study findings show that concern about falling is negatively associated with brain volumes in areas important for emotional control and for motor control, executive functions and visual processing in a large sample of older men and women. Regression analyses suggest that these relationships were primarily accounted for by psychological factors (generalized anxiety and neuroticism) and not by physical fall risk or vision. © The Author 2015. Published by Oxford University Press on behalf of The Gerontological Society of America. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Assessment of Intervertebral Disc Degeneration Based on Quantitative MRI Analysis: an in vivo study

PubMed Central

Grunert, Peter; Hudson, Katherine D.; Macielak, Michael R.; Aronowitz, Eric; Borde, Brandon H.; Alimi, Marjan; Njoku, Innocent; Ballon, Douglas; Tsiouris, Apostolos John; Bonassar, Lawrence J.; Härtl, Roger

2015-01-01

Study design Animal experimental study Objective To evaluate a novel quantitative imaging technique for assessing disc degeneration. Summary of Background Data T2-relaxation time (T2-RT) measurements have been used to quantitatively assess disc degeneration. T2 values correlate with the water content of inter vertebral disc tissue and thereby allow for the indirect measurement of nucleus pulposus (NP) hydration. Methods We developed an algorithm to subtract out MRI voxels not representing NP tissue based on T2-RT values. Filtered NP voxels were used to measure nuclear size by their amount and nuclear hydration by their mean T2-RT. This technique was applied to 24 rat-tail intervertebral discs’ (IVDs), which had been punctured with an 18-gauge needle according to different techniques to induce varying degrees of degeneration. NP voxel count and average T2-RT were used as parameters to assess the degeneration process at 1 and 3 months post puncture. NP voxel counts were evaluated against X-ray disc height measurements and qualitative MRI studies based on the Pfirrmann grading system. Tails were collected for histology to correlate NP voxel counts to histological disc degeneration grades and to NP cross-sectional area measurements. Results NP voxel count measurements showed strong correlations to qualitative MRI analyses (R2=0.79, p<0.0001), histological degeneration grades (R2=0.902, p<0.0001) and histological NP cross-sectional area measurements (R2=0.887, p<0.0001). In contrast to NP voxel counts, the mean T2-RT for each punctured group remained constant between months 1 and 3. The mean T2-RTs for the punctured groups did not show a statistically significant difference from those of healthy IVDs (63.55ms ±5.88ms month 1 and 62.61ms ±5.02ms) at either time point. Conclusion The NP voxel count proved to be a valid parameter to quantitatively assess disc degeneration in a needle puncture model. The mean NP T2-RT does not change significantly in needle-puncture induced degenerated IVDs. IVDs can be segmented into different tissue components according to their innate T2-RT. PMID:24384655

Multisite Reliability of Cognitive BOLD Data

PubMed Central

Brown, Gregory G.; Mathalon, Daniel H.; Stern, Hal; Ford, Judith; Mueller, Bryon; Greve, Douglas N.; McCarthy, Gregory; Voyvodic, Jim; Glover, Gary; Diaz, Michele; Yetter, Elizabeth; Burak Ozyurt, I.; Jorgensen, Kasper W.; Wible, Cynthia G.; Turner, Jessica A.; Thompson, Wesley K.; Potkin, Steven G.

2010-01-01

Investigators perform multi-site functional magnetic resonance imaging studies to increase statistical power, to enhance generalizability, and to improve the likelihood of sampling relevant subgroups. Yet undesired site variation in imaging methods could off-set these potential advantages. We used variance components analysis to investigate sources of variation in the blood oxygen level dependent (BOLD) signal across four 3T magnets in voxelwise and region of interest (ROI) analyses. Eighteen participants traveled to four magnet sites to complete eight runs of a working memory task involving emotional or neutral distraction. Person variance was more than 10 times larger than site variance for five of six ROIs studied. Person-by-site interactions, however, contributed sizable unwanted variance to the total. Averaging over runs increased between-site reliability, with many voxels showing good to excellent between-site reliability when eight runs were averaged and regions of interest showing fair to good reliability. Between-site reliability depended on the specific functional contrast analyzed in addition to the number of runs averaged. Although median effect size was correlated with between-site reliability, dissociations were observed for many voxels. Brain regions where the pooled effect size was large but between-site reliability was poor were associated with reduced individual differences. Brain regions where the pooled effect size was small but between-site reliability was excellent were associated with a balance of participants who displayed consistently positive or consistently negative BOLD responses. Although between-site reliability of BOLD data can be good to excellent, acquiring highly reliable data requires robust activation paradigms, ongoing quality assurance, and careful experimental control. PMID:20932915

Influence of parameter settings in voxel-based morphometry 8. Using DARTEL and region-of-interest on reproducibility in gray matter volumetry.

PubMed

Goto, M; Abe, O; Aoki, S; Hayashi, N; Miyati, T; Takao, H; Matsuda, H; Yamashita, F; Iwatsubo, T; Mori, H; Kunimatsu, A; Ino, K; Yano, K; Ohtomo, K

2015-01-01

To investigate whether reproducibility of gray matter volumetry is influenced by parameter settings for VBM 8 using Diffeomorphic Anatomical Registration Through Exponentiated Lie Algebra (DARTEL) with region-of-interest (ROI) analyses. We prepared three-dimensional T1-weighted magnetic resonance images (3D-T1WIs) of 21 healthy subjects. All subjects were imaged with each of five MRI systems. Voxel-based morphometry 8 (VBM 8) and WFU PickAtlas software were used for gray matter volumetry. The bilateral ROI labels used were those provided as default settings with the software: Frontal Lobe, Hippocampus, Occipital Lobe, Orbital Gyrus, Parietal Lobe, Putamen, and Temporal Lobe. All 3D-T1WIs were segmented to gray matter with six parameters of VBM 8, with each parameter having between three and eight selectable levels. Reproducibility was evaluated as the standard deviation (mm³) of measured values for the five MRI systems. Reproducibility was influenced by 'Bias regularization (BiasR)', 'Bias FWHM', and 'De-noising filter' settings, but not by 'MRF weighting', 'Sampling distance', or 'Warping regularization' settings. Reproducibility in BiasR was influenced by ROI. Superior reproducibility was observed in Frontal Lobe with the BiasR1 setting, and in Hippocampus, Parietal Lobe, and Putamen with the BiasR3*, BiasR1, and BiasR5 settings, respectively. Reproducibility of gray matter volumetry was influenced by parameter settings in VBM 8 using DARTEL and ROI. In multi-center studies, the use of appropriate settings in VBM 8 with DARTEL results in reduced scanner effect.

Secondary Progressive and Relapsing Remitting Multiple Sclerosis Leads to Motor-Related Decreased Anatomical Connectivity

PubMed Central

Lyksborg, Mark; Siebner, Hartwig R.; Sørensen, Per S.; Blinkenberg, Morten; Parker, Geoff J. M.; Dogonowski, Anne-Marie; Garde, Ellen; Larsen, Rasmus; Dyrby, Tim B.

2014-01-01

Multiple sclerosis (MS) damages central white matter pathways which has considerable impact on disease-related disability. To identify disease-related alterations in anatomical connectivity, 34 patients (19 with relapsing remitting MS (RR-MS), 15 with secondary progressive MS (SP-MS) and 20 healthy subjects underwent diffusion magnetic resonance imaging (dMRI) of the brain. Based on the dMRI, anatomical connectivity mapping (ACM) yielded a voxel-based metric reflecting the connectivity shared between each individual voxel and all other brain voxels. To avoid biases caused by inter-individual brain-shape differences, they were estimated in a spatially normalized space. Voxel-based statistical analyses using ACM were compared with analyses based on the localized microstructural indices of fractional anisotropy (FA). In both RR-MS and SP-MS patients, considerable portions of the motor-related white matter revealed decreases in ACM and FA when compared with healthy subjects. Patients with SP-MS exhibited reduced ACM values relative to RR-MS in the motor-related tracts, whereas there were no consistent decreases in FA between SP-MS and RR-MS patients. Regional ACM statistics exhibited moderate correlation with clinical disability as reflected by the expanded disability status scale (EDSS). The correlation between these statistics and EDSS was either similar to or stronger than the correlation between FA statistics and the EDSS. Together, the results reveal an improved relationship between ACM, the clinical phenotype, and impairment. This highlights the potential of the ACM connectivity indices to be used as a marker which can identify disease related-alterations due to MS which may not be seen using localized microstructural indices. PMID:24748023

Visual Exploration of Genetic Association with Voxel-based Imaging Phenotypes in an MCI/AD Study

PubMed Central

Kim, Sungeun; Shen, Li; Saykin, Andrew J.; West, John D.

2010-01-01

Neuroimaging genomics is a new transdisciplinary research field, which aims to examine genetic effects on brain via integrated analyses of high throughput neuroimaging and genomic data. We report our recent work on (1) developing an imaging genomic browsing system that allows for whole genome and entire brain analyses based on visual exploration and (2) applying the system to the imaging genomic analysis of an existing MCI/AD cohort. Voxel-based morphometry is used to define imaging phenotypes. ANCOVA is employed to evaluate the effect of the interaction of genotypes and diagnosis in relation to imaging phenotypes while controlling for relevant covariates. Encouraging experimental results suggest that the proposed system has substantial potential for enabling discovery of imaging genomic associations through visual evaluation and for localizing candidate imaging regions and genomic regions for refined statistical modeling. PMID:19963597

Neural correlates of cognitive dysfunction in Lewy body diseases and tauopathies: combined assessment with FDG-PET and the CERAD test battery.

PubMed

Hellwig, Sabine; Frings, Lars; Bormann, Tobias; Kreft, Annabelle; Amtage, Florian; Spehl, Timo S; Weiller, Cornelius; Tüscher, Oliver; Meyer, Philipp T

2013-11-01

We investigated disease-specific cognitive profiles and their neural correlates in Lewy-body diseases (LBD) and tauopathies by CERAD assessment and FDG-PET. Analyses revealed a significant interaction between reduced semantic fluency in tauopathies and impaired verbal learning in LBD. Semantic fluency discriminated between groups with high accuracy (83%). Compared to LBD, tauopathy patients showed bilateral hypometabolism of midbrain, thalamus, middle cingulate gyrus and supplementary motor/premotor cortex. In the reverse contrast, LBD patients exhibited bilateral hypometabolism in posterior parietal cortex, precuneus and inferior temporal gyrus extending into occipital and frontal cortices. In diagnosis-independent voxel-based analyses, verbal learning/memory correlated with left temporal and right parietal metabolism, while fluency was coupled to bilateral striatal and frontal metabolism. Naming correlated with left frontal metabolism and drawing with metabolism in bilateral temporal and left frontal regions. In line with disease-specific patterns of regional glucose metabolism, tauopathies and LBD show distinct cognitive profiles, which may assist clinical differentiation. Copyright © 2013 Elsevier Inc. All rights reserved.

Linear Representation of Emotions in Whole Persons by Combining Facial and Bodily Expressions in the Extrastriate Body Area

PubMed Central

Yang, Xiaoli; Xu, Junhai; Cao, Linjing; Li, Xianglin; Wang, Peiyuan; Wang, Bin; Liu, Baolin

2018-01-01

Our human brain can rapidly and effortlessly perceive a person’s emotional state by integrating the isolated emotional faces and bodies into a whole. Behavioral studies have suggested that the human brain encodes whole persons in a holistic rather than part-based manner. Neuroimaging studies have also shown that body-selective areas prefer whole persons to the sum of their parts. The body-selective areas played a crucial role in representing the relationships between emotions expressed by different parts. However, it remains unclear in which regions the perception of whole persons is represented by a combination of faces and bodies, and to what extent the combination can be influenced by the whole person’s emotions. In the present study, functional magnetic resonance imaging data were collected when participants performed an emotion distinction task. Multi-voxel pattern analysis was conducted to examine how the whole person-evoked responses were associated with the face- and body-evoked responses in several specific brain areas. We found that in the extrastriate body area (EBA), the whole person patterns were most closely correlated with weighted sums of face and body patterns, using different weights for happy expressions but equal weights for angry and fearful ones. These results were unique for the EBA. Our findings tentatively support the idea that the whole person patterns are represented in a part-based manner in the EBA, and modulated by emotions. These data will further our understanding of the neural mechanism underlying perceiving emotional persons. PMID:29375348

Algebraic Topology of Multi-Brain Connectivity Networks Reveals Dissimilarity in Functional Patterns during Spoken Communications

PubMed Central

Tadić, Bosiljka; Andjelković, Miroslav; Boshkoska, Biljana Mileva; Levnajić, Zoran

2016-01-01

Human behaviour in various circumstances mirrors the corresponding brain connectivity patterns, which are suitably represented by functional brain networks. While the objective analysis of these networks by graph theory tools deepened our understanding of brain functions, the multi-brain structures and connections underlying human social behaviour remain largely unexplored. In this study, we analyse the aggregate graph that maps coordination of EEG signals previously recorded during spoken communications in two groups of six listeners and two speakers. Applying an innovative approach based on the algebraic topology of graphs, we analyse higher-order topological complexes consisting of mutually interwoven cliques of a high order to which the identified functional connections organise. Our results reveal that the topological quantifiers provide new suitable measures for differences in the brain activity patterns and inter-brain synchronisation between speakers and listeners. Moreover, the higher topological complexity correlates with the listener’s concentration to the story, confirmed by self-rating, and closeness to the speaker’s brain activity pattern, which is measured by network-to-network distance. The connectivity structures of the frontal and parietal lobe consistently constitute distinct clusters, which extend across the listener’s group. Formally, the topology quantifiers of the multi-brain communities exceed the sum of those of the participating individuals and also reflect the listener’s rated attributes of the speaker and the narrated subject. In the broader context, the presented study exposes the relevance of higher topological structures (besides standard graph measures) for characterising functional brain networks under different stimuli. PMID:27880802

Heuristics for connectivity-based brain parcellation of SMA/pre-SMA through force-directed graph layout.

PubMed

Crippa, Alessandro; Cerliani, Leonardo; Nanetti, Luca; Roerdink, Jos B T M

2011-02-01

We propose the use of force-directed graph layout as an explorative tool for connectivity-based brain parcellation studies. The method can be used as a heuristic to find the number of clusters intrinsically present in the data (if any) and to investigate their organisation. It provides an intuitive representation of the structure of the data and facilitates interactive exploration of properties of single seed voxels as well as relations among (groups of) voxels. We validate the method on synthetic data sets and we investigate the changes in connectivity in the supplementary motor cortex, a brain region whose parcellation has been previously investigated via connectivity studies. This region is supposed to present two easily distinguishable connectivity patterns, putatively denoted by SMA (supplementary motor area) and pre-SMA. Our method provides insights with respect to the connectivity patterns of the premotor cortex. These present a substantial variation among subjects, and their subdivision into two well-separated clusters is not always straightforward. Copyright © 2010 Elsevier Inc. All rights reserved.

Tracing multi-habitat support of coastal fishes

EPA Science Inventory

Hydrologic linkages among coastal wetland and nearshore areas allow coastal fish to move among the habitats, which has led to a variety of habitat use patterns. In the Great Lakes, fine-scale microchemical analyses of yellow perch otoliths have revealed life-history categories th...

Multimodal Imaging Evidence for Axonal and Myelin Deterioration in Amnestic Mild Cognitive Impairment

PubMed Central

Gold, Brian T.; Jiang, Yang; Powell, David K.; Smith, Charles D.

2012-01-01

White matter (WM) microstructural declines have been demonstrated in Alzheimer’s disease and amnestic mild cognitive impairment (aMCI). However, the pattern of WM microstructural changes in aMCI after controlling for WM atrophy is unknown. Here, we address this issue through joint consideration of aMCI alterations in fractional anisotropy, mean diffusivity, axial diffusivity, and radial diffusivity, as well as macrostructural volume in WM and gray matter compartments. Participants were 18 individuals with aMCI and 24 healthy seniors. Voxelwise analyses of diffusion tensor imaging data was carried out using tract-based spatial statistics (TBSS) and voxelwise analyses of high-resolution structural data was conducted using voxel based morphometry. After controlling for WM atrophy, the main pattern of TBSS findings indicated reduced fractional anisotropy with only small alterations in mean diffusivity/radial diffusivity/axial diffusivity. These WM microstructural declines bordered and/or were connected to gray matter structures showing volumetric declines. However, none of the potential relationships between WM integrity and volume in connected gray matter structures was significant, and adding fractional anisotropy information improved the classificatory accuracy of aMCI compared to the use of hippocampal atrophy alone. These results suggest that WM microstructural declines provide unique information not captured by atrophy measures that may aid the magnetic resonance imaging contribution to aMCI detection. PMID:22460327

Grey-matter volume as a potential feature for the classification of Alzheimer's disease and mild cognitive impairment: an exploratory study.

PubMed

Guo, Yane; Zhang, Zengqiang; Zhou, Bo; Wang, Pan; Yao, Hongxiang; Yuan, Minshao; An, Ningyu; Dai, Haitao; Wang, Luning; Zhang, Xi; Liu, Yong

2014-06-01

Specific patterns of brain atrophy may be helpful in the diagnosis of Alzheimer's disease (AD). In the present study, we set out to evaluate the utility of grey-matter volume in the classification of AD and amnestic mild cognitive impairment (aMCI) compared to normal control (NC) individuals. Voxel-based morphometric analyses were performed on structural MRIs from 35 AD patients, 27 aMCI patients, and 27 NC participants. A two-sample two-tailed t-test was computed between the NC and AD groups to create a map of abnormal grey matter in AD. The brain areas with significant differences were extracted as regions of interest (ROIs), and the grey-matter volumes in the ROIs of the aMCI patients were included to evaluate the patterns of change across different disease severities. Next, correlation analyses between the grey-matter volumes in the ROIs and all clinical variables were performed in aMCI and AD patients to determine whether they varied with disease progression. The results revealed significantly decreased grey matter in the bilateral hippocampus/parahippocampus, the bilateral superior/middle temporal gyri, and the right precuneus in AD patients. The grey-matter volumes were positively correlated with clinical variables. Finally, we performed exploratory linear discriminative analyses to assess the classifying capacity of grey-matter volumes in the bilateral hippocampus and parahippocampus among AD, aMCI, and NC. Leave-one-out cross-validation analyses demonstrated that grey-matter volumes in hippocampus and parahippocampus accurately distinguished AD from NC. These findings indicate that grey-matter volumes are useful in the classification of AD.

Isolation and measurement of the features of arrays of cell aggregates formed by dielectrophoresis using the user-specified Multi Regions Masking (MRM) technique

NASA Astrophysics Data System (ADS)

Yusvana, Rama; Headon, Denis; Markx, Gerard H.

2009-08-01

The use of dielectrophoresis for the construction of artificial skin tissue with skin cells in follicle-like 3D cell aggregates in well-defined patterns is demonstrated. To analyse the patterns produced and to study their development after their formation a Virtual Instrument (VI) system was developed using the LabVIEW IMAQ Vision Development Module. A series of programming functions (algorithms) was used to isolate the features on the image (in our case; the patterned aggregates) and separate them from all other unwanted regions on the image. The image was subsequently converted into a binary version, covering only the desired microarray regions which could then be analysed by computer for automatic object measurements. The analysis utilized the simple and easy-to-use User-Specified Multi-Regions Masking (MRM) technique, which allows one to concentrate the analysis on the desired regions specified in the mask. This simplified the algorithms for the analysis of images of cell arrays having similar geometrical properties. By having a collection of scripts containing masks of different patterns, it was possible to quickly and efficiently develop sets of custom virtual instruments for the offline or online analysis of images of cell arrays in the database.

Identification of Amnestic Mild Cognitive Impairment Using Multi-Modal Brain Features: A Combined Structural MRI and Diffusion Tensor Imaging Study.

PubMed

Xie, Yunyan; Cui, Zaixu; Zhang, Zhongmin; Sun, Yu; Sheng, Can; Li, Kuncheng; Gong, Gaolang; Han, Ying; Jia, Jianping

2015-01-01

Identifying amnestic mild cognitive impairment (aMCI) is of great clinical importance because aMCI is a putative prodromal stage of Alzheimer's disease. The present study aimed to explore the feasibility of accurately identifying aMCI with a magnetic resonance imaging (MRI) biomarker. We integrated measures of both gray matter (GM) abnormalities derived from structural MRI and white matter (WM) alterations acquired from diffusion tensor imaging at the voxel level across the entire brain. In particular, multi-modal brain features, including GM volume, WM fractional anisotropy, and mean diffusivity, were extracted from a relatively large sample of 64 Han Chinese aMCI patients and 64 matched controls. Then, support vector machine classifiers for GM volume, FA, and MD were fused to distinguish the aMCI patients from the controls. The fused classifier was evaluated with the leave-one-out and the 10-fold cross-validations, and the classifier had an accuracy of 83.59% and an area under the curve of 0.862. The most discriminative regions of GM were mainly located in the medial temporal lobe, temporal lobe, precuneus, cingulate gyrus, parietal lobe, and frontal lobe, whereas the most discriminative regions of WM were mainly located in the corpus callosum, cingulum, corona radiata, frontal lobe, and parietal lobe. Our findings suggest that aMCI is characterized by a distributed pattern of GM abnormalities and WM alterations that represent discriminative power and reflect relevant pathological changes in the brain, and these changes further highlight the advantage of multi-modal feature integration for identifying aMCI.

Covariance PET patterns in early Alzheimer's disease and subjects with cognitive impairment but no dementia: utility in group discrimination and correlations with functional performance

PubMed Central

Scarmeas, Nikolaos; Habeck, Christian G.; Zarahn, Eric; Anderson, Karen E.; Park, Aileen; Hilton, John; Pelton, Gregory H.; Tabert, Matthias H.; Honig, Lawrence S.; Moeller, James R.; Devanand, Davangere P.; Stern, Yaakov

2011-01-01

Although multivariate analytic techniques might identify diagnostic patterns that are not captured by univariate methods, they have rarely been used to study the neural correlates of Alzheimer's disease (AD) or cognitive impairment. Nonquantitative H215O PET scans were acquired during rest in 17 probable AD subjects selected for mild severity [mean-modified Mini Mental Status Examination (mMMS) 46/57; SD 5.1], 16 control subjects (mMMS 54; SD 2.5) and 23 subjects with minimal to mild cognitive impairment but no dementia (mMMS 53; SD 2.8). Expert clinical reading had low success in discriminating AD and controls. There were no significant mean flow differences among groups in traditional univariate SPM Voxel-wise analyses or region of interest (ROI) analyses. A covariance pattern was identified whose mean expression was significantly higher in the AD as compared to controls (P = 0.03; sensitivity 76–94%; specificity 63–81%). Sites of increased concomitant flow included insula, cuneus, pulvinar, lingual, fusiform, superior occipital and parahippocampal gyri, whereas decreased concomitant flow was found in cingulate, inferior parietal lobule, middle and inferior frontal, supramarginal and precentral gyri. The covariance analysis-derived pattern was then prospectively applied to the cognitively impaired subjects: as compared to subjects with Clinical Dementia Rating (CDR) = 0, subjects with CDR = 0.5 had significantly higher mean covariance pattern expression (P = 0.009). Expression of this pattern correlated inversely with Selective Reminding Test total recall (r = −0.401, P = 0.002), delayed recall (r = −0.351, P = 0.008) and mMMS scores (r = −0.401, P = 0.002) in all three groups combined. We conclude that patients with AD may differentially express resting cerebral blood flow covariance patterns even at very early disease stages. Significant alterations in expression of resting flow covariance patterns occur even for subjects with cognitive impairment. Expression of covariance patterns correlates with cognitive and functional performance measures, holding promise for meaningful associations with underlying biopathological processes. PMID:15325350

Image Statistics and the Representation of Material Properties in the Visual Cortex

PubMed Central

Baumgartner, Elisabeth; Gegenfurtner, Karl R.

2016-01-01

We explored perceived material properties (roughness, texturedness, and hardness) with a novel approach that compares perception, image statistics and brain activation, as measured with fMRI. We initially asked participants to rate 84 material images with respect to the above mentioned properties, and then scanned 15 of the participants with fMRI while they viewed the material images. The images were analyzed with a set of image statistics capturing their spatial frequency and texture properties. Linear classifiers were then applied to the image statistics as well as the voxel patterns of visually responsive voxels and early visual areas to discriminate between images with high and low perceptual ratings. Roughness and texturedness could be classified above chance level based on image statistics. Roughness and texturedness could also be classified based on the brain activation patterns in visual cortex, whereas hardness could not. Importantly, the agreement in classification based on image statistics and brain activation was also above chance level. Our results show that information about visual material properties is to a large degree contained in low-level image statistics, and that these image statistics are also partially reflected in brain activity patterns induced by the perception of material images. PMID:27582714

Image Statistics and the Representation of Material Properties in the Visual Cortex.

PubMed

Baumgartner, Elisabeth; Gegenfurtner, Karl R

2016-01-01

We explored perceived material properties (roughness, texturedness, and hardness) with a novel approach that compares perception, image statistics and brain activation, as measured with fMRI. We initially asked participants to rate 84 material images with respect to the above mentioned properties, and then scanned 15 of the participants with fMRI while they viewed the material images. The images were analyzed with a set of image statistics capturing their spatial frequency and texture properties. Linear classifiers were then applied to the image statistics as well as the voxel patterns of visually responsive voxels and early visual areas to discriminate between images with high and low perceptual ratings. Roughness and texturedness could be classified above chance level based on image statistics. Roughness and texturedness could also be classified based on the brain activation patterns in visual cortex, whereas hardness could not. Importantly, the agreement in classification based on image statistics and brain activation was also above chance level. Our results show that information about visual material properties is to a large degree contained in low-level image statistics, and that these image statistics are also partially reflected in brain activity patterns induced by the perception of material images.

Brain structural changes in spasmodic dysphonia: A multimodal magnetic resonance imaging study.

PubMed

Kostic, Vladimir S; Agosta, Federica; Sarro, Lidia; Tomić, Aleksandra; Kresojević, Nikola; Galantucci, Sebastiano; Svetel, Marina; Valsasina, Paola; Filippi, Massimo

2016-04-01

The pathophysiology of spasmodic dysphonia is poorly understood. This study evaluated patterns of cortical morphology, basal ganglia, and white matter microstructural alterations in patients with spasmodic dysphonia relative to healthy controls. T1-weighted and diffusion tensor magnetic resonance imaging (MRI) scans were obtained from 13 spasmodic dysphonia patients and 30 controls. Tract-based spatial statistics was applied to compare diffusion tensor MRI indices (i.e., mean, radial and axial diffusivities, and fractional anisotropy) between groups on a voxel-by-voxel basis. Cortical measures were analyzed using surface-based morphometry. Basal ganglia were segmented on T1-weighted images, and volumes and diffusion tensor MRI metrics of nuclei were measured. Relative to controls, patients with spasmodic dysphonia showed increased cortical surface area of the primary somatosensory cortex bilaterally in a region consistent with the buccal sensory representation, as well as right primary motor cortex, left superior temporal, supramarginal and superior frontal gyri. A decreased cortical area was found in the rolandic operculum bilaterally, left superior/inferior parietal and lingual gyri, as well as in the right angular gyrus. Compared to controls, spasmodic dysphonia patients showed increased diffusivities and decreased fractional anisotropy of the corpus callosum and major white matter tracts, in the right hemisphere. Altered diffusion tensor MRI measures were found in the right caudate and putamen nuclei with no volumetric changes. Multi-level alterations in voice-controlling networks, that included regions devoted not only to sensorimotor integration, motor preparation and motor execution, but also processing of auditory and visual information during speech, might have a role in the pathophysiology of spasmodic dysphonia. Copyright © 2016 Elsevier Ltd. All rights reserved.

Anomalous Gray Matter Patterns in Specific Reading Comprehension Deficits are Independent of Dyslexia

PubMed Central

Bailey, Stephen; Hoeft, Fumiko; Aboud, Katherine; Cutting, Laurie

2016-01-01

Specific reading comprehension deficits (SRCD) affects up to 10% of all children. SRCD is distinct from dyslexia (DYS) in that individuals with SRCD show poor comprehension despite adequate decoding skills. Despite its prevalence and considerable behavioral research, there is not yet a unified cognitive explanation of SRCD. While its neuroanatomical basis is unknown, SRCD could be anomalous in regions subserving their commonly reported cognitive weaknesses in semantic processing and/or executive function. Here we investigated, for the first time, patterns of gray matter volume difference in SRCD as compared to DYS and typical developing (TD) adolescent readers (N=41). A linear support vector machine algorithm was applied to whole brain gray matter volumes generated through voxel-based morphometry. As expected, analyses revealed that DYS differed significantly from TD in a pattern that included features from left fusiform and supramarginal gyri (DYS vs. TD: 80.0%, p < 0.01). SRCD was well differentiated not only from TD (92.5%, p < 0.001) but also from DYS (88.0%, p < 0.001). Of particular interest were findings of reduced gray matter volume in right frontal areas that were also supported by univariate analysis. These areas are thought to subserve executive processes relevant for reading, such as monitoring and manipulating mental representations. Thus, preliminary analyses suggest that SRCD readers possess a distinct neural profile compared to both TD and DYS readers and that these differences might be linked to domain-general abilities. This work provides a foundation for further investigation into variants of reading disability beyond DYS. PMID:27324343

Training of verbal creativity modulates brain activity in regions associated with language- and memory-related demands.

PubMed

Fink, Andreas; Benedek, Mathias; Koschutnig, Karl; Pirker, Eva; Berger, Elisabeth; Meister, Sabrina; Neubauer, Aljoscha C; Papousek, Ilona; Weiss, Elisabeth M

2015-10-01

This functional magnetic resonance (fMRI) study was designed to investigate changes in functional patterns of brain activity during creative ideation as a result of a computerized, 3-week verbal creativity training. The training was composed of various verbal divergent thinking exercises requiring participants to train approximately 20 min per day. Fifty-three participants were tested three times (psychometric tests and fMRI assessment) with an intertest-interval of 4 weeks each. Participants were randomly assigned to two different training groups, which received the training time-delayed: The first training group was trained between the first and the second test, while the second group accomplished the training between the second and the third test session. At the behavioral level, only one training group showed improvements in different facets of verbal creativity right after the training. Yet, functional patterns of brain activity during creative ideation were strikingly similar across both training groups. Whole-brain voxel-wise analyses (along with supplementary region of interest analyses) revealed that the training was associated with activity changes in well-known creativity-related brain regions such as the left inferior parietal cortex and the left middle temporal gyrus, which have been shown as being particularly sensitive to the originality facet of creativity in previous research. Taken together, this study demonstrates that continuous engagement in a specific complex cognitive task like divergent thinking is associated with reliable changes of activity patterns in relevant brain areas, suggesting more effective search, retrieval, and integration from internal memory representations as a result of the training. © 2015 Wiley Periodicals, Inc.

Voxel-based plaque classification in coronary intravascular optical coherence tomography images using decision trees

NASA Astrophysics Data System (ADS)

Kolluru, Chaitanya; Prabhu, David; Gharaibeh, Yazan; Wu, Hao; Wilson, David L.

2018-02-01

Intravascular Optical Coherence Tomography (IVOCT) is a high contrast, 3D microscopic imaging technique that can be used to assess atherosclerosis and guide stent interventions. Despite its advantages, IVOCT image interpretation is challenging and time consuming with over 500 image frames generated in a single pullback volume. We have developed a method to classify voxel plaque types in IVOCT images using machine learning. To train and test the classifier, we have used our unique database of labeled cadaver vessel IVOCT images accurately registered to gold standard cryoimages. This database currently contains 300 images and is growing. Each voxel is labeled as fibrotic, lipid-rich, calcified or other. Optical attenuation, intensity and texture features were extracted for each voxel and were used to build a decision tree classifier for multi-class classification. Five-fold cross-validation across images gave accuracies of 96 % +/- 0.01 %, 90 +/- 0.02% and 90 % +/- 0.01 % for fibrotic, lipid-rich and calcified classes respectively. To rectify performance degradation seen in left out vessel specimens as opposed to left out images, we are adding data and reducing features to limit overfitting. Following spatial noise cleaning, important vascular regions were unambiguous in display. We developed displays that enable physicians to make rapid determination of calcified and lipid regions. This will inform treatment decisions such as the need for devices (e.g., atherectomy or scoring balloon in the case of calcifications) or extended stent lengths to ensure coverage of lipid regions prone to injury at the edge of a stent.

Sparse PDF Volumes for Consistent Multi-Resolution Volume Rendering.

PubMed

Sicat, Ronell; Krüger, Jens; Möller, Torsten; Hadwiger, Markus

2014-12-01

This paper presents a new multi-resolution volume representation called sparse pdf volumes, which enables consistent multi-resolution volume rendering based on probability density functions (pdfs) of voxel neighborhoods. These pdfs are defined in the 4D domain jointly comprising the 3D volume and its 1D intensity range. Crucially, the computation of sparse pdf volumes exploits data coherence in 4D, resulting in a sparse representation with surprisingly low storage requirements. At run time, we dynamically apply transfer functions to the pdfs using simple and fast convolutions. Whereas standard low-pass filtering and down-sampling incur visible differences between resolution levels, the use of pdfs facilitates consistent results independent of the resolution level used. We describe the efficient out-of-core computation of large-scale sparse pdf volumes, using a novel iterative simplification procedure of a mixture of 4D Gaussians. Finally, our data structure is optimized to facilitate interactive multi-resolution volume rendering on GPUs.

A Sparse Bayesian Learning Algorithm for White Matter Parameter Estimation from Compressed Multi-shell Diffusion MRI.

PubMed

Pisharady, Pramod Kumar; Sotiropoulos, Stamatios N; Sapiro, Guillermo; Lenglet, Christophe

2017-09-01

We propose a sparse Bayesian learning algorithm for improved estimation of white matter fiber parameters from compressed (under-sampled q-space) multi-shell diffusion MRI data. The multi-shell data is represented in a dictionary form using a non-monoexponential decay model of diffusion, based on continuous gamma distribution of diffusivities. The fiber volume fractions with predefined orientations, which are the unknown parameters, form the dictionary weights. These unknown parameters are estimated with a linear un-mixing framework, using a sparse Bayesian learning algorithm. A localized learning of hyperparameters at each voxel and for each possible fiber orientations improves the parameter estimation. Our experiments using synthetic data from the ISBI 2012 HARDI reconstruction challenge and in-vivo data from the Human Connectome Project demonstrate the improvements.

Simultaneous delivery time and aperture shape optimization for the volumetric-modulated arc therapy (VMAT) treatment planning problem

NASA Astrophysics Data System (ADS)

Mahnam, Mehdi; Gendreau, Michel; Lahrichi, Nadia; Rousseau, Louis-Martin

2017-07-01

In this paper, we propose a novel heuristic algorithm for the volumetric-modulated arc therapy treatment planning problem, optimizing the trade-off between delivery time and treatment quality. We present a new mixed integer programming model in which the multi-leaf collimator leaf positions, gantry speed, and dose rate are determined simultaneously. Our heuristic is based on column generation; the aperture configuration is modeled in the columns and the dose distribution and time restriction in the rows. To reduce the number of voxels and increase the efficiency of the master model, we aggregate similar voxels using a clustering technique. The efficiency of the algorithm and the treatment quality are evaluated on a benchmark clinical prostate cancer case. The computational results show that a high-quality treatment is achievable using a four-thread CPU. Finally, we analyze the effects of the various parameters and two leaf-motion strategies.

Voxel Datacubes for 3D Visualization in Blender

NASA Astrophysics Data System (ADS)

Gárate, Matías

2017-05-01

The growth of computational astrophysics and the complexity of multi-dimensional data sets evidences the need for new versatile visualization tools for both the analysis and presentation of the data. In this work, we show how to use the open-source software Blender as a three-dimensional (3D) visualization tool to study and visualize numerical simulation results, focusing on astrophysical hydrodynamic experiments. With a datacube as input, the software can generate a volume rendering of the 3D data, show the evolution of a simulation in time, and do a fly-around camera animation to highlight the points of interest. We explain the process to import simulation outputs into Blender using the voxel data format, and how to set up a visualization scene in the software interface. This method allows scientists to perform a complementary visual analysis of their data and display their results in an appealing way, both for outreach and science presentations.

Compressive Sensing for Background Subtraction

DTIC Science & Technology

2009-12-20

i) reconstructing an image using only a single optical pho- todiode (infrared, hyperspectral, etc.) along with a digital micromirror device (DMD... curves , we use the full images, run the background subtraction algorithm proposed in [19], and obtain baseline background subtracted images. We then...the images to generate the ROC curve . 5.5 Silhouettes vs. Difference Images We have used a multi camera set up for a 3D voxel reconstruction using the

Sex-related differences in amygdala functional connectivity during resting conditions.

PubMed

Kilpatrick, L A; Zald, D H; Pardo, J V; Cahill, L F

2006-04-01

Recent neuroimaging studies have established a sex-related hemispheric lateralization of amygdala involvement in memory for emotionally arousing material. Here, we examine the possibility that sex-related differences in amygdala involvement in memory for emotional material develop from differential patterns of amygdala functional connectivity evident in the resting brain. Seed voxel partial least square analyses of regional cerebral blood flow data revealed significant sex-related differences in amygdala functional connectivity during resting conditions. The right amygdala was associated with greater functional connectivity in men than in women. In contrast, the left amygdala was associated with greater functional connectivity in women than in men. Furthermore, the regions displaying stronger functional connectivity with the right amygdala in males (sensorimotor cortex, striatum, pulvinar) differed from those displaying stronger functional connectivity with the left amygdala in females (subgenual cortex, hypothalamus). These differences in functional connectivity at rest may link to sex-related differences in medical and psychiatric disorders.

Functional imaging of cerebral blood flow and glucose metabolism in Parkinson's disease and Huntington's disease.

PubMed

Ma, Yilong; Eidelberg, David

2007-01-01

Brain imaging of cerebral blood flow and glucose metabolism has been playing key roles in describing pathophysiology of Parkinson's disease (PD) and Huntington's disease (HD), respectively. Many biomarkers have been developed in recent years to investigate the abnormality in molecular substrate, track the time course of disease progression, and evaluate the efficacy of novel experimental therapeutics. A growing body of literature has emerged on neurobiology of these two movement disorders in resting states and in response to brain activation tasks. In this paper, we review the latest applications of these approaches in patients and normal volunteers at rest conditions. The discussions focus on brain mapping studies with univariate and multivariate statistical analyses on a voxel basis. In particular, we present data to validate the reproducibility and reliability of unique spatial covariance patterns related with PD and HD.

Approximations of noise covariance in multi-slice helical CT scans: impact on lung nodule size estimation.

PubMed

Zeng, Rongping; Petrick, Nicholas; Gavrielides, Marios A; Myers, Kyle J

2011-10-07

Multi-slice computed tomography (MSCT) scanners have become popular volumetric imaging tools. Deterministic and random properties of the resulting CT scans have been studied in the literature. Due to the large number of voxels in the three-dimensional (3D) volumetric dataset, full characterization of the noise covariance in MSCT scans is difficult to tackle. However, as usage of such datasets for quantitative disease diagnosis grows, so does the importance of understanding the noise properties because of their effect on the accuracy of the clinical outcome. The goal of this work is to study noise covariance in the helical MSCT volumetric dataset. We explore possible approximations to the noise covariance matrix with reduced degrees of freedom, including voxel-based variance, one-dimensional (1D) correlation, two-dimensional (2D) in-plane correlation and the noise power spectrum (NPS). We further examine the effect of various noise covariance models on the accuracy of a prewhitening matched filter nodule size estimation strategy. Our simulation results suggest that the 1D longitudinal, 2D in-plane and NPS prewhitening approaches can improve the performance of nodule size estimation algorithms. When taking into account computational costs in determining noise characterizations, the NPS model may be the most efficient approximation to the MSCT noise covariance matrix.

Voxel-based morphometric multisite collaborative study on schizophrenia.

PubMed

Segall, Judith M; Turner, Jessica A; van Erp, Theo G M; White, Tonya; Bockholt, H Jeremy; Gollub, Randy L; Ho, Beng C; Magnotta, Vince; Jung, Rex E; McCarley, Robert W; Schulz, S Charles; Lauriello, John; Clark, Vince P; Voyvodic, James T; Diaz, Michele T; Calhoun, Vince D

2009-01-01

Regional gray matter (GM) abnormalities are well known to exist in patients with chronic schizophrenia. Voxel-based morphometry (VBM) has been previously used on structural magnetic resonance images (MRI) data to characterize these abnormalities. Two multisite schizophrenia studies, the Functional Biomedical Informatics Research Network and the Mind Clinical Imaging Consortium, which include 9 data collection sites, are evaluating the efficacy of pooling structural imaging data across imaging centers. Such a pooling of data could yield the increased statistical power needed to elucidate effects that may not be seen with smaller samples. VBM analyses were performed to evaluate the consistency of patient versus control gray matter concentration (GMC) differences across the study sites, as well as the effects of combining multisite data. Integration of data from both studies yielded a large sample of 503 subjects, including 266 controls and 237 patients diagnosed with schizophrenia, schizoaffective or schizophreniform disorder. The data were analyzed using the combined sample, as well as analyzing each of the 2 multisite studies separately. A consistent pattern of reduced relative GMC in schizophrenia patients compared with controls was found across all study sites. Imaging center-specific effects were evaluated using a region of interest analysis. Overall, the findings support the use of VBM in combined multisite studies. This analysis of schizophrenics and controls from around the United States provides continued supporting evidence for GM deficits in the temporal lobes, anterior cingulate, and frontal regions in patients with schizophrenia spectrum disorders.

Impact of correction factors in human brain lesion-behavior inference.

PubMed

Sperber, Christoph; Karnath, Hans-Otto

2017-03-01

Statistical voxel-based lesion-behavior mapping (VLBM) in neurological patients with brain lesions is frequently used to examine the relationship between structure and function of the healthy human brain. Only recently, two simulation studies noted reduced anatomical validity of this method, observing the results of VLBM to be systematically misplaced by about 16 mm. However, both simulation studies differed from VLBM analyses of real data in that they lacked the proper use of two correction factors: lesion size and "sufficient lesion affection." In simulation experiments on a sample of 274 real stroke patients, we found that the use of these two correction factors reduced misplacement markedly compared to uncorrected VLBM. Apparently, the misplacement is due to physiological effects of brain lesion anatomy. Voxel-wise topographies of collateral damage in the real data were generated and used to compute a metric for the inter-voxel relation of brain damage. "Anatomical bias" vectors that were solely calculated from these inter-voxel relations in the patients' real anatomical data, successfully predicted the VLBM misplacement. The latter has the potential to help in the development of new VLBM methods that provide even higher anatomical validity than currently available by the proper use of correction factors. Hum Brain Mapp 38:1692-1701, 2017. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

Representations of Invariant Musical Categories Are Decodable by Pattern Analysis of Locally Distributed BOLD Responses in Superior Temporal and Intraparietal Sulci

PubMed Central

Klein, Mike E.; Zatorre, Robert J.

2015-01-01

In categorical perception (CP), continuous physical signals are mapped to discrete perceptual bins: mental categories not found in the physical world. CP has been demonstrated across multiple sensory modalities and, in audition, for certain over-learned speech and musical sounds. The neural basis of auditory CP, however, remains ambiguous, including its robustness in nonspeech processes and the relative roles of left/right hemispheres; primary/nonprimary cortices; and ventral/dorsal perceptual processing streams. Here, highly trained musicians listened to 2-tone musical intervals, which they perceive categorically while undergoing functional magnetic resonance imaging. Multivariate pattern analyses were performed after grouping sounds by interval quality (determined by frequency ratio between tones) or pitch height (perceived noncategorically, frequency ratios remain constant). Distributed activity patterns in spheres of voxels were used to determine sound sample identities. For intervals, significant decoding accuracy was observed in the right superior temporal and left intraparietal sulci, with smaller peaks observed homologously in contralateral hemispheres. For pitch height, no significant decoding accuracy was observed, consistent with the non-CP of this dimension. These results suggest that similar mechanisms are operative for nonspeech categories as for speech; espouse roles for 2 segregated processing streams; and support hierarchical processing models for CP. PMID:24488957

Gray Matter Atrophy in the Cerebellum-Evidence of Increased Vulnerability of the Crus and Vermis with Advancing Age.

PubMed

Yu, Teresa; Korgaonkar, Mayuresh S; Grieve, Stuart M

2017-04-01

This study examined patterns of cerebellar volumetric gray matter (GM) loss across the adult lifespan in a large cross-sectional sample. Four hundred and seventy-nine healthy participants (age range: 7-86 years) were drawn from the Brain Resource International Database who provided T1-weighted MRI scans. The spatially unbiased infratentorial template (SUIT) toolbox in SPM8 was used for normalisation of the cerebellum structures. Global volumetric and voxel-based morphometry analyses were performed to evaluate age-associated trends and gender-specific age-patterns. Global cerebellar GM shows a cross-sectional reduction with advancing age of 2.5 % per decade-approximately half the rate seen in the whole brain. The male cerebellum is larger with a lower percentage of GM, however, after controlling for total brain volume, no gender difference was detected. Analysis of age-related changes in GM volume revealed large bilateral clusters involving the vermis and cerebellar crus where regional loss occurred at nearly twice the average cerebellar rate. No gender-specific patterns were detected. These data confirm that regionally specific GM loss occurs in the cerebellum with age, and form a solid base for further investigation to find functional correlates for this global and focal loss.

White matter structural connectivity is associated with sensorimotor function in stroke survivors☆

PubMed Central

Kalinosky, Benjamin T.; Schindler-Ivens, Sheila; Schmit, Brian D.

2013-01-01

Purpose Diffusion tensor imaging (DTI) provides functionally relevant information about white matter structure. Local anatomical connectivity information combined with fractional anisotropy (FA) and mean diffusivity (MD) may predict functional outcomes in stroke survivors. Imaging methods for predicting functional outcomes in stroke survivors are not well established. This work uses DTI to objectively assess the effects of a stroke lesion on white matter structure and sensorimotor function. Methods A voxel-based approach is introduced to assess a stroke lesion's global impact on motor function. Anatomical T1-weighted and diffusion tensor images of the brain were acquired for nineteen subjects (10 post-stroke and 9 age-matched controls). A manually selected volume of interest was used to alleviate the effects of stroke lesions on image registration. Images from all subjects were registered to the images of the control subject that was anatomically closest to Talairach space. Each subject's transformed image was uniformly seeded for DTI tractography. Each seed was inversely transformed into the individual subject space, where DTI tractography was conducted and then the results were transformed back to the reference space. A voxel-wise connectivity matrix was constructed from the fibers, which was then used to calculate the number of directly and indirectly connected neighbors of each voxel. A novel voxel-wise indirect structural connectivity (VISC) index was computed as the average number of direct connections to a voxel's indirect neighbors. Voxel-based analyses (VBA) were performed to compare VISC, FA, and MD for the detection of lesion-induced changes in sensorimotor function. For each voxel, a t-value was computed from the differences between each stroke brain and the 9 controls. A series of linear regressions was performed between Fugl-Meyer (FM) assessment scores of sensorimotor impairment and each DTI metric's log number of voxels that differed from the control group. Results Correlation between the logarithm of the number of significant voxels in the ipsilesional hemisphere and total Fugl-Meyer score was moderate for MD (R2 = 0.512), and greater for VISC (R2 = 0.796) and FA (R2 = 0.674). The slopes of FA (p = 0.0036), VISC (p = 0.0005), and MD (p = 0.0199) versus the total FM score were significant. However, these correlations were driven by the upper extremity motor component of the FM score (VISC: R2 = 0.879) with little influence of the lower extremity motor component (FA: R2 = 0.177). Conclusion The results suggest that a voxel-wise metric based on DTI tractography can predict upper extremity sensorimotor function of stroke survivors, and that supraspinal intraconnectivity may have a less dominant role in lower extremity function. PMID:24179827

Simultaneous Multi-Scale Diffusion Estimation and Tractography Guided by Entropy Spectrum Pathways

PubMed Central

Galinsky, Vitaly L.; Frank, Lawrence R.

2015-01-01

We have developed a method for the simultaneous estimation of local diffusion and the global fiber tracts based upon the information entropy flow that computes the maximum entropy trajectories between locations and depends upon the global structure of the multi-dimensional and multi-modal diffusion field. Computation of the entropy spectrum pathways requires only solving a simple eigenvector problem for the probability distribution for which efficient numerical routines exist, and a straight forward integration of the probability conservation through ray tracing of the convective modes guided by a global structure of the entropy spectrum coupled with a small scale local diffusion. The intervoxel diffusion is sampled by multi b-shell multi q-angle DWI data expanded in spherical waves. This novel approach to fiber tracking incorporates global information about multiple fiber crossings in every individual voxel and ranks it in the most scientifically rigorous way. This method has potential significance for a wide range of applications, including studies of brain connectivity. PMID:25532167

The partial volume effect in the quantification of 1H magnetic resonance spectroscopy in Alzheimer's disease and aging.

PubMed

Mato Abad, Virginia; Quirós, Alicia; García-Álvarez, Roberto; Loureiro, Javier Pereira; Alvarez-Linera, Juan; Frank, Ana; Hernández-Tamames, Juan Antonio

2014-01-01

1H-MRS variability increases due to normal aging and also as a result of atrophy in grey and white matter caused by neurodegeneration. In this work, an automatic process was developed to integrate data from spectra and high-resolution anatomical images to quantify metabolites, taking into account tissue partial volumes within the voxel of interest avoiding additional spectra acquisitions required for partial volume correction. To evaluate this method, we use a cohort of 135 subjects (47 male and 88 female, aged between 57 and 99 years) classified into 4 groups: 38 healthy participants, 20 amnesic mild cognitive impairment patients, 22 multi-domain mild cognitive impairment patients, and 55 Alzheimer's disease patients. Our findings suggest that knowing the voxel composition of white and grey matter and cerebrospinal fluid is necessary to avoid partial volume variations in a single-voxel study and to decrease part of the variability found in metabolites quantification, particularly in those studies involving elder patients and neurodegenerative diseases. The proposed method facilitates the use of 1H-MRS techniques in statistical studies in Alzheimer's disease, because it provides more accurate quantitative measurements, reduces the inter-subject variability, and improves statistical results when performing group comparisons.

Regional patterns of grey matter atrophy and magnetisation transfer ratio abnormalities in multiple sclerosis clinical subgroups: a voxel-based analysis study.

PubMed

Mallik, Shahrukh; Muhlert, Nils; Samson, Rebecca S; Sethi, Varun; Wheeler-Kingshott, Claudia A M; Miller, David H; Chard, Declan T

2015-04-01

In multiple sclerosis (MS), demyelination and neuro-axonal loss occur in the brain grey matter (GM). We used magnetic resonance imaging (MRI) measures of GM magnetisation transfer ratio (MTR) and volume to assess the regional localisation of reduced MTR (reflecting demyelination) and atrophy (reflecting neuro-axonal loss) in relapsing-remitting MS (RRMS), secondary progressive MS (SPMS) and primary progressive MS (PPMS). A total of 98 people with MS (51 RRMS, 28 SPMS, 19 PPMS) and 29 controls had T1-weighted volumetric and magnetisation transfer scans. SPM8 was used to undertake voxel-based analysis (VBA) of GM tissue volumes and MTR. MS subgroups were compared with controls, adjusting for age and gender. A voxel-by-voxel basis correlation analysis between MTR and volume within each subject group was performed, using biological parametric mapping. MTR reduction was more extensive than atrophy. RRMS and SPMS patients showed proportionately more atrophy in the deep GM. SPMS and PPMS patients showed proportionately greater cortical MTR reduction. RRMS patients demonstrated the most correlation of MTR reduction and atrophy in deep GM. In SPMS and PPMS patients, there was less extensive correlation. These results suggest that in the deep GM of RRMS patients, demyelination and neuro-axonal loss may be linked, while in SPMS and PPMS patients, neuro-axonal loss and demyelination may occur mostly independently. © The Author(s), 2014.

Stimulus-related independent component and voxel-wise analysis of human brain activity during free viewing of a feature film.

PubMed

Lahnakoski, Juha M; Salmi, Juha; Jääskeläinen, Iiro P; Lampinen, Jouko; Glerean, Enrico; Tikka, Pia; Sams, Mikko

2012-01-01

Understanding how the brain processes stimuli in a rich natural environment is a fundamental goal of neuroscience. Here, we showed a feature film to 10 healthy volunteers during functional magnetic resonance imaging (fMRI) of hemodynamic brain activity. We then annotated auditory and visual features of the motion picture to inform analysis of the hemodynamic data. The annotations were fitted to both voxel-wise data and brain network time courses extracted by independent component analysis (ICA). Auditory annotations correlated with two independent components (IC) disclosing two functional networks, one responding to variety of auditory stimulation and another responding preferentially to speech but parts of the network also responding to non-verbal communication. Visual feature annotations correlated with four ICs delineating visual areas according to their sensitivity to different visual stimulus features. In comparison, a separate voxel-wise general linear model based analysis disclosed brain areas preferentially responding to sound energy, speech, music, visual contrast edges, body motion and hand motion which largely overlapped the results revealed by ICA. Differences between the results of IC- and voxel-based analyses demonstrate that thorough analysis of voxel time courses is important for understanding the activity of specific sub-areas of the functional networks, while ICA is a valuable tool for revealing novel information about functional connectivity which need not be explained by the predefined model. Our results encourage the use of naturalistic stimuli and tasks in cognitive neuroimaging to study how the brain processes stimuli in rich natural environments.

Stimulus-Related Independent Component and Voxel-Wise Analysis of Human Brain Activity during Free Viewing of a Feature Film

PubMed Central

Lahnakoski, Juha M.; Salmi, Juha; Jääskeläinen, Iiro P.; Lampinen, Jouko; Glerean, Enrico; Tikka, Pia; Sams, Mikko

2012-01-01

Understanding how the brain processes stimuli in a rich natural environment is a fundamental goal of neuroscience. Here, we showed a feature film to 10 healthy volunteers during functional magnetic resonance imaging (fMRI) of hemodynamic brain activity. We then annotated auditory and visual features of the motion picture to inform analysis of the hemodynamic data. The annotations were fitted to both voxel-wise data and brain network time courses extracted by independent component analysis (ICA). Auditory annotations correlated with two independent components (IC) disclosing two functional networks, one responding to variety of auditory stimulation and another responding preferentially to speech but parts of the network also responding to non-verbal communication. Visual feature annotations correlated with four ICs delineating visual areas according to their sensitivity to different visual stimulus features. In comparison, a separate voxel-wise general linear model based analysis disclosed brain areas preferentially responding to sound energy, speech, music, visual contrast edges, body motion and hand motion which largely overlapped the results revealed by ICA. Differences between the results of IC- and voxel-based analyses demonstrate that thorough analysis of voxel time courses is important for understanding the activity of specific sub-areas of the functional networks, while ICA is a valuable tool for revealing novel information about functional connectivity which need not be explained by the predefined model. Our results encourage the use of naturalistic stimuli and tasks in cognitive neuroimaging to study how the brain processes stimuli in rich natural environments. PMID:22496909

Comparison of a non-stationary voxelation-corrected cluster-size test with TFCE for group-Level MRI inference.

PubMed

Li, Huanjie; Nickerson, Lisa D; Nichols, Thomas E; Gao, Jia-Hong

2017-03-01

Two powerful methods for statistical inference on MRI brain images have been proposed recently, a non-stationary voxelation-corrected cluster-size test (CST) based on random field theory and threshold-free cluster enhancement (TFCE) based on calculating the level of local support for a cluster, then using permutation testing for inference. Unlike other statistical approaches, these two methods do not rest on the assumptions of a uniform and high degree of spatial smoothness of the statistic image. Thus, they are strongly recommended for group-level fMRI analysis compared to other statistical methods. In this work, the non-stationary voxelation-corrected CST and TFCE methods for group-level analysis were evaluated for both stationary and non-stationary images under varying smoothness levels, degrees of freedom and signal to noise ratios. Our results suggest that, both methods provide adequate control for the number of voxel-wise statistical tests being performed during inference on fMRI data and they are both superior to current CSTs implemented in popular MRI data analysis software packages. However, TFCE is more sensitive and stable for group-level analysis of VBM data. Thus, the voxelation-corrected CST approach may confer some advantages by being computationally less demanding for fMRI data analysis than TFCE with permutation testing and by also being applicable for single-subject fMRI analyses, while the TFCE approach is advantageous for VBM data. Hum Brain Mapp 38:1269-1280, 2017. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

Altered structural covariance of the striatum in functional dyspepsia patients.

PubMed

Liu, P; Zeng, F; Yang, F; Wang, J; Liu, X; Wang, Q; Zhou, G; Zhang, D; Zhu, M; Zhao, R; Wang, A; Gong, Q; Liang, F

2014-08-01

Functional dyspepsia (FD) is thought to be involved in dysregulation within the brain-gut axis. Recently, altered striatum activation has been reported in patients with FD. However, the gray matter (GM) volumes in the striatum and structural covariance patterns of this area are rarely explored. The purpose of this study was to examine the GM volumes and structural covariance patterns of the striatum between FD patients and healthy controls (HCs). T1-weighted magnetic resonance images were obtained from 44 FD patients and 39 HCs. Voxel-based morphometry (VBM) analysis was adopted to examine the GM volumes in the two groups. The caudate- or putamen-related regions identified from VBM analysis were then used as seeds to map the whole brain voxel-wise structural covariance patterns. Finally, a correlation analysis was used to investigate the effects of FD symptoms on the striatum. The results showed increased GM volumes in the bilateral putamen and right caudate. Compared with the structural covariance patterns of the HCs, the FD-related differences were mainly located in the amygdala, hippocampus/parahippocampus (HIPP/paraHIPP), thalamus, lingual gyrus, and cerebellum. And significant positive correlations were found between the volumes in the striatum and the FD duration in the patients. These findings provided preliminary evidence for GM changes in the striatum and different structural covariance patterns in patients with FD. The current results might expand our understanding of the pathophysiology of FD. © 2014 John Wiley & Sons Ltd.

Spatially aggregated multiclass pattern classification in functional MRI using optimally selected functional brain areas.

PubMed

Zheng, Weili; Ackley, Elena S; Martínez-Ramón, Manel; Posse, Stefan

2013-02-01

In previous works, boosting aggregation of classifier outputs from discrete brain areas has been demonstrated to reduce dimensionality and improve the robustness and accuracy of functional magnetic resonance imaging (fMRI) classification. However, dimensionality reduction and classification of mixed activation patterns of multiple classes remain challenging. In the present study, the goals were (a) to reduce dimensionality by combining feature reduction at the voxel level and backward elimination of optimally aggregated classifiers at the region level, (b) to compare region selection for spatially aggregated classification using boosting and partial least squares regression methods and (c) to resolve mixed activation patterns using probabilistic prediction of individual tasks. Brain activation maps from interleaved visual, motor, auditory and cognitive tasks were segmented into 144 functional regions. Feature selection reduced the number of feature voxels by more than 50%, leaving 95 regions. The two aggregation approaches further reduced the number of regions to 30, resulting in more than 75% reduction of classification time and misclassification rates of less than 3%. Boosting and partial least squares (PLS) were compared to select the most discriminative and the most task correlated regions, respectively. Successful task prediction in mixed activation patterns was feasible within the first block of task activation in real-time fMRI experiments. This methodology is suitable for sparsifying activation patterns in real-time fMRI and for neurofeedback from distributed networks of brain activation. Copyright © 2013 Elsevier Inc. All rights reserved.

Neural evidence of the strategic choice between working memory and episodic memory in prospective remembering.

PubMed

Lewis-Peacock, Jarrod A; Cohen, Jonathan D; Norman, Kenneth A

2016-12-01

Theories of prospective memory (PM) posit that it can be subserved either by working memory (WM) or episodic memory (EM). Testing and refining these multiprocess theories of PM requires a way of tracking participants' reliance on WM versus EM. Here we use multi-voxel pattern analysis (MVPA) to derive a trial-by-trial measure of WM use in prospective memory. We manipulated strategy demands by varying the degree of proactive interference (which impairs EM) and the memory load required to perform the secondary task (which impairs WM). For the condition in which participants were pushed to rely more on WM, our MVPA measures showed 1) greater WM use and 2) a trial-by-trial correlation between WM use and PM behavior. Finally, we also showed that MVPA measures of WM use are not redundant with other behavioral measures: in the condition in which participants were pushed more to rely on WM, using neural and behavioral measures together led to better prediction of PM accuracy than either measure on its own. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

The neural representation of objects formed through the spatiotemporal integration of visual transients

PubMed Central

Erlikhman, Gennady; Gurariy, Gennadiy; Mruczek, Ryan E.B.; Caplovitz, Gideon P.

2016-01-01

Oftentimes, objects are only partially and transiently visible as parts of them become occluded during observer or object motion. The visual system can integrate such object fragments across space and time into perceptual wholes or spatiotemporal objects. This integrative and dynamic process may involve both ventral and dorsal visual processing pathways, along which shape and spatial representations are thought to arise. We measured fMRI BOLD response to spatiotemporal objects and used multi-voxel pattern analysis (MVPA) to decode shape information across 20 topographic regions of visual cortex. Object identity could be decoded throughout visual cortex, including intermediate (V3A, V3B, hV4, LO1-2,) and dorsal (TO1-2, and IPS0-1) visual areas. Shape-specific information, therefore, may not be limited to early and ventral visual areas, particularly when it is dynamic and must be integrated. Contrary to the classic view that the representation of objects is the purview of the ventral stream, intermediate and dorsal areas may play a distinct and critical role in the construction of object representations across space and time. PMID:27033688

- and Scene-Guided Integration of Tls and Photogrammetric Point Clouds for Landslide Monitoring

NASA Astrophysics Data System (ADS)

Zieher, T.; Toschi, I.; Remondino, F.; Rutzinger, M.; Kofler, Ch.; Mejia-Aguilar, A.; Schlögel, R.

2018-05-01

Terrestrial and airborne 3D imaging sensors are well-suited data acquisition systems for the area-wide monitoring of landslide activity. State-of-the-art surveying techniques, such as terrestrial laser scanning (TLS) and photogrammetry based on unmanned aerial vehicle (UAV) imagery or terrestrial acquisitions have advantages and limitations associated with their individual measurement principles. In this study we present an integration approach for 3D point clouds derived from these techniques, aiming at improving the topographic representation of landslide features while enabling a more accurate assessment of landslide-induced changes. Four expert-based rules involving local morphometric features computed from eigenvectors, elevation and the agreement of the individual point clouds, are used to choose within voxels of selectable size which sensor's data to keep. Based on the integrated point clouds, digital surface models and shaded reliefs are computed. Using an image correlation technique, displacement vectors are finally derived from the multi-temporal shaded reliefs. All results show comparable patterns of landslide movement rates and directions. However, depending on the applied integration rule, differences in spatial coverage and correlation strength emerge.

Dietary patterns, insulin sensitivity and adiposity in the multi-ethnic Insulin Resistance Atherosclerosis Study population.

PubMed

Liese, Angela D; Schulz, Mandy; Moore, Charity G; Mayer-Davis, Elizabeth J

2004-12-01

Epidemiological investigations increasingly employ dietary-pattern techniques to fully integrate dietary data. The present study evaluated the relationship of dietary patterns identified by cluster analysis with measures of insulin sensitivity (SI) and adiposity in the multi-ethnic, multi-centre Insulin Resistance Atherosclerosis Study (IRAS, 1992-94). Cross-sectional data from 980 middle-aged adults, of whom 67 % had normal and 33 % had impaired glucose tolerance, were analysed. Usual dietary intake was obtained by an interviewer-administered, validated food-frequency questionnaire. Outcomes included SI, fasting insulin (FI), BMI and waist circumference. The relationship of dietary patterns to log(SI+1), log(FI), BMI and waist circumference was modelled with multivariable linear regressions. Cluster analysis identified six distinct diet patterns--'dark bread', 'wine', 'fruits', 'low-frequency eaters', 'fries' and 'white bread'. The 'white bread' and the 'fries' patterns over-represented the Hispanic IRAS population predominantly from two centres, while the 'wine' and 'dark bread' groups were dominated by non-Hispanic whites. The dietary patterns were associated significantly with each of the outcomes first at the crude, clinical level (P<0.001). Furthermore, they were significantly associated with FI, BMI and waist circumference independent of age, sex, race or ethnicity, clinic, family history of diabetes, smoking and activity (P<0.004), whereas significance was lost for SI. Studying the total dietary behaviour via a pattern approach allowed us to focus both on the qualitative and quantitative dimensions of diet. The present study identified highly consistent associations of distinct dietary patterns with measures of insulin resistance and adiposity, which are risk factors for diabetes and heart disease.

Sci-Thur AM: YIS – 08: Automated Imaging Quality Assurance for Image-Guided Small Animal Irradiators

DOE Office of Scientific and Technical Information (OSTI.GOV)

Johnstone, Chris; Bazalova-Carter, Magdalena

Purpose: To develop quality assurance (QA) standards and tolerance levels for image quality of small animal irradiators. Methods: A fully automated in-house QA software for image analysis of a commercial microCT phantom was created. Quantitative analyses of CT linearity, signal-to-noise ratio (SNR), uniformity and noise, geometric accuracy, modulation transfer function (MTF), and CT number evaluation was performed. Phantom microCT scans from seven institutions acquired with varying parameters (kVp, mA, time, voxel size, and frame rate) and five irradiator units (Xstrahl SARRP, PXI X-RAD 225Cx, PXI X-RAD SmART, GE explore CT/RT 140, and GE Explore CT 120) were analyzed. Multi-institutional datamore » sets were compared using our in-house software to establish pass/fail criteria for each QA test. Results: CT linearity (R2>0.996) was excellent at all but Institution 2. Acceptable SNR (>35) and noise levels (<55HU) were obtained at four of the seven institutions, where failing scans were acquired with less than 120mAs. Acceptable MTF (>1.5 lp/mm for MTF=0.2) was obtained at all but Institution 6 due to the largest scan voxel size (0.35mm). The geometric accuracy passed (<1.5%) at five of the seven institutions. Conclusion: Our QA software can be used to rapidly perform quantitative imaging QA for small animal irradiators, accumulate results over time, and display possible changes in imaging functionality from its original performance and/or from the recommended tolerance levels. This tool will aid researchers in maintaining high image quality, enabling precise conformal dose delivery to small animals.« less

Prospective multi-centre Voxel Based Morphometry study employing scanner specific segmentations: Procedure development using CaliBrain structural MRI data

PubMed Central

2009-01-01

Background Structural Magnetic Resonance Imaging (sMRI) of the brain is employed in the assessment of a wide range of neuropsychiatric disorders. In order to improve statistical power in such studies it is desirable to pool scanning resources from multiple centres. The CaliBrain project was designed to provide for an assessment of scanner differences at three centres in Scotland, and to assess the practicality of pooling scans from multiple-centres. Methods We scanned healthy subjects twice on each of the 3 scanners in the CaliBrain project with T1-weighted sequences. The tissue classifier supplied within the Statistical Parametric Mapping (SPM5) application was used to map the grey and white tissue for each scan. We were thus able to assess within scanner variability and between scanner differences. We have sought to correct for between scanner differences by adjusting the probability mappings of tissue occupancy (tissue priors) used in SPM5 for tissue classification. The adjustment procedure resulted in separate sets of tissue priors being developed for each scanner and we refer to these as scanner specific priors. Results Voxel Based Morphometry (VBM) analyses and metric tests indicated that the use of scanner specific priors reduced tissue classification differences between scanners. However, the metric results also demonstrated that the between scanner differences were not reduced to the level of within scanner variability, the ideal for scanner harmonisation. Conclusion Our results indicate the development of scanner specific priors for SPM can assist in pooling of scan resources from different research centres. This can facilitate improvements in the statistical power of quantitative brain imaging studies. PMID:19445668

Exploring connectivity with large-scale Granger causality on resting-state functional MRI.

PubMed

DSouza, Adora M; Abidin, Anas Z; Leistritz, Lutz; Wismüller, Axel

2017-08-01

Large-scale Granger causality (lsGC) is a recently developed, resting-state functional MRI (fMRI) connectivity analysis approach that estimates multivariate voxel-resolution connectivity. Unlike most commonly used multivariate approaches, which establish coarse-resolution connectivity by aggregating voxel time-series avoiding an underdetermined problem, lsGC estimates voxel-resolution, fine-grained connectivity by incorporating an embedded dimension reduction. We investigate application of lsGC on realistic fMRI simulations, modeling smoothing of neuronal activity by the hemodynamic response function and repetition time (TR), and empirical resting-state fMRI data. Subsequently, functional subnetworks are extracted from lsGC connectivity measures for both datasets and validated quantitatively. We also provide guidelines to select lsGC free parameters. Results indicate that lsGC reliably recovers underlying network structure with area under receiver operator characteristic curve (AUC) of 0.93 at TR=1.5s for a 10-min session of fMRI simulations. Furthermore, subnetworks of closely interacting modules are recovered from the aforementioned lsGC networks. Results on empirical resting-state fMRI data demonstrate recovery of visual and motor cortex in close agreement with spatial maps obtained from (i) visuo-motor fMRI stimulation task-sequence (Accuracy=0.76) and (ii) independent component analysis (ICA) of resting-state fMRI (Accuracy=0.86). Compared with conventional Granger causality approach (AUC=0.75), lsGC produces better network recovery on fMRI simulations. Furthermore, it cannot recover functional subnetworks from empirical fMRI data, since quantifying voxel-resolution connectivity is not possible as consequence of encountering an underdetermined problem. Functional network recovery from fMRI data suggests that lsGC gives useful insight into connectivity patterns from resting-state fMRI at a multivariate voxel-resolution. Copyright © 2017 Elsevier B.V. All rights reserved.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Frølich, S.; Leemreize, H.; Jakus, A.

A model sample consisting of two different hydroxyapatite (hAp) powders was used as a bone phantom to investigate the extent to which X-ray diffraction tomography could map differences in hAp lattice constants and crystallite size. The diffraction data were collected at beamline 1-ID, the Advanced Photon Source, using monochromatic 65 keV X-radiation, a 25 × 25 µm pinhole beam and translation/rotation data collection. The diffraction pattern was reconstructed for each volume element (voxel) in the sample, and Rietveld refinement was used to determine the hAp lattice constants. The crystallite size for each voxel was also determined from the 00.2 hApmore » diffraction peak width. The results clearly show that differences between hAp powders could be measured with diffraction tomography.« less

Relationship between BOLD amplitude and pattern classification of orientation-selective activity in the human visual cortex.

PubMed

Tong, Frank; Harrison, Stephenie A; Dewey, John A; Kamitani, Yukiyasu

2012-11-15

Orientation-selective responses can be decoded from fMRI activity patterns in the human visual cortex, using multivariate pattern analysis (MVPA). To what extent do these feature-selective activity patterns depend on the strength and quality of the sensory input, and might the reliability of these activity patterns be predicted by the gross amplitude of the stimulus-driven BOLD response? Observers viewed oriented gratings that varied in luminance contrast (4, 20 or 100%) or spatial frequency (0.25, 1.0 or 4.0 cpd). As predicted, activity patterns in early visual areas led to better discrimination of orientations presented at high than low contrast, with greater effects of contrast found in area V1 than in V3. A second experiment revealed generally better decoding of orientations at low or moderate as compared to high spatial frequencies. Interestingly however, V1 exhibited a relative advantage at discriminating high spatial frequency orientations, consistent with the finer scale of representation in the primary visual cortex. In both experiments, the reliability of these orientation-selective activity patterns was well predicted by the average BOLD amplitude in each region of interest, as indicated by correlation analyses, as well as decoding applied to a simple model of voxel responses to simulated orientation columns. Moreover, individual differences in decoding accuracy could be predicted by the signal-to-noise ratio of an individual's BOLD response. Our results indicate that decoding accuracy can be well predicted by incorporating the amplitude of the BOLD response into simple simulation models of cortical selectivity; such models could prove useful in future applications of fMRI pattern classification. Copyright © 2012 Elsevier Inc. All rights reserved.

Relationship between BOLD amplitude and pattern classification of orientation-selective activity in the human visual cortex

PubMed Central

Tong, Frank; Harrison, Stephenie A.; Dewey, John A.; Kamitani, Yukiyasu

2012-01-01

Orientation-selective responses can be decoded from fMRI activity patterns in the human visual cortex, using multivariate pattern analysis (MVPA). To what extent do these feature-selective activity patterns depend on the strength and quality of the sensory input, and might the reliability of these activity patterns be predicted by the gross amplitude of the stimulus-driven BOLD response? Observers viewed oriented gratings that varied in luminance contrast (4, 20 or 100%) or spatial frequency (0.25, 1.0 or 4.0 cpd). As predicted, activity patterns in early visual areas led to better discrimination of orientations presented at high than low contrast, with greater effects of contrast found in area V1 than in V3. A second experiment revealed generally better decoding of orientations at low or moderate as compared to high spatial frequencies. Interestingly however, V1 exhibited a relative advantage at discriminating high spatial frequency orientations, consistent with the finer scale of representation in the primary visual cortex. In both experiments, the reliability of these orientation-selective activity patterns was well predicted by the average BOLD amplitude in each region of interest, as indicated by correlation analyses, as well as decoding applied to a simple model of voxel responses to simulated orientation columns. Moreover, individual differences in decoding accuracy could be predicted by the signal-to-noise ratio of an individual's BOLD response. Our results indicate that decoding accuracy can be well predicted by incorporating the amplitude of the BOLD response into simple simulation models of cortical selectivity; such models could prove useful in future applications of fMRI pattern classification. PMID:22917989

Voxel-Space Ambient Occlusion

DTIC Science & Technology

2012-02-01

a collection of information if it does not display a currently valid OMB control number. PLEASE DO NOT RETURN YOUR FORM TO THE ABOVE ADDRESS. a...represents the BRDF of the surface material, for incoming direction ψ and an outgoing direction. L(y) is the incoming radiance in the direction ψ from a...10-1-0338). Models come from the Stanford repository. References [BS09] L. Bavoil, M.Sainz Multi-layer dual-resolution screen-space ambient occlusion

Impact of Strategically Located White Matter Hyperintensities on Cognition in Memory Clinic Patients with Small Vessel Disease

PubMed Central

Hilal, Saima; Kuijf, Hugo J.; Ikram, Mohammad Kamran; Xu, Xin; Tan, Boon Yeow; Venketasubramanian, Narayanaswamy; Postma, Albert; Biessels, Geert Jan; Chen, Christopher P. L. H.

2016-01-01

Background and Purpose Studies on the impact of small vessel disease (SVD) on cognition generally focus on white matter hyperintensity (WMH) volume. The extent to which WMH location relates to cognitive performance has received less attention, but is likely to be functionally important. We examined the relation between WMH location and cognition in a memory clinic cohort of patients with sporadic SVD. Methods A total of 167 patients with SVD were recruited from memory clinics. Assumption-free region of interest-based analyses based on major white matter tracts and voxel-wise analyses were used to determine the association between WMH location and executive functioning, visuomotor speed and memory. Results Region of interest-based analyses showed that WMHs located particularly within the anterior thalamic radiation and forceps minor were inversely associated with both executive functioning and visuomotor speed, independent of total WMH volume. Memory was significantly associated with WMH volume in the forceps minor, independent of total WMH volume. An independent assumption-free voxel-wise analysis identified strategic voxels in these same tracts. Region of interest-based analyses showed that WMH volume within the anterior thalamic radiation explained 6.8% of variance in executive functioning, compared to 3.9% for total WMH volume; WMH volume within the forceps minor explained 4.6% of variance in visuomotor speed and 4.2% of variance in memory, compared to 1.8% and 1.3% respectively for total WMH volume. Conclusions Our findings identify the anterior thalamic radiation and forceps minor as strategic white matter tracts in which WMHs are most strongly associated with cognitive impairment in memory clinic patients with SVD. WMH volumes in individual tracts explained more variance in cognition than total WMH burden, emphasizing the importance of lesion location when addressing the functional consequences of WMHs. PMID:27824925

VBM-DTI correlates of verbal intelligence: a potential link to Broca's area.

PubMed

Konrad, Andreas; Vucurevic, Goran; Musso, Francesco; Winterer, Georg

2012-04-01

Human brain lesion studies first investigated the biological roots of cognitive functions including language in the late 1800s. Neuroimaging studies have reported correlation findings with general intelligence predominantly in fronto-parietal cortical areas. However, there is still little evidence about the relationship between verbal intelligence and structural properties of the brain. We predicted that verbal performance is related to language regions of Broca's and Wernicke's areas. Verbal intelligence quotient (vIQ) was assessed in 30 healthy young subjects. T1-weighted MRI and diffusion tensor imaging data sets were acquired. Voxel-wise regression analyses were used to correlate fractional anisotropy (FA) and mean diffusivity values with vIQ. Moreover, regression analyses of regional brain volume with vIQ were performed adopting voxel-based morphometry (VBM) and ROI methodology. Our analyses revealed a significant negative correlation between vIQ and FA and a significant positive correlation between vIQ and mean diffusivity in the left-hemispheric Broca's area. VBM regression analyses did not show significant results, whereas a subsequent ROI analysis of Broca's area FA peak cluster demonstrated a positive correlation of gray matter volume and vIQ. These findings suggest that cortical thickness in Broca's area contributes to verbal intelligence. Diffusion parameters predicted gray matter ratio in Broca's area more sensitive than VBM methodology.

Students' Self-Identified Long-Term Leadership Development Goals: An Analysis by Gender and Race

ERIC Educational Resources Information Center

Rosch, David M.; Boyd, Barry L.; Duran, Kristina M.

2014-01-01

Leadership development goal statements of 92 undergraduate students enrolled in a multi-year self-directed leadership development program were analyzed using content and thematic analyses to investigate patterns of similarities and differences across gender and race. This qualitative analysis utilized a theoretical framework that approached…

Species-level assessment of secondary metabolite diversity among Hamigera species and a taxonomic note on the genus

USDA-ARS?s Scientific Manuscript database

Secondary metabolite phenotypes in nine species of the Hamigera clade were analysed to assess their correlations to a multi-gene species-level phylogeny. High-pressure-liquid-chromatography-based chemical analysis revealed three distinctive patterns of secondary metabolite production: (1) the nine s...

Decoding the Traumatic Memory among Women with PTSD: Implications for Neurocircuitry Models of PTSD and Real-Time fMRI Neurofeedback

PubMed Central

Cisler, Josh M.; Bush, Keith; James, G. Andrew; Smitherman, Sonet; Kilts, Clinton D.

2015-01-01

Posttraumatic Stress Disorder (PTSD) is characterized by intrusive recall of the traumatic memory. While numerous studies have investigated the neural processing mechanisms engaged during trauma memory recall in PTSD, these analyses have only focused on group-level contrasts that reveal little about the predictive validity of the identified brain regions. By contrast, a multivariate pattern analysis (MVPA) approach towards identifying the neural mechanisms engaged during trauma memory recall would entail testing whether a multivariate set of brain regions is reliably predictive of (i.e., discriminates) whether an individual is engaging in trauma or non-trauma memory recall. Here, we use a MVPA approach to test 1) whether trauma memory vs neutral memory recall can be predicted reliably using a multivariate set of brain regions among women with PTSD related to assaultive violence exposure (N=16), 2) the methodological parameters (e.g., spatial smoothing, number of memory recall repetitions, etc.) that optimize classification accuracy and reproducibility of the feature weight spatial maps, and 3) the correspondence between brain regions that discriminate trauma memory recall and the brain regions predicted by neurocircuitry models of PTSD. Cross-validation classification accuracy was significantly above chance for all methodological permutations tested; mean accuracy across participants was 76% for the methodological parameters selected as optimal for both efficiency and accuracy. Classification accuracy was significantly better for a voxel-wise approach relative to voxels within restricted regions-of-interest (ROIs); classification accuracy did not differ when using PTSD-related ROIs compared to randomly generated ROIs. ROI-based analyses suggested the reliable involvement of the left hippocampus in discriminating memory recall across participants and that the contribution of the left amygdala to the decision function was dependent upon PTSD symptom severity. These results have methodological implications for real-time fMRI neurofeedback of the trauma memory in PTSD and conceptual implications for neurocircuitry models of PTSD that attempt to explain core neural processing mechanisms mediating PTSD. PMID:26241958

Decoding the Traumatic Memory among Women with PTSD: Implications for Neurocircuitry Models of PTSD and Real-Time fMRI Neurofeedback.

PubMed

Cisler, Josh M; Bush, Keith; James, G Andrew; Smitherman, Sonet; Kilts, Clinton D

2015-01-01

Posttraumatic Stress Disorder (PTSD) is characterized by intrusive recall of the traumatic memory. While numerous studies have investigated the neural processing mechanisms engaged during trauma memory recall in PTSD, these analyses have only focused on group-level contrasts that reveal little about the predictive validity of the identified brain regions. By contrast, a multivariate pattern analysis (MVPA) approach towards identifying the neural mechanisms engaged during trauma memory recall would entail testing whether a multivariate set of brain regions is reliably predictive of (i.e., discriminates) whether an individual is engaging in trauma or non-trauma memory recall. Here, we use a MVPA approach to test 1) whether trauma memory vs neutral memory recall can be predicted reliably using a multivariate set of brain regions among women with PTSD related to assaultive violence exposure (N=16), 2) the methodological parameters (e.g., spatial smoothing, number of memory recall repetitions, etc.) that optimize classification accuracy and reproducibility of the feature weight spatial maps, and 3) the correspondence between brain regions that discriminate trauma memory recall and the brain regions predicted by neurocircuitry models of PTSD. Cross-validation classification accuracy was significantly above chance for all methodological permutations tested; mean accuracy across participants was 76% for the methodological parameters selected as optimal for both efficiency and accuracy. Classification accuracy was significantly better for a voxel-wise approach relative to voxels within restricted regions-of-interest (ROIs); classification accuracy did not differ when using PTSD-related ROIs compared to randomly generated ROIs. ROI-based analyses suggested the reliable involvement of the left hippocampus in discriminating memory recall across participants and that the contribution of the left amygdala to the decision function was dependent upon PTSD symptom severity. These results have methodological implications for real-time fMRI neurofeedback of the trauma memory in PTSD and conceptual implications for neurocircuitry models of PTSD that attempt to explain core neural processing mechanisms mediating PTSD.

Study on the characteristics of multi-infeed HVDC

NASA Astrophysics Data System (ADS)

Li, Ming; Song, Xinli; Liu, Wenzhuo; Xiang, Yinxing; Zhao, Shutao; Su, Zhida; Meng, Hang

2017-09-01

China has built more than ten HVDC transmission projects in recent years [1]. Now, east China has formed a multi-HVDC feed pattern grid. It is imminent to study the interaction of the multi-HVDC and the characteristics of it. In this paper, an electromechanical-electromagnetic hybrid model is built with electromechanical data of a certain power network. We use electromagnetic models to simulate the HVDC section and electromechanical models simulate the AC power network [2]. In order to study the characteristics of the grid, this paper adds some faults to the line and analysed the fault characteristics. At last give analysis of the fault characteristics.

Implementation errors in the GingerALE Software: Description and recommendations.

PubMed

Eickhoff, Simon B; Laird, Angela R; Fox, P Mickle; Lancaster, Jack L; Fox, Peter T

2017-01-01

Neuroscience imaging is a burgeoning, highly sophisticated field the growth of which has been fostered by grant-funded, freely distributed software libraries that perform voxel-wise analyses in anatomically standardized three-dimensional space on multi-subject, whole-brain, primary datasets. Despite the ongoing advances made using these non-commercial computational tools, the replicability of individual studies is an acknowledged limitation. Coordinate-based meta-analysis offers a practical solution to this limitation and, consequently, plays an important role in filtering and consolidating the enormous corpus of functional and structural neuroimaging results reported in the peer-reviewed literature. In both primary data and meta-analytic neuroimaging analyses, correction for multiple comparisons is a complex but critical step for ensuring statistical rigor. Reports of errors in multiple-comparison corrections in primary-data analyses have recently appeared. Here, we report two such errors in GingerALE, a widely used, US National Institutes of Health (NIH)-funded, freely distributed software package for coordinate-based meta-analysis. These errors have given rise to published reports with more liberal statistical inferences than were specified by the authors. The intent of this technical report is threefold. First, we inform authors who used GingerALE of these errors so that they can take appropriate actions including re-analyses and corrective publications. Second, we seek to exemplify and promote an open approach to error management. Third, we discuss the implications of these and similar errors in a scientific environment dependent on third-party software. Hum Brain Mapp 38:7-11, 2017. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

Multi-criteria Resource Mapping and its Relevance in the Assessment of Habitat Changes

NASA Astrophysics Data System (ADS)

Van Lancker, V. R.; Kint, L.; van Heteren, S.

2016-02-01

Mineral and geological resources can be considered to be non-renewable on time scales relevant for decision makers. Once exhausted by humans, they are not replenished rapidly enough by nature, meaning that truly sustainable resource exploitation is not possible. Comprehensive knowledge on the distribution, composition and dynamics of geological resources and on the environmental impact of aggregate extraction is therefore critical. For the Belgian and southern Netherlands part of the North Sea, being representative of a typical sandbank system, a 4D resource decision-support system is being developed that links 3D geological models with environmental impact models. Aim is to quantify natural and man-made changes and to define from these sustainable exploitation thresholds. These are needed to ensure that recovery from perturbations is rapid and secure, and that the range of natural variation is maintained, a prerequisite stated in Europe's Marine Strategy Framework Directive, the environmental pillar of Europe's Maritime Policy. The geological subsurface is parameterised using a voxel modelling approach. Primarily, the voxels, or volume blocks of information, are constrained by the geology, based on coring and seismic data, but they are open to any resource-relevant information. The primary geological data entering the voxels are subdued to uncertainty modelling, a necessary step to produce data products with confidence limits. The presentation will focus on the novelty this approach brings for seabed and habitat mapping. In our model this is the upper voxel, providing the advantage of having a dynamical coupling to the geology and a suite of environmental parameters. In the context of assessing habitat changes, this coupling enables to account for spatial and temporal variability, seabed heterogeneity, as well as data uncertainty. The project is funded by Belgian Science Policy and is further valorised through EMODnet-Geology (DG MARE).

Interactive lesion segmentation on dynamic contrast enhanced breast MRI using a Markov model

NASA Astrophysics Data System (ADS)

Wu, Qiu; Salganicoff, Marcos; Krishnan, Arun; Fussell, Donald S.; Markey, Mia K.

2006-03-01

The purpose of this study is to develop a method for segmenting lesions on Dynamic Contrast-Enhanced (DCE) breast MRI. DCE breast MRI, in which the breast is imaged before, during, and after the administration of a contrast agent, enables a truly 3D examination of breast tissues. This functional angiogenic imaging technique provides noninvasive assessment of microcirculatory characteristics of tissues in addition to traditional anatomical structure information. Since morphological features and kinetic curves from segmented lesions are to be used for diagnosis and treatment decisions, lesion segmentation is a key pre-processing step for classification. In our study, the ROI is defined by a bounding box containing the enhancement region in the subtraction image, which is generated by subtracting the pre-contrast image from 1st post-contrast image. A maximum a posteriori (MAP) estimate of the class membership (lesion vs. non-lesion) for each voxel is obtained using the Iterative Conditional Mode (ICM) method. The prior distribution of the class membership is modeled as a multi-level logistic model, a Markov Random Field model in which the class membership of each voxel is assumed to depend upon its nearest neighbors only. The likelihood distribution is assumed to be Gaussian. The parameters of each Gaussian distribution are estimated from a dozen voxels manually selected as representative of the class. The experimental segmentation results demonstrate anatomically plausible breast tissue segmentation and the predicted class membership of voxels from the interactive segmentation algorithm agrees with the manual classifications made by inspection of the kinetic enhancement curves. The proposed method is advantageous in that it is efficient, flexible, and robust.

Domain Selectivity in the Parahippocampal Gyrus Is Predicted by the Same Structural Connectivity Patterns in Blind and Sighted Individuals.

PubMed

Wang, Xiaoying; He, Chenxi; Peelen, Marius V; Zhong, Suyu; Gong, Gaolang; Caramazza, Alfonso; Bi, Yanchao

2017-05-03

Human ventral occipital temporal cortex contains clusters of neurons that show domain-preferring responses during visual perception. Recent studies have reported that some of these clusters show surprisingly similar domain selectivity in congenitally blind participants performing nonvisual tasks. An important open question is whether these functional similarities are driven by similar innate connections in blind and sighted groups. Here we addressed this question focusing on the parahippocampal gyrus (PHG), a region that is selective for large objects and scenes. Based on the assumption that patterns of long-range connectivity shape local computation, we examined whether domain selectivity in PHG is driven by similar structural connectivity patterns in the two populations. Multiple regression models were built to predict the selectivity of PHG voxels for large human-made objects from white matter (WM) connectivity patterns in both groups. These models were then tested using independent data from participants with similar visual experience (two sighted groups) and using data from participants with different visual experience (blind and sighted groups). Strikingly, the WM-based predictions between blind and sighted groups were as successful as predictions between two independent sighted groups. That is, the functional selectivity for large objects of a PHG voxel in a blind participant could be accurately predicted by its WM pattern using the connection-to-function model built from the sighted group data, and vice versa. Regions that significantly predicted PHG selectivity were located in temporal and frontal cortices in both sighted and blind populations. These results show that the large-scale network driving domain selectivity in PHG is independent of vision. SIGNIFICANCE STATEMENT Recent studies have reported intriguingly similar domain selectivity in sighted and congenitally blind individuals in regions within the ventral visual cortex. To examine whether these similarities originate from similar innate connectional roots, we investigated whether the domain selectivity in one population could be predicted by the structural connectivity pattern of the other. We found that the selectivity for large objects of a PHG voxel in a blind participant could be predicted by its structural connectivity pattern using the connection-to-function model built from the sighted group data, and vice versa. These results reveal that the structural connectivity underlying domain selectivity in the PHG is independent of visual experience, providing evidence for nonvisual representations in this region. Copyright © 2017 the authors 0270-6474/17/374706-12$15.00/0.

Right fusiform response patterns reflect visual object identity rather than semantic similarity.

PubMed

Bruffaerts, Rose; Dupont, Patrick; De Grauwe, Sophie; Peeters, Ronald; De Deyne, Simon; Storms, Gerrit; Vandenberghe, Rik

2013-12-01

We previously reported the neuropsychological consequences of a lesion confined to the middle and posterior part of the right fusiform gyrus (case JA) causing a partial loss of knowledge of visual attributes of concrete entities in the absence of category-selectivity (animate versus inanimate). We interpreted this in the context of a two-step model that distinguishes structural description knowledge from associative-semantic processing and implicated the lesioned area in the former process. To test this hypothesis in the intact brain, multi-voxel pattern analysis was used in a series of event-related fMRI studies in a total of 46 healthy subjects. We predicted that activity patterns in this region would be determined by the identity of rather than the conceptual similarity between concrete entities. In a prior behavioral experiment features were generated for each entity by more than 1000 subjects. Based on a hierarchical clustering analysis the entities were organised into 3 semantic clusters (musical instruments, vehicles, tools). Entities were presented as words or pictures. With foveal presentation of pictures, cosine similarity between fMRI response patterns in right fusiform cortex appeared to reflect both the identity of and the semantic similarity between the entities. No such effects were found for words in this region. The effect of object identity was invariant for location, scaling, orientation axis and color (grayscale versus color). It also persisted for different exemplars referring to a same concrete entity. The apparent semantic similarity effect however was not invariant. This study provides further support for a neurobiological distinction between structural description knowledge and processing of semantic relationships and confirms the role of right mid-posterior fusiform cortex in the former process, in accordance with previous lesion evidence. © 2013.

Automated measurement and classification of pulmonary blood-flow velocity patterns using phase-contrast MRI and correlation analysis.

PubMed

van Amerom, Joshua F P; Kellenberger, Christian J; Yoo, Shi-Joon; Macgowan, Christopher K

2009-01-01

An automated method was evaluated to detect blood flow in small pulmonary arteries and classify each as artery or vein, based on a temporal correlation analysis of their blood-flow velocity patterns. The method was evaluated using velocity-sensitive phase-contrast magnetic resonance data collected in vitro with a pulsatile flow phantom and in vivo in 11 human volunteers. The accuracy of the method was validated in vitro, which showed relative velocity errors of 12% at low spatial resolution (four voxels per diameter), but was reduced to 5% at increased spatial resolution (16 voxels per diameter). The performance of the method was evaluated in vivo according to its reproducibility and agreement with manual velocity measurements by an experienced radiologist. In all volunteers, the correlation analysis was able to detect and segment peripheral pulmonary vessels and distinguish arterial from venous velocity patterns. The intrasubject variability of repeated measurements was approximately 10% of peak velocity, or 2.8 cm/s root-mean-variance, demonstrating the high reproducibility of the method. Excellent agreement was obtained between the correlation analysis and radiologist measurements of pulmonary velocities, with a correlation of R2=0.98 (P<.001) and a slope of 0.99+/-0.01.

68Ga-HBED-CC-PSMA PET/CT Versus Histopathology in Primary Localized Prostate Cancer: A Voxel-Wise Comparison

PubMed Central

Zamboglou, Constantinos; Schiller, Florian; Fechter, Tobias; Wieser, Gesche; Jilg, Cordula Annette; Chirindel, Alin; Salman, Nasr; Drendel, Vanessa; Werner, Martin; Mix, Michael; Meyer, Philipp Tobias; Grosu, Anca Ligia

2016-01-01

Purpose: We performed a voxel-wise comparison of 68Ga-HBED-CC-PSMA PET/CT with prostate histopathology to evaluate the performance of 68Ga-HBED-CC-PSMA for the detection and delineation of primary prostate cancer (PCa). Methodology: Nine patients with histopathological proven primary PCa underwent 68Ga-HBED-CC-PSMA PET/CT followed by radical prostatectomy. Resected prostates were scanned by ex-vivo CT in a special localizer and histopathologically prepared. Histopathological information was matched to ex-vivo CT. PCa volume (PCa-histo) and non-PCa tissue in the prostate (NPCa-histo) were processed to obtain a PCa-model, which was adjusted to PET-resolution (histo-PET). Each histo-PET was coregistered to in-vivo PSMA-PET/CT data. Results: Analysis of spatial overlap between histo-PET and PSMA PET revealed highly significant correlations (p < 10-5) in nine patients and moderate to high coefficients of determination (R²) from 42 to 82 % with an average of 60 ± 14 % in eight patients (in one patient R2 = 7 %). Mean SUVmean in PCa-histo and NPCa-histo was 5.6 ± 6.1 and 3.3 ± 2.5 (p = 0.012). Voxel-wise receiver-operating characteristic (ROC) analyses comparing the prediction by PSMA-PET with the non-smoothed tumor distribution from histopathology yielded an average area under the curve of 0.83 ± 0.12. Absolute and relative SUV (normalized to SUVmax) thresholds for achieving at least 90 % sensitivity were 3.19 ± 3.35 and 0.28 ± 0.09, respectively. Conclusions: Voxel-wise analyses revealed good correlations of 68Ga-HBED-CC-PSMA PET/CT and histopathology in eight out of nine patients. Thus, PSMA-PET allows a reliable detection and delineation of PCa as basis for PET-guided focal therapies. PMID:27446496

The brain in myotonic dystrophy 1 and 2: evidence for a predominant white matter disease

PubMed Central

Weber, Bernd; Schoene-Bake, Jan-Christoph; Roeske, Sandra; Mirbach, Sandra; Anspach, Christian; Schneider-Gold, Christiane; Betz, Regina C.; Helmstaedter, Christoph; Tittgemeyer, Marc; Klockgether, Thomas; Kornblum, Cornelia

2011-01-01

Myotonic dystrophy types 1 and 2 are progressive multisystemic disorders with potential brain involvement. We compared 22 myotonic dystrophy type 1 and 22 myotonic dystrophy type 2 clinically and neuropsychologically well-characterized patients and a corresponding healthy control group using structural brain magnetic resonance imaging at 3 T (T1/T2/diffusion-weighted). Voxel-based morphometry and diffusion tensor imaging with tract-based spatial statistics were applied for voxel-wise analysis of cerebral grey and white matter affection (Pcorrected < 0.05). We further examined the association of structural brain changes with clinical and neuropsychological data. White matter lesions rated visually were more prevalent and severe in myotonic dystrophy type 1 compared with controls, with frontal white matter most prominently affected in both disorders, and temporal lesions restricted to myotonic dystrophy type 1. Voxel-based morphometry analyses demonstrated extensive white matter involvement in all cerebral lobes, brainstem and corpus callosum in myotonic dystrophy types 1 and 2, while grey matter decrease (cortical areas, thalamus, putamen) was restricted to myotonic dystrophy type 1. Accordingly, we found more prominent white matter affection in myotonic dystrophy type 1 than myotonic dystrophy type 2 by diffusion tensor imaging. Association fibres throughout the whole brain, limbic system fibre tracts, the callosal body and projection fibres (e.g. internal/external capsules) were affected in myotonic dystrophy types 1 and 2. Central motor pathways were exclusively impaired in myotonic dystrophy type 1. We found mild executive and attentional deficits in our patients when neuropsychological tests were corrected for manual motor dysfunctioning. Regression analyses revealed associations of white matter affection with several clinical parameters in both disease entities, but not with neuropsychological performance. We showed that depressed mood and fatigue were more prominent in patients with myotonic dystrophy type 1 with less white matter affection (early disease stages), contrary to patients with myotonic dystrophy type 2. Thus, depression in myotonic dystrophies might be a reactive adjustment disorder rather than a direct consequence of structural brain damage. Associations of white matter affection with age/disease duration as well as patterns of cerebral water diffusion parameters pointed towards an ongoing process of myelin destruction and/or axonal loss in our cross-sectional study design. Our data suggest that both myotonic dystrophy types 1 and 2 are serious white matter diseases with prominent callosal body and limbic system affection. White matter changes dominated the extent of grey matter changes, which might argue against Wallerian degeneration as the major cause of white matter affection in myotonic dystrophies. PMID:22131273

Evaluating Climate Causation of Conflict in Darfur Using Multi-temporal, Multi-resolution Satellite Image Datasets With Novel Analyses

NASA Astrophysics Data System (ADS)

Brown, I.; Wennbom, M.

2013-12-01

Climate change, population growth and changes in traditional lifestyles have led to instabilities in traditional demarcations between neighboring ethic and religious groups in the Sahel region. This has resulted in a number of conflicts as groups resort to arms to settle disputes. Such disputes often centre on or are justified by competition for resources. The conflict in Darfur has been controversially explained by resource scarcity resulting from climate change. Here we analyse established methods of using satellite imagery to assess vegetation health in Darfur. Multi-decadal time series of observations are available using low spatial resolution visible-near infrared imagery. Typically normalized difference vegetation index (NDVI) analyses are produced to describe changes in vegetation ';greenness' or ';health'. Such approaches have been widely used to evaluate the long term development of vegetation in relation to climate variations across a wide range of environments from the Arctic to the Sahel. These datasets typically measure peak NDVI observed over a given interval and may introduce bias. It is furthermore unclear how the spatial organization of sparse vegetation may affect low resolution NDVI products. We develop and assess alternative measures of vegetation including descriptors of the growing season, wetness and resource availability. Expanding the range of parameters used in the analysis reduces our dependence on peak NDVI. Furthermore, these descriptors provide a better characterization of the growing season than the single NDVI measure. Using multi-sensor data we combine high temporal/moderate spatial resolution data with low temporal/high spatial resolution data to improve the spatial representativity of the observations and to provide improved spatial analysis of vegetation patterns. The approach places the high resolution observations in the NDVI context space using a longer time series of lower resolution imagery. The vegetation descriptors derived are evaluated using independent high spatial resolution datasets that reveal the pattern and health of vegetation at metre scales. We also use climate variables to support the interpretation of these data. We conclude that the spatio-temporal patterns in Darfur vegetation and climate datasets suggest that labelling the conflict a climate-change conflict is inaccurate and premature.

Identification of a common neurobiological substrate for mental illness.

PubMed

Goodkind, Madeleine; Eickhoff, Simon B; Oathes, Desmond J; Jiang, Ying; Chang, Andrew; Jones-Hagata, Laura B; Ortega, Brissa N; Zaiko, Yevgeniya V; Roach, Erika L; Korgaonkar, Mayuresh S; Grieve, Stuart M; Galatzer-Levy, Isaac; Fox, Peter T; Etkin, Amit

2015-04-01

Psychiatric diagnoses are currently distinguished based on sets of specific symptoms. However, genetic and clinical analyses find similarities across a wide variety of diagnoses, suggesting that a common neurobiological substrate may exist across mental illness. To conduct a meta-analysis of structural neuroimaging studies across multiple psychiatric diagnoses, followed by parallel analyses of 3 large-scale healthy participant data sets to help interpret structural findings in the meta-analysis. PubMed was searched to identify voxel-based morphometry studies through July 2012 comparing psychiatric patients to healthy control individuals for the meta-analysis. The 3 parallel healthy participant data sets included resting-state functional magnetic resonance imaging, a database of activation foci across thousands of neuroimaging experiments, and a data set with structural imaging and cognitive task performance data. Studies were included in the meta-analysis if they reported voxel-based morphometry differences between patients with an Axis I diagnosis and control individuals in stereotactic coordinates across the whole brain, did not present predominantly in childhood, and had at least 10 studies contributing to that diagnosis (or across closely related diagnoses). The meta-analysis was conducted on peak voxel coordinates using an activation likelihood estimation approach. We tested for areas of common gray matter volume increase or decrease across Axis I diagnoses, as well as areas differing between diagnoses. Follow-up analyses on other healthy participant data sets tested connectivity related to regions arising from the meta-analysis and the relationship of gray matter volume to cognition. Based on the voxel-based morphometry meta-analysis of 193 studies comprising 15 892 individuals across 6 diverse diagnostic groups (schizophrenia, bipolar disorder, depression, addiction, obsessive-compulsive disorder, and anxiety), we found that gray matter loss converged across diagnoses in 3 regions: the dorsal anterior cingulate, right insula, and left insula. By contrast, there were few diagnosis-specific effects, distinguishing only schizophrenia and depression from other diagnoses. In the parallel follow-up analyses of the 3 independent healthy participant data sets, we found that the common gray matter loss regions formed a tightly interconnected network during tasks and at resting and that lower gray matter in this network was associated with poor executive functioning. We identified a concordance across psychiatric diagnoses in terms of integrity of an anterior insula/dorsal anterior cingulate-based network, which may relate to executive function deficits observed across diagnoses. This concordance provides an organizing model that emphasizes the importance of shared neural substrates across psychopathology, despite likely diverse etiologies, which is currently not an explicit component of psychiatric nosology.

Selective activation around the left occipito-temporal sulcus for words relative to pictures: individual variability or false positives?

PubMed

Wright, Nicholas D; Mechelli, Andrea; Noppeney, Uta; Veltman, Dick J; Rombouts, Serge A R B; Glensman, Janice; Haynes, John-Dylan; Price, Cathy J

2008-08-01

We used high-resolution fMRI to investigate claims that learning to read results in greater left occipito-temporal (OT) activation for written words relative to pictures of objects. In the first experiment, 9/16 subjects performing a one-back task showed activation in > or =1 left OT voxel for words relative to pictures (P < 0.05 uncorrected). In a second experiment, another 9/15 subjects performing a semantic decision task activated > or =1 left OT voxel for words relative to pictures. However, at this low statistical threshold false positives need to be excluded. The semantic decision paradigm was therefore repeated, within subject, in two different scanners (1.5 and 3 T). Both scanners consistently localised left OT activation for words relative to fixation and pictures relative to words, but there were no consistent effects for words relative to pictures. Finally, in a third experiment, we minimised the voxel size (1.5 x 1.5 x 1.5 mm(3)) and demonstrated a striking concordance between the voxels activated for words and pictures, irrespective of task (naming vs. one-back) or script (English vs. Hebrew). In summary, although we detected differential activation for words relative to pictures, these effects: (i) do not withstand statistical rigour; (ii) do not replicate within or between subjects; and (iii) are observed in voxels that also respond to pictures of objects. Our findings have implications for the role of left OT activation during reading. More generally, they show that studies using low statistical thresholds in single subject analyses should correct the statistical threshold for the number of comparisons made or replicate effects within subject. (c) 2007 Wiley-Liss, Inc.

Selective Activation Around the Left Occipito-Temporal Sulcus for Words Relative to Pictures: Individual Variability or False Positives?

PubMed Central

Wright, Nicholas D; Mechelli, Andrea; Noppeney, Uta; Veltman, Dick J; Rombouts, Serge ARB; Glensman, Janice; Haynes, John-Dylan; Price, Cathy J

2008-01-01

We used high-resolution fMRI to investigate claims that learning to read results in greater left occipito-temporal (OT) activation for written words relative to pictures of objects. In the first experiment, 9/16 subjects performing a one-back task showed activation in ≥1 left OT voxel for words relative to pictures (P < 0.05 uncorrected). In a second experiment, another 9/15 subjects performing a semantic decision task activated ≥1 left OT voxel for words relative to pictures. However, at this low statistical threshold false positives need to be excluded. The semantic decision paradigm was therefore repeated, within subject, in two different scanners (1.5 and 3 T). Both scanners consistently localised left OT activation for words relative to fixation and pictures relative to words, but there were no consistent effects for words relative to pictures. Finally, in a third experiment, we minimised the voxel size (1.5 × 1.5 × 1.5 mm3) and demonstrated a striking concordance between the voxels activated for words and pictures, irrespective of task (naming vs. one-back) or script (English vs. Hebrew). In summary, although we detected differential activation for words relative to pictures, these effects: (i) do not withstand statistical rigour; (ii) do not replicate within or between subjects; and (iii) are observed in voxels that also respond to pictures of objects. Our findings have implications for the role of left OT activation during reading. More generally, they show that studies using low statistical thresholds in single subject analyses should correct the statistical threshold for the number of comparisons made or replicate effects within subject. Hum Brain Mapp 2008. © 2007 Wiley-Liss, Inc. PMID:17712786

Distinct patterns of brain atrophy in Genetic Frontotemporal Dementia Initiative (GENFI) cohort revealed by visual rating scales.

PubMed

Fumagalli, Giorgio G; Basilico, Paola; Arighi, Andrea; Bocchetta, Martina; Dick, Katrina M; Cash, David M; Harding, Sophie; Mercurio, Matteo; Fenoglio, Chiara; Pietroboni, Anna M; Ghezzi, Laura; van Swieten, John; Borroni, Barbara; de Mendonça, Alexandre; Masellis, Mario; Tartaglia, Maria C; Rowe, James B; Graff, Caroline; Tagliavini, Fabrizio; Frisoni, Giovanni B; Laforce, Robert; Finger, Elizabeth; Sorbi, Sandro; Scarpini, Elio; Rohrer, Jonathan D; Galimberti, Daniela

2018-05-24

In patients with frontotemporal dementia, it has been shown that brain atrophy occurs earliest in the anterior cingulate, insula and frontal lobes. We used visual rating scales to investigate whether identifying atrophy in these areas may be helpful in distinguishing symptomatic patients carrying different causal mutations in the microtubule-associated protein tau (MAPT), progranulin (GRN) and chromosome 9 open reading frame (C9ORF72) genes. We also analysed asymptomatic carriers to see whether it was possible to visually identify brain atrophy before the appearance of symptoms. Magnetic resonance imaging of 343 subjects (63 symptomatic mutation carriers, 132 presymptomatic mutation carriers and 148 control subjects) from the Genetic Frontotemporal Dementia Initiative study were analysed by two trained raters using a protocol of six visual rating scales that identified atrophy in key regions of the brain (orbitofrontal, anterior cingulate, frontoinsula, anterior and medial temporal lobes and posterior cortical areas). Intra- and interrater agreement were greater than 0.73 for all the scales. Voxel-based morphometric analysis demonstrated a strong correlation between the visual rating scale scores and grey matter atrophy in the same region for each of the scales. Typical patterns of atrophy were identified: symmetric anterior and medial temporal lobe involvement for MAPT, asymmetric frontal and parietal loss for GRN, and a more widespread pattern for C9ORF72. Presymptomatic MAPT carriers showed greater atrophy in the medial temporal region than control subjects, but the visual rating scales could not identify presymptomatic atrophy in GRN or C9ORF72 carriers. These simple-to-use and reproducible scales may be useful tools in the clinical setting for the discrimination of different mutations of frontotemporal dementia, and they may even help to identify atrophy prior to onset in those with MAPT mutations.

Adaptive kernel regression for freehand 3D ultrasound reconstruction

NASA Astrophysics Data System (ADS)

Alshalalfah, Abdel-Latif; Daoud, Mohammad I.; Al-Najar, Mahasen

2017-03-01

Freehand three-dimensional (3D) ultrasound imaging enables low-cost and flexible 3D scanning of arbitrary-shaped organs, where the operator can freely move a two-dimensional (2D) ultrasound probe to acquire a sequence of tracked cross-sectional images of the anatomy. Often, the acquired 2D ultrasound images are irregularly and sparsely distributed in the 3D space. Several 3D reconstruction algorithms have been proposed to synthesize 3D ultrasound volumes based on the acquired 2D images. A challenging task during the reconstruction process is to preserve the texture patterns in the synthesized volume and ensure that all gaps in the volume are correctly filled. This paper presents an adaptive kernel regression algorithm that can effectively reconstruct high-quality freehand 3D ultrasound volumes. The algorithm employs a kernel regression model that enables nonparametric interpolation of the voxel gray-level values. The kernel size of the regression model is adaptively adjusted based on the characteristics of the voxel that is being interpolated. In particular, when the algorithm is employed to interpolate a voxel located in a region with dense ultrasound data samples, the size of the kernel is reduced to preserve the texture patterns. On the other hand, the size of the kernel is increased in areas that include large gaps to enable effective gap filling. The performance of the proposed algorithm was compared with seven previous interpolation approaches by synthesizing freehand 3D ultrasound volumes of a benign breast tumor. The experimental results show that the proposed algorithm outperforms the other interpolation approaches.

Lesion identification using unified segmentation-normalisation models and fuzzy clustering

PubMed Central

Seghier, Mohamed L.; Ramlackhansingh, Anil; Crinion, Jenny; Leff, Alexander P.; Price, Cathy J.

2008-01-01

In this paper, we propose a new automated procedure for lesion identification from single images based on the detection of outlier voxels. We demonstrate the utility of this procedure using artificial and real lesions. The scheme rests on two innovations: First, we augment the generative model used for combined segmentation and normalization of images, with an empirical prior for an atypical tissue class, which can be optimised iteratively. Second, we adopt a fuzzy clustering procedure to identify outlier voxels in normalised gray and white matter segments. These two advances suppress misclassification of voxels and restrict lesion identification to gray/white matter lesions respectively. Our analyses show a high sensitivity for detecting and delineating brain lesions with different sizes, locations, and textures. Our approach has important implications for the generation of lesion overlap maps of a given population and the assessment of lesion-deficit mappings. From a clinical perspective, our method should help to compute the total volume of lesion or to trace precisely lesion boundaries that might be pertinent for surgical or diagnostic purposes. PMID:18482850

Quantification of Dynamic [18F]FDG Pet Studies in Acute Lung Injury.

PubMed

Grecchi, Elisabetta; Veronese, Mattia; Moresco, Rosa Maria; Bellani, Giacomo; Pesenti, Antonio; Messa, Cristina; Bertoldo, Alessandra

2016-02-01

This work aims to investigate lung glucose metabolism using 2-deoxy-2-[(18)F]fluoro-D-glucose ([(18)F]FDG) positron emission tomography (PET) imaging in acute lung injury (ALI) patients. Eleven ALI patients and five healthy controls underwent a dynamic [(18)F]FDG PET/X-ray computed tomography (CT) scan. The standardized uptake values (SUV) and three different methods for the quantification of glucose metabolism (i.e., ratio, Patlak, and spectral analysis iterative filter, SAIF) were applied both at the region and the voxel levels. SUV reported a lower correlation than the ratio with the net tracer uptake. Patlak and SAIF analyses did not show any significant spatial or quantitative (R(2) > 0.80) difference. The additional information provided by SAIF showed that in lung inflammation, elevated tracer uptake is coupled with abnormal tracer exchanges within and between lung tissue compartments. Full kinetic modeling provides a multi-parametric description of glucose metabolism in the lungs. This allows characterizing the spatial distribution of lung inflammation as well as returning the functional state of the tissues.

Brain 18F-FDG PET Metabolic Abnormalities in Patients with Long-Lasting Macrophagic Myofascitis.

PubMed

Van Der Gucht, Axel; Aoun Sebaiti, Mehdi; Guedj, Eric; Aouizerate, Jessie; Yara, Sabrina; Gherardi, Romain K; Evangelista, Eva; Chalaye, Julia; Cottereau, Anne-Ségolène; Verger, Antoine; Bachoud-Levi, Anne-Catherine; Abulizi, Mukedaisi; Itti, Emmanuel; Authier, François-Jérôme

2017-03-01

The aim of this study was to characterize brain metabolic abnormalities in patients with macrophagic myofascitis (MMF) and the relationship with cognitive dysfunction through the use of PET with 18 F-FDG. Methods: 18 F-FDG PET brain imaging and a comprehensive battery of neuropsychological tests were performed in 100 consecutive MMF patients (age [mean ± SD], 45.9 ± 12 y; 74% women). Images were analyzed with statistical parametric mapping (SPM12). Through the use of analysis of covariance, all 18 F-FDG PET brain images of MMF patients were compared with those of a reference population of 44 healthy subjects similar in age (45.4 ± 16 y; P = 0.87) and sex (73% women; P = 0.88). The neuropsychological assessment identified 4 categories of patients: those with no significant cognitive impairment ( n = 42), those with frontal subcortical (FSC) dysfunction ( n = 29), those with Papez circuit dysfunction ( n = 22), and those with callosal disconnection ( n = 7). Results: In comparison with healthy subjects, the whole population of patients with MMF exhibited a spatial pattern of cerebral glucose hypometabolism ( P < 0.001) involving the occipital lobes, temporal lobes, limbic system, cerebellum, and frontoparietal cortices, as shown by analysis of covariance. The subgroup of patients with FSC dysfunction exhibited a larger extent of involved areas (35,223 voxels vs. 13,680 voxels in the subgroup with Papez circuit dysfunction and 5,453 voxels in patients without cognitive impairment). Nonsignificant results were obtained for the last subgroup because of its small population size. Conclusion: Our study identified a peculiar spatial pattern of cerebral glucose hypometabolism that was most marked in MMF patients with FSC dysfunction. Further studies are needed to determine whether this pattern could represent a diagnostic biomarker of MMF in patients with chronic fatigue syndrome and cognitive dysfunction. © 2017 by the Society of Nuclear Medicine and Molecular Imaging.

Information Flow Between Resting-State Networks.

PubMed

Diez, Ibai; Erramuzpe, Asier; Escudero, Iñaki; Mateos, Beatriz; Cabrera, Alberto; Marinazzo, Daniele; Sanz-Arigita, Ernesto J; Stramaglia, Sebastiano; Cortes Diaz, Jesus M

2015-11-01

The resting brain dynamics self-organize into a finite number of correlated patterns known as resting-state networks (RSNs). It is well known that techniques such as independent component analysis can separate the brain activity at rest to provide such RSNs, but the specific pattern of interaction between RSNs is not yet fully understood. To this aim, we propose here a novel method to compute the information flow (IF) between different RSNs from resting-state magnetic resonance imaging. After hemodynamic response function blind deconvolution of all voxel signals, and under the hypothesis that RSNs define regions of interest, our method first uses principal component analysis to reduce dimensionality in each RSN to next compute IF (estimated here in terms of transfer entropy) between the different RSNs by systematically increasing k (the number of principal components used in the calculation). When k=1, this method is equivalent to computing IF using the average of all voxel activities in each RSN. For k≥1, our method calculates the k multivariate IF between the different RSNs. We find that the average IF among RSNs is dimension dependent, increasing from k=1 (i.e., the average voxel activity) up to a maximum occurring at k=5 and to finally decay to zero for k≥10. This suggests that a small number of components (close to five) is sufficient to describe the IF pattern between RSNs. Our method--addressing differences in IF between RSNs for any generic data--can be used for group comparison in health or disease. To illustrate this, we have calculated the inter-RSN IF in a data set of Alzheimer's disease (AD) to find that the most significant differences between AD and controls occurred for k=2, in addition to AD showing increased IF w.r.t. The spatial localization of the k=2 component, within RSNs, allows the characterization of IF differences between AD and controls.

Multi-parameter MRI in the 6-OPRI variant of inherited prion disease

PubMed Central

De Vita, Enrico; Ridgway, Gerard R.; Scahill, Rachael I; Caine, Diana; Rudge, Peter; Yousry, Tarek A; Mead, Simon; Collinge, John; Jäger, H R; Thornton, John S; Hyare, Harpreet

2013-01-01

Background and Purpose To define the distribution of cerebral volumetric and microstructural parenchymal tissue changes in a specific mutation within inherited human prion diseases (IPD) combining voxel-based morphometry (VBM) with voxel-based analysis (VBA) of cerebral magnetization transfer ratio (MTR) and mean diffusivity (MD). Materials and Methods VBM and VBA of cerebral MTR and MD were performed in 16 healthy controls and 9 patients with the 6-octapeptide repeat insertion (6-OPRI) mutation. An ANCOVA consisting of diagnostic grouping with age and total intracranial volume as covariates was performed. Results On VBM there was significant grey matter (GM) volume reduction in patients compared with controls in the basal ganglia, perisylvian cortex, lingual gyrus and precuneus. Significant MTR reduction and MD increases were more anatomically extensive than volume differences on VBM in the same cortical areas, but MTR and MD changes were not seen in the basal ganglia. Conclusions GM and WM changes were seen in brain areas associated with motor and cognitive functions known to be impaired in patients with the 6-OPRI mutation. There were some differences in the anatomical distribution of MTR-VBA and MDVBA changes compared to VBM, likely to reflect regional variations in the type and degree of the respective pathophysiological substrates. Combined analysis of complementary multi-parameter MRI data furthers our understanding of prion disease pathophysiology. PMID:23538406

Resting State Network Estimation in Individual Subjects

PubMed Central

Hacker, Carl D.; Laumann, Timothy O.; Szrama, Nicholas P.; Baldassarre, Antonello; Snyder, Abraham Z.

2014-01-01

Resting-state functional magnetic resonance imaging (fMRI) has been used to study brain networks associated with both normal and pathological cognitive function. The objective of this work is to reliably compute resting state network (RSN) topography in single participants. We trained a supervised classifier (multi-layer perceptron; MLP) to associate blood oxygen level dependent (BOLD) correlation maps corresponding to pre-defined seeds with specific RSN identities. Hard classification of maps obtained from a priori seeds was highly reliable across new participants. Interestingly, continuous estimates of RSN membership retained substantial residual error. This result is consistent with the view that RSNs are hierarchically organized, and therefore not fully separable into spatially independent components. After training on a priori seed-based maps, we propagated voxel-wise correlation maps through the MLP to produce estimates of RSN membership throughout the brain. The MLP generated RSN topography estimates in individuals consistent with previous studies, even in brain regions not represented in the training data. This method could be used in future studies to relate RSN topography to other measures of functional brain organization (e.g., task-evoked responses, stimulation mapping, and deficits associated with lesions) in individuals. The multi-layer perceptron was directly compared to two alternative voxel classification procedures, specifically, dual regression and linear discriminant analysis; the perceptron generated more spatially specific RSN maps than either alternative. PMID:23735260

Persistence of Amygdala-Hippocampal Connectivity and Multi-Voxel Correlation Structures During Awake Rest After Fear Learning Predicts Long-Term Expression of Fear.

PubMed

Hermans, Erno J; Kanen, Jonathan W; Tambini, Arielle; Fernández, Guillén; Davachi, Lila; Phelps, Elizabeth A

2017-05-01

After encoding, memories undergo a process of consolidation that determines long-term retention. For conditioned fear, animal models postulate that consolidation involves reactivations of neuronal assemblies supporting fear learning during postlearning "offline" periods. However, no human studies to date have investigated such processes, particularly in relation to long-term expression of fear. We tested 24 participants using functional MRI on 2 consecutive days in a fear conditioning paradigm involving 1 habituation block, 2 acquisition blocks, and 2 extinction blocks on day 1, and 2 re-extinction blocks on day 2. Conditioning blocks were preceded and followed by 4.5-min rest blocks. Strength of spontaneous recovery of fear on day 2 served as a measure of long-term expression of fear. Amygdala connectivity primarily with hippocampus increased progressively during postacquisition and postextinction rest on day 1. Intraregional multi-voxel correlation structures within amygdala and hippocampus sampled during a block of differential fear conditioning furthermore persisted after fear learning. Critically, both these main findings were stronger in participants who exhibited spontaneous recovery 24 h later. Our findings indicate that neural circuits activated during fear conditioning exhibit persistent postlearning activity that may be functionally relevant in promoting consolidation of the fear memory. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Regional patterns of grey matter atrophy and magnetisation transfer ratio abnormalities in multiple sclerosis clinical subgroups: A voxel-based analysis study

PubMed Central

Muhlert, Nils; Samson, Rebecca S; Sethi, Varun; Wheeler-Kingshott, Claudia AM; Miller, David H; Chard, Declan T

2015-01-01

Background: In multiple sclerosis (MS), demyelination and neuro-axonal loss occur in the brain grey matter (GM). We used magnetic resonance imaging (MRI) measures of GM magnetisation transfer ratio (MTR) and volume to assess the regional localisation of reduced MTR (reflecting demyelination) and atrophy (reflecting neuro-axonal loss) in relapsing–remitting MS (RRMS), secondary progressive MS (SPMS) and primary progressive MS (PPMS). Methods: A total of 98 people with MS (51 RRMS, 28 SPMS, 19 PPMS) and 29 controls had T1-weighted volumetric and magnetisation transfer scans. SPM8 was used to undertake voxel-based analysis (VBA) of GM tissue volumes and MTR. MS subgroups were compared with controls, adjusting for age and gender. A voxel-by-voxel basis correlation analysis between MTR and volume within each subject group was performed, using biological parametric mapping. Results: MTR reduction was more extensive than atrophy. RRMS and SPMS patients showed proportionately more atrophy in the deep GM. SPMS and PPMS patients showed proportionately greater cortical MTR reduction. RRMS patients demonstrated the most correlation of MTR reduction and atrophy in deep GM. In SPMS and PPMS patients, there was less extensive correlation. Conclusions: These results suggest that in the deep GM of RRMS patients, demyelination and neuro-axonal loss may be linked, while in SPMS and PPMS patients, neuro-axonal loss and demyelination may occur mostly independently. PMID:25145689

Actuating materials. Voxelated liquid crystal elastomers.

PubMed

Ware, Taylor H; McConney, Michael E; Wie, Jeong Jae; Tondiglia, Vincent P; White, Timothy J

2015-02-27

Dynamic control of shape can bring multifunctionality to devices. Soft materials capable of programmable shape change require localized control of the magnitude and directionality of a mechanical response. We report the preparation of soft, ordered materials referred to as liquid crystal elastomers. The direction of molecular order, known as the director, is written within local volume elements (voxels) as small as 0.0005 cubic millimeters. Locally, the director controls the inherent mechanical response (55% strain) within the material. In monoliths with spatially patterned director, thermal or chemical stimuli transform flat sheets into three-dimensional objects through controlled bending and stretching. The programmable mechanical response of these materials could yield monolithic multifunctional devices or serve as reconfigurable substrates for flexible devices in aerospace, medicine, or consumer goods. Copyright © 2015, American Association for the Advancement of Science.

Distributed neural signatures of natural audiovisual speech and music in the human auditory cortex.

PubMed

Salmi, Juha; Koistinen, Olli-Pekka; Glerean, Enrico; Jylänki, Pasi; Vehtari, Aki; Jääskeläinen, Iiro P; Mäkelä, Sasu; Nummenmaa, Lauri; Nummi-Kuisma, Katarina; Nummi, Ilari; Sams, Mikko

2017-08-15

During a conversation or when listening to music, auditory and visual information are combined automatically into audiovisual objects. However, it is still poorly understood how specific type of visual information shapes neural processing of sounds in lifelike stimulus environments. Here we applied multi-voxel pattern analysis to investigate how naturally matching visual input modulates supratemporal cortex activity during processing of naturalistic acoustic speech, singing and instrumental music. Bayesian logistic regression classifiers with sparsity-promoting priors were trained to predict whether the stimulus was audiovisual or auditory, and whether it contained piano playing, speech, or singing. The predictive performances of the classifiers were tested by leaving one participant at a time for testing and training the model using the remaining 15 participants. The signature patterns associated with unimodal auditory stimuli encompassed distributed locations mostly in the middle and superior temporal gyrus (STG/MTG). A pattern regression analysis, based on a continuous acoustic model, revealed that activity in some of these MTG and STG areas were associated with acoustic features present in speech and music stimuli. Concurrent visual stimulus modulated activity in bilateral MTG (speech), lateral aspect of right anterior STG (singing), and bilateral parietal opercular cortex (piano). Our results suggest that specific supratemporal brain areas are involved in processing complex natural speech, singing, and piano playing, and other brain areas located in anterior (facial speech) and posterior (music-related hand actions) supratemporal cortex are influenced by related visual information. Those anterior and posterior supratemporal areas have been linked to stimulus identification and sensory-motor integration, respectively. Copyright © 2017 Elsevier Inc. All rights reserved.

Disentangling visual imagery and perception of real-world objects

PubMed Central

Lee, Sue-Hyun; Kravitz, Dwight J.; Baker, Chris I.

2011-01-01

During mental imagery, visual representations can be evoked in the absence of “bottom-up” sensory input. Prior studies have reported similar neural substrates for imagery and perception, but studies of brain-damaged patients have revealed a double dissociation with some patients showing preserved imagery in spite of impaired perception and others vice versa. Here, we used fMRI and multi-voxel pattern analysis to investigate the specificity, distribution, and similarity of information for individual seen and imagined objects to try and resolve this apparent contradiction. In an event-related design, participants either viewed or imagined individual named object images on which they had been trained prior to the scan. We found that the identity of both seen and imagined objects could be decoded from the pattern of activity throughout the ventral visual processing stream. Further, there was enough correspondence between imagery and perception to allow discrimination of individual imagined objects based on the response during perception. However, the distribution of object information across visual areas was strikingly different during imagery and perception. While there was an obvious posterior-anterior gradient along the ventral visual stream for seen objects, there was an opposite gradient for imagined objects. Moreover, the structure of representations (i.e. the pattern of similarity between responses to all objects) was more similar during imagery than perception in all regions along the visual stream. These results suggest that while imagery and perception have similar neural substrates, they involve different network dynamics, resolving the tension between previous imaging and neuropsychological studies. PMID:22040738

Differential patterns of 2D location versus depth decoding along the visual hierarchy.

PubMed

Finlayson, Nonie J; Zhang, Xiaoli; Golomb, Julie D

2017-02-15

Visual information is initially represented as 2D images on the retina, but our brains are able to transform this input to perceive our rich 3D environment. While many studies have explored 2D spatial representations or depth perception in isolation, it remains unknown if or how these processes interact in human visual cortex. Here we used functional MRI and multi-voxel pattern analysis to investigate the relationship between 2D location and position-in-depth information. We stimulated different 3D locations in a blocked design: each location was defined by horizontal, vertical, and depth position. Participants remained fixated at the center of the screen while passively viewing the peripheral stimuli with red/green anaglyph glasses. Our results revealed a widespread, systematic transition throughout visual cortex. As expected, 2D location information (horizontal and vertical) could be strongly decoded in early visual areas, with reduced decoding higher along the visual hierarchy, consistent with known changes in receptive field sizes. Critically, we found that the decoding of position-in-depth information tracked inversely with the 2D location pattern, with the magnitude of depth decoding gradually increasing from intermediate to higher visual and category regions. Representations of 2D location information became increasingly location-tolerant in later areas, where depth information was also tolerant to changes in 2D location. We propose that spatial representations gradually transition from 2D-dominant to balanced 3D (2D and depth) along the visual hierarchy. Copyright © 2016 Elsevier Inc. All rights reserved.

FracPaQ: a MATLAB™ toolbox for the quantification of fracture patterns

NASA Astrophysics Data System (ADS)

Healy, David; Rizzo, Roberto; Farrell, Natalie; Watkins, Hannah; Cornwell, David; Gomez-Rivas, Enrique; Timms, Nick

2017-04-01

The patterns of fractures in deformed rocks are rarely uniform or random. Fracture orientations, sizes, shapes and spatial distributions often exhibit some kind of order. In detail, there may be relationships among the different fracture attributes e.g. small fractures dominated by one orientation, larger fractures by another. These relationships are important because the mechanical (e.g. strength, anisotropy) and transport (e.g. fluids, heat) properties of rock depend on these fracture patterns and fracture attributes. This presentation describes an open source toolbox to quantify fracture patterns, including distributions in fracture attributes and their spatial variation. Software has been developed to quantify fracture patterns from 2-D digital images, such as thin section micrographs, geological maps, outcrop or aerial photographs or satellite images. The toolbox comprises a suite of MATLAB™ scripts based on published quantitative methods for the analysis of fracture attributes: orientations, lengths, intensity, density and connectivity. An estimate of permeability in 2-D is made using a parallel plate model. The software provides an objective and consistent methodology for quantifying fracture patterns and their variations in 2-D across a wide range of length scales. Our current focus for the application of the software is on quantifying crack and fracture patterns in and around fault zones. There is a large body of published work on the quantification of relatively simple joint patterns, but fault zones present a bigger, and arguably more important, challenge. The methods presented are inherently scale independent, and a key task will be to analyse and integrate quantitative fracture pattern data from micro- to macro-scales. New features in this release include multi-scale analyses based on a wavelet method to look for scale transitions, support for multi-colour traces in the input file processed as separate fracture sets, and combining fracture traces from multiple 2-D images to derive the statistically equivalent 3-D fracture pattern expressed as a 2nd rank crack tensor.

Spatial scaling and multi-model inference in landscape genetics: Martes americana in northern Idaho

Treesearch

Tzeidle N. Wasserman; Samuel A. Cushman; Michael K. Schwartz; David O. Wallin

2010-01-01

Individual-based analyses relating landscape structure to genetic distances across complex landscapes enable rigorous evaluation of multiple alternative hypotheses linking landscape structure to gene flow. We utilize two extensions to increase the rigor of the individual-based causal modeling approach to inferring relationships between landscape patterns and gene flow...

Sparse PDF Volumes for Consistent Multi-Resolution Volume Rendering

PubMed Central

Sicat, Ronell; Krüger, Jens; Möller, Torsten; Hadwiger, Markus

2015-01-01

This paper presents a new multi-resolution volume representation called sparse pdf volumes, which enables consistent multi-resolution volume rendering based on probability density functions (pdfs) of voxel neighborhoods. These pdfs are defined in the 4D domain jointly comprising the 3D volume and its 1D intensity range. Crucially, the computation of sparse pdf volumes exploits data coherence in 4D, resulting in a sparse representation with surprisingly low storage requirements. At run time, we dynamically apply transfer functions to the pdfs using simple and fast convolutions. Whereas standard low-pass filtering and down-sampling incur visible differences between resolution levels, the use of pdfs facilitates consistent results independent of the resolution level used. We describe the efficient out-of-core computation of large-scale sparse pdf volumes, using a novel iterative simplification procedure of a mixture of 4D Gaussians. Finally, our data structure is optimized to facilitate interactive multi-resolution volume rendering on GPUs. PMID:26146475

Increased glutamate/GABA+ ratio in a shared autistic and schizotypal trait phenotype termed Social Disorganisation.

PubMed

Ford, Talitha C; Nibbs, Richard; Crewther, David P

2017-01-01

Autism and schizophrenia are multi-dimensional spectrum disorders that have substantial phenotypic overlap. This overlap is readily identified in the non-clinical population, and has been conceptualised as Social Disorganisation (SD). This study investigates the balance of excitatory glutamate and inhibitory γ -aminobutyric acid (GABA) concentrations in a non-clinical sample with high and low trait SD, as glutamate and GABA abnormalities are reported across the autism and schizophrenia spectrum disorders. Participants were 18 low (10 females) and 19 high (9 females) SD scorers aged 18 to 40 years who underwent 1 H-MRS for glutamate and GABA+macromolecule (GABA+) concentrations in right and left hemisphere superior temporal (ST) voxels. Reduced GABA+ concentration ( p  = 0.03) and increased glutamate/GABA+ ratio ( p  = 0.003) in the right ST voxel for the high SD group was found, and there was increased GABA+ concentration in the left compared to right ST voxel ( p  = 0.047). Bilateral glutamate concentration was increased for the high SD group ( p  = 0.006); there was no hemisphere by group interaction ( p  = 0.772). Results suggest that a higher expression of the SD phenotype may be associated with increased glutamate/GABA+ ratio in the right ST region, which may affect speech prosody processing, and lead behavioural characteristics that are shared within the autistic and schizotypal spectra.

An Multivariate Distance-Based Analytic Framework for Connectome-Wide Association Studies

PubMed Central

Shehzad, Zarrar; Kelly, Clare; Reiss, Philip T.; Craddock, R. Cameron; Emerson, John W.; McMahon, Katie; Copland, David A.; Castellanos, F. Xavier; Milham, Michael P.

2014-01-01

The identification of phenotypic associations in high-dimensional brain connectivity data represents the next frontier in the neuroimaging connectomics era. Exploration of brain-phenotype relationships remains limited by statistical approaches that are computationally intensive, depend on a priori hypotheses, or require stringent correction for multiple comparisons. Here, we propose a computationally efficient, data-driven technique for connectome-wide association studies (CWAS) that provides a comprehensive voxel-wise survey of brain-behavior relationships across the connectome; the approach identifies voxels whose whole-brain connectivity patterns vary significantly with a phenotypic variable. Using resting state fMRI data, we demonstrate the utility of our analytic framework by identifying significant connectivity-phenotype relationships for full-scale IQ and assessing their overlap with existent neuroimaging findings, as synthesized by openly available automated meta-analysis (www.neurosynth.org). The results appeared to be robust to the removal of nuisance covariates (i.e., mean connectivity, global signal, and motion) and varying brain resolution (i.e., voxelwise results are highly similar to results using 800 parcellations). We show that CWAS findings can be used to guide subsequent seed-based correlation analyses. Finally, we demonstrate the applicability of the approach by examining CWAS for three additional datasets, each encompassing a distinct phenotypic variable: neurotypical development, Attention-Deficit/Hyperactivity Disorder diagnostic status, and L-dopa pharmacological manipulation. For each phenotype, our approach to CWAS identified distinct connectome-wide association profiles, not previously attainable in a single study utilizing traditional univariate approaches. As a computationally efficient, extensible, and scalable method, our CWAS framework can accelerate the discovery of brain-behavior relationships in the connectome. PMID:24583255

Multimodal Magnetic Resonance Imaging in Alzheimer's Disease Patients at Prodromal Stage.

PubMed

Eustache, Pierre; Nemmi, Federico; Saint-Aubert, Laure; Pariente, Jeremie; Péran, Patrice

2016-01-01

One objective of modern neuroimaging is to identify markers that can aid in diagnosis, monitor disease progression, and impact long-term drug analysis. In this study, physiopathological modifications in seven subcortical structures of patients with mild cognitive impairment (MCI) due to Alzheimer's disease (AD) were characterized by simultaneously measuring quantitative magnetic resonance parameters that are sensitive to complementary tissue characteristics (e.g., volume atrophy, shape changes, microstructural damage, and iron deposition). Fourteen MCI patients and fourteen matched, healthy subjects underwent 3T-magnetic resonance imaging with whole-brain, T1-weighted, T2*-weighted, and diffusion-tensor imaging scans. Volume, shape, mean R2*, mean diffusivity (MD), and mean fractional anisotropy (FA) in the thalamus, hippocampus, putamen, amygdala, caudate nucleus, pallidum, and accumbens were compared between MCI patients and healthy subjects. Comparisons were then performed using voxel-based analyses of R2*, MD, FA maps, and voxel-based morphometry to determine which subregions showed the greatest difference for each parameter. With respect to the micro- and macro-structural patterns of damage, our results suggest that different and distinct physiopathological processes are present in the prodromal phase of AD. MCI patients had significant atrophy and microstructural changes within their hippocampi and amygdalae, which are known to be affected in the prodromal stage of AD. This suggests that the amygdala is affected in the same, direct physiopathological process as the hippocampus. Conversely, atrophy alone was observed within the thalamus and putamen, which are not directly involved in AD pathogenesis. This latter result may reflect another mechanism, whereby atrophy is linked to indirect physiopathological processes.

AICHA: An atlas of intrinsic connectivity of homotopic areas.

PubMed

Joliot, Marc; Jobard, Gaël; Naveau, Mikaël; Delcroix, Nicolas; Petit, Laurent; Zago, Laure; Crivello, Fabrice; Mellet, Emmanuel; Mazoyer, Bernard; Tzourio-Mazoyer, Nathalie

2015-10-30

Atlases of brain anatomical ROIs are widely used for functional MRI data analysis. Recently, it was proposed that an atlas of ROIs derived from a functional brain parcellation could be advantageous, in particular for understanding how different regions share information. However, functional atlases so far proposed do not account for a crucial aspect of cerebral organization, namely homotopy, i.e. that each region in one hemisphere has a homologue in the other hemisphere. We present AICHA (for Atlas of Intrinsic Connectivity of Homotopic Areas), a functional brain ROIs atlas based on resting-state fMRI data acquired in 281 individuals. AICHA ROIs cover the whole cerebrum, each having 1-homogeneity of its constituting voxels intrinsic activity, and 2-a unique homotopic contralateral counterpart with which it has maximal intrinsic connectivity. AICHA was built in 4 steps: (1) estimation of resting-state networks (RSNs) using individual resting-state fMRI independent components, (2) k-means clustering of voxel-wise group level profiles of connectivity, (3) homotopic regional grouping based on maximal inter-hemispheric functional correlation, and (4) ROI labeling. AICHA includes 192 homotopic region pairs (122 gyral, 50 sulcal, and 20 gray nuclei). As an application, we report inter-hemispheric (homotopic and heterotopic) and intra-hemispheric connectivity patterns at different sparsities. ROI functional homogeneity was higher for AICHA than for anatomical ROI atlases, but slightly lower than for another functional ROI atlas not accounting for homotopy. AICHA is ideally suited for intrinsic/effective connectivity analyses, as well as for investigating brain hemispheric specialization. Copyright © 2015 Elsevier B.V. All rights reserved.

SU-E-J-178: A Normalization Method Can Remove Discrepancy in Ventilation Function Due to Different Breathing Patterns

DOE Office of Scientific and Technical Information (OSTI.GOV)

Qu, H; Yu, N; Stephans, K

2014-06-01

Purpose: To develop a normalization method to remove discrepancy in ventilation function due to different breathing patterns. Methods: Twenty five early stage non-small cell lung cancer patients were included in this study. For each patient, a ten phase 4D-CT and the voluntarily maximum inhale and exhale CTs were acquired clinically and retrospectively used for this study. For each patient, two ventilation maps were calculated from voxel-to-voxel CT density variations from two phases of the quiet breathing and two phases of the extreme breathing. For the quiet breathing, 0% (inhale) and 50% (exhale) phases from 4D-CT were used. An in-house toolmore » was developed to calculate and display the ventilation maps. To enable normalization, the whole lung of each patient was evenly divided into three parts in the longitude direction at a coronal image with a maximum lung cross section. The ratio of cumulated ventilation from the top one-third region to the middle one-third region of the lung was calculated for each breathing pattern. Pearson's correlation coefficient was calculated on the ratios of the two breathing patterns for the group. Results: For each patient, the ventilation map from the quiet breathing was different from that of the extreme breathing. When the cumulative ventilation was normalized to the middle one-third of the lung region for each patient, the normalized ventilation functions from the two breathing patterns were consistent. For this group of patients, the correlation coefficient of the normalized ventilations for the two breathing patterns was 0.76 (p < 0.01), indicating a strong correlation in the ventilation function measured from the two breathing patterns. Conclusion: For each patient, the ventilation map is dependent of the breathing pattern. Using a regional normalization method, the discrepancy in ventilation function induced by the different breathing patterns thus different tidal volumes can be removed.« less

Performing label-fusion-based segmentation using multiple automatically generated templates.

PubMed

Chakravarty, M Mallar; Steadman, Patrick; van Eede, Matthijs C; Calcott, Rebecca D; Gu, Victoria; Shaw, Philip; Raznahan, Armin; Collins, D Louis; Lerch, Jason P

2013-10-01

Classically, model-based segmentation procedures match magnetic resonance imaging (MRI) volumes to an expertly labeled atlas using nonlinear registration. The accuracy of these techniques are limited due to atlas biases, misregistration, and resampling error. Multi-atlas-based approaches are used as a remedy and involve matching each subject to a number of manually labeled templates. This approach yields numerous independent segmentations that are fused using a voxel-by-voxel label-voting procedure. In this article, we demonstrate how the multi-atlas approach can be extended to work with input atlases that are unique and extremely time consuming to construct by generating a library of multiple automatically generated templates of different brains (MAGeT Brain). We demonstrate the efficacy of our method for the mouse and human using two different nonlinear registration algorithms (ANIMAL and ANTs). The input atlases consist a high-resolution mouse brain atlas and an atlas of the human basal ganglia and thalamus derived from serial histological data. MAGeT Brain segmentation improves the identification of the mouse anterior commissure (mean Dice Kappa values (κ = 0.801), but may be encountering a ceiling effect for hippocampal segmentations. Applying MAGeT Brain to human subcortical structures improves segmentation accuracy for all structures compared to regular model-based techniques (κ = 0.845, 0.752, and 0.861 for the striatum, globus pallidus, and thalamus, respectively). Experiments performed with three manually derived input templates suggest that MAGeT Brain can approach or exceed the accuracy of multi-atlas label-fusion segmentation (κ = 0.894, 0.815, and 0.895 for the striatum, globus pallidus, and thalamus, respectively). Copyright © 2012 Wiley Periodicals, Inc.

Advanced Three-Dimensional Display System

NASA Technical Reports Server (NTRS)

Geng, Jason

2005-01-01

A desktop-scale, computer-controlled display system, initially developed for NASA and now known as the VolumeViewer(TradeMark), generates three-dimensional (3D) images of 3D objects in a display volume. This system differs fundamentally from stereoscopic and holographic display systems: The images generated by this system are truly 3D in that they can be viewed from almost any angle, without the aid of special eyeglasses. It is possible to walk around the system while gazing at its display volume to see a displayed object from a changing perspective, and multiple observers standing at different positions around the display can view the object simultaneously from their individual perspectives, as though the displayed object were a real 3D object. At the time of writing this article, only partial information on the design and principle of operation of the system was available. It is known that the system includes a high-speed, silicon-backplane, ferroelectric-liquid-crystal spatial light modulator (SLM), multiple high-power lasers for projecting images in multiple colors, a rotating helix that serves as a moving screen for displaying voxels [volume cells or volume elements, in analogy to pixels (picture cells or picture elements) in two-dimensional (2D) images], and a host computer. The rotating helix and its motor drive are the only moving parts. Under control by the host computer, a stream of 2D image patterns is generated on the SLM and projected through optics onto the surface of the rotating helix. The system utilizes a parallel pixel/voxel-addressing scheme: All the pixels of the 2D pattern on the SLM are addressed simultaneously by laser beams. This parallel addressing scheme overcomes the difficulty of achieving both high resolution and a high frame rate in a raster scanning or serial addressing scheme. It has been reported that the structure of the system is simple and easy to build, that the optical design and alignment are not difficult, and that the system can be built by use of commercial off-the-shelf products. A prototype of the system displays an image of 1,024 by 768 by 170 (=133,693,440) voxels. In future designs, the resolution could be increased. The maximum number of voxels that can be generated depends upon the spatial resolution of SLM and the speed of rotation of the helix. For example, one could use an available SLM that has 1,024 by 1,024 pixels. Incidentally, this SLM is capable of operation at a switching speed of 300,000 frames per second. Implementation of full-color displays in future versions of the system would be straightforward: One could use three SLMs for red, green, and blue, respectively, and the colors of the voxels could be automatically controlled. An optically simpler alternative would be to use a single red/green/ blue light projector and synchronize the projection of each color with the generation of patterns for that color on a single SLM.

A custom correlation coefficient (CCC) approach for fast identification of multi-SNP association patterns in genome-wide SNPs data.

PubMed

Climer, Sharlee; Yang, Wei; de las Fuentes, Lisa; Dávila-Román, Victor G; Gu, C Charles

2014-11-01

Complex diseases are often associated with sets of multiple interacting genetic factors and possibly with unique sets of the genetic factors in different groups of individuals (genetic heterogeneity). We introduce a novel concept of custom correlation coefficient (CCC) between single nucleotide polymorphisms (SNPs) that address genetic heterogeneity by measuring subset correlations autonomously. It is used to develop a 3-step process to identify candidate multi-SNP patterns: (1) pairwise (SNP-SNP) correlations are computed using CCC; (2) clusters of so-correlated SNPs identified; and (3) frequencies of these clusters in disease cases and controls compared to identify disease-associated multi-SNP patterns. This method identified 42 candidate multi-SNP associations with hypertensive heart disease (HHD), among which one cluster of 22 SNPs (six genes) included 13 in SLC8A1 (aka NCX1, an essential component of cardiac excitation-contraction coupling) and another of 32 SNPs had 29 from a different segment of SLC8A1. While allele frequencies show little difference between cases and controls, the cluster of 22 associated alleles were found in 20% of controls but no cases and the other in 3% of controls but 20% of cases. These suggest that both protective and risk effects on HHD could be exerted by combinations of variants in different regions of SLC8A1, modified by variants from other genes. The results demonstrate that this new correlation metric identifies disease-associated multi-SNP patterns overlooked by commonly used correlation measures. Furthermore, computation time using CCC is a small fraction of that required by other methods, thereby enabling the analyses of large GWAS datasets. © 2014 WILEY PERIODICALS, INC.

A custom correlation coefficient (CCC) approach for fast identification of multi-SNP association patterns in genome-wide SNPs data

PubMed Central

Climer, Sharlee; Yang, Wei; de las Fuentes, Lisa; Dávila-Román, Victor G.; Gu, C. Charles

2014-01-01

Complex diseases are often associated with sets of multiple interacting genetic factors and possibly with unique sets of the genetic factors in different groups of individuals (genetic heterogeneity). We introduce a novel concept of Custom Correlation Coefficient (CCC) between single nucleotide polymorphisms (SNPs) that address genetic heterogeneity by measuring subset correlations autonomously. It is used to develop a 3-step process to identify candidate multi-SNP patterns: (1) pairwise (SNP-SNP) correlations are computed using CCC; (2) clusters of so-correlated SNPs identified; and (3) frequencies of these clusters in disease cases and controls compared to identify disease-associated multi-SNP patterns. This method identified 42 candidate multi-SNP associations with hypertensive heart disease (HHD), among which one cluster of 22 SNPs (6 genes) included 13 in SLC8A1 (aka NCX1, an essential component of cardiac excitation-contraction coupling) and another of 32 SNPs had 29 from a different segment of SLC8A1. While allele frequencies show little difference between cases and controls, the cluster of 22 associated alleles were found in 20% of controls but no cases and the other in 3% of controls but 20% of cases. These suggest that both protective and risk effects on HHD could be exerted by combinations of variants in different regions of SLC8A1, modified by variants from other genes. The results demonstrate that this new correlation metric identifies disease-associated multi-SNP patterns overlooked by commonly used correlation measures. Furthermore, computation time using CCC is a small fraction of that required by other methods, thereby enabling the analyses of large GWAS datasets. PMID:25168954

Deep feature learning for knee cartilage segmentation using a triplanar convolutional neural network.

PubMed

Prasoon, Adhish; Petersen, Kersten; Igel, Christian; Lauze, François; Dam, Erik; Nielsen, Mads

2013-01-01

Segmentation of anatomical structures in medical images is often based on a voxel/pixel classification approach. Deep learning systems, such as convolutional neural networks (CNNs), can infer a hierarchical representation of images that fosters categorization. We propose a novel system for voxel classification integrating three 2D CNNs, which have a one-to-one association with the xy, yz and zx planes of 3D image, respectively. We applied our method to the segmentation of tibial cartilage in low field knee MRI scans and tested it on 114 unseen scans. Although our method uses only 2D features at a single scale, it performs better than a state-of-the-art method using 3D multi-scale features. In the latter approach, the features and the classifier have been carefully adapted to the problem at hand. That we were able to get better results by a deep learning architecture that autonomously learns the features from the images is the main insight of this study.

Voxel based parallel post processor for void nucleation and growth analysis of atomistic simulations of material fracture.

PubMed

Hemani, H; Warrier, M; Sakthivel, N; Chaturvedi, S

2014-05-01

Molecular dynamics (MD) simulations are used in the study of void nucleation and growth in crystals that are subjected to tensile deformation. These simulations are run for typically several hundred thousand time steps depending on the problem. We output the atom positions at a required frequency for post processing to determine the void nucleation, growth and coalescence due to tensile deformation. The simulation volume is broken up into voxels of size equal to the unit cell size of crystal. In this paper, we present the algorithm to identify the empty unit cells (voids), their connections (void size) and dynamic changes (growth and coalescence of voids) for MD simulations of large atomic systems (multi-million atoms). We discuss the parallel algorithms that were implemented and discuss their relative applicability in terms of their speedup and scalability. We also present the results on scalability of our algorithm when it is incorporated into MD software LAMMPS. Copyright © 2014 Elsevier Inc. All rights reserved.

Neural substrates of spontaneous narrative production in focal neurodegenerative disease.

PubMed

Gola, Kelly A; Thorne, Avril; Veldhuisen, Lisa D; Felix, Cordula M; Hankinson, Sarah; Pham, Julie; Shany-Ur, Tal; Schauer, Guido P; Stanley, Christine M; Glenn, Shenly; Miller, Bruce L; Rankin, Katherine P

2015-12-01

Conversational storytelling integrates diverse cognitive and socio-emotional abilities that critically differ across neurodegenerative disease groups. Storytelling patterns may have diagnostic relevance and predict anatomic changes. The present study employed mixed methods discourse and quantitative analyses to delineate patterns of storytelling across focal neurodegenerative disease groups, and to clarify the neuroanatomical contributions to common storytelling characteristics. Transcripts of spontaneous social interactions of 46 participants (15 behavioral variant frontotemporal dementia (bvFTD), 7 semantic variant primary progressive aphasia (svPPA), 12 Alzheimer's disease (AD), and 12 healthy older normal controls (NC)) were analyzed for storytelling frequency and characteristics, and videos of the interactions were rated for patients' level of social attentiveness. Compared to controls, svPPAs told more stories and autobiographical stories, and perseverated on aspects of self during the interaction, whereas ADs told fewer autobiographical stories than NCs. svPPAs and bvFTDs were rated as less attentive to social cues. Aspects of storytelling were related to diverse cognitive and socio-emotional functions, and voxel-based anatomic analysis of structural magnetic resonance imaging revealed that temporal organization, narrative evaluations patterns, and social attentiveness correlated with atrophy corresponding to known intrinsic connectivity networks, including the default mode, limbic, salience, and stable task control networks. Differences in spontaneous storytelling among neurodegenerative groups elucidated diverse cognitive, socio-emotional, and neural contributions to narrative production, with implications for diagnostic screening and therapeutic intervention. Copyright © 2015 Elsevier Ltd. All rights reserved.

Improved superficial brain hemorrhage visualization in susceptibility weighted images by constrained minimum intensity projection

NASA Astrophysics Data System (ADS)

Castro, Marcelo A.; Pham, Dzung L.; Butman, John

2016-03-01

Minimum intensity projection is a technique commonly used to display magnetic resonance susceptibility weighted images, allowing the observer to better visualize hemorrhages and vasculature. The technique displays the minimum intensity in a given projection within a thick slab, allowing different connectivity patterns to be easily revealed. Unfortunately, the low signal intensity of the skull within the thick slab can mask superficial tissues near the skull base and other regions. Because superficial microhemorrhages are a common feature of traumatic brain injury, this effect limits the ability to proper diagnose and follow up patients. In order to overcome this limitation, we developed a method to allow minimum intensity projection to properly display superficial tissues adjacent to the skull. Our approach is based on two brain masks, the largest of which includes extracerebral voxels. The analysis of the rind within both masks containing the actual brain boundary allows reclassification of those voxels initially missed in the smaller mask. Morphological operations are applied to guarantee accuracy and topological correctness, and the mean intensity within the mask is assigned to all outer voxels. This prevents bone from dominating superficial regions in the projection, enabling superior visualization of cortical hemorrhages and vessels.

Evaluation of the Lactate-to-N-Acetyl-aspartate Ratio Defined With Magnetic Resonance Spectroscopic Imaging Before Radiation Therapy as a New Predictive Marker of the Site of Relapse in Patients With Glioblastoma Multiforme

DOE Office of Scientific and Technical Information (OSTI.GOV)

Deviers, Alexandra; UMR; INP

Purpose: Because lactate accumulation is considered a surrogate for hypoxia and tumor radiation resistance, we studied the spatial distribution of the lactate-to-N-acetyl-aspartate ratio (LNR) before radiation therapy (RT) with 3D proton magnetic resonance spectroscopic imaging (3D-{sup 1}H-MRSI) and assessed its impact on local tumor control in glioblastoma (GBM). Methods and Materials: Fourteen patients with newly diagnosed GBM included in a phase 2 chemoradiation therapy trial constituted our database. Magnetic resonance imaging (MRI) and MRSI data before RT were evaluated and correlated to MRI data at relapse. The optimal threshold for tumor-associated LNR was determined with receiver-operating-characteristic (ROC) curve analysis ofmore » the pre-RT LNR values and MRI characteristics of the tumor. This threshold was used to segment pre-RT normalized LNR maps. Two spatial analyses were performed: (1) a pre-RT volumetric comparison of abnormal LNR areas with regions of MRI-defined lesions and a choline (Cho)-to- N-acetyl-aspartate (NAA) ratio ≥2 (CNR2); and (2) a voxel-by-voxel spatial analysis of 4,186,185 voxels with the intention of evaluating whether pre-RT abnormal LNR areas were predictive of the site of local recurrence. Results: A LNR of ≥0.4 (LNR-0.4) discriminated between tumor-associated and normal LNR values with 88.8% sensitivity and 97.6% specificity. LNR-0.4 voxels were spatially different from those of MRI-defined lesions, representing 44% of contrast enhancement, 64% of central necrosis, and 26% of fluid-attenuated inversion recovery (FLAIR) abnormality volumes before RT. They extended beyond the overlap with CNR2 for most patients (median: 20 cm{sup 3}; range: 6-49 cm{sup 3}). LNR-0.4 voxels were significantly predictive of local recurrence, regarded as contrast enhancement at relapse: 71% of voxels with a LNR-0.4 before RT were contrast enhanced at relapse versus 10% of voxels with a normal LNR (P<.01). Conclusions: Pre-RT LNR-0.4 in GBM indicates tumor areas that are likely to relapse. Further investigations are needed to confirm lactate imaging as a tool to define additional biological target volumes for dose painting.« less

Outbreak of mastitis in sheep caused by multi-drug resistant Enterococcus faecalis in Sardinia, Italy.

PubMed

Sanciu, G; Marogna, G; Paglietti, B; Cappuccinelli, P; Leori, G; Rappelli, P

2013-03-01

An outbreak of infective mastitis due to Enterococcus faecalis occurred in an intensive sheep farm in north Sardinia (Italy). E. faecalis, which is only rarely isolated from sheep milk, was unexpectedly found in 22·3% of positive samples at microbiological examination. Forty-five out of the 48 E. faecalis isolates showed the same multi-drug resistance pattern (cloxacillin, streptomycin, kanamycin, clindamycin, oxytetracycline). E. faecalis isolates were analysed by pulsed-field gel electrophoresis, and all 45 multi-drug resistant strains showed an indistinguishable macrorestiction profile, indicating their clonal origin. To our knowledge, this is the first report of an outbreak of mastitis in sheep caused by E. faecalis.

Analysis of multiplex gene expression maps obtained by voxelation.

PubMed

An, Li; Xie, Hongbo; Chin, Mark H; Obradovic, Zoran; Smith, Desmond J; Megalooikonomou, Vasileios

2009-04-29

Gene expression signatures in the mammalian brain hold the key to understanding neural development and neurological disease. Researchers have previously used voxelation in combination with microarrays for acquisition of genome-wide atlases of expression patterns in the mouse brain. On the other hand, some work has been performed on studying gene functions, without taking into account the location information of a gene's expression in a mouse brain. In this paper, we present an approach for identifying the relation between gene expression maps obtained by voxelation and gene functions. To analyze the dataset, we chose typical genes as queries and aimed at discovering similar gene groups. Gene similarity was determined by using the wavelet features extracted from the left and right hemispheres averaged gene expression maps, and by the Euclidean distance between each pair of feature vectors. We also performed a multiple clustering approach on the gene expression maps, combined with hierarchical clustering. Among each group of similar genes and clusters, the gene function similarity was measured by calculating the average gene function distances in the gene ontology structure. By applying our methodology to find similar genes to certain target genes we were able to improve our understanding of gene expression patterns and gene functions. By applying the clustering analysis method, we obtained significant clusters, which have both very similar gene expression maps and very similar gene functions respectively to their corresponding gene ontologies. The cellular component ontology resulted in prominent clusters expressed in cortex and corpus callosum. The molecular function ontology gave prominent clusters in cortex, corpus callosum and hypothalamus. The biological process ontology resulted in clusters in cortex, hypothalamus and choroid plexus. Clusters from all three ontologies combined were most prominently expressed in cortex and corpus callosum. The experimental results confirm the hypothesis that genes with similar gene expression maps might have similar gene functions. The voxelation data takes into account the location information of gene expression level in mouse brain, which is novel in related research. The proposed approach can potentially be used to predict gene functions and provide helpful suggestions to biologists.

Lifetime use of cannabis from longitudinal assessments, cannabinoid receptor (CNR1) variation, and reduced volume of the right anterior cingulate

PubMed Central

Hill, Shirley Y.; Sharma, Vinod; Jones, Bobby L.

2016-01-01

Lifetime measures of cannabis use and co-occurring exposures were obtained from a longitudinal cohort followed an average of 13 years at the time they received a structural MRI scan. MRI scans were analyzed for 88 participants (mean age=25.9 years), 34 of whom were regular users of cannabis. Whole brain voxel based morphometry analyses (SPM8) were conducted using 50 voxel clusters at p=0.005. Controlling for age, familial risk, and gender, we found reduced volume in Regular Users compared to Non-Users, in the lingual gyrus, anterior cingulum (right and left), and the rolandic operculum (right). The right anterior cingulum reached family-wise error statistical significance at p=0.001, controlling for personal lifetime use of alcohol and cigarettes and any prenatal exposures. CNR1 haplotypes were formed from four CNR1 SNPs (rs806368, rs1049353, rs2023239, and rs6454674) and tested with level of cannabis exposure to assess their interactive effects on the lingual gyrus, cingulum (right and left) and rolandic operculum, regions showing cannabis exposure effects in the SPM8 analyses. These analyses used mixed model analyses (SPSS) to control for multiple potentially confounding variables. Level of cannabis exposure was associated with decreased volume of the right anterior cingulum and showed interaction effects with haplotype variation. PMID:27500453

Robust Estimation of Electron Density From Anatomic Magnetic Resonance Imaging of the Brain Using a Unifying Multi-Atlas Approach

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ren, Shangjie; Department of Radiation Oncology, Stanford University School of Medicine, Palo Alto, California; Hara, Wendy

Purpose: To develop a reliable method to estimate electron density based on anatomic magnetic resonance imaging (MRI) of the brain. Methods and Materials: We proposed a unifying multi-atlas approach for electron density estimation based on standard T1- and T2-weighted MRI. First, a composite atlas was constructed through a voxelwise matching process using multiple atlases, with the goal of mitigating effects of inherent anatomic variations between patients. Next we computed for each voxel 2 kinds of conditional probabilities: (1) electron density given its image intensity on T1- and T2-weighted MR images; and (2) electron density given its spatial location in a referencemore » anatomy, obtained by deformable image registration. These were combined into a unifying posterior probability density function using the Bayesian formalism, which provided the optimal estimates for electron density. We evaluated the method on 10 patients using leave-one-patient-out cross-validation. Receiver operating characteristic analyses for detecting different tissue types were performed. Results: The proposed method significantly reduced the errors in electron density estimation, with a mean absolute Hounsfield unit error of 119, compared with 140 and 144 (P<.0001) using conventional T1-weighted intensity and geometry-based approaches, respectively. For detection of bony anatomy, the proposed method achieved an 89% area under the curve, 86% sensitivity, 88% specificity, and 90% accuracy, which improved upon intensity and geometry-based approaches (area under the curve: 79% and 80%, respectively). Conclusion: The proposed multi-atlas approach provides robust electron density estimation and bone detection based on anatomic MRI. If validated on a larger population, our work could enable the use of MRI as a primary modality for radiation treatment planning.« less

Discriminative confidence estimation for probabilistic multi-atlas label fusion.

PubMed

Benkarim, Oualid M; Piella, Gemma; González Ballester, Miguel Angel; Sanroma, Gerard

2017-12-01

Quantitative neuroimaging analyses often rely on the accurate segmentation of anatomical brain structures. In contrast to manual segmentation, automatic methods offer reproducible outputs and provide scalability to study large databases. Among existing approaches, multi-atlas segmentation has recently shown to yield state-of-the-art performance in automatic segmentation of brain images. It consists in propagating the labelmaps from a set of atlases to the anatomy of a target image using image registration, and then fusing these multiple warped labelmaps into a consensus segmentation on the target image. Accurately estimating the contribution of each atlas labelmap to the final segmentation is a critical step for the success of multi-atlas segmentation. Common approaches to label fusion either rely on local patch similarity, probabilistic statistical frameworks or a combination of both. In this work, we propose a probabilistic label fusion framework based on atlas label confidences computed at each voxel of the structure of interest. Maximum likelihood atlas confidences are estimated using a supervised approach, explicitly modeling the relationship between local image appearances and segmentation errors produced by each of the atlases. We evaluate different spatial pooling strategies for modeling local segmentation errors. We also present a novel type of label-dependent appearance features based on atlas labelmaps that are used during confidence estimation to increase the accuracy of our label fusion. Our approach is evaluated on the segmentation of seven subcortical brain structures from the MICCAI 2013 SATA Challenge dataset and the hippocampi from the ADNI dataset. Overall, our results indicate that the proposed label fusion framework achieves superior performance to state-of-the-art approaches in the majority of the evaluated brain structures and shows more robustness to registration errors. Copyright © 2017 Elsevier B.V. All rights reserved.

An Effective Algorithm Research of Scenario Voxelization Organization and Occlusion Culling

NASA Astrophysics Data System (ADS)

Lai, Guangling; Ding, Lu; Qin, Zhiyuan; Tong, Xiaochong

2016-11-01

Compared with the traditional triangulation approaches, the voxelized point cloud data can reduce the sensitivity of scenario and complexity of calculation. While on the base of the point cloud data, implementation scenario organization could be accomplishment by subtle voxel, but it will add more memory consumption. Therefore, an effective voxel representation method is very necessary. At present, the specific study of voxel visualization algorithm is less. This paper improved the ray tracing algorithm by the characteristics of voxel configuration. Firstly, according to the scope of point cloud data, determined the scope of the pixels on the screen. Then, calculated the light vector came from each pixel. Lastly, used the rules of voxel configuration to calculate all the voxel penetrated through by light. The voxels closest to viewpoint were named visible ones, the rest were all obscured ones. This experimental showed that the method could realize voxelization organization and voxel occlusion culling of implementation scenario efficiently, and increased the render efficiency.

A Novel Multi-voxel Based Quantitation of Metabolites and Lipids Non-invasively Combined with Diffusion Weighted Imaging in Breast Cancer

DTIC Science & Technology

2011-09-01

SATURATED AND UNSATURATED LIPIDS 16. SECURITY CLASSIFICATION OF: 17. LIMITATION OF ABSTRACT 18. NUMBER OF PAGES 19a. NAME OF RESPONSIBLE PERSON... saturated fatty acid, mono- unsaturated and poly unsaturated fatty acids. 6 Figure 3. Prior-knowledge COSY spectra for the breast metabolites (top...In addition to water, presence of 2D diagonal and cross peaks from the methyl, methylene, and olefenic protons of unsaturated and saturated 8

Using Laser-Induced Thermal Voxels to Pattern Diverse Materials at the Solid-Liquid Interface.

PubMed

Zarzar, Lauren D; Swartzentruber, B S; Donovan, Brian F; Hopkins, Patrick E; Kaehr, Bryan

2016-08-24

We describe a high-resolution patterning approach that combines the spatial control inherent to laser direct writing with the versatility of benchtop chemical synthesis. By taking advantage of the steep thermal gradient that occurs while laser heating a metal edge in contact with solution, diverse materials comprising transition metals are patterned with feature size resolution nearing 1 μm. We demonstrate fabrication of reduced metallic nickel in one step and examine electrical properties and air stability through direct-write integration onto a device platform. This strategy expands the chemistries and materials that can be used in combination with laser direct writing.

Using laser-induced thermal voxels to pattern diverse materials at the solid–liquid interface

DOE PAGES

Zarzar, Lauren D.; Swartzentruber, B. S.; Donovan, Brian F.; ...

2016-08-05

We describe a high-resolution patterning approach that combines the spatial control inherent to laser direct writing with the versatility of benchtop chemical synthesis. By taking advantage of the steep thermal gradient that occurs while laser heating a metal edge in contact with solution, diverse materials comprising transition metals are patterned with feature size resolution nearing 1 μm. We demonstrate fabrication of reduced metallic nickel in one step and examine electrical properties and air stability through direct-write integration onto a device platform. In conclusion, this strategy expands the chemistries and materials that can be used in combination with laser direct writing.

Micro CT characterization of a coastal mine tailings deposit, Portmán Bay, SE Spain

NASA Astrophysics Data System (ADS)

Frigola, Jaime; Cerdà-Domènech, Marc; Barriuso, Eduardo; Sanchez-Vidal, Anna; Amblas, David; Canals, Miquel

2017-04-01

Scanning of sediment cores by means of high-resolution non-destructive techniques provides researchers with huge amounts of highly valuable data allowing fast and detailed characterization of the materials. In the last decades several devoted instruments have been developed and applied to the study of sedimentary sequences, mainly multi-sensor core loggers (MSCL) for the physical properties and XRF core scanners for the chemical elemental composition. The geoscientific community started using computed tomography (CT) systems about two decades ago. These were mainly medical systems as dedicated instruments were essentially lacking by that time. The resolution of those medical systems was limited to several hundreds of micrometres voxel size. Micro computed tomography (micro-CT) systems have also spread into geoscientific research, although their limited workspace dimensions prevents their use for large objects, such as long sediment cores. Recently, a new micro-CT system, the MultiTom Core X-ray CT, conceived by University of Barcelona (UB) researchers and developed by X-ray Engineering, became operational. It is able of scanning sediment cores up to 1.5 m long, and allows adjustable resolutions from 300 microns down to 3-4 microns. The system is now installed at UB's CORELAB Laboratory for non-destructive analyses of geological materials. Here we present, as an example, the results of MultiTom scans of a set of sediment cores recovered offshore Portmán Bay, SE Spain, in order to characterize at very high-resolution the metal-enriched deposit generated after 33 years of direct discharge into the sea of mine tailings resulting from the exploitation of Pb and Zn ores. In total 52 short cores and 6 long gravity cores from the mine tailings infilled bay were scanned with the MultiTom system at a mean voxel resolution of 125 microns. The integrated study of micro-CT data allowed differentiating the main tailings units from deposits formed after disposal cessation. Tailings units show higher radio-density values, which correspond to metal enrichments. A lower unit consists of highly laminated interbedded low radio-density and very high radio-density layers, while an upper mine tailings unit is more homogeneous and shows intermediate radio-density values. The limit between the tailings and the post-mining deposits is defined by a sharp surface associated with an abrupt decrease in the radio-densities. Post-mining deposits are also characterized by an increment in bioturbation marks, which are practically absent in the tailings units, and an increase in carbonate particles and organic matter patches. Micro CT scans allow observation of very small structures, which are indicative of the complexity of the sedimentation processes involved in the transport and final deposition of the mine tailings. Integration of micro CT scans together with XRF core scanner and MSCL data allows a better characterization of the metal concentrations and their distribution within the deposit, directly demonstrating the great value of non-destructive techniques for actually high-resolution sedimentological studies.

Automatic Generation of Indoor Navigable Space Using a Point Cloud and its Scanner Trajectory

NASA Astrophysics Data System (ADS)

Staats, B. R.; Diakité, A. A.; Voûte, R. L.; Zlatanova, S.

2017-09-01

Automatic generation of indoor navigable models is mostly based on 2D floor plans. However, in many cases the floor plans are out of date. Buildings are not always built according to their blue prints, interiors might change after a few years because of modified walls and doors, and furniture may be repositioned to the user's preferences. Therefore, new approaches for the quick recording of indoor environments should be investigated. This paper concentrates on laser scanning with a Mobile Laser Scanner (MLS) device. The MLS device stores a point cloud and its trajectory. If the MLS device is operated by a human, the trajectory contains information which can be used to distinguish different surfaces. In this paper a method is presented for the identification of walkable surfaces based on the analysis of the point cloud and the trajectory of the MLS scanner. This method consists of several steps. First, the point cloud is voxelized. Second, the trajectory is analysing and projecting to acquire seed voxels. Third, these seed voxels are generated into floor regions by the use of a region growing process. By identifying dynamic objects, doors and furniture, these floor regions can be modified so that each region represents a specific navigable space inside a building as a free navigable voxel space. By combining the point cloud and its corresponding trajectory, the walkable space can be identified for any type of building even if the interior is scanned during business hours.

Childhood neglect predicts disorganization in schizophrenia through grey matter decrease in dorsolateral prefrontal cortex.

PubMed

Cancel, A; Comte, M; Truillet, R; Boukezzi, S; Rousseau, P-F; Zendjidjian, X Y; Sage, T; Lazerges, P-E; Guedj, E; Khalfa, S; Azorin, J-M; Blin, O; Fakra, E

2015-10-01

Psychosocial trauma during childhood is associated with schizophrenia vulnerability. The pattern of grey matter decrease is similar to brain alterations seen in schizophrenia. Our objective was to explore the links between childhood trauma, brain morphology and schizophrenia symptoms. Twenty-one patients with schizophrenia stabilized with atypical antipsychotic monotherapy and 30 healthy control subjects completed the study. Anatomical MRI images were analysed using optimized voxel-based morphometry (VBM). Childhood trauma was assessed with the Childhood Trauma Questionnaire, and symptoms were rated on the Scale for the Assessment of Negative Symptoms (SANS) and Scale for the Assessment of Positive Symptoms (SAPS) (disorganization, positive and negative symptoms). In the schizophrenia group, we used structural equation modelling in a path analysis. Total grey matter volume was negatively associated with emotional neglect (EN) in patients with schizophrenia. Whole-brain VBM analyses of grey matter in the schizophrenia group revealed a specific inversed association between EN and the right dorsolateral prefrontal cortex (DLPFC). Path analyses identified a well-fitted model in which EN predicted grey matter density in DLPFC, which in turn predicted the disorganization score. Our findings suggest that EN during childhood could have an impact on psychopathology in schizophrenia, which would be mediated by developmental effects on brain regions such as the DLPFC. © 2015 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Cortical grey matter content is associated with both age and bimanual performance, but is not observed to mediate age-related behavioural decline.

PubMed

van Ruitenbeek, Peter; Serbruyns, Leen; Solesio-Jofre, Elena; Meesen, Raf; Cuypers, Koen; Swinnen, Stephan P

2017-01-01

Declines in both cortical grey matter and bimanual coordination performance are evident in healthy ageing. However, the relationship between ageing, bimanual performance, and grey matter loss remains unclear, particularly across the whole adult lifespan. Therefore, participants (N = 93, range 20-80 years) performed a complex Bimanual Tracking Task, and structural brain images were obtained using magnetic resonance imaging. Analyses revealed that age correlated negatively with task performance. Voxel-based morphometry analysis revealed that age was associated with grey matter declines in task-relevant cortical areas and that grey matter in these areas was negatively associated with task performance. However, no evidence for a mediating effect of grey matter in age-related bimanual performance decline was observed. We propose a new hypothesis that functional compensation may account for the observed absence of mediation, which is in line with the observed pattern of increased inter-individual variance in performance with age.

Voxel-based correlation between coregistered single-photon emission computed tomography and dynamic susceptibility contrast magnetic resonance imaging in subjects with suspected Alzheimer disease.

PubMed

Cavallin, L; Axelsson, R; Wahlund, L O; Oksengard, A R; Svensson, L; Juhlin, P; Wiberg, M Kristoffersen; Frank, A

2008-12-01

Current diagnosis of Alzheimer disease is made by clinical, neuropsychologic, and neuroimaging assessments. Neuroimaging techniques such as magnetic resonance imaging (MRI) and single-photon emission computed tomography (SPECT) could be valuable in the differential diagnosis of Alzheimer disease, as well as in assessing prognosis. To compare SPECT and MRI in a cohort of patients examined for suspected dementia, including patients with no objective cognitive impairment (control group), mild cognitive impairment (MCI), and Alzheimer disease (AD). 24 patients, eight with AD, 10 with MCI, and six controls, were investigated with SPECT using (99m)Tc-hexamethylpropyleneamine oxime (HMPAO, Ceretec; GE Healthcare Ltd., Little Chalsont UK) and dynamic susceptibility contrast magnetic resonance imaging (DSC-MRI) with a contrast-enhancing gadobutrol formula (Gadovist; Bayer Schering Pharma, Berlin, Germany). Voxel-based correlation between coregistered SPECT and DSC-MR images was calculated. Region-of-interest (ROI) analyses were then performed in 24 different brain areas using brain registration and analysis of SPECT studies (BRASS; Nuclear Diagnostics AB, Stockholm, Sweden) on both SPECT and DSC-MRI. Voxel-based correlation between coregistered SPECT and DSC-MR showed a high correlation, with a mean correlation coefficient of 0.94. ROI analyses of 24 regions showed significant differences between the control group and AD patients in 10 regions using SPECT and five regions in DSC-MR. SPECT remains superior to DSC-MRI in differentiating normal from pathological perfusion, and DSC-MRI could not replace SPECT in the diagnosis of patients with Alzheimer disease.

Abnormal degree centrality in Alzheimer's disease patients with depression: A resting-state functional magnetic resonance imaging study.

PubMed

Guo, Zhongwei; Liu, Xiaozheng; Hou, Hongtao; Wei, Fuquan; Liu, Jian; Chen, Xingli

2016-06-15

Depression is common in Alzheimer's disease (AD) and occurs in AD patients with a prevalence of up to 40%. It reduces cognitive function and increases the burden on caregivers. Currently, there are very few medications that are useful for treating depression in AD patients. Therefore, understanding the brain abnormalities in AD patients with depression (D-AD) is crucial for developing effective interventions. The aim of this study was to investigate the intrinsic dysconnectivity pattern of whole-brain functional networks at the voxel level in D-AD patients based on degree centrality (DC) as measured by resting-state functional magnetic resonance imaging (R-fMRI). Our study included 32 AD patients. All patients were evaluated using the Neuropsychiatric Inventory and Hamilton Depression Rating Scale and further divided into two groups: 15 D-AD patients and 17 non-depressed AD (nD-AD) patients. R-fMRI datasets were acquired from these D-AD and nD-AD patients. First, we performed a DC analysis to identify voxels that showed altered whole brain functional connectivity (FC) with other voxels. We then further investigated FC using the abnormal DC regions to examine in more detail the connectivity patterns of the identified DC changes. D-AD patients had lower DC values in the right middle frontal, precentral, and postcentral gyrus than nD-AD patients. Seed-based analysis revealed decreased connectivity between the precentral and postcentral gyrus to the supplementary motor area and middle cingulum. FC also decreased in the right middle frontal, precentral, and postcentral gyrus. Thus, AD patients with depression fit a 'network dysfunction model' distinct from major depressive disorder and AD. Copyright © 2016. Published by Elsevier Inc.

Age-related differences in regional brain volumes: A comparison of optimized voxel-based morphometry to manual volumetry

PubMed Central

Kennedy, Kristen M.; Erickson, Kirk I.; Rodrigue, Karen M.; Voss, Michelle W.; Colcombe, Stan J.; Kramer, Arthur F.; Acker, James D.; Raz, Naftali

2009-01-01

Regional manual volumetry is the gold standard of in vivo neuroanatomy, but is labor-intensive, can be imperfectly reliable, and allows for measuring limited number of regions. Voxel-based morphometry (VBM) has perfect repeatability and assesses local structure across the whole brain. However, its anatomic validity is unclear, and with its increasing popularity, a systematic comparison of VBM to manual volumetry is necessary. The few existing comparison studies are limited by small samples, qualitative comparisons, and limited selection and modest reliability of manual measures. Our goal was to overcome those limitations by quantitatively comparing optimized VBM findings with highly reliable multiple regional measures in a large sample (N = 200) across a wide agespan (18–81). We report a complex pattern of similarities and differences. Peak values of VBM volume estimates (modulated density) produced stronger age differences and a different spatial distribution from manual measures. However, when we aggregated VBM-derived information across voxels contained in specific anatomically defined regions (masks), the patterns of age differences became more similar, although important discrepancies emerged. Notably, VBM revealed stronger age differences in the regions bordering CSF and white matter areas prone to leukoaraiosis, and VBM was more likely to report nonlinearities in age-volume relationships. In the white matter regions, manual measures showed stronger negative associations with age than the corresponding VBM-based masks. We conclude that VBM provides realistic estimates of age differences in the regional gray matter only when applied to anatomically defined regions, but overestimates effects when individual peaks are interpreted. It may be beneficial to use VBM as a first-pass strategy, followed by manual measurement of anatomically-defined regions. PMID:18276037

Cross-scale analysis of cluster correspondence using different operational neighborhoods

NASA Astrophysics Data System (ADS)

Lu, Yongmei; Thill, Jean-Claude

2008-09-01

Cluster correspondence analysis examines the spatial autocorrelation of multi-location events at the local scale. This paper argues that patterns of cluster correspondence are highly sensitive to the definition of operational neighborhoods that form the spatial units of analysis. A subset of multi-location events is examined for cluster correspondence if they are associated with the same operational neighborhood. This paper discusses the construction of operational neighborhoods for cluster correspondence analysis based on the spatial properties of the underlying zoning system and the scales at which the zones are aggregated into neighborhoods. Impacts of this construction on the degree of cluster correspondence are also analyzed. Empirical analyses of cluster correspondence between paired vehicle theft and recovery locations are conducted on different zoning methods and across a series of geographic scales and the dynamics of cluster correspondence patterns are discussed.

Brain volumes in healthy adults aged 40 years and over: a voxel-based morphometry study.

PubMed

Riello, Roberta; Sabattoli, Francesca; Beltramello, Alberto; Bonetti, Matteo; Bono, Giorgio; Falini, Andrea; Magnani, Giuseppe; Minonzio, Giorgio; Piovan, Enrico; Alaimo, Giuseppina; Ettori, Monica; Galluzzi, Samantha; Locatelli, Enrico; Noiszewska, Malgorzata; Testa, Cristina; Frisoni, Giovanni B

2005-08-01

Gender and age effect on brain morphology have been extensively investigated. However, the great variety in methods applied to morphology partly explain the conflicting results of linear patterns of tissue changes and lateral asymmetry in men and women. The aim of the present study was to assess the effect of age, gender and laterality on the volumes of gray matter (GM) and white matter (WM) in a large group of healthy adults by means of voxel-based morphometry. This technique, based on observer-independent algorithms, automatically segments the 3 types of tissue and computes the amount of tissue in each single voxel. Subjects were 229 healthy subjects of 40 years of age or older, who underwent magnetic resonance (MR) for reasons other than cognitive impairment. MR images were reoriented following the AC-PC line and, after removing the voxels below the cerebellum, were processed by Statistical Parametric Mapping (SPM99). GM and WM volumes were normalized for intracranial volume. Women had more fractional GM and WM volumes than men. Age was negatively correlated with both fractional GM and WM, and a gender x age interaction effect was found for WM, men having greater WM loss with advancing age. Pairwise differences between left and right GM were negative (greater GM in right hemisphere) in men, and positive (greater GM in left hemisphere) in women (-0.56+/-4.2 vs 0.99+/-4.8; p=0.019). These results support side-specific accelerated WM loss in men, and may help our better understanding of changes in regional brain structures associated with pathological aging.

Multi-Element Unstructured Analyses of Complex Valve Systems

NASA Technical Reports Server (NTRS)

Sulyma, Peter (Technical Monitor); Ahuja, Vineet; Hosangadi, Ashvin; Shipman, Jeremy

2004-01-01

The safe and reliable operation of high pressure test stands for rocket engine and component testing places an increased emphasis on the performance of control valves and flow metering devices. In this paper, we will present a series of high fidelity computational analyses of systems ranging from cryogenic control valves and pressure regulator systems to cavitating venturis that are used to support rocket engine and component testing at NASA Stennis Space Center. A generalized multi-element framework with sub-models for grid adaption, grid movement and multi-phase flow dynamics has been used to carry out the simulations. Such a framework provides the flexibility of resolving the structural and functional complexities that are typically associated with valve-based high pressure feed systems and have been difficult to deal with traditional CFD methods. Our simulations revealed a rich variety of flow phenomena such as secondary flow patterns, hydrodynamic instabilities, fluctuating vapor pockets etc. In the paper, we will discuss performance losses related to cryogenic control valves, and provide insight into the physics of the dominant multi-phase fluid transport phenomena that are responsible for the choking like behavior in cryogenic control elements. Additionally, we will provide detailed analyses of the modal instability that is observed in the operation of the dome pressure regulator valve. Such instabilities are usually not localized and manifest themselves as a system wide phenomena leading to an undesirable chatter at high flow conditions.

Laser-induced Forward Transfer of Ag Nanopaste.

PubMed

Breckenfeld, Eric; Kim, Heungsoo; Auyeung, Raymond C Y; Piqué, Alberto

2016-03-31

Over the past decade, there has been much development of non-lithographic methods(1-3) for printing metallic inks or other functional materials. Many of these processes such as inkjet(3) and laser-induced forward transfer (LIFT)(4) have become increasingly popular as interest in printable electronics and maskless patterning has grown. These additive manufacturing processes are inexpensive, environmentally friendly, and well suited for rapid prototyping, when compared to more traditional semiconductor processing techniques. While most direct-write processes are confined to two-dimensional structures and cannot handle materials with high viscosity (particularly inkjet), LIFT can transcend both constraints if performed properly. Congruent transfer of three dimensional pixels (called voxels), also referred to as laser decal transfer (LDT)(5-9), has recently been demonstrated with the LIFT technique using highly viscous Ag nanopastes to fabricate freestanding interconnects, complex voxel shapes, and high-aspect-ratio structures. In this paper, we demonstrate a simple yet versatile process for fabricating a variety of micro- and macroscale Ag structures. Structures include simple shapes for patterning electrical contacts, bridging and cantilever structures, high-aspect-ratio structures, and single-shot, large area transfers using a commercial digital micromirror device (DMD) chip.

Laser-induced Forward Transfer of Ag Nanopaste

PubMed Central

Breckenfeld, Eric; Kim, Heungsoo; Auyeung, Raymond C. Y.; Piqué, Alberto

2016-01-01

Over the past decade, there has been much development of non-lithographic methods1-3 for printing metallic inks or other functional materials. Many of these processes such as inkjet3 and laser-induced forward transfer (LIFT)4 have become increasingly popular as interest in printable electronics and maskless patterning has grown. These additive manufacturing processes are inexpensive, environmentally friendly, and well suited for rapid prototyping, when compared to more traditional semiconductor processing techniques. While most direct-write processes are confined to two-dimensional structures and cannot handle materials with high viscosity (particularly inkjet), LIFT can transcend both constraints if performed properly. Congruent transfer of three dimensional pixels (called voxels), also referred to as laser decal transfer (LDT)5-9, has recently been demonstrated with the LIFT technique using highly viscous Ag nanopastes to fabricate freestanding interconnects, complex voxel shapes, and high-aspect-ratio structures. In this paper, we demonstrate a simple yet versatile process for fabricating a variety of micro- and macroscale Ag structures. Structures include simple shapes for patterning electrical contacts, bridging and cantilever structures, high-aspect-ratio structures, and single-shot, large area transfers using a commercial digital micromirror device (DMD) chip. PMID:27077645

Neurodevelopmental perspectives on dance learning: Insights from early adolescence and young adulthood.

PubMed

Sumanapala, Dilini K; Walbrin, Jon; Kirsch, Louise P; Cross, Emily S

2018-01-01

Studies investigating human motor learning and movement perception have shown that similar sensorimotor brain regions are engaged when we observe or perform action sequences. However, the way these networks enable translation of complex observed actions into motor commands-such as in the context of dance-remains poorly understood. Emerging evidence suggests that the ability to encode specific visuospatial and kinematic movement properties encountered via different routes of sensorimotor experience may be an integral component of action learning throughout development. Using a video game-based dance training paradigm, we demonstrate that patterns of voxel activity in visual and sensorimotor brain regions when perceiving movements following training are related to the sensory modalities through which these movements were encountered during whole-body dance training. Compared to adolescents, young adults in this study demonstrated more distinctive patterns of voxel activity in visual cortices in relation to different types of sensorimotor experience. This finding suggests that cortical maturity might influence the extent to which prior sensorimotor experiences shape brain activity when watching others in action, and potentially impact how we acquire new motor skills. © 2018 Elsevier B.V. All rights reserved.

Biomarkers of Eating Disorders Using Support Vector Machine Analysis of Structural Neuroimaging Data: Preliminary Results

PubMed Central

Cerasa, Antonio; Castiglioni, Isabella; Salvatore, Christian; Funaro, Angela; Martino, Iolanda; Alfano, Stefania; Donzuso, Giulia; Perrotta, Paolo; Gioia, Maria Cecilia; Gilardi, Maria Carla; Quattrone, Aldo

2015-01-01

Presently, there are no valid biomarkers to identify individuals with eating disorders (ED). The aim of this work was to assess the feasibility of a machine learning method for extracting reliable neuroimaging features allowing individual categorization of patients with ED. Support Vector Machine (SVM) technique, combined with a pattern recognition method, was employed utilizing structural magnetic resonance images. Seventeen females with ED (six with diagnosis of anorexia nervosa and 11 with bulimia nervosa) were compared against 17 body mass index-matched healthy controls (HC). Machine learning allowed individual diagnosis of ED versus HC with an Accuracy ≥ 0.80. Voxel-based pattern recognition analysis demonstrated that voxels influencing the classification Accuracy involved the occipital cortex, the posterior cerebellar lobule, precuneus, sensorimotor/premotor cortices, and the medial prefrontal cortex, all critical regions known to be strongly involved in the pathophysiological mechanisms of ED. Although these findings should be considered preliminary given the small size investigated, SVM analysis highlights the role of well-known brain regions as possible biomarkers to distinguish ED from HC at an individual level, thus encouraging the translational implementation of this new multivariate approach in the clinical practice. PMID:26648660

European Climate and Pinot Noir Grape-Harvest Dates in Burgundy, since the 17th Century

NASA Astrophysics Data System (ADS)

Tourre, Y. M.

2011-12-01

Time-series of growing season air temperature anomalies in the Parisian region and of 'Pinot Noir' grape-harvest dates (GHD) in Burgundy (1676-2004) are analyzed in the frequency-domain. Variability of both time-series display three significant frequency-bands (peaks significant at the 5% level) i.e., a low-frequency band (multi-decadal) with a 25-year peak period; a 3-to-8 year band period (inter-annual) with a 3.1-year peak period; and a 2-to-3 year band period (quasi-biennial) with a 2.4-year peak period. Joint sea surface temperature/sea level pressure (SST/SLP) empirical orthogonal functions (EOF) analyses during the 20th century, along with spatio-temporal patterns for the above frequency-bands are presented. It is found that SST anomalies display early significant spatial SST patterns in the North Atlantic Ocean (air temperature lagging by 6 months) similar to those obtained from EOF analyses. It is thus proposed that the robust power spectra for the above frequency-bands could be linked with Atlantic climate variability metrics modulating Western European climate i.e., 1) the global Multi-decadal Oscillation (MDO) with its Atlantic Multi-decadal Oscillation (AMO) footprint; 2) the Atlantic Inter-Annual (IA) fluctuations; and 3) the Atlantic Quasi-Biennial (QB) fluctuations, respectively. Moreover these specific Western European climate signals have effects on ecosystem health and can be perceived as contributors to the length of the growing season and the timing of GHD in Burgundy. Thus advance knowledge on the evolution and phasing of the above climate fluctuations become important elements for viticulture and wine industry management. It is recognized that anthropogenic effects could have modified time-series patterns presented here, particularly since the mid 1980s.

Fast computation of voxel-level brain connectivity maps from resting-state functional MRI using l₁-norm as approximation of Pearson's temporal correlation: proof-of-concept and example vector hardware implementation.

PubMed

Minati, Ludovico; Zacà, Domenico; D'Incerti, Ludovico; Jovicich, Jorge

2014-09-01

An outstanding issue in graph-based analysis of resting-state functional MRI is choice of network nodes. Individual consideration of entire brain voxels may represent a less biased approach than parcellating the cortex according to pre-determined atlases, but entails establishing connectedness for 1(9)-1(11) links, with often prohibitive computational cost. Using a representative Human Connectome Project dataset, we show that, following appropriate time-series normalization, it may be possible to accelerate connectivity determination replacing Pearson correlation with l1-norm. Even though the adjacency matrices derived from correlation coefficients and l1-norms are not identical, their similarity is high. Further, we describe and provide in full an example vector hardware implementation of l1-norm on an array of 4096 zero instruction-set processors. Calculation times <1000 s are attainable, removing the major deterrent to voxel-based resting-sate network mapping and revealing fine-grained node degree heterogeneity. L1-norm should be given consideration as a substitute for correlation in very high-density resting-state functional connectivity analyses. Copyright © 2014 IPEM. Published by Elsevier Ltd. All rights reserved.

Evaluation of non-negative matrix factorization of grey matter in age prediction.

PubMed

Varikuti, Deepthi P; Genon, Sarah; Sotiras, Aristeidis; Schwender, Holger; Hoffstaedter, Felix; Patil, Kaustubh R; Jockwitz, Christiane; Caspers, Svenja; Moebus, Susanne; Amunts, Katrin; Davatzikos, Christos; Eickhoff, Simon B

2018-06-01

The relationship between grey matter volume (GMV) patterns and age can be captured by multivariate pattern analysis, allowing prediction of individuals' age based on structural imaging. Raw data, voxel-wise GMV and non-sparse factorization (with Principal Component Analysis, PCA) show good performance but do not promote relatively localized brain components for post-hoc examinations. Here we evaluated a non-negative matrix factorization (NNMF) approach to provide a reduced, but also interpretable representation of GMV data in age prediction frameworks in healthy and clinical populations. This examination was performed using three datasets: a multi-site cohort of life-span healthy adults, a single site cohort of older adults and clinical samples from the ADNI dataset with healthy subjects, participants with Mild Cognitive Impairment and patients with Alzheimer's disease (AD) subsamples. T1-weighted images were preprocessed with VBM8 standard settings to compute GMV values after normalization, segmentation and modulation for non-linear transformations only. Non-negative matrix factorization was computed on the GM voxel-wise values for a range of granularities (50-690 components) and LASSO (Least Absolute Shrinkage and Selection Operator) regression were used for age prediction. First, we compared the performance of our data compression procedure (i.e., NNMF) to various other approaches (i.e., uncompressed VBM data, PCA-based factorization and parcellation-based compression). We then investigated the impact of the granularity on the accuracy of age prediction, as well as the transferability of the factorization and model generalization across datasets. We finally validated our framework by examining age prediction in ADNI samples. Our results showed that our framework favorably compares with other approaches. They also demonstrated that the NNMF based factorization derived from one dataset could be efficiently applied to compress VBM data of another dataset and that granularities between 300 and 500 components give an optimal representation for age prediction. In addition to the good performance in healthy subjects our framework provided relatively localized brain regions as the features contributing to the prediction, thereby offering further insights into structural changes due to brain aging. Finally, our validation in clinical populations showed that our framework is sensitive to deviance from normal structural variations in pathological aging. Copyright © 2018 Elsevier Inc. All rights reserved.

Accurate Segmentation of CT Male Pelvic Organs via Regression-based Deformable Models and Multi-task Random Forests

PubMed Central

Gao, Yaozong; Shao, Yeqin; Lian, Jun; Wang, Andrew Z.; Chen, Ronald C.

2016-01-01

Segmenting male pelvic organs from CT images is a prerequisite for prostate cancer radiotherapy. The efficacy of radiation treatment highly depends on segmentation accuracy. However, accurate segmentation of male pelvic organs is challenging due to low tissue contrast of CT images, as well as large variations of shape and appearance of the pelvic organs. Among existing segmentation methods, deformable models are the most popular, as shape prior can be easily incorporated to regularize the segmentation. Nonetheless, the sensitivity to initialization often limits their performance, especially for segmenting organs with large shape variations. In this paper, we propose a novel approach to guide deformable models, thus making them robust against arbitrary initializations. Specifically, we learn a displacement regressor, which predicts 3D displacement from any image voxel to the target organ boundary based on the local patch appearance. This regressor provides a nonlocal external force for each vertex of deformable model, thus overcoming the initialization problem suffered by the traditional deformable models. To learn a reliable displacement regressor, two strategies are particularly proposed. 1) A multi-task random forest is proposed to learn the displacement regressor jointly with the organ classifier; 2) an auto-context model is used to iteratively enforce structural information during voxel-wise prediction. Extensive experiments on 313 planning CT scans of 313 patients show that our method achieves better results than alternative classification or regression based methods, and also several other existing methods in CT pelvic organ segmentation. PMID:26800531

Fully refocused multi-shot spatiotemporally encoded MRI: robust imaging in the presence of metallic implants.

PubMed

Ben-Eliezer, Noam; Solomon, Eddy; Harel, Elad; Nevo, Nava; Frydman, Lucio

2012-12-01

An approach has been recently introduced for acquiring arbitrary 2D NMR spectra or images in a single scan, based on the use of frequency-swept RF pulses for the sequential excitation and acquisition of the spins response. This spatiotemporal-encoding (SPEN) approach enables a unique, voxel-by-voxel refocusing of all frequency shifts in the sample, for all instants throughout the data acquisition. The present study investigates the use of this full-refocusing aspect of SPEN-based imaging in the multi-shot MRI of objects, subject to sizable field inhomogeneities that complicate conventional imaging approaches. 2D MRI experiments were performed at 7 T on phantoms and on mice in vivo, focusing on imaging in proximity to metallic objects. Fully refocused SPEN-based spin echo imaging sequences were implemented, using both Cartesian and back-projection trajectories, and compared with k-space encoded spin echo imaging schemes collected on identical samples under equal bandwidths and acquisition timing conditions. In all cases assayed, the fully refocused spatiotemporally encoded experiments evidenced a ca. 50 % reduction in signal dephasing in the proximity of the metal, as compared to analogous results stemming from the k-space encoded spin echo counterparts. The results in this study suggest that SPEN-based acquisition schemes carry the potential to overcome strong field inhomogeneities, of the kind that currently preclude high-field, high-resolution tissue characterizations in the neighborhood of metallic implants.

Voxel-based Gaussian naïve Bayes classification of ischemic stroke lesions in individual T1-weighted MRI scans.

PubMed

Griffis, Joseph C; Allendorfer, Jane B; Szaflarski, Jerzy P

2016-01-15

Manual lesion delineation by an expert is the standard for lesion identification in MRI scans, but it is time-consuming and can introduce subjective bias. Alternative methods often require multi-modal MRI data, user interaction, scans from a control population, and/or arbitrary statistical thresholding. We present an approach for automatically identifying stroke lesions in individual T1-weighted MRI scans using naïve Bayes classification. Probabilistic tissue segmentation and image algebra were used to create feature maps encoding information about missing and abnormal tissue. Leave-one-case-out training and cross-validation was used to obtain out-of-sample predictions for each of 30 cases with left hemisphere stroke lesions. Our method correctly predicted lesion locations for 30/30 un-trained cases. Post-processing with smoothing (8mm FWHM) and cluster-extent thresholding (100 voxels) was found to improve performance. Quantitative evaluations of post-processed out-of-sample predictions on 30 cases revealed high spatial overlap (mean Dice similarity coefficient=0.66) and volume agreement (mean percent volume difference=28.91; Pearson's r=0.97) with manual lesion delineations. Our automated approach agrees with manual tracing. It provides an alternative to automated methods that require multi-modal MRI data, additional control scans, or user interaction to achieve optimal performance. Our fully trained classifier has applications in neuroimaging and clinical contexts. Copyright © 2015 Elsevier B.V. All rights reserved.

Lesion correlates of patholinguistic profiles in chronic aphasia: comparisons of syndrome-, modality- and symptom-level assessment.

PubMed

Henseler, Ilona; Regenbrecht, Frank; Obrig, Hellmuth

2014-03-01

One way to investigate the neuronal underpinnings of language competence is to correlate patholinguistic profiles of aphasic patients to corresponding lesion sites. Constituting the beginnings of aphasiology and neurolinguistics over a century ago, this approach has been revived and refined in the past decade by statistical approaches mapping continuous variables (providing metrics that are not simply categorical) on voxel-wise lesion information (voxel-based lesion-symptom mapping). Here we investigate whether and how voxel-based lesion-symptom mapping allows us to delineate specific lesion patterns for differentially fine-grained clinical classifications. The latter encompass 'classical' syndrome-based approaches (e.g. Broca's aphasia), more symptom-oriented descriptions (e.g. agrammatism) and further refinement to linguistic sub-functions (e.g. lexico-semantic deficits for inanimate versus animate items). From a large database of patients treated for aphasia of different aetiologies (n = 1167) a carefully selected group of 102 first ever ischaemic stroke patients with chronic aphasia (∅ 12 months) were included in a VLSM analysis. Specifically, we investigated how performance in the Aachen Aphasia Test-the standard clinical test battery for chronic aphasia in German-relates to distinct brain lesions. The Aachen Aphasia Test evaluates aphasia on different levels: a non-parametric discriminant procedure yields probabilities for the allocation to one of the four 'standard' syndromes (Broca, Wernicke, global and amnestic aphasia), whereas standardized subtests target linguistic modalities (e.g. repetition), or even more specific symptoms (e.g. phoneme repetition). Because some subtests of the Aachen Aphasia Test (e.g. for the linguistic level of lexico-semantics) rely on rather coarse and heterogeneous test items we complemented the analysis with a number of more detailed clinically used tests in selected mostly mildly affected subgroups of patients. Our results indicate that: (i) Aachen Aphasia Test-based syndrome allocation allows for an unexpectedly concise differentiation between 'Broca's' and 'Wernicke's' aphasia corresponding to non-overlapping anterior and posterior lesion sites; whereas (ii) analyses for modalities and specific symptoms yielded more circumscribed but partially overlapping lesion foci, often cutting across the above syndrome territories; and (iii) especially for lexico-semantic capacities more specialized clinical test-batteries are required to delineate precise lesion patterns at this linguistic level. In sum this is the first report on a successful lesion-delineation of syndrome-based aphasia classification highlighting the relevance of vascular distribution for the syndrome level while confirming and extending a number of more linguistically motivated differentiations, based on clinically used tests. We consider such a comprehensive view reaching from the syndrome to a fine-grained symptom-oriented assessment mandatory to converge neurolinguistic, patholinguistic and clinical-therapeutic knowledge on language-competence and impairment.

Microscopic validation of whole mouse micro-metastatic tumor imaging agents using cryo-imaging and sliding organ image registration.

PubMed

Liu, Yiqiao; Zhou, Bo; Qutaish, Mohammed; Wilson, David L

2016-01-01

We created a metastasis imaging, analysis platform consisting of software and multi-spectral cryo-imaging system suitable for evaluating emerging imaging agents targeting micro-metastatic tumor. We analyzed CREKA-Gd in MRI, followed by cryo-imaging which repeatedly sectioned and tiled microscope images of the tissue block face, providing anatomical bright field and molecular fluorescence, enabling 3D microscopic imaging of the entire mouse with single metastatic cell sensitivity. To register MRI volumes to the cryo bright field reference, we used our standard mutual information, non-rigid registration which proceeded: preprocess → affine → B-spline non-rigid 3D registration. In this report, we created two modified approaches: mask where we registered locally over a smaller rectangular solid, and sliding organ . Briefly, in sliding organ , we segmented the organ, registered the organ and body volumes separately and combined results. Though s liding organ required manual annotation, it provided the best result as a standard to measure other registration methods. Regularization parameters for standard and mask methods were optimized in a grid search. Evaluations consisted of DICE, and visual scoring of a checkerboard display. Standard had accuracy of 2 voxels in all regions except near the kidney, where there were 5 voxels sliding. After mask and sliding organ correction, kidneys sliding were within 2 voxels, and Dice overlap increased 4%-10% in mask compared to standard . Mask generated comparable results with sliding organ and allowed a semi-automatic process.

Medial prefrontal aberrations in major depressive disorder revealed by cytoarchitectonically informed voxel-based morphometry

PubMed Central

Bludau, Sebastian; Bzdok, Danilo; Gruber, Oliver; Kohn, Nils; Riedl, Valentin; Sorg, Christian; Palomero-Gallagher, Nicola; Müller, Veronika I.; Hoffstaedter, Felix; Amunts, Katrin; Eickhoff, Simon B.

2017-01-01

Objective The heterogeneous human frontal pole has been identified as a node in the dysfunctional network of major depressive disorder. The contribution of the medial (socio-affective) versus lateral (cognitive) frontal pole to major depression pathogenesis is currently unclear. The present study performs morphometric comparison of the microstructurally informed subdivisions of human frontal pole between depressed patients and controls using both uni- and multivariate statistics. Methods Multi-site voxel- and region-based morphometric MRI analysis of 73 depressed patients and 73 matched controls without psychiatric history. Frontal pole volume was first compared between depressed patients and controls by subdivision-wise classical morphometric analysis. In a second approach, frontal pole volume was compared by subdivision-naive multivariate searchlight analysis based on support vector machines. Results Subdivision-wise morphometric analysis found a significantly smaller medial frontal pole in depressed patients with a negative correlation of disease severity and duration. Histologically uninformed multivariate voxel-wise statistics provided converging evidence for structural aberrations specific to the microstructurally defined medial area of the frontal pole in depressed patients. Conclusions Across disparate methods, we demonstrated subregion specificity in the left medial frontal pole volume in depressed patients. Indeed, the frontal pole was shown to structurally and functionally connect to other key regions in major depression pathology like the anterior cingulate cortex and the amygdala via the uncinate fasciculus. Present and previous findings consolidate the left medial portion of the frontal pole as particularly altered in major depression. PMID:26621569

Central focused convolutional neural networks: Developing a data-driven model for lung nodule segmentation.

PubMed

Wang, Shuo; Zhou, Mu; Liu, Zaiyi; Liu, Zhenyu; Gu, Dongsheng; Zang, Yali; Dong, Di; Gevaert, Olivier; Tian, Jie

2017-08-01

Accurate lung nodule segmentation from computed tomography (CT) images is of great importance for image-driven lung cancer analysis. However, the heterogeneity of lung nodules and the presence of similar visual characteristics between nodules and their surroundings make it difficult for robust nodule segmentation. In this study, we propose a data-driven model, termed the Central Focused Convolutional Neural Networks (CF-CNN), to segment lung nodules from heterogeneous CT images. Our approach combines two key insights: 1) the proposed model captures a diverse set of nodule-sensitive features from both 3-D and 2-D CT images simultaneously; 2) when classifying an image voxel, the effects of its neighbor voxels can vary according to their spatial locations. We describe this phenomenon by proposing a novel central pooling layer retaining much information on voxel patch center, followed by a multi-scale patch learning strategy. Moreover, we design a weighted sampling to facilitate the model training, where training samples are selected according to their degree of segmentation difficulty. The proposed method has been extensively evaluated on the public LIDC dataset including 893 nodules and an independent dataset with 74 nodules from Guangdong General Hospital (GDGH). We showed that CF-CNN achieved superior segmentation performance with average dice scores of 82.15% and 80.02% for the two datasets respectively. Moreover, we compared our results with the inter-radiologists consistency on LIDC dataset, showing a difference in average dice score of only 1.98%. Copyright © 2017. Published by Elsevier B.V.

Microscopic validation of whole mouse micro-metastatic tumor imaging agents using cryo-imaging and sliding organ image registration

NASA Astrophysics Data System (ADS)

Liu, Yiqiao; Zhou, Bo; Qutaish, Mohammed; Wilson, David L.

2016-03-01

We created a metastasis imaging, analysis platform consisting of software and multi-spectral cryo-imaging system suitable for evaluating emerging imaging agents targeting micro-metastatic tumor. We analyzed CREKA-Gd in MRI, followed by cryo-imaging which repeatedly sectioned and tiled microscope images of the tissue block face, providing anatomical bright field and molecular fluorescence, enabling 3D microscopic imaging of the entire mouse with single metastatic cell sensitivity. To register MRI volumes to the cryo bright field reference, we used our standard mutual information, non-rigid registration which proceeded: preprocess --> affine --> B-spline non-rigid 3D registration. In this report, we created two modified approaches: mask where we registered locally over a smaller rectangular solid, and sliding organ. Briefly, in sliding organ, we segmented the organ, registered the organ and body volumes separately and combined results. Though sliding organ required manual annotation, it provided the best result as a standard to measure other registration methods. Regularization parameters for standard and mask methods were optimized in a grid search. Evaluations consisted of DICE, and visual scoring of a checkerboard display. Standard had accuracy of 2 voxels in all regions except near the kidney, where there were 5 voxels sliding. After mask and sliding organ correction, kidneys sliding were within 2 voxels, and Dice overlap increased 4%-10% in mask compared to standard. Mask generated comparable results with sliding organ and allowed a semi-automatic process.

Diffusion tensor imaging of nigral degeneration in Parkinson's disease: A region-of-interest and voxel-based study at 3 T and systematic review with meta-analysis☆

PubMed Central

Schwarz, Stefan T.; Abaei, Maryam; Gontu, Vamsi; Morgan, Paul S.; Bajaj, Nin; Auer, Dorothee P.

2013-01-01

There is increasing interest in developing a reliable, affordable and accessible disease biomarker of Parkinson's disease (PD) to facilitate disease modifying PD-trials. Imaging biomarkers using magnetic resonance imaging (MRI) and diffusion tensor imaging (DTI) can describe parameters such as fractional anisotropy (FA), mean diffusivity (MD) or apparent diffusion coefficient (ADC). These parameters, when measured in the substantia nigra (SN), have not only shown promising but also varying and controversial results. To clarify the potential diagnostic value of nigral DTI in PD and its dependency on selection of region-of-interest, we undertook a high resolution DTI study at 3 T. 59 subjects (32 PD patients, 27 age and sex matched healthy controls) were analysed using manual outlining of SN and substructures, and voxel-based analysis (VBA). We also performed a systematic literature review and meta-analysis to estimate the effect size (DES) of disease related nigral DTI changes. We found a regional increase in nigral mean diffusivity in PD (mean ± SD, PD 0.80 ± 0.10 vs. controls 0.73 ± 0.06 · 10− 3 mm2/s, p = 0.002), but no difference using a voxel based approach. No significant disease effect was seen using meta-analysis of nigral MD changes (10 studies, DES = + 0.26, p = 0.17, I2 = 30%). None of the nigral regional or voxel based analyses of this study showed altered fractional anisotropy. Meta-analysis of 11 studies on nigral FA changes revealed a significant PD induced FA decrease. There was, however, a very large variation in results (I2 = 86%) comparing all studies. After exclusion of five studies with unusual high values of nigral FA in the control group, an acceptable heterogeneity was reached, but there was non-significant disease effect (DES = − 0.5, p = 0.22, I2 = 28%). The small PD related nigral MD changes in conjunction with the negative findings on VBA and meta-analysis limit the usefulness of nigral MD measures as biomarker of Parkinson's disease. The negative results of nigral FA measurements at regional, sub-regional and voxel level in conjunction with the results of the meta-analysis of nigral FA changes question the stability and validity of this measure as a PD biomarker. PMID:24273730

Perceived freedom of choice is associated with neural encoding of option availability.

PubMed

Rens, Natalie; Bode, Stefan; Cunnington, Ross

2018-05-03

Freedom of choice has been defined as the opportunity to choose alternative plans of action. In this fMRI study, we investigated how the perceived freedom of choice and the underlying neural correlates are influenced by the availability of options. Participants made an initial free choice between left or right doors before beginning a virtual walk along a corridor. At the mid-point of the corridor, lock cues appeared to reveal whether one or both doors remained available, requiring participants either to select a particular door or allowing them to freely choose to stay or switch their choice. We found that participants rated trials as free when they were able to carry out their initial choice, but even more so when both doors remained available. Multi-voxel pattern analysis showed that upcoming choices could initially be decoded from visual cortices before the appearance of the lock cues, and additionally from the motor cortex after the lock cues had confirmed which doors were open. When participants were able to maintain the same choice that they originally selected, the availability of alternative options was represented in fine-grained patterns of activity in the dorsolateral prefrontal cortex. Further, decoding accuracy in this region correlated with the subjective level of freedom that participants reported. These results suggest that there is neural encoding of the availability of alternative options in the dorsolateral prefrontal cortex, and the degree of this encoding predicts an individual's perceived freedom of choice. Copyright © 2018 Elsevier Inc. All rights reserved.

Assessing the mechanism of response in the retrosplenial cortex of good and poor navigators☆

PubMed Central

Auger, Stephen D.; Maguire, Eleanor A.

2013-01-01

The retrosplenial cortex (RSC) is consistently engaged by a range of tasks that examine episodic memory, imagining the future, spatial navigation, and scene processing. Despite this, an account of its exact contribution to these cognitive functions remains elusive. Here, using functional MRI (fMRI) and multi-voxel pattern analysis (MVPA) we found that the RSC coded for the specific number of permanent outdoor items that were in view, that is, items which are fixed and never change their location. Moreover, this effect was selective, and was not apparent for other item features such as size and visual salience. This detailed detection of the number of permanent items in view was echoed in the parahippocampal cortex (PHC), although the two brain structures diverged when participants were divided into good and poor navigators. There was no difference in the responsivity of the PHC between the two groups, while significantly better decoding of the number of permanent items in view was possible from patterns of activity in the RSC of good compared to poor navigators. Within good navigators, the RSC also facilitated significantly better prediction of item permanence than the PHC. Overall, these findings suggest that the RSC in particular is concerned with coding the presence of every permanent item that is in view. This mechanism may represent a key building block for spatial and scene representations that are central to episodic memories and imagining the future, and could also be a prerequisite for successful navigation. PMID:24012136

Detecting changes resulting from human pressure in a naturally quick-changing and heterogeneous environment: Spatial and temporal scales of variability in coastal lagoons

NASA Astrophysics Data System (ADS)

Pérez-Ruzafa, A.; Marcos, C.; Pérez-Ruzafa, I. M.; Barcala, E.; Hegazi, M. I.; Quispe, J.

2007-10-01

To detect changes in ecosystems due to human impact, experimental designs must include replicates at the appropriate scale to avoid pseudoreplication. Although coastal lagoons, with their highly variable environmental factors and biological assemblages, are relatively well-studied systems, very little is known about their natural scales of variation. In this study, we investigate the spatio-temporal scales of variability in the Mar Menor coastal lagoon (SE Spain) using structured hierarchical sampling designs, mixed and permutational multi-variate analyses of variance, and ordination multi-variate analyses applied to hydrographical parameters, nutrients, chlorophyll a and ichthyoplankton in the water column, and to macrophyte and fish benthic assemblages. Lagoon processes in the Mar Menor show heterogeneous patterns at different temporal and spatial scales. The water column characteristics (including nutrient concentration) showed small-scale spatio-temporal variability, from 10 0 to 10 1 km and from fortnightly to seasonally. Biological features (chlorophyll a concentration and ichthyoplankton assemblage descriptors) showed monthly changes and spatial patterns at the scale of 10 0 (chlorophyll a) - 10 1 km (ichthyoplankton). Benthic assemblages (macrophytes and fishes) showed significant differences between types of substrates in the same locality and between localities, according to horizontal gradients related with confinement in the lagoon, at the scale of 10 0-10 1 km. The vertical zonation of macrophyte assemblages (at scales of 10 1-10 2 cm) overlaps changes in substrata and horizontal gradients. Seasonal patterns in vegetation biomass were not significant, but the significant interaction between Locality and Season indicated that the seasons of maximum and minimum biomass depend on local environmental conditions. Benthic fish assemblages showed no significant patterns at the monthly scale but did show seasonal patterns.

From Solidification Processing to Microstructure to Mechanical Properties: A Multi-scale X-ray Study of an Al-Cu Alloy Sample

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tourret, D.; Mertens, J. C. E.; Lieberman, E.

We follow an Al-12 at. pct Cu alloy sample from the liquid state to mechanical failure, using in situ X-ray radiography during directional solidification and tensile testing, as well as three-dimensional computed tomography of the microstructure before and after mechanical testing. The solidification processing stage is simulated with a multi-scale dendritic needle network model, and the micromechanical behavior of the solidified microstructure is simulated using voxelized tomography data and an elasto-viscoplastic fast Fourier transform model. This study demonstrates the feasibility of direct in situ monitoring of a metal alloy microstructure from the liquid processing stage up to its mechanical failure,more » supported by quantitative simulations of microstructure formation and its mechanical behavior.« less

From Solidification Processing to Microstructure to Mechanical Properties: A Multi-scale X-ray Study of an Al-Cu Alloy Sample

DOE PAGES

Tourret, D.; Mertens, J. C. E.; Lieberman, E.; ...

2017-09-13

We follow an Al-12 at. pct Cu alloy sample from the liquid state to mechanical failure, using in situ X-ray radiography during directional solidification and tensile testing, as well as three-dimensional computed tomography of the microstructure before and after mechanical testing. The solidification processing stage is simulated with a multi-scale dendritic needle network model, and the micromechanical behavior of the solidified microstructure is simulated using voxelized tomography data and an elasto-viscoplastic fast Fourier transform model. This study demonstrates the feasibility of direct in situ monitoring of a metal alloy microstructure from the liquid processing stage up to its mechanical failure,more » supported by quantitative simulations of microstructure formation and its mechanical behavior.« less

From Solidification Processing to Microstructure to Mechanical Properties: A Multi-scale X-ray Study of an Al-Cu Alloy Sample

NASA Astrophysics Data System (ADS)

Tourret, D.; Mertens, J. C. E.; Lieberman, E.; Imhoff, S. D.; Gibbs, J. W.; Henderson, K.; Fezzaa, K.; Deriy, A. L.; Sun, T.; Lebensohn, R. A.; Patterson, B. M.; Clarke, A. J.

2017-11-01

We follow an Al-12 at. pct Cu alloy sample from the liquid state to mechanical failure, using in situ X-ray radiography during directional solidification and tensile testing, as well as three-dimensional computed tomography of the microstructure before and after mechanical testing. The solidification processing stage is simulated with a multi-scale dendritic needle network model, and the micromechanical behavior of the solidified microstructure is simulated using voxelized tomography data and an elasto-viscoplastic fast Fourier transform model. This study demonstrates the feasibility of direct in situ monitoring of a metal alloy microstructure from the liquid processing stage up to its mechanical failure, supported by quantitative simulations of microstructure formation and its mechanical behavior.

Comparison of quantitative Y-90 SPECT and non-time-of-flight PET imaging in post-therapy radioembolization of liver cancer

PubMed Central

Yue, Jianting; Mauxion, Thibault; Reyes, Diane K.; Lodge, Martin A.; Hobbs, Robert F.; Rong, Xing; Dong, Yinfeng; Herman, Joseph M.; Wahl, Richard L.; Geschwind, Jean-François H.; Frey, Eric C.

2016-01-01

Purpose: Radioembolization with yttrium-90 microspheres may be optimized with patient-specific pretherapy treatment planning. Dose verification and validation of treatment planning methods require quantitative imaging of the post-therapy distribution of yttrium-90 (Y-90). Methods for quantitative imaging of Y-90 using both bremsstrahlung SPECT and PET have previously been described. The purpose of this study was to compare the two modalities quantitatively in humans. Methods: Calibration correction factors for both quantitative Y-90 bremsstrahlung SPECT and a non-time-of-flight PET system without compensation for prompt coincidences were developed by imaging three phantoms. The consistency of these calibration correction factors for the different phantoms was evaluated. Post-therapy images from both modalities were obtained from 15 patients with hepatocellular carcinoma who underwent hepatic radioembolization using Y-90 glass microspheres. Quantitative SPECT and PET images were rigidly registered and the total liver activities and activity distributions estimated for each modality were compared. The activity distributions were compared using profiles, voxel-by-voxel correlation and Bland–Altman analyses, and activity-volume histograms. Results: The mean ± standard deviation of difference in the total activity in the liver between the two modalities was 0% ± 9% (range −21%–18%). Voxel-by-voxel comparisons showed a good agreement in regions corresponding roughly to treated tumor and treated normal liver; the agreement was poorer in regions with low or no expected activity, where PET appeared to overestimate the activity. The correlation coefficients between intrahepatic voxel pairs for the two modalities ranged from 0.86 to 0.94. Cumulative activity volume histograms were in good agreement. Conclusions: These data indicate that, with appropriate reconstruction methods and measured calibration correction factors, either Y-90 SPECT/CT or Y-90 PET/CT can be used for quantitative post-therapy monitoring of Y-90 activity distribution following hepatic radioembolization. PMID:27782730

Comparison of quantitative Y-90 SPECT and non-time-of-flight PET imaging in post-therapy radioembolization of liver cancer.

PubMed

Yue, Jianting; Mauxion, Thibault; Reyes, Diane K; Lodge, Martin A; Hobbs, Robert F; Rong, Xing; Dong, Yinfeng; Herman, Joseph M; Wahl, Richard L; Geschwind, Jean-François H; Frey, Eric C

2016-10-01

Radioembolization with yttrium-90 microspheres may be optimized with patient-specific pretherapy treatment planning. Dose verification and validation of treatment planning methods require quantitative imaging of the post-therapy distribution of yttrium-90 (Y-90). Methods for quantitative imaging of Y-90 using both bremsstrahlung SPECT and PET have previously been described. The purpose of this study was to compare the two modalities quantitatively in humans. Calibration correction factors for both quantitative Y-90 bremsstrahlung SPECT and a non-time-of-flight PET system without compensation for prompt coincidences were developed by imaging three phantoms. The consistency of these calibration correction factors for the different phantoms was evaluated. Post-therapy images from both modalities were obtained from 15 patients with hepatocellular carcinoma who underwent hepatic radioembolization using Y-90 glass microspheres. Quantitative SPECT and PET images were rigidly registered and the total liver activities and activity distributions estimated for each modality were compared. The activity distributions were compared using profiles, voxel-by-voxel correlation and Bland-Altman analyses, and activity-volume histograms. The mean ± standard deviation of difference in the total activity in the liver between the two modalities was 0% ± 9% (range -21%-18%). Voxel-by-voxel comparisons showed a good agreement in regions corresponding roughly to treated tumor and treated normal liver; the agreement was poorer in regions with low or no expected activity, where PET appeared to overestimate the activity. The correlation coefficients between intrahepatic voxel pairs for the two modalities ranged from 0.86 to 0.94. Cumulative activity volume histograms were in good agreement. These data indicate that, with appropriate reconstruction methods and measured calibration correction factors, either Y-90 SPECT/CT or Y-90 PET/CT can be used for quantitative post-therapy monitoring of Y-90 activity distribution following hepatic radioembolization.

Multi-session complex averaging for high resolution high SNR 3T MR visualization of ex vivo hippocampus and insula

NASA Astrophysics Data System (ADS)

Stamm, Aymeric; Singh, Jolene M.; Scherrer, Benoit; Afacan, Onur; Warfield, Simon K.

2015-03-01

The hippocampus and the insula are responsible for episodic memory formation and retrieval. Hence, visualization of the cytoarchitecture of such structures is of primary importance to understand the underpinnings of conscious experience. Magnetic Resonance Imaging (MRI) offers an opportunity to non-invasively image these crucial structures. However, current clinical MR imaging operates at the millimeter scale while these anatomical landmarks are organized into sub-millimeter structures. For instance, the hippocampus contains several layers, including the CA3-dentate network responsible for encoding events and experiences. To investigate whether memory loss is a result of injury or degradation of CA3/dentate, spatial resolution must exceed one hundred micron, isotropic, voxel size. Going from one millimeter voxels to one hundred micron voxels results in a 1000× signal loss, making the measured signal close to or even way below the precision of the receiving coils. Consequently, the signal magnitude that forms the structural images will be biased and noisy, which results in inaccurate contrast and less than optimal signal-to-noise ratio (SNR). In this paper, we propose a strategy to perform high spatial resolution MR imaging of the hippocampus and insula with 3T scanners that enables accurate contrast (no systematic bias) and arbitrarily high SNR. This requires the collection of additional repeated measurements of the same image and a proper averaging of the k-space data in the complex domain. This comes at the cost of additional scan time, but long single-session scan times are not practical for obvious reasons. Hence, we also develop an approach to combine k-space data from multiple sessions, which enables the total scan time to be split into arbitrarily short sessions, where the patient is allowed to move and rest in-between. For validation, we hereby illustrate our multi-session complex averaging strategy by providing high spatial resolution 3T MR visualization of the hippocampus and insula using an ex-vivo specimen, so that the number of sessions and the duration of each session are not limited by physiological motion or poor subject compliance.

Analysis of a municipal wastewater treatment plant using a neural network-based pattern analysis

USGS Publications Warehouse

Hong, Y.-S.T.; Rosen, Michael R.; Bhamidimarri, R.

2003-01-01

This paper addresses the problem of how to capture the complex relationships that exist between process variables and to diagnose the dynamic behaviour of a municipal wastewater treatment plant (WTP). Due to the complex biological reaction mechanisms, the highly time-varying, and multivariable aspects of the real WTP, the diagnosis of the WTP are still difficult in practice. The application of intelligent techniques, which can analyse the multi-dimensional process data using a sophisticated visualisation technique, can be useful for analysing and diagnosing the activated-sludge WTP. In this paper, the Kohonen Self-Organising Feature Maps (KSOFM) neural network is applied to analyse the multi-dimensional process data, and to diagnose the inter-relationship of the process variables in a real activated-sludge WTP. By using component planes, some detailed local relationships between the process variables, e.g., responses of the process variables under different operating conditions, as well as the global information is discovered. The operating condition and the inter-relationship among the process variables in the WTP have been diagnosed and extracted by the information obtained from the clustering analysis of the maps. It is concluded that the KSOFM technique provides an effective analysing and diagnosing tool to understand the system behaviour and to extract knowledge contained in multi-dimensional data of a large-scale WTP. ?? 2003 Elsevier Science Ltd. All rights reserved.

Abnormal hubs of white matter networks in the frontal-parieto circuit contribute to depression discrimination via pattern classification.

PubMed

Qin, Jiaolong; Wei, Maobin; Liu, Haiyan; Chen, Jianhuai; Yan, Rui; Hua, Lingling; Zhao, Ke; Yao, Zhijian; Lu, Qing

2014-12-01

Previous studies had explored the diagnostic and prognostic value of the structural neuroimaging data of MDD and treated the whole brain voxels, the fractional anisotropy and the structural connectivity as classification features. To our best knowledge, no study examined the potential diagnostic value of the hubs of anatomical brain networks in MDD. The purpose of the current study was to provide an exploratory examination of the potential diagnostic and prognostic values of hubs of white matter brain networks in MDD discrimination and the corresponding impaired hub pattern via a multi-pattern analysis. We constructed white matter brain networks from 29 depressions and 30 healthy controls based on diffusion tensor imaging data, calculated nodal measures and identified hubs. Using these measures as features, two types of feature architectures were established, one only included hubs (HUB) and the other contained both hubs and non hubs. The support vector machine classifiers with Gaussian radial basis kernel were used after the feature selection. Moreover, the relative contribution of the features was estimated by means of the consensus features. Our results presented that the hubs (including the bilateral dorsolateral part of superior frontal gyrus, the left middle frontal gyrus, the bilateral middle temporal gyrus, and the bilateral inferior temporal gyrus) played an important role in distinguishing the depressions from healthy controls with the best accuracy of 83.05%. Moreover, most of the HUB consensus features located in the frontal-parieto circuit. These findings provided evidence that the hubs could be served as valuable potential diagnostic measure for MDD, and the hub-concentrated lesion distribution of MDD was primarily anchored within the frontal-parieto circuit. Copyright © 2014 Elsevier Inc. All rights reserved.

Representational Similarity Analysis – Connecting the Branches of Systems Neuroscience

PubMed Central

Kriegeskorte, Nikolaus; Mur, Marieke; Bandettini, Peter

2008-01-01

A fundamental challenge for systems neuroscience is to quantitatively relate its three major branches of research: brain-activity measurement, behavioral measurement, and computational modeling. Using measured brain-activity patterns to evaluate computational network models is complicated by the need to define the correspondency between the units of the model and the channels of the brain-activity data, e.g., single-cell recordings or voxels from functional magnetic resonance imaging (fMRI). Similar correspondency problems complicate relating activity patterns between different modalities of brain-activity measurement (e.g., fMRI and invasive or scalp electrophysiology), and between subjects and species. In order to bridge these divides, we suggest abstracting from the activity patterns themselves and computing representational dissimilarity matrices (RDMs), which characterize the information carried by a given representation in a brain or model. Building on a rich psychological and mathematical literature on similarity analysis, we propose a new experimental and data-analytical framework called representational similarity analysis (RSA), in which multi-channel measures of neural activity are quantitatively related to each other and to computational theory and behavior by comparing RDMs. We demonstrate RSA by relating representations of visual objects as measured with fMRI in early visual cortex and the fusiform face area to computational models spanning a wide range of complexities. The RDMs are simultaneously related via second-level application of multidimensional scaling and tested using randomization and bootstrap techniques. We discuss the broad potential of RSA, including novel approaches to experimental design, and argue that these ideas, which have deep roots in psychology and neuroscience, will allow the integrated quantitative analysis of data from all three branches, thus contributing to a more unified systems neuroscience. PMID:19104670

Good Exemplars of Natural Scene Categories Elicit Clearer Patterns than Bad Exemplars but Not Greater BOLD Activity

PubMed Central

Torralbo, Ana; Walther, Dirk B.; Chai, Barry; Caddigan, Eamon; Fei-Fei, Li; Beck, Diane M.

2013-01-01

Within the range of images that we might categorize as a “beach”, for example, some will be more representative of that category than others. Here we first confirmed that humans could categorize “good” exemplars better than “bad” exemplars of six scene categories and then explored whether brain regions previously implicated in natural scene categorization showed a similar sensitivity to how well an image exemplifies a category. In a behavioral experiment participants were more accurate and faster at categorizing good than bad exemplars of natural scenes. In an fMRI experiment participants passively viewed blocks of good or bad exemplars from the same six categories. A multi-voxel pattern classifier trained to discriminate among category blocks showed higher decoding accuracy for good than bad exemplars in the PPA, RSC and V1. This difference in decoding accuracy cannot be explained by differences in overall BOLD signal, as average BOLD activity was either equivalent or higher for bad than good scenes in these areas. These results provide further evidence that V1, RSC and the PPA not only contain information relevant for natural scene categorization, but their activity patterns mirror the fundamentally graded nature of human categories. Analysis of the image statistics of our good and bad exemplars shows that variability in low-level features and image structure is higher among bad than good exemplars. A simulation of our neuroimaging experiment suggests that such a difference in variance could account for the observed differences in decoding accuracy. These results are consistent with both low-level models of scene categorization and models that build categories around a prototype. PMID:23555588

Increased spatial granularity of left brain activation and unique age/gender signatures: a 4D frequency domain approach to cerebral lateralization at rest.

PubMed

Agcaoglu, O; Miller, R; Mayer, A R; Hugdahl, K; Calhoun, V D

2016-12-01

Cerebral lateralization is a well-studied topic. However, most of the research to date in functional magnetic resonance imaging (fMRI) has been carried out on hemodynamic fluctuations of voxels, networks, or regions of interest (ROIs). For example, cerebral differences can be revealed by comparing the temporal activation of an ROI in one hemisphere with the corresponding homotopic region in the other hemisphere. While this approach can reveal significant information about cerebral organization, it does not provide information about the full spatiotemporal organization of the hemispheres. The cerebral differences revealed in literature suggest that hemispheres have different spatiotemporal organization in the resting state. In this study, we evaluate cerebral lateralization in the 4D spatiotemporal frequency domain to compare the hemispheres in the context of general activation patterns at different spatial and temporal scales. We use a gender-balanced resting fMRI dataset comprising over 600 healthy subjects ranging in age from 12 to 71, that have previously been studied with a network specific voxel-wise and global analysis of lateralization (Agcaoglu, et al. NeuroImage, 2014). Our analysis elucidates significant differences in the spatiotemporal organization of brain activity between hemispheres, and generally more spatiotemporal fluctuation in the left hemisphere especially in the high spatial frequency bands, and more power in the right hemisphere in the low and middle spatial frequencies. Importantly, the identified effects are not visible in the context of a typical assessment of voxelwise, regional, or even global laterality, thus our study highlights the value of 4D spatiotemporal frequency domain analyses as a complementary and powerful tool for studying brain function.

Reduced prefrontal efficiency for visuospatial working memory in attention-deficit/hyperactivity disorder.

PubMed

Bédard, Anne-Claude V; Newcorn, Jeffrey H; Clerkin, Suzanne M; Krone, Beth; Fan, Jin; Halperin, Jeffrey M; Schulz, Kurt P

2014-09-01

Visuospatial working memory impairments have been implicated in the pathophysiology of attention-deficit/hyperactivity disorder (ADHD). However, most ADHD research has focused on the neural correlates of nonspatial mnemonic processes. This study examined brain activation and functional connectivity for visuospatial working memory in youth with and without ADHD. Twenty-four youth with ADHD and 21 age- and sex-matched healthy controls were scanned with functional magnetic resonance imaging while performing an N-back test of working memory for spatial position. Block-design analyses contrasted activation and functional connectivity separately for high (2-back) and low (1-back) working memory load conditions versus the control condition (0-back). The effect of working memory load was modeled with linear contrasts. The 2 groups performed comparably on the task and demonstrated similar patterns of frontoparietal activation, with no differences in linear gains in activation as working memory load increased. However, youth with ADHD showed greater activation in the left dorsolateral prefrontal cortex (DLPFC) and left posterior cingulate cortex (PCC), greater functional connectivity between the left DLPFC and left intraparietal sulcus, and reduced left DLPFC connectivity with left midcingulate cortex and PCC for the high load contrast compared to controls (p < .01; k > 100 voxels). Reanalysis using a more conservative statistical approach (p < .001; k > 100 voxels) yielded group differences in PCC activation and DLPFC-midcingulate connectivity. Youth with ADHD show decreased efficiency of DLPFC for high-load visuospatial working memory and greater reliance on posterior spatial attention circuits to store and update spatial position than healthy control youth. Findings should be replicated in larger samples. Copyright © 2014 American Academy of Child and Adolescent Psychiatry. Published by Elsevier Inc. All rights reserved.

A Hybrid CPU-GPU Accelerated Framework for Fast Mapping of High-Resolution Human Brain Connectome

PubMed Central

Ren, Ling; Xu, Mo; Xie, Teng; Gong, Gaolang; Xu, Ningyi; Yang, Huazhong; He, Yong

2013-01-01

Recently, a combination of non-invasive neuroimaging techniques and graph theoretical approaches has provided a unique opportunity for understanding the patterns of the structural and functional connectivity of the human brain (referred to as the human brain connectome). Currently, there is a very large amount of brain imaging data that have been collected, and there are very high requirements for the computational capabilities that are used in high-resolution connectome research. In this paper, we propose a hybrid CPU-GPU framework to accelerate the computation of the human brain connectome. We applied this framework to a publicly available resting-state functional MRI dataset from 197 participants. For each subject, we first computed Pearson’s Correlation coefficient between any pairs of the time series of gray-matter voxels, and then we constructed unweighted undirected brain networks with 58 k nodes and a sparsity range from 0.02% to 0.17%. Next, graphic properties of the functional brain networks were quantified, analyzed and compared with those of 15 corresponding random networks. With our proposed accelerating framework, the above process for each network cost 80∼150 minutes, depending on the network sparsity. Further analyses revealed that high-resolution functional brain networks have efficient small-world properties, significant modular structure, a power law degree distribution and highly connected nodes in the medial frontal and parietal cortical regions. These results are largely compatible with previous human brain network studies. Taken together, our proposed framework can substantially enhance the applicability and efficacy of high-resolution (voxel-based) brain network analysis, and have the potential to accelerate the mapping of the human brain connectome in normal and disease states. PMID:23675425

Cortical hypometabolism and hypoperfusion in Parkinson's disease is extensive: probably even at early disease stages.

PubMed

Borghammer, Per; Chakravarty, Mallar; Jonsdottir, Kristjana Yr; Sato, Noriko; Matsuda, Hiroshi; Ito, Kengo; Arahata, Yutaka; Kato, Takashi; Gjedde, Albert

2010-05-01

Recent cerebral blood flow (CBF) and glucose consumption (CMRglc) studies of Parkinson's disease (PD) revealed conflicting results. Using simulated data, we previously demonstrated that the often-reported subcortical hypermetabolism in PD could be explained as an artifact of biased global mean (GM) normalization, and that low-magnitude, extensive cortical hypometabolism is best detected by alternative data-driven normalization methods. Thus, we hypothesized that PD is characterized by extensive cortical hypometabolism but no concurrent widespread subcortical hypermetabolism and tested it on three independent samples of PD patients. We compared SPECT CBF images of 32 early-stage and 33 late-stage PD patients with that of 60 matched controls. We also compared PET FDG images from 23 late-stage PD patients with that of 13 controls. Three different normalization methods were compared: (1) GM normalization, (2) cerebellum normalization, (3) reference cluster normalization (Yakushev et al.). We employed standard voxel-based statistics (fMRIstat) and principal component analysis (SSM). Additionally, we performed a meta-analysis of all quantitative CBF and CMRglc studies in the literature to investigate whether the global mean (GM) values in PD are decreased. Voxel-based analysis with GM normalization and the SSM method performed similarly, i.e., both detected decreases in small cortical clusters and concomitant increases in extensive subcortical regions. Cerebellum normalization revealed more widespread cortical decreases but no subcortical increase. In all comparisons, the Yakushev method detected nearly identical patterns of very extensive cortical hypometabolism. Lastly, the meta-analyses demonstrated that global CBF and CMRglc values are decreased in PD. Based on the results, we conclude that PD most likely has widespread cortical hypometabolism, even at early disease stages. In contrast, extensive subcortical hypermetabolism is probably not a feature of PD.

Reduced variance in monozygous twins for multiple MR parameters: implications for disease studies and the genetic basis of brain structure.

PubMed

Pell, Gaby S; Briellmann, Regula S; Lawrence, Kate M; Glencross, Deborah; Wellard, R Mark; Berkovic, Samuel F; Jackson, Graeme D

2010-01-15

Twin studies offer the opportunity to determine the relative contribution of genes versus environment in traits of interest. Here, we investigate the extent to which variance in brain structure is reduced in monozygous twins with identical genetic make-up. We investigate whether using twins as compared to a control population reduces variability in a number of common magnetic resonance (MR) structural measures, and we investigate the location of areas under major genetic influences. This is fundamental to understanding the benefit of using twins in studies where structure is the phenotype of interest. Twenty-three pairs of healthy MZ twins were compared to matched control pairs. Volume, T2 and diffusion MR imaging were performed as well as spectroscopy (MRS). Images were compared using (i) global measures of standard deviation and effect size, (ii) voxel-based analysis of similarity and (iii) intra-pair correlation. Global measures indicated a consistent increase in structural similarity in twins. The voxel-based and correlation analyses indicated a widespread pattern of increased similarity in twin pairs, particularly in frontal and temporal regions. The areas of increased similarity were most widespread for the diffusion trace and least widespread for T2. MRS showed consistent reduction in metabolite variation that was significant in the temporal lobe N-acetylaspartate (NAA). This study has shown the distribution and magnitude of reduced variability in brain volume, diffusion, T2 and metabolites in twins. The data suggest that evaluation of twins discordant for disease is indeed a valid way to attribute genetic or environmental influences to observed abnormalities in patients since evidence is provided for the underlying assumption of decreased variability in twins.

Striatal dopamine (D2) receptor availability predicts socially desirable responding.

PubMed

Reeves, Suzanne J; Mehta, Mitul A; Montgomery, Andrew J; Amiras, Dimitri; Egerton, Alice; Howard, Robert J; Grasby, Paul M

2007-02-15

Research in non-human primates has implicated striatal dopamine (D2) receptor function in the expression of social dominance--a fundamental component of social extraversion. We predicted that trait extraversion - indexed by the revised Eysenck Personality Questionnaire (EPQ-R) - would correlate with striatal DA (D2) receptor measures - indexed by [(11)C]-Raclopride binding potential (BP) - in 28 healthy post-menopausal females (mean age=75 years; range=58-91 years). Region of interest (ROI) and voxel-based statistical parametric mapping (SPM) analyses were performed, using a reference tissue model for [(11)C]-Raclopride. ROI analysis showed moderately significant negative correlations between extraversion and BP measures in the left caudate and between psychoticism scores and BP in the right putamen. Unexpectedly, scores on the Lie scale, a measure of socially desirable responding, were significantly and negatively correlated with BP measures in the putamen and survived Bonferroni correction on the right side. After controlling for the potential confounding of self-report bias in high Lie scorers, only the correlation between Lie scores and BP measures in the right putamen remained significant. Voxel-based analysis showed only Lie scores to be significantly and negatively correlated with BP measures in the right putamen. We explored this association further by applying an ROI-based approach to data on a previously scanned sample of young adults (n=13) and found a similar pattern of association, which achieved trend level significance in the right putamen. Although unanticipated, the relationship observed between BP measures in the right putamen and Lie scores is consistent with dopaminergic involvement in socially rewarding behaviour. How this relates to dopaminergic tone will need to be further explored.

A feasibility study on estimation of tissue mixture contributions in 3D arterial spin labeling sequence

NASA Astrophysics Data System (ADS)

Liu, Yang; Pu, Huangsheng; Zhang, Xi; Li, Baojuan; Liang, Zhengrong; Lu, Hongbing

2017-03-01

Arterial spin labeling (ASL) provides a noninvasive measurement of cerebral blood flow (CBF). Due to relatively low spatial resolution, the accuracy of CBF measurement is affected by the partial volume (PV) effect. To obtain accurate CBF estimation, the contribution of each tissue type in the mixture is desirable. In general, this can be obtained according to the registration of ASL and structural image in current ASL studies. This approach can obtain probability of each tissue type inside each voxel, but it also introduces error, which include error of registration algorithm and imaging itself error in scanning of ASL and structural image. Therefore, estimation of mixture percentage directly from ASL data is greatly needed. Under the assumption that ASL signal followed the Gaussian distribution and each tissue type is independent, a maximum a posteriori expectation-maximization (MAP-EM) approach was formulated to estimate the contribution of each tissue type to the observed perfusion signal at each voxel. Considering the sensitivity of MAP-EM to the initialization, an approximately accurate initialization was obtain using 3D Fuzzy c-means method. Our preliminary results demonstrated that the GM and WM pattern across the perfusion image can be sufficiently visualized by the voxel-wise tissue mixtures, which may be promising for the diagnosis of various brain diseases.

Stability of whole brain and regional network topology within and between resting and cognitive states.

PubMed

Rzucidlo, Justyna K; Roseman, Paige L; Laurienti, Paul J; Dagenbach, Dale

2013-01-01

Graph-theory based analyses of resting state functional Magnetic Resonance Imaging (fMRI) data have been used to map the network organization of the brain. While numerous analyses of resting state brain organization exist, many questions remain unexplored. The present study examines the stability of findings based on this approach over repeated resting state and working memory state sessions within the same individuals. This allows assessment of stability of network topology within the same state for both rest and working memory, and between rest and working memory as well. fMRI scans were performed on five participants while at rest and while performing the 2-back working memory task five times each, with task state alternating while they were in the scanner. Voxel-based whole brain network analyses were performed on the resulting data along with analyses of functional connectivity in regions associated with resting state and working memory. Network topology was fairly stable across repeated sessions of the same task, but varied significantly between rest and working memory. In the whole brain analysis, local efficiency, Eloc, differed significantly between rest and working memory. Analyses of network statistics for the precuneus and dorsolateral prefrontal cortex revealed significant differences in degree as a function of task state for both regions and in local efficiency for the precuneus. Conversely, no significant differences were observed across repeated sessions of the same state. These findings suggest that network topology is fairly stable within individuals across time for the same state, but also fluid between states. Whole brain voxel-based network analyses may prove to be a valuable tool for exploring how functional connectivity changes in response to task demands.

Fully automatic multi-atlas segmentation of CTA for partial volume correction in cardiac SPECT/CT

NASA Astrophysics Data System (ADS)

Liu, Qingyi; Mohy-ud-Din, Hassan; Boutagy, Nabil E.; Jiang, Mingyan; Ren, Silin; Stendahl, John C.; Sinusas, Albert J.; Liu, Chi

2017-05-01

Anatomical-based partial volume correction (PVC) has been shown to improve image quality and quantitative accuracy in cardiac SPECT/CT. However, this method requires manual segmentation of various organs from contrast-enhanced computed tomography angiography (CTA) data. In order to achieve fully automatic CTA segmentation for clinical translation, we investigated the most common multi-atlas segmentation methods. We also modified the multi-atlas segmentation method by introducing a novel label fusion algorithm for multiple organ segmentation to eliminate overlap and gap voxels. To evaluate our proposed automatic segmentation, eight canine 99mTc-labeled red blood cell SPECT/CT datasets that incorporated PVC were analyzed, using the leave-one-out approach. The Dice similarity coefficient of each organ was computed. Compared to the conventional label fusion method, our proposed label fusion method effectively eliminated gaps and overlaps and improved the CTA segmentation accuracy. The anatomical-based PVC of cardiac SPECT images with automatic multi-atlas segmentation provided consistent image quality and quantitative estimation of intramyocardial blood volume, as compared to those derived using manual segmentation. In conclusion, our proposed automatic multi-atlas segmentation method of CTAs is feasible, practical, and facilitates anatomical-based PVC of cardiac SPECT/CT images.

Integrated optimization of location assignment and sequencing in multi-shuttle automated storage and retrieval systems under modified 2n-command cycle pattern

NASA Astrophysics Data System (ADS)

Yang, Peng; Peng, Yongfei; Ye, Bin; Miao, Lixin

2017-09-01

This article explores the integrated optimization problem of location assignment and sequencing in multi-shuttle automated storage/retrieval systems under the modified 2n-command cycle pattern. The decision of storage and retrieval (S/R) location assignment and S/R request sequencing are jointly considered. An integer quadratic programming model is formulated to describe this integrated optimization problem. The optimal travel cycles for multi-shuttle S/R machines can be obtained to process S/R requests in the storage and retrieval request order lists by solving the model. The small-sized instances are optimally solved using CPLEX. For large-sized problems, two tabu search algorithms are proposed, in which the first come, first served and nearest neighbour are used to generate initial solutions. Various numerical experiments are conducted to examine the heuristics' performance and the sensitivity of algorithm parameters. Furthermore, the experimental results are analysed from the viewpoint of practical application, and a parameter list for applying the proposed heuristics is recommended under different real-life scenarios.

Brain Volume Differences Associated With Hearing Impairment in Adults

PubMed Central

Vriend, Chris; Heslenfeld, Dirk J.; Versfeld, Niek J.; Kramer, Sophia E.

2018-01-01

Speech comprehension depends on the successful operation of a network of brain regions. Processing of degraded speech is associated with different patterns of brain activity in comparison with that of high-quality speech. In this exploratory study, we studied whether processing degraded auditory input in daily life because of hearing impairment is associated with differences in brain volume. We compared T1-weighted structural magnetic resonance images of 17 hearing-impaired (HI) adults with those of 17 normal-hearing (NH) controls using a voxel-based morphometry analysis. HI adults were individually matched with NH adults based on age and educational level. Gray and white matter brain volumes were compared between the groups by region-of-interest analyses in structures associated with speech processing, and by whole-brain analyses. The results suggest increased gray matter volume in the right angular gyrus and decreased white matter volume in the left fusiform gyrus in HI listeners as compared with NH ones. In the HI group, there was a significant correlation between hearing acuity and cluster volume of the gray matter cluster in the right angular gyrus. This correlation supports the link between partial hearing loss and altered brain volume. The alterations in volume may reflect the operation of compensatory mechanisms that are related to decoding meaning from degraded auditory input. PMID:29557274

Efficient visibility-driven medical image visualisation via adaptive binned visibility histogram.

PubMed

Jung, Younhyun; Kim, Jinman; Kumar, Ashnil; Feng, David Dagan; Fulham, Michael

2016-07-01

'Visibility' is a fundamental optical property that represents the observable, by users, proportion of the voxels in a volume during interactive volume rendering. The manipulation of this 'visibility' improves the volume rendering processes; for instance by ensuring the visibility of regions of interest (ROIs) or by guiding the identification of an optimal rendering view-point. The construction of visibility histograms (VHs), which represent the distribution of all the visibility of all voxels in the rendered volume, enables users to explore the volume with real-time feedback about occlusion patterns among spatially related structures during volume rendering manipulations. Volume rendered medical images have been a primary beneficiary of VH given the need to ensure that specific ROIs are visible relative to the surrounding structures, e.g. the visualisation of tumours that may otherwise be occluded by neighbouring structures. VH construction and its subsequent manipulations, however, are computationally expensive due to the histogram binning of the visibilities. This limits the real-time application of VH to medical images that have large intensity ranges and volume dimensions and require a large number of histogram bins. In this study, we introduce an efficient adaptive binned visibility histogram (AB-VH) in which a smaller number of histogram bins are used to represent the visibility distribution of the full VH. We adaptively bin medical images by using a cluster analysis algorithm that groups the voxels according to their intensity similarities into a smaller subset of bins while preserving the distribution of the intensity range of the original images. We increase efficiency by exploiting the parallel computation and multiple render targets (MRT) extension of the modern graphical processing units (GPUs) and this enables efficient computation of the histogram. We show the application of our method to single-modality computed tomography (CT), magnetic resonance (MR) imaging and multi-modality positron emission tomography-CT (PET-CT). In our experiments, the AB-VH markedly improved the computational efficiency for the VH construction and thus improved the subsequent VH-driven volume manipulations. This efficiency was achieved without major degradation in the VH visually and numerical differences between the AB-VH and its full-bin counterpart. We applied several variants of the K-means clustering algorithm with varying Ks (the number of clusters) and found that higher values of K resulted in better performance at a lower computational gain. The AB-VH also had an improved performance when compared to the conventional method of down-sampling of the histogram bins (equal binning) for volume rendering visualisation. Copyright © 2016 Elsevier Ltd. All rights reserved.

A voxel visualization and analysis system based on AutoCAD

NASA Astrophysics Data System (ADS)

Marschallinger, Robert

1996-05-01

A collection of AutoLISP programs is presented which enable the visualization and analysis of voxel models by AutoCAD rel. 12/rel. 13. The programs serve as an interactive, graphical front end for manipulating the results of three-dimensional modeling software producing block estimation data. ASCII data files describing geometry and attributes per estimation block are imported and stored as a voxel array. Each voxel may contain multiple attributes, therefore different parameters may be incorporated in one voxel array. Voxel classification is implemented on a layer basis providing flexible treatment of voxel classes such as recoloring, peeling, or volumetry. A versatile clipping tool enables slicing voxel arrays according to combinations of three perpendicular clipping planes. The programs feature an up-to-date, graphical user interface for user-friendly operation by non AutoCAD specialists.

Mapping the bycatch seascape: multispecies and multi-scale spatial patterns of fisheries bycatch.

PubMed

Lewison, Rebecca L; Soykan, Candan U; Franklin, Janet

2009-06-01

Fisheries bycatch is a worldwide conservation issue. Despite a growing awareness of bycatch problems in particular ocean regions, there have been few efforts to identify spatial patterns in bycatch events. Furthermore, many studies of fisheries bycatch have been myopic, focusing on a single species or a single region. Using a range of analytical approaches to identify spatial patterns in bycatch data, we demonstrate the utility and applications of area and point pattern analyses to single and multispecies bycatch seascapes of pelagic longline fisheries in the Atlantic and Pacific Oceans. We find clear evidence of spatial clustering within bycatch species in both ocean basins, both in terms of the underlying pattern of the locations of bycatch events relative to fishing locations and for areas of high bycatch rates. Furthermore, we find significant spatial overlap in the pattern of bycatch across species relative to the spatial distribution in fishing effort and target catch. These results point to the importance of considering spatial patterns of both single and multispecies bycatch to meet the ultimate goal of reducing bycatch encounters. These analyses also highlight the importance of considering bycatch relative to target catch as a way of identifying areas where fishing effort reduction may help to reduce multispecies bycatch with minimal impact on target catch.

Image Matrix Processor for Volumetric Computations Final Report CRADA No. TSB-1148-95

DOE Office of Scientific and Technical Information (OSTI.GOV)

Roberson, G. Patrick; Browne, Jolyon

The development of an Image Matrix Processor (IMP) was proposed that would provide an economical means to perform rapid ray-tracing processes on volume "Giga Voxel" data sets. This was a multi-phased project. The objective of the first phase of the IMP project was to evaluate the practicality of implementing a workstation-based Image Matrix Processor for use in volumetric reconstruction and rendering using hardware simulation techniques. Additionally, ARACOR and LLNL worked together to identify and pursue further funding sources to complete a second phase of this project.

Harsh Corporal Punishment Is Associated With Increased T2 Relaxation Time in Dopamine-Rich Regions

PubMed Central

Sheu, Yi-Shin; Polcari, Ann; Anderson, Carl M.; Teicher, Martin H.

2010-01-01

Harsh corporal punishment (HCP) was defined as frequent parental administration of corporal punishment (CP) for discipline, with occasional use of objects such as straps, or paddles. CP is linked to increased risk for depression and substance abuse. We examine whether long-term exposure to HCP acts as sub-traumatic stressor that contributes to brain alterations, particularly in dopaminergic pathways, which may mediate their increased vulnerability to drug and alcohol abuse. Nineteen young adults who experienced early HCP but no other forms of maltreatment and twenty-three comparable controls were studied. T2 relaxation time (T2-RT) measurements were performed with an echo planar imaging TE stepping technique and T2 maps were calculated and analyzed voxel-by-voxel to locate regional T2-RT differences between groups. Previous studies indicated that T2-RT provides an indirect index of resting cerebral blood volume. Region of interest (ROI) analyses were also conducted in caudate, putamen, nucleus accumbens, anterior cingulate cortex, dorsolateral prefrontal cortex, thalamus, globus pallidus and cerebellar hemispheres. Voxel-based relaxometry showed that HCP was associated with increased T2-RT in right caudate and putamen. ROI analyses also revealed increased T2-RT in dorsolateral prefrontal cortex, substantia nigra, thalamus and accumbens but not globus pallidus or cerebellum. There were significant associations between T2-RT measures in dopamine target regions and use of drugs and alcohol, and memory performance. Alteration in the paramagnetic or hemodynamic properties of dopaminergic cell body and projection regions were observed in subjects with HCP, and these findings may relate to their increased risk for drug and alcohol abuse. PMID:20600981

Harsh corporal punishment is associated with increased T2 relaxation time in dopamine-rich regions.

PubMed

Sheu, Yi-Shin; Polcari, Ann; Anderson, Carl M; Teicher, Martin H

2010-11-01

Harsh corporal punishment (HCP) was defined as frequent parental administration of corporal punishment (CP) for discipline, with occasional use of objects such as straps, or paddles. CP is linked to increased risk for depression and substance abuse. We examine whether long-term exposure to HCP acts as sub-traumatic stressor that contributes to brain alterations, particularly in dopaminergic pathways, which may mediate their increased vulnerability to drug and alcohol abuse. Nineteen young adults who experienced early HCP but no other forms of maltreatment and twenty-three comparable controls were studied. T2 relaxation time (T2-RT) measurements were performed with an echo planar imaging TE stepping technique and T2 maps were calculated and analyzed voxel-by-voxel to locate regional T2-RT differences between groups. Previous studies indicated that T2-RT provides an indirect index of resting cerebral blood volume. Region of interest (ROI) analyses were also conducted in caudate, putamen, nucleus accumbens, anterior cingulate cortex, dorsolateral prefrontal cortex, thalamus, globus pallidus and cerebellar hemispheres. Voxel-based relaxometry showed that HCP was associated with increased T2-RT in right caudate and putamen. ROI analyses also revealed increased T2-RT in dorsolateral prefrontal cortex, substantia nigra, thalamus and accumbens but not globus pallidus or cerebellum. There were significant associations between T2-RT measures in dopamine target regions and use of drugs and alcohol, and memory performance. Alteration in the paramagnetic or hemodynamic properties of dopaminergic cell body and projection regions were observed in subjects with HCP, and these findings may relate to their increased risk for drug and alcohol abuse. Copyright 2010 Elsevier Inc. All rights reserved.

Association of emphysema-like lung on cardiac computed tomography and mortality in persons without airflow obstruction: the Multi-Ethnic Study of Atherosclerosis (MESA) Lung Study

PubMed Central

Oelsner, Elizabeth C.; Hoffman, Eric A.; Folsom, Aaron R.; Carr, J. Jeffrey; Enright, Paul L.; Kawut, Steven M.; Kronmal, Richard; Lederer, David; Lima, Joao A. C.; Lovasi, Gina S.; Shea, Steven; Barr, R. Graham

2015-01-01

Background Whereas low lung function is known to predict mortality in the general population, the prognostic significance of emphysema on computed tomography (CT) in persons without chronic obstructive pulmonary disease (COPD) remains uncertain. Objective To determine whether greater emphysema-like lung on CT is associated with all-cause mortality among persons without airflow obstruction or COPD in the general population. Design Prospective cohort study. Setting Population-based, multiethnic sample from 6 US communities. Participants 2965 participants ages 45-84 years without airflow obstruction on spirometry. Measurements Emphysema-like lung was defined on cardiac CT as the number of lung voxels less than -950 Hounsfield Units, and was adjusted for the number of total imaged lung voxels. Results Among 2965 participants, 50.9% of whom never smoked, there were 186 deaths over a median of 6.2 years. Greater emphysema-like lung was independently associated with increased mortality (adjusted hazard ratio [HR]1.14 per one-half of the interquartile range, 95% CI 1.04-1.24, P=0.004), adjusting for potential confounders including cardiovascular risk factors and the forced expiratory volume in one second. Generalized additive models supported a linear association between emphysema-like lung and mortality without evidence for a threshold. The association was of greatest magnitude among smokers, although multiplicative interaction terms did not support effect modification by smoking status. Limitations Cardiac CT scans did not include lung apices. The number of deaths was limited among subgroup analyses. Conclusions Emphysema-like lung on CT was associated with all-cause mortality among persons without airflow obstruction or COPD in a general population sample, particularly among smokers. Recognition of the independent prognostic significance of emphysema on CT among patients without COPD on spirometry is warranted. Primary Funding Source NIH/NHLBI. PMID:25506855

4D Cone-beam CT reconstruction using a motion model based on principal component analysis

PubMed Central

Staub, David; Docef, Alen; Brock, Robert S.; Vaman, Constantin; Murphy, Martin J.

2011-01-01

Purpose: To provide a proof of concept validation of a novel 4D cone-beam CT (4DCBCT) reconstruction algorithm and to determine the best methods to train and optimize the algorithm. Methods: The algorithm animates a patient fan-beam CT (FBCT) with a patient specific parametric motion model in order to generate a time series of deformed CTs (the reconstructed 4DCBCT) that track the motion of the patient anatomy on a voxel by voxel scale. The motion model is constrained by requiring that projections cast through the deformed CT time series match the projections of the raw patient 4DCBCT. The motion model uses a basis of eigenvectors that are generated via principal component analysis (PCA) of a training set of displacement vector fields (DVFs) that approximate patient motion. The eigenvectors are weighted by a parameterized function of the patient breathing trace recorded during 4DCBCT. The algorithm is demonstrated and tested via numerical simulation. Results: The algorithm is shown to produce accurate reconstruction results for the most complicated simulated motion, in which voxels move with a pseudo-periodic pattern and relative phase shifts exist between voxels. The tests show that principal component eigenvectors trained on DVFs from a novel 2D/3D registration method give substantially better results than eigenvectors trained on DVFs obtained by conventionally registering 4DCBCT phases reconstructed via filtered backprojection. Conclusions: Proof of concept testing has validated the 4DCBCT reconstruction approach for the types of simulated data considered. In addition, the authors found the 2D/3D registration approach to be our best choice for generating the DVF training set, and the Nelder-Mead simplex algorithm the most robust optimization routine. PMID:22149852

Rapid construction of pinhole SPECT system matrices by distance-weighted Gaussian interpolation method combined with geometric parameter estimations

NASA Astrophysics Data System (ADS)

Lee, Ming-Wei; Chen, Yi-Chun

2014-02-01

In pinhole SPECT applied to small-animal studies, it is essential to have an accurate imaging system matrix, called H matrix, for high-spatial-resolution image reconstructions. Generally, an H matrix can be obtained by various methods, such as measurements, simulations or some combinations of both methods. In this study, a distance-weighted Gaussian interpolation method combined with geometric parameter estimations (DW-GIMGPE) is proposed. It utilizes a simplified grid-scan experiment on selected voxels and parameterizes the measured point response functions (PRFs) into 2D Gaussians. The PRFs of missing voxels are interpolated by the relations between the Gaussian coefficients and the geometric parameters of the imaging system with distance-weighting factors. The weighting factors are related to the projected centroids of voxels on the detector plane. A full H matrix is constructed by combining the measured and interpolated PRFs of all voxels. The PRFs estimated by DW-GIMGPE showed similar profiles as the measured PRFs. OSEM reconstructed images of a hot-rod phantom and normal rat myocardium demonstrated the effectiveness of the proposed method. The detectability of a SKE/BKE task on a synthetic spherical test object verified that the constructed H matrix provided comparable detectability to that of the H matrix acquired by a full 3D grid-scan experiment. The reduction in the acquisition time of a full 1.0-mm grid H matrix was about 15.2 and 62.2 times with the simplified grid pattern on 2.0-mm and 4.0-mm grid, respectively. A finer-grid H matrix down to 0.5-mm spacing interpolated by the proposed method would shorten the acquisition time by 8 times, additionally.

Position Information Encoded by Population Activity in Hierarchical Visual Areas

PubMed Central

Majima, Kei; Horikawa, Tomoyasu

2017-01-01

Abstract Neurons in high-level visual areas respond to more complex visual features with broader receptive fields (RFs) compared to those in low-level visual areas. Thus, high-level visual areas are generally considered to carry less information regarding the position of seen objects in the visual field. However, larger RFs may not imply loss of position information at the population level. Here, we evaluated how accurately the position of a seen object could be predicted (decoded) from activity patterns in each of six representative visual areas with different RF sizes [V1–V4, lateral occipital complex (LOC), and fusiform face area (FFA)]. We collected functional magnetic resonance imaging (fMRI) responses while human subjects viewed a ball randomly moving in a two-dimensional field. To estimate population RF sizes of individual fMRI voxels, RF models were fitted for individual voxels in each brain area. The voxels in higher visual areas showed larger estimated RFs than those in lower visual areas. Then, the ball’s position in a separate session was predicted by maximum likelihood estimation using the RF models of individual voxels. We also tested a model-free multivoxel regression (support vector regression, SVR) to predict the position. We found that regardless of the difference in RF size, all visual areas showed similar prediction accuracies, especially on the horizontal dimension. Higher areas showed slightly lower accuracies on the vertical dimension, which appears to be attributed to the narrower spatial distributions of the RF centers. The results suggest that much position information is preserved in population activity through the hierarchical visual pathway regardless of RF sizes and is potentially available in later processing for recognition and behavior. PMID:28451634

Maternal Dietary Patterns and Gestational Diabetes Mellitus in a Multi-Ethnic Asian Cohort: The GUSTO Study.

PubMed

de Seymour, Jamie; Chia, Airu; Colega, Marjorelee; Jones, Beatrix; McKenzie, Elizabeth; Shirong, Cai; Godfrey, Keith; Kwek, Kenneth; Saw, Seang-Mei; Conlon, Cathryn; Chong, Yap-Seng; Baker, Philip; Chong, Mary F F

2016-09-20

Gestational Diabetes Mellitus (GDM) is associated with an increased risk of perinatal morbidity and long term health issues for both the mother and offspring. Previous research has demonstrated associations between maternal diet and GDM development, but evidence in Asian populations is limited. The objective of our study was to examine the cross-sectional relationship between maternal dietary patterns during pregnancy and the risk of GDM in a multi-ethnic Asian cohort. Maternal diet was ascertained using 24-h dietary recalls from participants in the Growing up in Singapore towards healthy outcomes (GUSTO) study-a prospective mother-offspring cohort, and GDM was diagnosed according to 1999 World Health Organisation guidelines. Dietary patterns were identified using factor analysis, and multivariate regression analyses performed to assess the association with GDM. Of 909 participants, 17.6% were diagnosed with GDM. Three dietary patterns were identified: a vegetable-fruit-rice-based-diet, a seafood-noodle-based-diet and a pasta-cheese-processed-meat-diet. After adjusting for confounding variables, the seafood-noodle-based-diet was associated with a lower likelihood of GDM (Odds Ratio (95% Confidence Interval)) = 0.74 (0.59, 0.93). The dietary pattern found to be associated with GDM in our study was substantially different to those reported previously in Western populations.

Clinical and imaging characterization of progressive spastic dysarthria

PubMed Central

Clark, Heather M.; Duffy, Joseph R.; Whitwell, Jennifer L.; Ahlskog, J. Eric; Sorenson, Eric J.; Josephs, Keith A.

2013-01-01

Objective To describe speech, neurological and imaging characteristics of a series of patients presenting with progressive spastic dysarthria (PSD) as the first and predominant sign of a presumed neurodegenerative disease. Methods Participants were 25 patients with spastic dysarthria as the only or predominant speech disorder. Clinical features, pattern of MRI volume loss on voxel-based morphometry, and pattern of hypometabolism with F18-Fluorodeoxyglucose (FDG-PET) scan are described. Results All patients demonstrated speech characteristics consistent with spastic dysarthria, including strained voice quality, slow speaking rate, monopitch and monoloudness, and slow and regular speech alternating motion rates. Eight patients did not have additional neurological findings on examination. Pseudobulbar affect, upper motor neuron pattern limb weakness, spasticity, Hoffman sign and positive Babinski reflexes were noted in some of the remaining patients. Twenty-three patients had electromyographic assessment and none had diffuse motor neuron disease or met El Escorial criteria for ALS. Voxel-based morphometry revealed striking bilateral white matter volume loss, , affecting the motor cortex (BA 4), including the frontoparietal operculum (BA 43) with extension into the middle cerebral peduncle. FDG-PET showed subtle hypometabolism affecting the premotor and motor cortices in some patients, particularly in those who had a disease duration longer than two years. Conclusions We have characterized a neurodegenerative disorder that begins focally with spastic dysarthria due to involvement of the motor and premotor cortex and descending corticospinal and corticobulbar pathways. We propose the descriptive label “progressive spastic dysarthria” to best capture the dominant presenting feature of the syndrome. PMID:24053325

Decoding and reconstructing color from responses in human visual cortex.

PubMed

Brouwer, Gijs Joost; Heeger, David J

2009-11-04

How is color represented by spatially distributed patterns of activity in visual cortex? Functional magnetic resonance imaging responses to several stimulus colors were analyzed with multivariate techniques: conventional pattern classification, a forward model of idealized color tuning, and principal component analysis (PCA). Stimulus color was accurately decoded from activity in V1, V2, V3, V4, and VO1 but not LO1, LO2, V3A/B, or MT+. The conventional classifier and forward model yielded similar accuracies, but the forward model (unlike the classifier) also reliably reconstructed novel stimulus colors not used to train (specify parameters of) the model. The mean responses, averaged across voxels in each visual area, were not reliably distinguishable for the different stimulus colors. Hence, each stimulus color was associated with a unique spatially distributed pattern of activity, presumably reflecting the color selectivity of cortical neurons. Using PCA, a color space was derived from the covariation, across voxels, in the responses to different colors. In V4 and VO1, the first two principal component scores (main source of variation) of the responses revealed a progression through perceptual color space, with perceptually similar colors evoking the most similar responses. This was not the case for any of the other visual cortical areas, including V1, although decoding was most accurate in V1. This dissociation implies a transformation from the color representation in V1 to reflect perceptual color space in V4 and VO1.

Explaining the Substantial Inter-Domain and Over-Time Correlations in Student Achievement: The Importance of Stable Student Attributes

ERIC Educational Resources Information Center

Marks, Gary N.

2016-01-01

Multi-domain and longitudinal studies of student achievement routinely find moderate to strong correlations across achievement domains and even stronger within-domain correlations over time. The purpose of this study is to examine the sources of these patterns analysing student achievement in 5 domains across Years 3, 5 and 7. The analysis is of…

Evaluating the Effects of Traffic on Driver Stopping and Turn Signal Use at a Stop Sign: A Systematic Replication

ERIC Educational Resources Information Center

Lebbon, Angela R.; Austin, John; Van Houten, Ron; Malenfant, Louis E.

2007-01-01

The current analyses of observational data found that oncoming traffic substantially affected driver stopping patterns and turn signal use at the target stop sign. The percentage of legal stops and turn signal use by drivers in the presence and absence of traffic was analyzed using a multi-element design. The results showed that legal stops were…

Horizontal and vertical combination of multi-tenancy patterns in service-oriented applications

NASA Astrophysics Data System (ADS)

Mietzner, Ralph; Leymann, Frank; Unger, Tobias

2011-02-01

Software as a service (SaaS) providers exploit economies of scale by offering the same instance of an application to multiple customers typically in a single-instance multi-tenant architecture model. Therefore the applications must be scalable, multi-tenant aware and configurable. In this article, we show how the services in a service-oriented SaaS application can be deployed using different multi-tenancy patterns. We describe how services in different multi-tenancy patterns can be composed on the application level. In addition to that, we also describe how these multi-tenancy patterns can be applied to middleware and hardware components. We then show with some real world examples how the different multi-tenancy patterns can be combined.

PIV measurements in a compact return diffuser under multi-conditions

NASA Astrophysics Data System (ADS)

Zhou, L.; Lu, W. G.; Shi, W. D.

2013-12-01

Due to the complex three-dimensional geometries of impellers and diffusers, their design is a delicate and difficult task. Slight change could lead to significant changes in hydraulic performance and internal flow structure. Conversely, the grasp of the pump's internal flow pattern could benefit from pump design improvement. The internal flow fields in a compact return diffuser have been investigated experimentally under multi-conditions. A special Particle Image Velocimetry (PIV) test rig is designed, and the two-dimensional PIV measurements are successfully conducted in the diffuser mid-plane to capture the complex flow patterns. The analysis of the obtained results has been focused on the flow structure in diffuser, especially under part-load conditions. The vortex and recirculation flow patterns in diffuser are captured and analysed accordingly. Strong flow separation and back flow appeared at the part-load flow rates. Under the design and over-load conditions, the flow fields in diffuser are uniform, and the flow separation and back flow appear at the part-load flow rates, strong back flow is captured at one diffuser passage under 0.2Qdes.

Converting Multi-Shell and Diffusion Spectrum Imaging to High Angular Resolution Diffusion Imaging

PubMed Central

Yeh, Fang-Cheng; Verstynen, Timothy D.

2016-01-01

Multi-shell and diffusion spectrum imaging (DSI) are becoming increasingly popular methods of acquiring diffusion MRI data in a research context. However, single-shell acquisitions, such as diffusion tensor imaging (DTI) and high angular resolution diffusion imaging (HARDI), still remain the most common acquisition schemes in practice. Here we tested whether multi-shell and DSI data have conversion flexibility to be interpolated into corresponding HARDI data. We acquired multi-shell and DSI data on both a phantom and in vivo human tissue and converted them to HARDI. The correlation and difference between their diffusion signals, anisotropy values, diffusivity measurements, fiber orientations, connectivity matrices, and network measures were examined. Our analysis result showed that the diffusion signals, anisotropy, diffusivity, and connectivity matrix of the HARDI converted from multi-shell and DSI were highly correlated with those of the HARDI acquired on the MR scanner, with correlation coefficients around 0.8~0.9. The average angular error between converted and original HARDI was 20.7° at voxels with signal-to-noise ratios greater than 5. The network topology measures had less than 2% difference, whereas the average nodal measures had a percentage difference around 4~7%. In general, multi-shell and DSI acquisitions can be converted to their corresponding single-shell HARDI with high fidelity. This supports multi-shell and DSI acquisitions over HARDI acquisition as the scheme of choice for diffusion acquisitions. PMID:27683539

Flow behaviour of supercritical CO2 and brine in Berea sandstone during drainage and imbibition revealed by medical X-ray CT images

NASA Astrophysics Data System (ADS)

Zhang, Yi; Nishizawa, Osamu; Kiyama, Tamotsu; Chiyonobu, Shun; Xue, Ziqiu

2014-06-01

We injected Berea sandstone with supercritical CO2 and imaged the results with a medical X-ray computed tomography (CT) scanner. The images were acquired by injecting CO2 into a core of brine-saturated sandstone (drainage), and additional images were acquired during reinjection of brine (imbibition) after drainage. We then analysed the temporal variations of CO2 saturation maps obtained from the CT images. The experiments were performed under a confining pressure of 12 MPa, a pore pressure of 10 MPa and a temperature of 40 °C. Porosity and CO2 saturation were calculated for each image voxel of the rock on the basis of the Hounsfield unit values (CT numbers) measured at three states of saturation: dry, full brine saturation and full CO2 saturation. The saturation maps indicated that the distributions of CO2 and brine were controlled by the sub-core-scale heterogeneities which consisted of a laminated structure (bedding) with high- and low-porosity layers. During drainage, CO2 preferentially flowed through the high-porosity layers where most of the CO2 was entrapped during low flow-rate imbibition. The entrapped CO2 was flushed out when high flow-rate imbibition commenced. Plots of the voxel's CT number against porosity revealed the relationship between fluid replacement and porosity. By reference to the CT numbers at the full brine-saturated stage, differential CT numbers were classified into three bins corresponding to voxel porosity: high, medium and low porosity. Distributions of the differential CT number for the three porosity bins were bimodal and in order with respect to the porosity bins during both drainage and imbibitions; however, the order differed between the two stages. This difference suggested that different replacement mechanisms operated for the two processes. Spatial autocorrelation of CO2 saturation maps on sections perpendicular to the flow direction revealed remarkable changes during passage of the replacement fronts during both drainage and imbibition, changes reflecting the interfingering pattern across the replacement fronts. Although the permeability differences between high- and low-porosity layers were not sufficiently large to disturb the uniform flow of brine, the CO2 concentration in the high-porosity layers may have been caused by the differences of capillary pressure between wide and narrow pore throats, perhaps enhanced by an invasion percolation mechanism in flow-path networks.

Evidence for similar patterns of neural activity elicited by picture- and word-based representations of natural scenes.

PubMed

Kumar, Manoj; Federmeier, Kara D; Fei-Fei, Li; Beck, Diane M

2017-07-15

A long-standing core question in cognitive science is whether different modalities and representation types (pictures, words, sounds, etc.) access a common store of semantic information. Although different input types have been shown to activate a shared network of brain regions, this does not necessitate that there is a common representation, as the neurons in these regions could still differentially process the different modalities. However, multi-voxel pattern analysis can be used to assess whether, e.g., pictures and words evoke a similar pattern of activity, such that the patterns that separate categories in one modality transfer to the other. Prior work using this method has found support for a common code, but has two limitations: they have either only examined disparate categories (e.g. animals vs. tools) that are known to activate different brain regions, raising the possibility that the pattern separation and inferred similarity reflects only large scale differences between the categories or they have been limited to individual object representations. By using natural scene categories, we not only extend the current literature on cross-modal representations beyond objects, but also, because natural scene categories activate a common set of brain regions, we identify a more fine-grained (i.e. higher spatial resolution) common representation. Specifically, we studied picture- and word-based representations of natural scene stimuli from four different categories: beaches, cities, highways, and mountains. Participants passively viewed blocks of either phrases (e.g. "sandy beach") describing scenes or photographs from those same scene categories. To determine whether the phrases and pictures evoke a common code, we asked whether a classifier trained on one stimulus type (e.g. phrase stimuli) would transfer (i.e. cross-decode) to the other stimulus type (e.g. picture stimuli). The analysis revealed cross-decoding in the occipitotemporal, posterior parietal and frontal cortices. This similarity of neural activity patterns across the two input types, for categories that co-activate local brain regions, provides strong evidence of a common semantic code for pictures and words in the brain. Copyright © 2017 Elsevier Inc. All rights reserved.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chang, J; Gu, X; Lu, W

Purpose: A novel distance-dose weighting method for label fusion was developed to increase segmentation accuracy in dosimetrically important regions for prostate radiation therapy. Methods: Label fusion as implemented in the original SIMPLE (OS) for multi-atlas segmentation relies iteratively on the majority vote to generate an estimated ground truth and DICE similarity measure to screen candidates. The proposed distance-dose weighting puts more values on dosimetrically important regions when calculating similarity measure. Specifically, we introduced distance-to-dose error (DDE), which converts distance to dosimetric importance, in performance evaluation. The DDE calculates an estimated DE error derived from surface distance differences between the candidatemore » and estimated ground truth label by multiplying a regression coefficient. To determine the coefficient at each simulation point on the rectum, we fitted DE error with respect to simulated voxel shift. The DEs were calculated by the multi-OAR geometry-dosimetry training model previously developed in our research group. Results: For both the OS and the distance-dose weighted SIMPLE (WS) results, the evaluation metrics for twenty patients were calculated using the ground truth segmentation. The mean difference of DICE, Hausdorff distance, and mean absolute distance (MAD) between OS and WS have shown 0, 0.10, and 0.11, respectively. In partial MAD of WS which calculates MAD within a certain PTV expansion voxel distance, the lower MADs were observed at the closer distances from 1 to 8 than those of OS. The DE results showed that the segmentation from WS produced more accurate results than OS. The mean DE error of V75, V70, V65, and V60 were decreased by 1.16%, 1.17%, 1.14%, and 1.12%, respectively. Conclusion: We have demonstrated that the method can increase the segmentation accuracy in rectum regions adjacent to PTV. As a result, segmentation using WS have shown improved dosimetric accuracy than OS. The WS will provide dosimetrically important label selection strategy in multi-atlas segmentation. CPRIT grant RP150485.« less

Simultaneous Quantitative MRI Mapping of T1, T2* and Magnetic Susceptibility with Multi-Echo MP2RAGE

PubMed Central

Kober, Tobias; Möller, Harald E.; Schäfer, Andreas

2017-01-01

The knowledge of relaxation times is essential for understanding the biophysical mechanisms underlying contrast in magnetic resonance imaging. Quantitative experiments, while offering major advantages in terms of reproducibility, may benefit from simultaneous acquisitions. In this work, we demonstrate the possibility of simultaneously recording relaxation-time and susceptibility maps with a prototype Multi-Echo (ME) Magnetization-Prepared 2 RApid Gradient Echoes (MP2RAGE) sequence. T1 maps can be obtained using the MP2RAGE sequence, which is relatively insensitive to inhomogeneities of the radio-frequency transmit field, B1+. As an extension, multiple gradient echoes can be acquired in each of the MP2RAGE readout blocks, which permits the calculation of T2* and susceptibility maps. We used computer simulations to explore the effects of the parameters on the precision and accuracy of the mapping. In vivo parameter maps up to 0.6 mm nominal resolution were acquired at 7 T in 19 healthy volunteers. Voxel-by-voxel correlations and the test-retest reproducibility were used to assess the reliability of the results. When using optimized paramenters, T1 maps obtained with ME-MP2RAGE and standard MP2RAGE showed excellent agreement for the whole range of values found in brain tissues. Simultaneously obtained T2* and susceptibility maps were of comparable quality as Fast Low-Angle SHot (FLASH) results. The acquisition times were more favorable for the ME-MP2RAGE (≈ 19 min) sequence as opposed to the sum of MP2RAGE (≈ 12 min) and FLASH (≈ 10 min) acquisitions. Without relevant sacrifice in accuracy, precision or flexibility, the multi-echo version may yield advantages in terms of reduced acquisition time and intrinsic co-registration, provided that an appropriate optimization of the acquisition parameters is performed. PMID:28081157

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kauweloa, K; Gutierrez, A; Bergamo, A

Purpose: There is growing interest about biological effective dose (BED) and its application in treatment plan evaluation due to its stronger correlation with treatment outcome. An approximate biological effective dose (BEDA) equation was introduced to simplify BED calculations by treatment planning systems in multi-phase treatments. The purpose of this work is to reveal its mathematical properties relative to the true, multi-phase BED (BEDT) equation. Methods: The BEDT equation was derived and used to reveal the mathematical properties of BEDA. MATLAB (MathWorks, Natick, MA) was used to simulate and analyze common and extreme clinical multi-phase cases. In those cases, percent errormore » (Perror) and Bland-Altman analysis were used to study the significance of the inaccuracies of BEDA for different combinations of total doses, numbers of fractions, doses per fractions and α over β values. All the calculations were performed on a voxel-basis in order to study how dose distributions would affect the accuracy of BEDA. Results: When the voxel dose-per-fractions (DPF) delivered by both phases are equal, BEDA and BEDT are equal. In heterogeneous dose distributions, which significantly vary between the phases, there are fewer occurrences of equal DPFs and hence the imprecision of BEDA is greater. It was shown that as the α over β ratio increased the accuracy of BEDA would improve. Examining twenty-four cases, it was shown that the range of DPF ratios for a 3 Perror varied from 0.32 to 7.50Gy, whereas for Perror of 1 the range varied from 0.50 to 2.96Gy. Conclusion: The DPF between the different phases should be equal in order to render BEDA accurate. OARs typically receive heterogeneous dose distributions hence the probability of equal DPFs is low. Consequently, the BEDA equation should only be used for targets or OARs that receive uniform or very similar dose distributions by the different treatment phases.« less

Enhancement of Temporal Resolution and BOLD Sensitivity in Real-Time fMRI using Multi-Slab Echo-Volumar Imaging

PubMed Central

Posse, Stefan; Ackley, Elena; Mutihac, Radu; Rick, Jochen; Shane, Matthew; Murray-Krezan, Cristina; Zaitsev, Maxim; Speck, Oliver

2012-01-01

In this study, a new approach to high-speed fMRI using multi-slab echo-volumar imaging (EVI) is developed that minimizes geometrical image distortion and spatial blurring, and enables nonaliased sampling of physiological signal fluctuation to increase BOLD sensitivity compared to conventional echo-planar imaging (EPI). Real-time fMRI using whole brain 4-slab EVI with 286 ms temporal resolution (4 mm isotropic voxel size) and partial brain 2-slab EVI with 136 ms temporal resolution (4×4×6 mm3 voxel size) was performed on a clinical 3 Tesla MRI scanner equipped with 12-channel head coil. Four-slab EVI of visual and motor tasks significantly increased mean (visual: 96%, motor: 66%) and maximum t-score (visual: 263%, motor: 124%) and mean (visual: 59%, motor: 131%) and maximum (visual: 29%, motor: 67%) BOLD signal amplitude compared with EPI. Time domain moving average filtering (2 s width) to suppress physiological noise from cardiac and respiratory fluctuations further improved mean (visual: 196%, motor: 140%) and maximum (visual: 384%, motor: 200%) t-scores and increased extents of activation (visual: 73%, motor: 70%) compared to EPI. Similar sensitivity enhancement, which is attributed to high sampling rate at only moderately reduced temporal signal-to-noise ratio (mean: − 52%) and longer sampling of the BOLD effect in the echo-time domain compared to EPI, was measured in auditory cortex. Two-slab EVI further improved temporal resolution for measuring task-related activation and enabled mapping of five major resting state networks (RSNs) in individual subjects in 5 min scans. The bilateral sensorimotor, the default mode and the occipital RSNs were detectable in time frames as short as 75 s. In conclusion, the high sampling rate of real-time multi-slab EVI significantly improves sensitivity for studying the temporal dynamics of hemodynamic responses and for characterizing functional networks at high field strength in short measurement times. PMID:22398395

Characterization of the Spatial Structure of Local Functional Connectivity Using Multidistance Average Correlation Measures.

PubMed

Macià, Dídac; Pujol, Jesus; Blanco-Hinojo, Laura; Martínez-Vilavella, Gerard; Martín-Santos, Rocío; Deus, Joan

2018-06-01

There is ample evidence from basic research in neuroscience of the importance of local corticocortical networks. Millimetric resolution is achievable with current functional magnetic resonance imaging (fMRI) scanners and sequences, and consequently a number of "local" activity similarity measures have been defined to describe patterns of segregation and integration at this spatial scale. We have introduced the use of IsoDistant Average Correlation (IDAC), easily defined as the average fMRI temporal correlation of a given voxel with other voxels placed at increasingly separated isodistant intervals, to characterize the curve of local fMRI signal similarities. IDAC curves can be statistically compared using parametric multivariate statistics. Furthermore, by using red-green-blue color coding to display jointly IDAC values belonging to three different distance lags, IDAC curves can also be displayed as multidistance IDAC maps. We applied IDAC analysis to a sample of 41 subjects scanned under two different conditions, a resting state and an auditory-visual continuous stimulation. Multidistance IDAC mapping was able to discriminate between gross anatomofunctional cortical areas and, moreover, was sensitive to modulation between the two brain conditions in areas known to activate and deactivate during audiovisual tasks. Unlike previous fMRI local similarity measures already in use, our approach draws special attention to the continuous smooth pattern of local functional connectivity.

Adaptive smoothing based on Gaussian processes regression increases the sensitivity and specificity of fMRI data.

PubMed

Strappini, Francesca; Gilboa, Elad; Pitzalis, Sabrina; Kay, Kendrick; McAvoy, Mark; Nehorai, Arye; Snyder, Abraham Z

2017-03-01

Temporal and spatial filtering of fMRI data is often used to improve statistical power. However, conventional methods, such as smoothing with fixed-width Gaussian filters, remove fine-scale structure in the data, necessitating a tradeoff between sensitivity and specificity. Specifically, smoothing may increase sensitivity (reduce noise and increase statistical power) but at the cost loss of specificity in that fine-scale structure in neural activity patterns is lost. Here, we propose an alternative smoothing method based on Gaussian processes (GP) regression for single subjects fMRI experiments. This method adapts the level of smoothing on a voxel by voxel basis according to the characteristics of the local neural activity patterns. GP-based fMRI analysis has been heretofore impractical owing to computational demands. Here, we demonstrate a new implementation of GP that makes it possible to handle the massive data dimensionality of the typical fMRI experiment. We demonstrate how GP can be used as a drop-in replacement to conventional preprocessing steps for temporal and spatial smoothing in a standard fMRI pipeline. We present simulated and experimental results that show the increased sensitivity and specificity compared to conventional smoothing strategies. Hum Brain Mapp 38:1438-1459, 2017. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

Functional quantitative susceptibility mapping (fQSM).

PubMed

Balla, Dávid Z; Sanchez-Panchuelo, Rosa M; Wharton, Samuel J; Hagberg, Gisela E; Scheffler, Klaus; Francis, Susan T; Bowtell, Richard

2014-10-15

Blood oxygenation level dependent (BOLD) functional magnetic resonance imaging (fMRI) is a powerful technique, typically based on the statistical analysis of the magnitude component of the complex time-series. Here, we additionally interrogated the phase data of the fMRI time-series and used quantitative susceptibility mapping (QSM) in order to investigate the potential of functional QSM (fQSM) relative to standard magnitude BOLD fMRI. High spatial resolution data (1mm isotropic) were acquired every 3 seconds using zoomed multi-slice gradient-echo EPI collected at 7 T in single orientation (SO) and multiple orientation (MO) experiments, the latter involving 4 repetitions with the subject's head rotated relative to B0. Statistical parametric maps (SPM) were reconstructed for magnitude, phase and QSM time-series and each was subjected to detailed analysis. Several fQSM pipelines were evaluated and compared based on the relative number of voxels that were coincidentally found to be significant in QSM and magnitude SPMs (common voxels). We found that sensitivity and spatial reliability of fQSM relative to the magnitude data depended strongly on the arbitrary significance threshold defining "activated" voxels in SPMs, and on the efficiency of spatio-temporal filtering of the phase time-series. Sensitivity and spatial reliability depended slightly on whether MO or SO fQSM was performed and on the QSM calculation approach used for SO data. Our results present the potential of fQSM as a quantitative method of mapping BOLD changes. We also critically discuss the technical challenges and issues linked to this intriguing new technique. Copyright © 2014 Elsevier Inc. All rights reserved.

Modelling dendritic ecological networks in space: anintegrated network perspective

USGS Publications Warehouse

Peterson, Erin E.; Ver Hoef, Jay M.; Isaak, Dan J.; Falke, Jeffrey A.; Fortin, Marie-Josée; Jordon, Chris E.; McNyset, Kristina; Monestiez, Pascal; Ruesch, Aaron S.; Sengupta, Aritra; Som, Nicholas; Steel, E. Ashley; Theobald, David M.; Torgersen, Christian E.; Wenger, Seth J.

2013-01-01

the context of stream ecology. Within this context, we summarise the key innovations of a new family of spatial statistical models that describe spatial relationships in DENs. Finally, we discuss how different network analyses may be combined to address more complex and novel research questions. While our main focus is streams, the taxonomy of network analyses is also relevant anywhere spatial patterns in both network and 2-D space can be used to explore the influence of multi-scale processes on biota and their habitat (e.g. plant morphology and pest infestation, or preferential migration along stream or road corridors).

WE-D-BRE-06: Quantification of Dose-Response for High Grade Esophagtis Patients Using a Novel Voxel-To-Voxel Method

DOE Office of Scientific and Technical Information (OSTI.GOV)

Niedzielski, J; Martel, M; Tucker, S

2014-06-15

Purpose: Radiation induces an inflammatory response in the esophagus, discernible on CT studies. This work objectively quantifies the voxel esophageal radiation-response for patients with acute esophagitis. This knowledge is an important first-step towards predicting the effect of complex dose distributions on patient esophagitis symptoms. Methods: A previously validated voxel-based methodology of quantifying radiation esophagitis severity was used to identify the voxel dose-response for 18 NSCLC patients with severe esophagitis (CTCAE grading criteria, grade2 or higher). The response is quantified as percent voxel volume change for a given dose. During treatment (6–8 weeks), patients had weekly 4DCT studies and esophagitis scoring.more » Planning CT esophageal contours were deformed to each weekly CT using a demons DIR algorithm. An algorithm using the Jacobian Map from the DIR of the planning CT to all weekly CTs was used to quantify voxel-volume change, along with corresponding delivered voxel dose, to the planning voxel. Dose for each voxel for each time-point was calculated on each previous weekly CT image, and accumulated using DIR. Thus, for each voxel, the volume-change and delivered dose was calculated for each time-point. The data was binned according to when the volume-change first increased by a threshold volume (10%–100%, in 10% increments), and the average delivered dose calculated for each bin. Results: The average dose resulting in a voxel volume increase of 10–100% was 21.6 to 45.9Gy, respectively. The mean population dose to give a 50% volume increase was 36.3±4.4Gy, (range:29.8 to 43.5Gy). The average week of 50% response was 4.1 (range:4.9 to 2.8 weeks). All 18 patients showed similar dose to first response curves, showing a common trend in the initial inflammatoryresponse. Conclusion: We extracted the dose-response curve of the esophagus on a voxel-to-voxel level. This may be useful for estimating the esophagus response (and patient symptoms) to complicated dose distributions.« less

Diagnostic implications of a small-voxel reconstruction for loco-regional lymph node characterization in breast cancer patients using FDG-PET/CT.

PubMed

Koopman, Daniëlle; van Dalen, Jorn A; Arkies, Hester; Oostdijk, Ad H J; Francken, Anne Brecht; Bart, Jos; Slump, Cornelis H; Knollema, Siert; Jager, Pieter L

2018-01-16

We evaluated the diagnostic implications of a small-voxel reconstruction for lymph node characterization in breast cancer patients, using state-of-the-art FDG-PET/CT. We included 69 FDG-PET/CT scans from breast cancer patients. PET data were reconstructed using standard 4 × 4 × 4 mm 3 and small 2 × 2 × 2 mm 3 voxels. Two hundred thirty loco-regional lymph nodes were included, of which 209 nodes were visualised on PET/CT. All nodes were visually scored as benign or malignant, and SUV max and TB ratio (=SUV max /SUV background ) were measured. Final diagnosis was based on histological or imaging information. We determined the accuracy, sensitivity and specificity for both reconstruction methods and calculated optimal cut-off values to distinguish benign from malignant nodes. Sixty-one benign and 169 malignant lymph nodes were included. Visual evaluation accuracy was 73% (sensitivity 67%, specificity 89%) on standard-voxel images and 77% (sensitivity 78%, specificity 74%) on small-voxel images (p = 0.13). Across malignant nodes visualised on PET/CT, the small-voxel score was more often correct compared with the standard-voxel score (89 vs. 76%, p <  0.001). In benign nodes, the standard-voxel score was more often correct (89 vs. 74%, p = 0.04). Quantitative data were based on the 61 benign and 148 malignant lymph nodes visualised on PET/CT. SUVs and TB ratio were on average 3.0 and 1.6 times higher in malignant nodes compared to those in benign nodes (p <  0.001), on standard- and small-voxel PET images respectively. Small-voxel PET showed average increases in SUV max and TB ratio of typically 40% over standard-voxel PET. The optimal SUV max cut-off using standard-voxels was 1.8 (sensitivity 81%, specificity 95%, accuracy 85%) while for small-voxels, the optimal SUV max cut-off was 2.6 (sensitivity 78%, specificity 98%, accuracy 84%). Differences in accuracy were non-significant. Small-voxel PET/CT improves the sensitivity of visual lymph node characterization and provides a higher detection rate of malignant lymph nodes. However, small-voxel PET/CT also introduced more false-positive results in benign nodes. Across all nodes, differences in accuracy were non-significant. Quantitatively, small-voxel images require higher cut-off values. Readers have to adapt their reference standards.

Brain changes following four weeks of unimanual motor training: Evidence from behavior, neural stimulation, cortical thickness, and functional MRI.

PubMed

Sale, Martin V; Reid, Lee B; Cocchi, Luca; Pagnozzi, Alex M; Rose, Stephen E; Mattingley, Jason B

2017-09-01

Although different aspects of neuroplasticity can be quantified with behavioral probes, brain stimulation, and brain imaging assessments, no study to date has combined all these approaches into one comprehensive assessment of brain plasticity. Here, 24 healthy right-handed participants practiced a sequence of finger-thumb opposition movements for 10 min each day with their left hand. After 4 weeks, performance for the practiced sequence improved significantly (P < 0.05 FWE) relative to a matched control sequence, with both the left (mean increase: 53.0% practiced, 6.5% control) and right (21.0%; 15.8%) hands. Training also induced significant (cluster p-FWE < 0.001) reductions in functional MRI activation for execution of the trained sequence, relative to the control sequence. These changes were observed as clusters in the premotor and supplementary motor cortices (right hemisphere, 301 voxel cluster; left hemisphere 700 voxel cluster), and sensorimotor cortices and superior parietal lobules (right hemisphere 864 voxel cluster; left hemisphere, 1947 voxel cluster). Transcranial magnetic stimulation over the right ("trained") primary motor cortex yielded a 58.6% mean increase in a measure of motor evoked potential amplitude, as recorded at the left abductor pollicis brevis muscle. Cortical thickness analyses based on structural MRI suggested changes in the right precentral gyrus, right post central gyrus, right dorsolateral prefrontal cortex, and potentially the right supplementary motor area. Such findings are consistent with LTP-like neuroplastic changes in areas that were already responsible for finger sequence execution, rather than improved recruitment of previously nonutilized tissue. Hum Brain Mapp 38:4773-4787, 2017. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

White matter microstructural changes as vulnerability factors and acquired signs of post-earthquake distress.

PubMed

Sekiguchi, Atsushi; Sugiura, Motoaki; Taki, Yasuyuki; Kotozaki, Yuka; Nouchi, Rui; Takeuchi, Hikaru; Araki, Tsuyoshi; Hanawa, Sugiko; Nakagawa, Seishu; Miyauchi, Carlos Makoto; Sakuma, Atsushi; Kawashima, Ryuta

2014-01-01

Many survivors of severe disasters need psychological support, even those not suffering post-traumatic stress disorder (PTSD). The critical issue in understanding the psychological response after experiencing severe disasters is to distinguish neurological microstructural underpinnings as vulnerability factors from signs of emotional distress acquired soon after the stressful life event. We collected diffusion-tensor magnetic resonance imaging (DTI) data from a group of healthy adolescents before the Great East Japan Earthquake and re-examined the DTIs and anxiety levels of 30 non-PTSD subjects from this group 3-4 months after the earthquake using voxel-based analyses in a longitudinal DTI study before and after the earthquake. We found that the state anxiety level after the earthquake was negatively associated with fractional anisotropy (FA) in the right anterior cingulum (Cg) before the earthquake (r = -0.61, voxel level p<0.0025, cluster level p<0.05 corrected), and positively associated with increased FA changes from before to after the earthquake in the left anterior Cg (r = 0.70, voxel level p<0.0025, cluster level p<0.05 corrected) and uncinate fasciculus (Uf) (r = 0.65, voxel level p<0.0025, cluster level p<0.05 corrected). The results demonstrated that lower FA in the right anterior Cg was a vulnerability factor and increased FA in the left anterior Cg and Uf was an acquired sign of state anxiety after the earthquake. We postulate that subjects with dysfunctions in processing fear and anxiety before the disaster were likely to have higher anxiety levels requiring frequent emotional regulation after the disaster. These findings provide new evidence of psychophysiological responses at the neural network level soon after a stressful life event and might contribute to the development of effective methods to prevent PTSD.

Brain regions associated with cognitive impairment in patients with Parkinson disease: quantitative analysis of cerebral blood flow using 123I iodoamphetamine SPECT.

PubMed

Hattori, Naoya; Yabe, Ichiro; Hirata, Kenji; Shiga, Tohru; Sakushima, Ken; Tsuji-Akimoto, Sachiko; Sasaki, Hidenao; Tamaki, Nagara

2013-05-01

Cognitive impairment is a representative neuropsychiatric presentation that accompanies Parkinson disease (PD). The purpose of this study was to localize the cerebral regions associated with cognitive impairment in patients with PD using quantitative SPECT. Thirty-two patients with PD (mean [SD] age, 75 [8] years; 25 women; Hoehn-Yahr scores from 2 to 5) underwent quantitative brain SPECT using 123I iodoamphetamine. Parametric images of regional cerebral blood flow (rCBF) were spatially normalized to the standard brain atlas. First, voxel-by-voxel comparison between patients with PD with versus without cognitive impairment was performed to visualize overall trend of regional differences. Next, the individual quantitative rCBF values were extracted in representative cortical regions using a standard region-of-interest template to compare the quantitative rCBF values. Patients with cognitive impairment showed trends of lower rCBF in the left frontal and temporal cortices as well as in the bilateral medial frontal and anterior cingulate cortices in the voxel-by-voxel analyses. Region-of-interest-based analysis demonstrated significantly lower rCBF in the bilateral anterior cingulate cortices (right, 25.8 [5.5] vs 28.9 [5.7] mL per 100 g/min, P < 0.05; left, 25.8 [5.8] vs 29.1 [5.7] mL per 100 g/min, P < 0.05) associated with cognitive impairment. Patients with cognitive impairment showed lower rCBF in the left frontal and temporal cortices as well as in the bilateral medial frontal and anterior cingulate cortices. The results suggested dysexecutive function as an underlining mechanism of cognitive impairment in patients with PD.

The temptation of suicide: striatal gray matter, discounting of delayed rewards, and suicide attempts in late-life depression

PubMed Central

Dombrovski, Alexandre Y.; Siegle, Greg J.; Szanto, Katalin; Clark, Luke; Reynolds, Charles F.; Aizenstein, Howard

2012-01-01

Background Converging evidence implicates basal ganglia alterations in impulsivity and suicidal behavior. For example, D2/D3 agonists and subthalamic nucleus stimulation in Parkinson’s disease trigger impulse control disorders and possibly suicidal behavior. Further, suicidal behavior has been associated with structural basal ganglia abnormalities. Finally, low-lethality, unplanned suicide attempts are associated with increased discounting of delayed rewards, a behavior dependent upon the striatum. Thus, we tested whether, in late-life depression, changes in the basal ganglia were associated with suicide attempts and with increased delay discounting. Methods Fifty-two persons aged ≥60 underwent extensive clinical and cognitive characterization: 33 with major depression (13 suicide attempters [SA], 20 non-suicidal depressed elderly), and 19 non-depressed controls. Participants had high-resolution T1-weighted MPRAGE MRI scans. Basal ganglia gray matter voxel counts were estimated using atlas-based segmentation, with a highly-deformable automated algorithm. Discounting of delayed rewards was assessed using the Monetary Choice Questionnaire, and delay aversion with the Cambridge Gamble Task. Results SA had lower putamen but not caudate or pallidum gray matter voxel counts, compared to the control groups. This difference persisted after accounting for substance use disorders and possible brain injury from suicide attempts. SA with lower putamen gray matter voxel counts displayed higher delay discounting on the MCQ, but not delay aversion on the CGT. Secondary analyses revealed that SA had lower voxel counts in associative and possibly ventral, but not sensorimotor striatum. Conclusions Our findings, while limited by small sample size and case-control design, suggest that striatal lesions could contribute to suicidal behavior by increasing impulsivity. PMID:21999930

The temptation of suicide: striatal gray matter, discounting of delayed rewards, and suicide attempts in late-life depression.

PubMed

Dombrovski, A Y; Siegle, G J; Szanto, K; Clark, L; Reynolds, C F; Aizenstein, H

2012-06-01

Converging evidence implicates basal ganglia alterations in impulsivity and suicidal behavior. For example, D2/D3 agonists and subthalamic nucleus stimulation in Parkinson's disease (PD) trigger impulse control disorders and possibly suicidal behavior. Furthermore, suicidal behavior has been associated with structural basal ganglia abnormalities. Finally, low-lethality, unplanned suicide attempts are associated with increased discounting of delayed rewards, a behavior dependent upon the striatum. Thus, we tested whether, in late-life depression, changes in the basal ganglia were associated with suicide attempts and with increased delay discounting. Fifty-two persons aged ≥ 60 years underwent extensive clinical and cognitive characterization: 33 with major depression [13 suicide attempters (SA), 20 non-suicidal depressed elderly] and 19 non-depressed controls. Participants had high-resolution T1-weighted magnetization prepared rapid acquisition gradient-echo (MPRAGE) magnetic resonance imaging (MRI) scans. Basal ganglia gray matter voxel counts were estimated using atlas-based segmentation, with a highly deformable automated algorithm. Discounting of delayed rewards was assessed using the Monetary Choice Questionnaire (MCQ) and delay aversion with the Cambridge Gamble Task (CGT). SA had lower putamen but not caudate or pallidum gray matter voxel counts, compared to the control groups. This difference persisted after accounting for substance use disorders and possible brain injury from suicide attempts. SA with lower putamen gray matter voxel counts displayed higher delay discounting but not delay aversion. Secondary analyses revealed that SA had lower voxel counts in associative and ventral but not sensorimotor striatum. Our findings, although limited by small sample size and the case-control design, suggest that striatal lesions could contribute to suicidal behavior by increasing impulsivity.

Hippocampal hypometabolism in older adults with memory complaints and increased amyloid burden.

PubMed

Vannini, Patrizia; Hanseeuw, Bernard; Munro, Catherine E; Amariglio, Rebecca E; Marshall, Gad A; Rentz, Dorene M; Pascual-Leone, Alvaro; Johnson, Keith A; Sperling, Reisa A

2017-05-02

To identify the functional and pathologic correlates underlying subjective memory complaints (SMCs) in cognitively normal older adults. Two hundred fifty-one older adults underwent resting-state fluorodeoxyglucose (FDG)-PET and Pittsburg compound B-PET β-amyloid (Aβ) imaging and filled out a questionnaire regarding SMCs. Participants were classified into 2 groups based on their Aβ burden. Age-adjusted voxel-wise correlations were used to examine SMCs, amyloid status (Aβ + vs Aβ - ), and the interaction between SMCs and Aβ status as predictors of metabolism. Region-of-interest (ROI) analyses were performed to confirm the whole-brain analyses and to test for additional covariates. Greater SMCs correlated with decreased FDG metabolism in the bilateral precuneus, bilateral inferior parietal lobes, right inferior temporal lobe, right medial frontal gyrus, and right orbitofrontal gyrus. A significant interaction effect between SMCs and amyloid burden was found such that Aβ + individuals with increased complaints had decreased FDG metabolism in the bilateral medial temporal lobes. ROI analyses confirmed the voxel-wise analyses result in that decreased precuneus metabolism was associated with greater SMCs regardless of Aβ status, age, or thickness, whereas the relationship between hippocampal metabolism and SMCs was a function of Aβ, even after adjustment for age, hippocampal volume, or depressive symptoms. These data show the relevant role of posterior and anterior midline regions in SMCs in older individuals. Decreased hippocampal metabolism may be a specific marker of subclinical changes in cognition due to amyloid pathology. However, longitudinal studies are needed to determine whether our findings foreshadow clinical decline. © 2017 American Academy of Neurology.

Influences of the land use pattern on water quality in low-order streams of the Dongjiang River basin, China: A multi-scale analysis.

PubMed

Ding, Jiao; Jiang, Yuan; Liu, Qi; Hou, Zhaojiang; Liao, Jianyu; Fu, Lan; Peng, Qiuzhi

2016-05-01

Understanding the relationships between land use patterns and water quality in low-order streams is useful for effective landscape planning to protect downstream water quality. A clear understanding of these relationships remains elusive due to the heterogeneity of land use patterns and scale effects. To better assess land use influences, we developed empirical models relating land use patterns to the water quality of low-order streams at different geomorphic regions across multi-scales in the Dongjiang River basin using multivariate statistical analyses. The land use pattern was quantified in terms of the composition, configuration and hydrological distance of land use types at the reach buffer, riparian corridor and catchment scales. Water was sampled under summer base flow at 56 low-order catchments, which were classified into two homogenous geomorphic groups. The results indicated that the water quality of low-order streams was most strongly affected by the configuration metrics of land use. Poorer water quality was associated with higher patch densities of cropland, orchards and grassland in the mountain catchments, whereas it was associated with a higher value for the largest patch index of urban land use in the plain catchments. The overall water quality variation was explained better by catchment scale than by riparian- or reach-scale land use, whereas the spatial scale over which land use influenced water quality also varied across specific water parameters and the geomorphic basis. Our study suggests that watershed management should adopt better landscape planning and multi-scale measures to improve water quality. Copyright © 2016 Elsevier B.V. All rights reserved.

Compressive spectral testbed imaging system based on thin-film color-patterned filter arrays.

PubMed

Rueda, Hoover; Arguello, Henry; Arce, Gonzalo R

2016-11-20

Compressive spectral imaging systems can reliably capture multispectral data using far fewer measurements than traditional scanning techniques. In this paper, a thin-film patterned filter array-based compressive spectral imager is demonstrated, including its optical design and implementation. The use of a patterned filter array entails a single-step three-dimensional spatial-spectral coding on the input data cube, which provides higher flexibility on the selection of voxels being multiplexed on the sensor. The patterned filter array is designed and fabricated with micrometer pitch size thin films, referred to as pixelated filters, with three different wavelengths. The performance of the system is evaluated in terms of references measured by a commercially available spectrometer and the visual quality of the reconstructed images. Different distributions of the pixelated filters, including random and optimized structures, are explored.

Direct voxel-based comparisons between grey matter shrinkage and glucose hypometabolism in chronic alcoholism.

PubMed

Ritz, Ludivine; Segobin, Shailendra; Lannuzel, Coralie; Boudehent, Céline; Vabret, François; Eustache, Francis; Beaunieux, Hélène; Pitel, Anne L

2016-09-01

Alcoholism is associated with widespread brain structural abnormalities affecting mainly the frontocerebellar and the Papez's circuits. Brain glucose metabolism has received limited attention, and few studies used regions of interest approach and showed reduced global brain metabolism predominantly in the frontal and parietal lobes. Even though these studies have examined the relationship between grey matter shrinkage and hypometabolism, none has performed a direct voxel-by-voxel comparison between the degrees of structural and metabolic abnormalities. Seventeen alcoholic patients and 16 control subjects underwent both structural magnetic resonance imaging and (18)F-2-fluoro-deoxy-glucose-positron emission tomography examinations. Structural abnormalities and hypometabolism were examined in alcoholic patients compared with control subjects using two-sample t-tests. Then, these two patterns of brain damage were directly compared with a paired t-test. Compared to controls, alcoholic patients had grey matter shrinkage and hypometabolism in the fronto-cerebellar circuit and several nodes of Papez's circuit. The direct comparison revealed greater shrinkage than hypometabolism in the cerebellum, cingulate cortex, thalamus and hippocampus and parahippocampal gyrus. Conversely, hypometabolism was more severe than shrinkage in the dorsolateral, premotor and parietal cortices. The distinct profiles of abnormalities found within the Papez's circuit, the fronto-cerebellar circuit and the parietal gyrus in chronic alcoholism suggest the involvement of different pathological mechanisms. © The Author(s) 2015.

Co-occurrence of Local Anisotropic Gradient Orientations (CoLlAGe): A new radiomics descriptor.

PubMed

Prasanna, Prateek; Tiwari, Pallavi; Madabhushi, Anant

2016-11-22

In this paper, we introduce a new radiomic descriptor, Co-occurrence of Local Anisotropic Gradient Orientations (CoLlAGe) for capturing subtle differences between benign and pathologic phenotypes which may be visually indistinguishable on routine anatomic imaging. CoLlAGe seeks to capture and exploit local anisotropic differences in voxel-level gradient orientations to distinguish similar appearing phenotypes. CoLlAGe involves assigning every image voxel an entropy value associated with the co-occurrence matrix of gradient orientations computed around every voxel. The hypothesis behind CoLlAGe is that benign and pathologic phenotypes even though they may appear similar on anatomic imaging, will differ in their local entropy patterns, in turn reflecting subtle local differences in tissue microarchitecture. We demonstrate CoLlAGe's utility in three clinically challenging classification problems: distinguishing (1) radiation necrosis, a benign yet confounding effect of radiation treatment, from recurrent tumors on T1-w MRI in 42 brain tumor patients, (2) different molecular sub-types of breast cancer on DCE-MRI in 65 studies and (3) non-small cell lung cancer (adenocarcinomas) from benign fungal infection (granulomas) on 120 non-contrast CT studies. For each of these classification problems, CoLlAGE in conjunction with a random forest classifier outperformed state of the art radiomic descriptors (Haralick, Gabor, Histogram of Gradient Orientations).

Direct voxel-based comparisons between grey matter shrinkage and glucose hypometabolism in chronic alcoholism

PubMed Central

Ritz, Ludivine; Segobin, Shailendra; Lannuzel, Coralie; Boudehent, Céline; Vabret, François; Eustache, Francis; Beaunieux, Hélène

2015-01-01

Alcoholism is associated with widespread brain structural abnormalities affecting mainly the frontocerebellar and the Papez’s circuits. Brain glucose metabolism has received limited attention, and few studies used regions of interest approach and showed reduced global brain metabolism predominantly in the frontal and parietal lobes. Even though these studies have examined the relationship between grey matter shrinkage and hypometabolism, none has performed a direct voxel-by-voxel comparison between the degrees of structural and metabolic abnormalities. Seventeen alcoholic patients and 16 control subjects underwent both structural magnetic resonance imaging and 18F-2-fluoro-deoxy-glucose-positron emission tomography examinations. Structural abnormalities and hypometabolism were examined in alcoholic patients compared with control subjects using two-sample t-tests. Then, these two patterns of brain damage were directly compared with a paired t-test. Compared to controls, alcoholic patients had grey matter shrinkage and hypometabolism in the fronto-cerebellar circuit and several nodes of Papez’s circuit. The direct comparison revealed greater shrinkage than hypometabolism in the cerebellum, cingulate cortex, thalamus and hippocampus and parahippocampal gyrus. Conversely, hypometabolism was more severe than shrinkage in the dorsolateral, premotor and parietal cortices. The distinct profiles of abnormalities found within the Papez’s circuit, the fronto-cerebellar circuit and the parietal gyrus in chronic alcoholism suggest the involvement of different pathological mechanisms. PMID:26661206

Unravelling the Intrinsic Functional Organization of the Human Striatum: A Parcellation and Connectivity Study Based on Resting-State fMRI

PubMed Central

Jung, Wi Hoon; Jang, Joon Hwan; Park, Jin Woo; Kim, Euitae; Goo, Eun-Hoe; Im, Oh-Soo; Kwon, Jun Soo

2014-01-01

As the main input hub of the basal ganglia, the striatum receives projections from the cerebral cortex. Many studies have provided evidence for multiple parallel corticostriatal loops based on the structural and functional connectivity profiles of the human striatum. A recent resting-state fMRI study revealed the topography of striatum by assigning each voxel in the striatum to its most strongly correlated cortical network among the cognitive, affective, and motor networks. However, it remains unclear what patterns of striatal parcellation would result from performing the clustering without subsequent assignment to cortical networks. Thus, we applied unsupervised clustering algorithms to parcellate the human striatum based on its functional connectivity patterns to other brain regions without any anatomically or functionally defined cortical targets. Functional connectivity maps of striatal subdivisions, identified through clustering analyses, were also computed. Our findings were consistent with recent accounts of the functional distinctions of the striatum as well as with recent studies about its functional and anatomical connectivity. For example, we found functional connections between dorsal and ventral striatal clusters and the areas involved in cognitive and affective processes, respectively, and between rostral and caudal putamen clusters and the areas involved in cognitive and motor processes, respectively. This study confirms prior findings, showing similar striatal parcellation patterns between the present and prior studies. Given such striking similarity, it is suggested that striatal subregions are functionally linked to cortical networks involving specific functions rather than discrete portions of cortical regions. Our findings also demonstrate that the clustering of functional connectivity patterns is a reliable feature in parcellating the striatum into anatomically and functionally meaningful subdivisions. The striatal subdivisions identified here may have important implications for understanding the relationship between corticostriatal dysfunction and various neurodegenerative and psychiatric disorders. PMID:25203441

Pittsburgh Compound B and AV-1451 positron emission tomography assessment of molecular pathologies of Alzheimer's disease in progressive supranuclear palsy.

PubMed

Whitwell, Jennifer L; Ahlskog, J Eric; Tosakulwong, Nirubol; Senjem, Matthew L; Spychalla, Anthony J; Petersen, Ronald C; Jack, Clifford R; Lowe, Val J; Josephs, Keith A

2018-03-01

Little is known about Alzheimer's disease molecular proteins, beta-amyloid and paired helical filament (PHF) tau, in progressive supranuclear palsy (PSP). Recent techniques have been developed to allow for investigations of these proteins in PSP. We determined the frequency of beta-amyloid deposition in PSP, and whether beta-amyloid deposition in PSP is associated with PHF-tau deposition pattern, or clinical features. Thirty probable PSP participants underwent MRI, [ 18 F]AV-1451 PET and Pittsburgh compound B (PiB) PET. Apolipoprotein (APOE) genotyping was also performed. A global PiB standard-uptake value ratio (SUVR) was calculated. AV-1451 SUVRs were calculated for a set of Alzheimer's disease (AD)-related regions and a set of PSP-related regions. Voxel-level analyses were conducted to assess for differences in AV-1451 uptake patterns and MRI atrophy between PiB(+) and PiB(-) cases compared to 60 normal PiB(-) controls. Statistical testing for correlations and associations between variables of interest were also performed. Twelve subjects (40%) showed beta-amyloid deposition. Higher PiB SUVR correlated with older age but not with AV-1451 SUVR in the AD- or PSP-related regions. Higher AV-1451 SUVR in AD-related regions was associated with higher AV-1451 SUVR in PSP-related regions. We found little evidence for beta-amyloid related differences in clinical metrics, proportion of APOE e4 carriers, pattern of AV-1451 uptake, or pattern of atrophy. Beta-amyloid deposition occurs in a relatively high proportion of PSP subjects. Unlike in Alzheimer's disease, however, there is little evidence that beta-amyloid, and PHF-tau, play a significant role in neurodegeneration in PSP. Copyright © 2017 Elsevier Ltd. All rights reserved.

Common and distinct neural correlates of facial emotion processing in social anxiety disorder and Williams syndrome: A systematic review and voxel-based meta-analysis of functional resonance imaging studies.

PubMed

Binelli, C; Subirà, S; Batalla, A; Muñiz, A; Sugranyés, G; Crippa, J A; Farré, M; Pérez-Jurado, L; Martín-Santos, R

2014-11-01

Social Anxiety Disorder (SAD) and Williams-Beuren Syndrome (WS) are two conditions which seem to be at opposite ends in the continuum of social fear but show compromised abilities in some overlapping areas, including some social interactions, gaze contact and processing of facial emotional cues. The increase in the number of neuroimaging studies has greatly expanded our knowledge of the neural bases of facial emotion processing in both conditions. However, to date, SAD and WS have not been compared. We conducted a systematic review of functional magnetic resonance imaging (fMRI) studies comparing SAD and WS cases to healthy control participants (HC) using facial emotion processing paradigms. Two researchers conducted comprehensive PubMed/Medline searches to identify all fMRI studies of facial emotion processing in SAD and WS. The following search key-words were used: "emotion processing"; "facial emotion"; "social anxiety"; "social phobia"; "Williams syndrome"; "neuroimaging"; "functional magnetic resonance"; "fMRI" and their combinations, as well as terms specifying individual facial emotions. We extracted spatial coordinates from each study and conducted two separate voxel-wise activation likelihood estimation meta-analyses, one for SAD and one for WS. Twenty-two studies met the inclusion criteria: 17 studies of SAD and five of WS. We found evidence for both common and distinct patterns of neural activation. Limbic engagement was common to SAD and WS during facial emotion processing, although we observed opposite patterns of activation for each disorder. Compared to HC, SAD cases showed hyperactivation of the amygdala, the parahippocampal gyrus and the globus pallidus. Compared to controls, participants with WS showed hypoactivation of these regions. Differential activation in a number of regions specific to either condition was also identified: SAD cases exhibited greater activation of the insula, putamen, the superior temporal gyrus, medial frontal regions and the cuneus, while WS subjects showed decreased activation in the inferior region of the parietal lobule. The identification of limbic structures as a shared correlate and the patterns of activation observed for each condition may reflect the aberrant patterns of facial emotion processing that the two conditions share, and may contribute to explaining part of the underlying neural substrate of exaggerated/diminished fear responses to social cues that characterize SAD and WS respectively. We believe that insights from WS and the inclusion of this syndrome as a control group in future experimental studies may improve our understanding of the neural correlates of social fear in general, and of SAD in particular. Copyright © 2014 Elsevier Ltd. All rights reserved.

Rhinal hypometabolism on FDG PET in healthy APO-E4 carriers: impact on memory function and metabolic networks.

PubMed

Didic, Mira; Felician, Olivier; Gour, Natalina; Bernard, Rafaelle; Pécheux, Christophe; Mundler, Olivier; Ceccaldi, Mathieu; Guedj, Eric

2015-09-01

The ε4 allele of the apolipoprotein E (APO-E4) gene, a genetic risk factor for Alzheimer's disease (AD), also modulates brain metabolism and function in healthy subjects. The aim of the present study was to explore cerebral metabolism using FDG PET in healthy APO-E4 carriers by comparing cognitively normal APO-E4 carriers to noncarriers and to assess if patterns of metabolism are correlated with performance on cognitive tasks. Moreover, metabolic connectivity patterns were established in order to assess if the organization of neural networks is influenced by genetic factors. Whole-brain PET statistical analysis was performed at voxel-level using SPM8 with a threshold of p < 0.005, corrected for volume, with age, gender and level of education as nuisance variables. Significant hypometabolism between APO-E4 carriers (n = 11) and noncarriers (n = 30) was first determined. Mean metabolic values with clinical/neuropsychological data were extracted at the individual level, and correlations were searched using Spearman's rank test in the whole group. To evaluate metabolic connectivity from metabolic cluster(s) previously identified in the intergroup comparison, voxel-wise interregional correlation analysis (IRCA) was performed between groups of subjects. APO-E4 carriers had reduced metabolism within the left anterior medial temporal lobe (MTL), where neuropathological changes first appear in AD, including the entorhinal and perirhinal cortices. A correlation between metabolism in this area and performance on the DMS48 (delayed matching to sample-48 items) was found, in line with converging evidence involving the perirhinal cortex in object-based memory. Finally, a voxel-wise IRCA revealed stronger metabolic connectivity of the MTL cluster with neocortical frontoparietal regions in carriers than in noncarriers, suggesting compensatory metabolic networks. Exploring cerebral metabolism using FDG PET can contribute to a better understanding of the influence of genetic factors on cerebral metabolism at both the local and network levels leading to phenotypical variations of the healthy brain and selective vulnerability.

Multi-scale graph-cut algorithm for efficient water-fat separation.

PubMed

Berglund, Johan; Skorpil, Mikael

2017-09-01

To improve the accuracy and robustness to noise in water-fat separation by unifying the multiscale and graph cut based approaches to B 0 -correction. A previously proposed water-fat separation algorithm that corrects for B 0 field inhomogeneity in 3D by a single quadratic pseudo-Boolean optimization (QPBO) graph cut was incorporated into a multi-scale framework, where field map solutions are propagated from coarse to fine scales for voxels that are not resolved by the graph cut. The accuracy of the single-scale and multi-scale QPBO algorithms was evaluated against benchmark reference datasets. The robustness to noise was evaluated by adding noise to the input data prior to water-fat separation. Both algorithms achieved the highest accuracy when compared with seven previously published methods, while computation times were acceptable for implementation in clinical routine. The multi-scale algorithm was more robust to noise than the single-scale algorithm, while causing only a small increase (+10%) of the reconstruction time. The proposed 3D multi-scale QPBO algorithm offers accurate water-fat separation, robustness to noise, and fast reconstruction. The software implementation is freely available to the research community. Magn Reson Med 78:941-949, 2017. © 2016 International Society for Magnetic Resonance in Medicine. © 2016 International Society for Magnetic Resonance in Medicine.

Neural Representations of Sensorimotor Memory- and Digit Position-Based Load Force Adjustments Before the Onset of Dexterous Object Manipulation.

PubMed

Marneweck, Michelle; Barany, Deborah A; Santello, Marco; Grafton, Scott T

2018-05-16

Anticipatory load forces for dexterous object manipulation in humans are modulated based on visual object property cues, sensorimotor memories of previous experiences with the object, and, when digit positioning varies from trial to trial, the integrating of this sensed variability with force modulation. Studies of the neural representations encoding these anticipatory mechanisms have not considered these mechanisms separately from each other or from feedback mechanisms emerging after lift onset. Here, representational similarity analyses of fMRI data were used to identify neural representations of sensorimotor memories and the sensing and integration of digit position. Cortical activity and movement kinematics were measured as 20 human subjects (11 women) minimized tilt of a symmetrically shaped object with a concealed asymmetric center of mass (CoM, left and right sided). This task required generating compensatory torques in opposite directions, which, without helpful visual CoM cues, relied primarily on sensorimotor memories of the same object and CoM. Digit position was constrained or unconstrained, the latter of which required modulating forces beyond what can be recalled from sensorimotor memories to compensate for digit position variability. Ventral premotor (PMv), somatosensory, and cerebellar lobule regions (CrusII, VIIIa) were sensitive to anticipatory behaviors that reflect sensorimotor memory content, as shown by larger voxel pattern differences for unmatched than matched CoM conditions. Cerebellar lobule I-IV, Broca area 44, and PMv showed greater voxel pattern differences for unconstrained than constrained grasping, which suggests their sensitivity to monitor the online coincidence of planned and actual digit positions and correct for a mismatch by force modulation. SIGNIFICANCE STATEMENT To pick up a water glass without slipping, tipping, or spilling requires anticipatory planning of fingertip load forces before the lift commences. This anticipation relies on object visual properties (e.g., mass/mass distribution), sensorimotor memories built from previous experiences (especially when object properties cannot be inferred visually), and online sensing of where the digits are positioned. There is limited understanding of how the brain represents each of these anticipatory mechanisms. We used fMRI measures of regional brain patterns and digit position kinematics before lift onset of an object with nonsalient visual cues specifically to isolate sensorimotor memories and integration of sensed digit position with force modulation. In doing so, we localized neural representations encoding these anticipatory mechanisms for dexterous object manipulation. Copyright © 2018 the authors 0270-6474/18/384724-14$15.00/0.

Detecting representations of recent and remote autobiographical memories in vmPFC and hippocampus

PubMed Central

Bonnici, Heidi M.; Chadwick, Martin J.; Lutti, Antoine; Hassabis, Demis; Weiskopf, Nikolaus; Maguire, Eleanor A.

2012-01-01

How autobiographical memories are represented in the human brain and whether this changes with time are questions central to memory neuroscience. Two regions in particular have been consistently implicated, the ventromedial prefrontal cortex (vmPFC) and the hippocampus, although their precise contributions are still contested. The key question in this debate, when reduced to its simplest form, concerns where information about specific autobiographical memories is located. Here we availed ourselves of the opportunity afforded by multi-voxel pattern analysis (MVPA) to provide an alternative to conventional neuropsychological and fMRI approaches, by detecting representations of individual autobiographical memories in patterns of fMRI activity. We examined whether information about specific recent (two weeks old) and remote (ten years old) autobiographical memories was represented in vmPFC and hippocampus, and other medial temporal and neocortical regions. vmPFC contained information about recent and remote autobiographical memories, although remote memories were more readily detected there, indicating that consolidation or a change of some kind had occurred. Information about both types of memory was also present in the hippocampus, suggesting it plays a role in the retrieval of vivid autobiographical memories regardless of remoteness. Interestingly, we also found that while recent and remote memories were both represented within anterior and posterior hippocampus, the latter nevertheless contained more information about remote memories. Thus, like vmPFC, the hippocampus too respected the distinction between recent and remote memories. Overall, these findings clarify and extend our view of vmPFC and hippocampus while also informing systems-level consolidation and providing clear targets for future studies. PMID:23175849

Phases of learning: How skill acquisition impacts cognitive processing.

PubMed

Tenison, Caitlin; Fincham, Jon M; Anderson, John R

2016-06-01

This fMRI study examines the changes in participants' information processing as they repeatedly solve the same mathematical problem. We show that the majority of practice-related speedup is produced by discrete changes in cognitive processing. Because the points at which these changes take place vary from problem to problem, and the underlying information processing steps vary in duration, the existence of such discrete changes can be hard to detect. Using two converging approaches, we establish the existence of three learning phases. When solving a problem in one of these learning phases, participants can go through three cognitive stages: Encoding, Solving, and Responding. Each cognitive stage is associated with a unique brain signature. Using a bottom-up approach combining multi-voxel pattern analysis and hidden semi-Markov modeling, we identify the duration of that stage on any particular trial from participants brain activation patterns. For our top-down approach we developed an ACT-R model of these cognitive stages and simulated how they change over the course of learning. The Solving stage of the first learning phase is long and involves a sequence of arithmetic computations. Participants transition to the second learning phase when they can retrieve the answer, thereby drastically reducing the duration of the Solving stage. With continued practice, participants then transition to the third learning phase when they recognize the problem as a single unit and produce the answer as an automatic response. The duration of this third learning phase is dominated by the Responding stage. Copyright © 2016 Elsevier Inc. All rights reserved.

Two efficient label-equivalence-based connected-component labeling algorithms for 3-D binary images.

PubMed

He, Lifeng; Chao, Yuyan; Suzuki, Kenji

2011-08-01

Whenever one wants to distinguish, recognize, and/or measure objects (connected components) in binary images, labeling is required. This paper presents two efficient label-equivalence-based connected-component labeling algorithms for 3-D binary images. One is voxel based and the other is run based. For the voxel-based one, we present an efficient method of deciding the order for checking voxels in the mask. For the run-based one, instead of assigning each foreground voxel, we assign each run a provisional label. Moreover, we use run data to label foreground voxels without scanning any background voxel in the second scan. Experimental results have demonstrated that our voxel-based algorithm is efficient for 3-D binary images with complicated connected components, that our run-based one is efficient for those with simple connected components, and that both are much more efficient than conventional 3-D labeling algorithms.

Localized shape abnormalities in the thalamus and pallidum are associated with secondarily generalized seizures in mesial temporal lobe epilepsy.

PubMed

Yang, Linglin; Li, Hong; Zhu, Lujia; Yu, Xinfeng; Jin, Bo; Chen, Cong; Wang, Shan; Ding, Meiping; Zhang, Minming; Chen, Zhong; Wang, Shuang

2017-05-01

Mesial temporal lobe epilepsy (mTLE) is a common type of drug-resistant epilepsy and secondarily generalized tonic-clonic seizures (sGTCS) have devastating consequences for patients' safety and quality of life. To probe the mechanism underlying the genesis of sGTCS, we investigated the structural differences between patients with and without sGTCS in a cohort of mTLE with radiologically defined unilateral hippocampal sclerosis. We performed voxel-based morphometric analysis of cortex and vertex-wise shape analysis of subcortical structures (the basal ganglia and thalamus) on MRI of 39 patients (21 with and 18 without sGTCS). Comparisons were initially made between sGTCS and non-sGTCS groups, and subsequently made between uncontrolled-sGTCS and controlled-sGTCS subgroups. Regional atrophy of the ipsilateral ventral pallidum (cluster size=450 voxels, corrected p=0.047, Max voxel coordinate=107, 120, 65), medial thalamus (cluster size=1128 voxels, corrected p=0.049, Max voxel coordinate=107, 93, 67), middle frontal gyrus (cluster size=60 voxels, corrected p<0.05, Max voxel coordinate=-30, 49.5, 6), and contralateral posterior cingulate cortex (cluster size=130 voxels, corrected p<0.05, Max voxel coordinate=16.5, -57, 27) was found in the sGTCS group relative to the non-sGTCS group. Furthermore, the uncontrolled-sGTCS subgroup showed more pronounced atrophy of the ipsilateral medial thalamus (cluster size=1240 voxels, corrected p=0.014, Max voxel coordinate=107, 93, 67) than the controlled-sGTCS subgroup. These findings indicate a central role of thalamus and pallidum in the pathophysiology of sGTCS in mTLE. Copyright © 2017 Elsevier Inc. All rights reserved.

Mapping the MRI voxel volume in which thermal noise matches physiological noise--implications for fMRI.

PubMed

Bodurka, J; Ye, F; Petridou, N; Murphy, K; Bandettini, P A

2007-01-15

This work addresses the choice of the imaging voxel volume in blood oxygen level dependent (BOLD) functional magnetic resonance imaging (fMRI). Noise of physiological origin that is present in the voxel time course is a prohibitive factor in the detection of small activation-induced BOLD signal changes. If the physiological noise contribution dominates over the temporal fluctuation contribution in the imaging voxel, further increases in the voxel signal-to-noise ratio (SNR) will have diminished corresponding increases in temporal signal-to-noise (TSNR), resulting in reduced corresponding increases in the ability to detect activation induced signal changes. On the other hand, if the thermal and system noise dominate (suggesting a relatively low SNR) further decreases in SNR can prohibit detection of activation-induced signal changes. Here we have proposed and called the "suggested" voxel volume for fMRI the volume where thermal plus system-related and physiological noise variances are equal. Based on this condition we have created maps of fMRI suggested voxel volume from our experimental data at 3T, since this value will spatially vary depending on the contribution of physiologic noise in each voxel. Based on our fast EPI segmentation technique we have found that for gray matter (GM), white matter (WM), and cerebral spinal fluid (CSF) brain compartments the mean suggested cubical voxel volume is: (1.8 mm)3, (2.1 mm)3 and (1.4 mm)3, respectively. Serendipitously, (1.8 mm)3 cubical voxel volume for GM approximately matches the cortical thickness, thus optimizing BOLD contrast by minimizing partial volume averaging. The introduced suggested fMRI voxel volume can be a useful parameter for choice of imaging volume for functional studies.

SU-E-CAMPUS-I-02: Estimation of the Dosimetric Error Caused by the Voxelization of Hybrid Computational Phantoms Using Triangle Mesh-Based Monte Carlo Transport

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lee, C; Badal, A

Purpose: Computational voxel phantom provides realistic anatomy but the voxel structure may result in dosimetric error compared to real anatomy composed of perfect surface. We analyzed the dosimetric error caused from the voxel structure in hybrid computational phantoms by comparing the voxel-based doses at different resolutions with triangle mesh-based doses. Methods: We incorporated the existing adult male UF/NCI hybrid phantom in mesh format into a Monte Carlo transport code, penMesh that supports triangle meshes. We calculated energy deposition to selected organs of interest for parallel photon beams with three mono energies (0.1, 1, and 10 MeV) in antero-posterior geometry. Wemore » also calculated organ energy deposition using three voxel phantoms with different voxel resolutions (1, 5, and 10 mm) using MCNPX2.7. Results: Comparison of organ energy deposition between the two methods showed that agreement overall improved for higher voxel resolution, but for many organs the differences were small. Difference in the energy deposition for 1 MeV, for example, decreased from 11.5% to 1.7% in muscle but only from 0.6% to 0.3% in liver as voxel resolution increased from 10 mm to 1 mm. The differences were smaller at higher energies. The number of photon histories processed per second in voxels were 6.4×10{sup 4}, 3.3×10{sup 4}, and 1.3×10{sup 4}, for 10, 5, and 1 mm resolutions at 10 MeV, respectively, while meshes ran at 4.0×10{sup 4} histories/sec. Conclusion: The combination of hybrid mesh phantom and penMesh was proved to be accurate and of similar speed compared to the voxel phantom and MCNPX. The lowest voxel resolution caused a maximum dosimetric error of 12.6% at 0.1 MeV and 6.8% at 10 MeV but the error was insignificant in some organs. We will apply the tool to calculate dose to very thin layer tissues (e.g., radiosensitive layer in gastro intestines) which cannot be modeled by voxel phantoms.« less

[Elastic registration method to compute deformation functions for mitral valve].

PubMed

Yang, Jinyu; Zhang, Wan; Yin, Ran; Deng, Yuxiao; Wei, Yunfeng; Zeng, Junyi; Wen, Tong; Ding, Lu; Liu, Xiaojian; Li, Yipeng

2014-10-01

Mitral valve disease is one of the most popular heart valve diseases. Precise positioning and displaying of the valve characteristics is necessary for the minimally invasive mitral valve repairing procedures. This paper presents a multi-resolution elastic registration method to compute the deformation functions constructed from cubic B-splines in three dimensional ultrasound images, in which the objective functional to be optimized was generated by maximum likelihood method based on the probabilistic distribution of the ultrasound speckle noise. The algorithm was then applied to register the mitral valve voxels. Numerical results proved the effectiveness of the algorithm.

Detecting the transition to failure: wavelet analysis of multi-scale crack patterns at different confining pressures

NASA Astrophysics Data System (ADS)

Rizzo, R. E.; Healy, D.; Farrell, N. J.

2017-12-01

Numerous laboratory brittle deformation experiments have shown that a rapid transition exists in the behaviour of porous materials under stress: at a certain point, early formed tensile cracks interact and coalesce into a `single' narrow zone, the shear plane, rather than remaining distributed throughout the material. In this work, we present and apply a novel image processing tool which is able to quantify this transition between distributed (`stable') damage accumulation and localised (`unstable') deformation, in terms of size, density, and orientation of cracks at the point of failure. Our technique, based on a two-dimensional (2D) continuous Morlet wavelet analysis, can recognise, extract and visually separate the multi-scale changes occurring in the fracture network during the deformation process. We have analysed high-resolution SEM-BSE images of thin sections of Hopeman Sandstone (Scotland, UK) taken from core plugs deformed under triaxial conditions, with increasing confining pressure. Through this analysis, we can determine the relationship between the initial orientation of tensile microcracks and the final geometry of the through-going shear fault, exploiting the total areal coverage of the analysed image. In addition, by comparing patterns of fractures in thin sections derived from triaxial (σ1>σ2=σ3=Pc) laboratory experiments conducted at different confining pressures (Pc), we can quantitatively explore the relationship between the observed geometry and the inferred mechanical processes. The methodology presented here can have important implications for larger-scale mechanical problems related to major fault propagation. Just as a core plug scale fault localises through extension and coalescence of microcracks, larger faults also grow by extension and coalescence of segments in a multi-scale process by which microscopic cracks can ultimately lead to macroscopic faulting. Consequently, wavelet analysis represents a useful tool for fracture pattern recognition, applicable to the detection of the transitions occurring at the time of catastrophic rupture.

Evidence of strategic periodicities in collective conflict dynamics.

PubMed

Dedeo, Simon; Krakauer, David; Flack, Jessica

2011-09-07

We analyse the timescales of conflict decision-making in a primate society. We present evidence for multiple, periodic timescales associated with social decision-making and behavioural patterns. We demonstrate the existence of periodicities that are not directly coupled to environmental cycles or known ultraridian mechanisms. Among specific biological and socially defined demographic classes, periodicities span timescales between hours and days. Our results indicate that these periodicities are not driven by exogenous or internal regularities but are instead driven by strategic responses to social interaction patterns. Analyses also reveal that a class of individuals, playing a critical functional role, policing, have a signature timescale of the order of 1 h. We propose a classification of behavioural timescales analogous to those of the nervous system, with high frequency, or α-scale, behaviour occurring on hour-long scales, through to multi-hour, or β-scale, behaviour, and, finally γ periodicities observed on a timescale of days.

Structural correlates of psychopathological symptom dimensions in schizophrenia: a voxel-based morphometric study.

PubMed

Koutsouleris, Nikolaos; Gaser, Christian; Jäger, Markus; Bottlender, Ronald; Frodl, Thomas; Holzinger, Silvia; Schmitt, Gisela J E; Zetzsche, Thomas; Burgermeister, Bernhard; Scheuerecker, Johanna; Born, Christine; Reiser, Maximilian; Möller, Hans-Jürgen; Meisenzahl, Eva M

2008-02-15

Structural neuroimaging has substantially advanced the neurobiological research of schizophrenia by describing a range of focal brain alterations as possible neuroanatomical underpinnings of the disease. Despite this progress, a considerable heterogeneity of structural findings persists that may reflect the phenomenological diversity of schizophrenia. It is unclear whether the range of possible clinical disease manifestations relates to a core structural brain deficit or to distinct structural correlates. Therefore, gray matter density (GMD) differences between 175 schizophrenic patients (SZ) and 177 matched healthy control subjects (HC) were examined in a three-step approach using cross-sectional and conjunctional voxel-based morphometry (VBM): (1) analysis of structural alterations irrespective of symptomatology; (2) subdivision of the patient sample according to a three-dimensional factor model of the PANSS and investigation of structural differences between these subsamples and healthy controls; (3) analysis of a common pattern of structural alterations present in all patient subsamples compared to healthy controls. Significant GMD reductions in patients compared to controls were identified within the prefrontal, limbic, paralimbic, temporal and thalamic regions. The disorganized symptom dimension was associated with bilateral alterations in temporal, insular and medial prefrontal cortices. Positive symptoms were associated with left-pronounced alterations in perisylvian regions and extended thalamic GMD losses. Negative symptoms were linked to the most extended alterations within orbitofrontal, medial prefrontal, lateral prefrontal and temporal cortices as well as limbic and subcortical structures. Thus, structural heterogeneity in schizophrenia may relate to specific patterns of GMD reductions that possibly share a common prefrontal-perisylvian pattern of structural brain alterations.

Right Limbic FDG-PET Hypometabolism Correlates with Emotion Recognition and Attribution in Probable Behavioral Variant of Frontotemporal Dementia Patients

PubMed Central

Cerami, Chiara; Dodich, Alessandra; Iannaccone, Sandro; Marcone, Alessandra; Lettieri, Giada; Crespi, Chiara; Gianolli, Luigi; Cappa, Stefano F.; Perani, Daniela

2015-01-01

The behavioural variant of frontotemporal dementia (bvFTD) is a rare disease mainly affecting the social brain. FDG-PET fronto-temporal hypometabolism is a supportive feature for the diagnosis. It may also provide specific functional metabolic signatures for altered socio-emotional processing. In this study, we evaluated the emotion recognition and attribution deficits and FDG-PET cerebral metabolic patterns at the group and individual levels in a sample of sporadic bvFTD patients, exploring the cognitive-functional correlations. Seventeen probable mild bvFTD patients (10 male and 7 female; age 67.8±9.9) were administered standardized and validated version of social cognition tasks assessing the recognition of basic emotions and the attribution of emotions and intentions (i.e., Ekman 60-Faces test-Ek60F and Story-based Empathy task-SET). FDG-PET was analysed using an optimized voxel-based SPM method at the single-subject and group levels. Severe deficits of emotion recognition and processing characterized the bvFTD condition. At the group level, metabolic dysfunction in the right amygdala, temporal pole, and middle cingulate cortex was highly correlated to the emotional recognition and attribution performances. At the single-subject level, however, heterogeneous impairments of social cognition tasks emerged, and different metabolic patterns, involving limbic structures and prefrontal cortices, were also observed. The derangement of a right limbic network is associated with altered socio-emotional processing in bvFTD patients, but different hypometabolic FDG-PET patterns and heterogeneous performances on social tasks at an individual level exist. PMID:26513651

Tensor-product kernel-based representation encoding joint MRI view similarity.

PubMed

Alvarez-Meza, A; Cardenas-Pena, D; Castro-Ospina, A E; Alvarez, M; Castellanos-Dominguez, G

2014-01-01

To support 3D magnetic resonance image (MRI) analysis, a marginal image similarity (MIS) matrix holding MR inter-slice relationship along every axis view (Axial, Coronal, and Sagittal) can be estimated. However, mutual inference from MIS view information poses a difficult task since relationships between axes are nonlinear. To overcome this issue, we introduce a Tensor-Product Kernel-based Representation (TKR) that allows encoding brain structure patterns due to patient differences, gathering all MIS matrices into a single joint image similarity framework. The TKR training strategy is carried out into a low dimensional projected space to get less influence of voxel-derived noise. Obtained results for classifying the considered patient categories (gender and age) on real MRI database shows that the proposed TKR training approach outperforms the conventional voxel-wise sum of squared differences. The proposed approach may be useful to support MRI clustering and similarity inference tasks, which are required on template-based image segmentation and atlas construction.

Show me the data: advances in multi-model benchmarking, assimilation, and forecasting

NASA Astrophysics Data System (ADS)

Dietze, M.; Raiho, A.; Fer, I.; Cowdery, E.; Kooper, R.; Kelly, R.; Shiklomanov, A. N.; Desai, A. R.; Simkins, J.; Gardella, A.; Serbin, S.

2016-12-01

Researchers want their data to inform carbon cycle predictions, but there are considerable bottlenecks between data collection and the use of data to calibrate and validate earth system models and inform predictions. This talk highlights recent advancements in the PEcAn project aimed at it making it easier for individual researchers to confront models with their own data: (1) The development of an easily extensible site-scale benchmarking system aimed at ensuring that models capture process rather than just reproducing pattern; (2) Efficient emulator-based Bayesian parameter data assimilation to constrain model parameters; (3) A novel, generalized approach to ensemble data assimilation to estimate carbon pools and fluxes and quantify process error; (4) automated processing and downscaling of CMIP climate scenarios to support forecasts that include driver uncertainty; (5) a large expansion in the number of models supported, with new tools for conducting multi-model and multi-site analyses; and (6) a network-based architecture that allows analyses to be shared with model developers and other collaborators. Application of these methods is illustrated with data across a wide range of time scales, from eddy-covariance to forest inventories to tree rings to paleoecological pollen proxies.

Organ dose calculations by Monte Carlo modeling of the updated VCH adult male phantom against idealized external proton exposure

NASA Astrophysics Data System (ADS)

Zhang, Guozhi; Liu, Qian; Zeng, Shaoqun; Luo, Qingming

2008-07-01

The voxel-based visible Chinese human (VCH) adult male phantom has offered a high-quality test bed for realistic Monte Carlo modeling in radiological dosimetry simulations. The phantom has been updated in recent effort by adding newly segmented organs, revising walled and smaller structures as well as recalibrating skeletal marrow distributions. The organ absorbed dose against external proton exposure was calculated at a voxel resolution of 2 × 2 × 2 mm3 using the MCNPX code for incident energies from 20 MeV to 10 GeV and for six idealized irradiation geometries: anterior-posterior (AP), posterior-anterior (PA), left-lateral (LLAT), right-lateral (RLAT), rotational (ROT) and isotropic (ISO), respectively. The effective dose on the VCH phantom was derived in compliance with the evaluation scheme for the reference male proposed in the 2007 recommendations of the International Commission on Radiological Protection (ICRP). Algorithm transitions from the revised radiation and tissue weighting factors are accountable for approximately 90% and 10% of effective dose discrepancies in proton dosimetry, respectively. Results are tabulated in terms of fluence-to-dose conversion coefficients for practical use and are compared with data from other models available in the literature. Anatomical variations between various computational phantoms lead to dose discrepancies ranging from a negligible level to 100% or more at proton energies below 200 MeV, corresponding to the spatial geometric locations of individual organs within the body. Doses show better agreement at higher energies and the deviations are mostly within 20%, to which the organ volume and mass differences should be of primary responsibility. The impact of body size on dose distributions was assessed by dosimetry of a scaled-up VCH phantom that was resized in accordance with the height and total mass of the ICRP reference man. The organ dose decreases with the directionally uniform enlargement of voxels. Potential pathways to improve the VCH phantom have also been briefly addressed. This work pertains to VCH-based systematic multi-particle dose investigations and will contribute to comparative dosimetry studies of ICRP standardized voxel phantoms in the near future.

SigVox - A 3D feature matching algorithm for automatic street object recognition in mobile laser scanning point clouds

NASA Astrophysics Data System (ADS)

Wang, Jinhu; Lindenbergh, Roderik; Menenti, Massimo

2017-06-01

Urban road environments contain a variety of objects including different types of lamp poles and traffic signs. Its monitoring is traditionally conducted by visual inspection, which is time consuming and expensive. Mobile laser scanning (MLS) systems sample the road environment efficiently by acquiring large and accurate point clouds. This work proposes a methodology for urban road object recognition from MLS point clouds. The proposed method uses, for the first time, shape descriptors of complete objects to match repetitive objects in large point clouds. To do so, a novel 3D multi-scale shape descriptor is introduced, that is embedded in a workflow that efficiently and automatically identifies different types of lamp poles and traffic signs. The workflow starts by tiling the raw point clouds along the scanning trajectory and by identifying non-ground points. After voxelization of the non-ground points, connected voxels are clustered to form candidate objects. For automatic recognition of lamp poles and street signs, a 3D significant eigenvector based shape descriptor using voxels (SigVox) is introduced. The 3D SigVox descriptor is constructed by first subdividing the points with an octree into several levels. Next, significant eigenvectors of the points in each voxel are determined by principal component analysis (PCA) and mapped onto the appropriate triangle of a sphere approximating icosahedron. This step is repeated for different scales. By determining the similarity of 3D SigVox descriptors between candidate point clusters and training objects, street furniture is automatically identified. The feasibility and quality of the proposed method is verified on two point clouds obtained in opposite direction of a stretch of road of 4 km. 6 types of lamp pole and 4 types of road sign were selected as objects of interest. Ground truth validation showed that the overall accuracy of the ∼170 automatically recognized objects is approximately 95%. The results demonstrate that the proposed method is able to recognize street furniture in a practical scenario. Remaining difficult cases are touching objects, like a lamp pole close to a tree.

The effect of fMRI task combinations on determining the hemispheric dominance of language functions.

PubMed

Niskanen, Eini; Könönen, Mervi; Villberg, Ville; Nissi, Mikko; Ranta-Aho, Perttu; Säisänen, Laura; Karjalainen, Pasi; Aikiä, Marja; Kälviäinen, Reetta; Mervaala, Esa; Vanninen, Ritva

2012-04-01

The purpose of this study is to establish the most suitable combination of functional magnetic resonance imaging (fMRI) language tasks for clinical use in determining language dominance and to define the variability in laterality index (LI) and activation power between different combinations of language tasks. Activation patterns of different fMRI analyses of five language tasks (word generation, responsive naming, letter task, sentence comprehension, and word pair) were defined for 20 healthy volunteers (16 right-handed). LIs and sums of T values were calculated for each task separately and for four combinations of tasks in predefined regions of interest. Variability in terms of activation power and lateralization was defined in each analysis. In addition, the visual assessment of lateralization of language functions based on the individual fMRI activation maps was conducted by an experienced neuroradiologist. A combination analysis of word generation, responsive naming, and sentence comprehension was the most suitable in terms of activation power, robustness to detect essential language areas, and scanning time. In general, combination analyses of the tasks provided higher overall activation levels than single tasks and reduced the number of outlier voxels disturbing the calculation of LI. A combination of auditory and visually presented tasks that activate different aspects of language functions with sufficient activation power may be a useful task battery for determining language dominance in patients.

A meta-analysis of neurofunctional imaging studies of emotion and cognition in major depression.

PubMed

Diener, Carsten; Kuehner, Christine; Brusniak, Wencke; Ubl, Bettina; Wessa, Michèle; Flor, Herta

2012-07-02

Major depressive disorder (MDD) is characterized by altered emotional and cognitive functioning. We performed a voxel-based whole-brain meta-analysis of functional neuroimaging data on altered emotion and cognition in MDD. Forty peer-reviewed studies in English-language published between 1998 and 2010 were included, which used functional neuroimaging during cognitive-emotional challenge in adult individuals with MDD and healthy controls. All studies reported between-groups differences for whole-brain analyses in standardized neuroanatomical space and were subjected to Activation Likelihood Estimation (ALE) of brain cluster showing altered responsivity in MDD. ALE resulted in thresholded and false discovery rate corrected hypo- and hyperactive brain regions. Against the background of a complex neural activation pattern, studies converged in predominantly hypoactive cluster in the anterior insular and rostral anterior cingulate cortex linked to affectively biased information processing and poor cognitive control. Frontal areas showed not only similar under- but also over-activation during cognitive-emotional challenge. On the subcortical level, we identified activation alterations in the thalamus and striatum which were involved in biased valence processing of emotional stimuli in MDD. These results for active conditions extend findings from ALE meta-analyses of resting state and antidepressant treatment studies and emphasize the key role of the anterior insular and rostral anterior cingulate cortex for altered emotion and cognition in MDD. Copyright © 2012 Elsevier Inc. All rights reserved.

Neuroanatomic substrates of semantic memory impairment in Alzheimer’s disease: Patterns of functional MRI activation

PubMed Central

SAYKIN, ANDREW J.; FLASHMAN, LAURA A.; FRUTIGER, SALLY A.; JOHNSON, STERLING C.; MAMOURIAN, ALEXANDER C.; MORITZ, CHAD H.; O’JILE, JUDITH R.; RIORDAN, HENRY J.; SANTULLI, ROBERT B.; SMITH, CYNTHIA A.; WEAVER, JOHN B.

2015-01-01

Impairment in semantic processing occurs early in Alzheimer’s disease (AD) and differential impact on subtypes of semantic relations have been reported, yet there is little data on the neuroanatomic basis of these deficits. Patients with mild AD and healthy controls underwent 3 functional MRI auditory stimulation tasks requiring semantic or phonological decisions (match–mismatch) about word pairs (category–exemplar, category–function, pseudoword). Patients showed a significant performance deficit only on the exemplar task. On voxel-based fMRI activation analyses, controls showed a clear activation focus in the left superior temporal gyrus for the phonological task; patients showed additional foci in the left dorsolateral prefrontal and bilateral cingulate areas. On the semantic tasks, predominant activation foci were seen in the inferior and middle frontal gyrus (left greater than right) in both groups but patients showed additional activation suggesting compensatory recruitment of locally expanded foci and remote regions, for example, right frontal activation during the exemplar task. Covariance analyses indicated that exemplar task performance was strongly related to signal increase in bilateral medial prefrontal cortex. The authors conclude that fMRI can reveal similarities and differences in functional neuroanatomical processing of semantic and phonological information in mild AD compared to healthy elderly, and can help to bridge cognitive and neural investigations of the integrity of semantic networks in AD. PMID:10439584

Dysfunctional role of parietal lobe during self-face recognition in schizophrenia.

PubMed

Yun, Je-Yeon; Hur, Ji-Won; Jung, Wi Hoon; Jang, Joon Hwan; Youn, Tak; Kang, Do-Hyung; Park, Sohee; Kwon, Jun Soo

2014-01-01

Anomalous sense of self is central to schizophrenia yet difficult to demonstrate empirically. The present study examined the effective neural network connectivity underlying self-face recognition in patients with schizophrenia (SZ) using [15O]H2O Positron Emission Tomography (PET) and Structural Equation Modeling. Eight SZ and eight age-matched healthy controls (CO) underwent six consecutive [15O]H2O PET scans during self-face (SF) and famous face (FF) recognition blocks, each of which was repeated three times. There were no behavioral performance differences between the SF and FF blocks in SZ. Moreover, voxel-based analyses of data from SZ revealed no significant differences in the regional cerebral blood flow (rCBF) levels between the SF and FF recognition conditions. Further effective connectivity analyses for SZ also showed a similar pattern of effective connectivity network across the SF and FF recognition. On the other hand, comparison of SF recognition effective connectivity network between SZ and CO demonstrated significantly attenuated effective connectivity strength not only between the right supramarginal gyrus and left inferior temporal gyrus, but also between the cuneus and right medial prefrontal cortex in SZ. These findings support a conceptual model that posits a causal relationship between disrupted self-other discrimination and attenuated effective connectivity among the right supramarginal gyrus, cuneus, and prefronto-temporal brain areas involved in the SF recognition network of SZ. © 2013.

Dyslexia susceptibility genes influence brain atrophy in frontotemporal dementia.

PubMed

Paternicó, Donata; Premi, Enrico; Alberici, Antonella; Archetti, Silvana; Bonomi, Elisa; Gualeni, Vera; Gasparotti, Roberto; Padovani, Alessandro; Borroni, Barbara

2015-10-01

In this study, we evaluated whether variations within genes specifically associated with dyslexia, namely KIAA0319, DCDC2, and CNTNAP2, were associated with greater damage of language-related regions in patients with frontotemporal dementia (FTD) and primary progressive aphasia (PPA) in particular. A total of 118 patients with FTD, 84 with the behavioral variant of FTD (bvFTD) and 34 with PPA, underwent neuropsychological examination, genetic analyses, and brain MRI. KIAA0319 rs17243157 G/A, DCDC2 rs793842 A/G, and CNTNAP2 rs17236239 A/G genetic variations were assessed. Patients were grouped according to clinical phenotype and genotype status (GA/AA or GG). Gray matter (GM) and white matter (WM) differences were assessed by voxel-based morphometry and structural intercorrelation pattern analyses. Patients carrying KIAA0319 A* (GA or AA) showed greater GM and WM atrophy in the left middle and inferior temporal gyri, as compared with KIAA0319 GG (p < 0.001). The effect of KIAA0319 polymorphism was mainly reported in patients with PPA. In patients with PPA carrying at-risk polymorphism, temporal damage led to loss of interhemispheric and intrahemispheric GM and WM structural association. No effect of DCDC2 and CNTNAP2 was found. Genes involved in dyslexia susceptibility, such as KIAA0319, result in language network vulnerability in FTD, and in PPA in particular.

Neural Substrates of Spontaneous Narrative Production in Focal Neurodegenerative Disease

PubMed Central

Gola, Kelly A.; Thorne, Avril; Veldhuisen, Lisa D.; Felix, Cordula M.; Hankinson, Sarah; Pham, Julie; Shany-Ur, Tal; Schauer, Guido P.; Stanley, Christine M.; Glenn, Shenly; Miller, Bruce L.; Rankin, Katherine P.

2016-01-01

Conversational storytelling integrates diverse cognitive and socio-emotional abilities that critically differ across neurodegenerative disease groups and may have diagnostic relevance and predict anatomic changes. The present study employed mixed methods discourse and quantitative analyses to delineate patterns of storytelling across focal neurodegenerative disease groups, and to clarify the neuroanatomical contributions to common storytelling characteristics in these patients. Transcripts of spontaneous social interactions of 46 participants (15 behavioral variant frontotemporal dementia (bvFTD), 7 semantic variant primary progressive aphasia (svPPA), 12 Alzheimer's disease (AD), and 12 healthy older normal controls) were analysed for storytelling characteristics and frequency, and videos of the interactions were rated for patients' social attentiveness. Compared to controls, svPPAs also told more stories and autobiographical stories, and perseverated on aspects of self during storytelling. ADs told fewer autobiographical stories than NCs, and svPPAs and bvFTDs failed to attend to social cues. Storytelling characteristics were associated with a processing speed and mental flexibility, and voxel-based anatomic analysis of structural magnetic resonance imaging revealed that temporal organization, evaluations, and social attention correlated with atrophy corresponding to known intrinsic connectivity networks, including the default mode, limbic, salience, and stable task control networks. Differences in spontaneous storytelling among neurodegenerative groups elucidated diverse cognitive, socio-emotional, and neural contributions to narrative production, with implications for diagnostic screening and therapeutic intervention. PMID:26485159

Hybrid computational phantoms of the male and female newborn patient: NURBS-based whole-body models

NASA Astrophysics Data System (ADS)

Lee, Choonsik; Lodwick, Daniel; Hasenauer, Deanna; Williams, Jonathan L.; Lee, Choonik; Bolch, Wesley E.

2007-07-01

Anthropomorphic computational phantoms are computer models of the human body for use in the evaluation of dose distributions resulting from either internal or external radiation sources. Currently, two classes of computational phantoms have been developed and widely utilized for organ dose assessment: (1) stylized phantoms and (2) voxel phantoms which describe the human anatomy via mathematical surface equations or 3D voxel matrices, respectively. Although stylized phantoms based on mathematical equations can be very flexible in regard to making changes in organ position and geometrical shape, they are limited in their ability to fully capture the anatomic complexities of human internal anatomy. In turn, voxel phantoms have been developed through image-based segmentation and correspondingly provide much better anatomical realism in comparison to simpler stylized phantoms. However, they themselves are limited in defining organs presented in low contrast within either magnetic resonance or computed tomography images—the two major sources in voxel phantom construction. By definition, voxel phantoms are typically constructed via segmentation of transaxial images, and thus while fine anatomic features are seen in this viewing plane, slice-to-slice discontinuities become apparent in viewing the anatomy of voxel phantoms in the sagittal or coronal planes. This study introduces the concept of a hybrid computational newborn phantom that takes full advantage of the best features of both its stylized and voxel counterparts: flexibility in phantom alterations and anatomic realism. Non-uniform rational B-spline (NURBS) surfaces, a mathematical modeling tool traditionally applied to graphical animation studies, was adopted to replace the limited mathematical surface equations of stylized phantoms. A previously developed whole-body voxel phantom of the newborn female was utilized as a realistic anatomical framework for hybrid phantom construction. The construction of a hybrid phantom is performed in three steps: polygonization of the voxel phantom, organ modeling via NURBS surfaces and phantom voxelization. Two 3D graphic tools, 3D-DOCTOR™ and Rhinoceros™, were utilized to polygonize the newborn voxel phantom and generate NURBS surfaces, while an in-house MATLAB™ code was used to voxelize the resulting NURBS model into a final computational phantom ready for use in Monte Carlo radiation transport calculations. A total of 126 anatomical organ and tissue models, including 38 skeletal sites and 31 cartilage sites, were described within the hybrid phantom using either NURBS or polygon surfaces. A male hybrid newborn phantom was constructed following the development of the female phantom through the replacement of female-specific organs with male-specific organs. The outer body contour and internal anatomy of the NURBS-based phantoms were adjusted to match anthropometric and reference newborn data reported by the International Commission on Radiological Protection in their Publication 89. The voxelization process was designed to accurately convert NURBS models to a voxel phantom with minimum volumetric change. A sensitivity study was additionally performed to better understand how the meshing tolerance and voxel resolution would affect volumetric changes between the hybrid-NURBS and hybrid-voxel phantoms. The male and female hybrid-NURBS phantoms were constructed in a manner so that all internal organs approached their ICRP reference masses to within 1%, with the exception of the skin (-6.5% relative error) and brain (-15.4% relative error). Both hybrid-voxel phantoms were constructed with an isotropic voxel resolution of 0.663 mm—equivalent to the ICRP 89 reference thickness of the newborn skin (dermis and epidermis). Hybrid-NURBS phantoms used to create their voxel counterpart retain the non-uniform scalability of stylized phantoms, while maintaining the anatomic realism of segmented voxel phantoms with respect to organ shape, depth and inter-organ positioning. This work was supported by the National Cancer Institute.

Quantification of micro-CT images of textile reinforcements

NASA Astrophysics Data System (ADS)

Straumit, Ilya; Lomov, Stepan V.; Wevers, Martine

2017-10-01

VoxTex software (KU Leuven) employs 3D image processing, which use the local directionality information, retrieved using analysis of local structure tensor. The processing results in a voxel 3D array, with each voxel carrying information on (1) material type (matrix; yarn/ply, with identification of the yarn/ply in the reinforcement architecture; void) and (2) fibre direction for fibrous yarns/plies. The knowledge of the material phase volume and known characterisation of the textile structure allows assigning to the voxels (3) fibre volume fraction. This basic voxel model can be further used for different type of the material analysis: Internal geometry and characterisation of defects; permeability; micromechanics; mesoFE voxel models. Apart from the voxel based analysis, approaches to reconstruction of the yarn paths are presented.

Tract-specific fractional anisotropy predicts cognitive outcome in a community sample of middle-aged participants with white matter lesions

PubMed Central

Soriano-Raya, Juan José; Miralbell, Júlia; López-Cancio, Elena; Bargalló, Núria; Arenillas, Juan Francisco; Barrios, Maite; Cáceres, Cynthia; Toran, Pere; Alzamora, Maite; Dávalos, Antoni; Mataró, Maria

2014-01-01

Cerebral white matter lesions (WMLs) have been consistently related to cognitive dysfunction but the role of white matter (WM) damage in cognitive impairment is not fully determined. Diffusion tensor imaging is a promising tool to explain impaired cognition related to WMLs. We investigated the separate association of high-grade periventricular hyperintensities (PVHs) and deep white matter hyperintensities (DWMHs) with fractional anisotropy (FA) in middle-aged individuals. We also assessed the predictive value to cognition of FA within specific WM tracts associated with high-grade WMLs. One hundred participants from the Barcelona-AsIA Neuropsychology Study were divided into groups based on low- and high-grade WMLs. Voxel-by-voxel FA were compared between groups, with separate analyses for high-grade PVHs and DWMHs. The mean FA within areas showing differences between groups was extracted in each tract for linear regression analyses. Participants with high-grade PVHs and participants with high-grade DWMHs showed lower FA in different areas of specific tracts. Areas showing decreased FA in high-grade DWMHs predicted lower cognition, whereas areas with decreased FA in high-grade PVHs did not. The predictive value to cognition of specific WM tracts supports the involvement of cortico-subcortical circuits in cognitive deficits only in DWMHs. PMID:24549185

Tract-specific fractional anisotropy predicts cognitive outcome in a community sample of middle-aged participants with white matter lesions.

PubMed

Soriano-Raya, Juan José; Miralbell, Júlia; López-Cancio, Elena; Bargalló, Núria; Arenillas, Juan Francisco; Barrios, Maite; Cáceres, Cynthia; Toran, Pere; Alzamora, Maite; Dávalos, Antoni; Mataró, Maria

2014-05-01

Cerebral white matter lesions (WMLs) have been consistently related to cognitive dysfunction but the role of white matter (WM) damage in cognitive impairment is not fully determined. Diffusion tensor imaging is a promising tool to explain impaired cognition related to WMLs. We investigated the separate association of high-grade periventricular hyperintensities (PVHs) and deep white matter hyperintensities (DWMHs) with fractional anisotropy (FA) in middle-aged individuals. We also assessed the predictive value to cognition of FA within specific WM tracts associated with high-grade WMLs. One hundred participants from the Barcelona-AsIA Neuropsychology Study were divided into groups based on low- and high-grade WMLs. Voxel-by-voxel FA were compared between groups, with separate analyses for high-grade PVHs and DWMHs. The mean FA within areas showing differences between groups was extracted in each tract for linear regression analyses. Participants with high-grade PVHs and participants with high-grade DWMHs showed lower FA in different areas of specific tracts. Areas showing decreased FA in high-grade DWMHs predicted lower cognition, whereas areas with decreased FA in high-grade PVHs did not. The predictive value to cognition of specific WM tracts supports the involvement of cortico-subcortical circuits in cognitive deficits only in DWMHs.

Reduced Gray Matter Volume of the Thalamus and Hippocampal Region in Elderly Healthy Adults with no Impact of APOE ɛ4: A Longitudinal Voxel-Based Morphometry Study.

PubMed

Squarzoni, Paula; Duran, Fabio Luis Souza; Busatto, Geraldo F; Alves, Tania Correa Toledo de Ferraz

2018-01-01

Many cross-sectional voxel-based morphometry (VBM) investigations have shown significant inverse correlations between chronological age and gray matter (GM) volume in several brain regions in healthy humans. However, few VBM studies have documented GM decrements in the healthy elderly with repeated MRI measurements obtained in the same subjects. Also, the extent to which the APOE ɛ4 allele influences longitudinal findings of GM reduction in the healthy elderly is unclear. Verify whether regional GM changes are associated with significant decrements in cognitive performance taking in account the presence of the APOE ɛ4 allele. Using structural MRI datasets acquired in 55 cognitively intact elderly subjects at two time-points separated by approximately three years, we searched for voxels showing significant GM reductions taking into account differences in APOE genotype. We found global GM reductions as well as regional GM decrements in the right thalamus and left parahippocampal gyrus (p < 0.05, family-wise error corrected for multiple comparisons over the whole brain). These findings were not affected by APOE ɛ4. Irrespective of APOE ɛ4, longitudinal VBM analyses show that the hippocampal region and thalamus are critical sites where GM shrinkage is greater than the degree of global volume reduction in healthy elderly subjects.

Voxel-based lesion mapping of meningioma: a comprehensive lesion location mapping of 260 lesions.

PubMed

Hirayama, Ryuichi; Kinoshita, Manabu; Arita, Hideyuki; Kagawa, Naoki; Kishima, Haruhiko; Hashimoto, Naoya; Fujimoto, Yasunori; Yoshimine, Toshiki

2018-06-01

OBJECTIVE In the present study the authors aimed to determine preferred locations of meningiomas by avoiding descriptive analysis and instead using voxel-based lesion mapping and 3D image-rendering techniques. METHODS Magnetic resonance images obtained in 248 treatment-naïve meningioma patients with 260 lesions were retrospectively and consecutively collected. All images were registered to a 1-mm isotropic, high-resolution, T1-weighted brain atlas provided by the Montreal Neurological Institute (the MNI152), and a lesion frequency map was created, followed by 3D volume rendering to visualize the preferred locations of meningiomas in 3D. RESULTS The 3D lesion frequency map clearly showed that skull base structures such as parasellar, sphenoid wing, and petroclival regions were commonly affected by the tumor. The middle one-third of the superior sagittal sinus was most commonly affected in parasagittal tumors. Substantial lesion accumulation was observed around the leptomeninges covering the central sulcus and the sylvian fissure, with very few lesions observed at the frontal, parietal, and occipital convexities. CONCLUSIONS Using an objective visualization method, meningiomas were shown to be located around the middle third of the superior sagittal sinus, the perisylvian convexity, and the skull base. These observations, which are in line with previous descriptive analyses, justify further use of voxel-based lesion mapping techniques to help understand the biological nature of this disease.

Large-scale brain network associated with creative insight: combined voxel-based morphometry and resting-state functional connectivity analyses.

PubMed

Ogawa, Takeshi; Aihara, Takatsugu; Shimokawa, Takeaki; Yamashita, Okito

2018-04-24

Creative insight occurs with an "Aha!" experience when solving a difficult problem. Here, we investigated large-scale networks associated with insight problem solving. We recruited 232 healthy participants aged 21-69 years old. Participants completed a magnetic resonance imaging study (MRI; structural imaging and a 10 min resting-state functional MRI) and an insight test battery (ITB) consisting of written questionnaires (matchstick arithmetic task, remote associates test, and insight problem solving task). To identify the resting-state functional connectivity (RSFC) associated with individual creative insight, we conducted an exploratory voxel-based morphometry (VBM)-constrained RSFC analysis. We identified positive correlations between ITB score and grey matter volume (GMV) in the right insula and middle cingulate cortex/precuneus, and a negative correlation between ITB score and GMV in the left cerebellum crus 1 and right supplementary motor area. We applied seed-based RSFC analysis to whole brain voxels using the seeds obtained from the VBM and identified insight-positive/negative connections, i.e. a positive/negative correlation between the ITB score and individual RSFCs between two brain regions. Insight-specific connections included motor-related regions whereas creative-common connections included a default mode network. Our results indicate that creative insight requires a coupling of multiple networks, such as the default mode, semantic and cerebral-cerebellum networks.

Non-Water-Suppressed 1H MR Spectroscopy with Orientational Prior Knowledge Shows Potential for Separating Intra- and Extramyocellular Lipid Signals in Human Myocardium.

PubMed

Fillmer, Ariane; Hock, Andreas; Cameron, Donnie; Henning, Anke

2017-12-04

Conditions such as type II diabetes are linked with elevated lipid levels in the heart, and significantly increased risk of heart failure; however, metabolic processes underlying the development of cardiac disease in type II diabetes are not fully understood. Here we present a non-invasive method for in vivo investigation of cardiac lipid metabolism: namely, IVS-McPRESS. This technique uses metabolite-cycled, non-water suppressed 1 H cardiac magnetic resonance spectroscopy with prospective and retrospective motion correction. High-quality IVS-McPRESS data acquired from healthy volunteers allowed us to investigate the frequency shift of extramyocellular lipid signals, which depends on the myocardial fibre orientation. Assuming consistent voxel positioning relative to myofibres, the myofibre angle with the magnetic field was derived from the voxel orientation. For separation and individual analysis of intra- and extramyocellular lipid signals, the angle myocardial fibres in the spectroscopy voxel take with the magnetic field should be within ±24.5°. Metabolite and lipid concentrations were analysed with respect to BMI. Significant correlations between BMI and unsaturated fatty acids in intramyocellular lipids, and methylene groups in extramyocellular lipids were found. The proposed IVS-McPRESS technique enables non-invasive investigation of cardiac lipid metabolism and may thus be a useful tool to study healthy and pathological conditions.

Displacement behaviors in chimpanzees (Pan troglodytes): A neurogenomics investigation of the RDoC Negative Valence Systems domain.

PubMed

Latzman, Robert D; Young, Larry J; Hopkins, William D

2016-03-01

The current study aimed to systematically investigate genetic and neuroanatomical correlates of individual variation in scratching behaviors, a well-validated animal-behavioral indicator of negative emotional states with clear links to the NIMH Research Domain Criteria (RDoC) response to potential harm ("anxiety") construct within the Negative Valence Systems domain. Utilizing data from a sample of 76 captive chimpanzees (Pan troglodytes), we (a) examined the association between scratching and presence or absence of the RS3-containing DupB element in the AVPR1A 5' flanking region, (b) utilized voxel-based morphometry (VBM) to identify gray matter (GM) voxel clusters that differentiated AVPR1A genotype, and (c) conducted a VBM-guided voxel-of-interest analysis to examine the association between GM intensity and scratching. AVPR1A evidenced sexually dimorphic associations with scratching. VBM analyses revealed significant differences in GM by genotype across twelve clusters largely in the frontal cortex. Regions differentiating AVPR1A genotype showed sex-specific associations with scratching. Results suggest that sexually dimorphic associations between AVPR1A and scratching may be explained by genotype-specific neuroanatomical variation. The current study provides an example of the way in which chimpanzee research is uniquely poised for multilevel, systematic investigations of psychopathology-relevant constructs within the context of the RDoC framework. © 2016 Society for Psychophysiological Research.

Optimized digital speckle patterns for digital image correlation by consideration of both accuracy and efficiency.

PubMed

Chen, Zhenning; Shao, Xinxing; Xu, Xiangyang; He, Xiaoyuan

2018-02-01

The technique of digital image correlation (DIC), which has been widely used for noncontact deformation measurements in both the scientific and engineering fields, is greatly affected by the quality of speckle patterns in terms of its performance. This study was concerned with the optimization of the digital speckle pattern (DSP) for DIC in consideration of both the accuracy and efficiency. The root-mean-square error of the inverse compositional Gauss-Newton algorithm and the average number of iterations were used as quality metrics. Moreover, the influence of subset sizes and the noise level of images, which are the basic parameters in the quality assessment formulations, were also considered. The simulated binary speckle patterns were first compared with the Gaussian speckle patterns and captured DSPs. Both the single-radius and multi-radius DSPs were optimized. Experimental tests and analyses were conducted to obtain the optimized and recommended DSP. The vector diagram of the optimized speckle pattern was also uploaded as reference.

Interstitial diffusion and the relationship between compartment modelling and multi-scale spatial-temporal modelling of (18)F-FLT tumour uptake dynamics.

PubMed

Liu, Dan; Chalkidou, Anastasia; Landau, David B; Marsden, Paul K; Fenwick, John D

2014-09-07

Tumour cell proliferation can be imaged via positron emission tomography of the radiotracer 3'-deoxy-3'-18F-fluorothymidine (18F-FLT). Conceptually, the number of proliferating cells might be expected to correlate more closely with the kinetics of 18F-FLT uptake than with uptake at a fixed time. Radiotracer uptake kinetics are standardly visualized using parametric maps of compartment model fits to time-activity-curves (TACs) of individual voxels. However the relationship between the underlying spatiotemporal accumulation of FLT and the kinetics described by compartment models has not yet been explored. In this work tumour tracer uptake is simulated using a mechanistic spatial-temporal model based on a convection-diffusion-reaction equation solved via the finite difference method. The model describes a chain of processes: the flow of FLT between the spatially heterogeneous tumour vasculature and interstitium; diffusion and convection of FLT within the interstitium; transport of FLT into cells; and intracellular phosphorylation. Using values of model parameters estimated from the biological literature, simulated FLT TACs are generated with shapes and magnitudes similar to those seen clinically. Results show that the kinetics of the spatial-temporal model can be recovered accurately by fitting a 3-tissue compartment model to FLT TACs simulated for those tumours or tumour sub-volumes that can be viewed as approximately closed, for which tracer diffusion throughout the interstitium makes only a small fractional change to the quantity of FLT they contain. For a single PET voxel of width 2.5-5 mm we show that this condition is roughly equivalent to requiring that the relative difference in tracer uptake between the voxel and its neighbours is much less than one.

Comparison of classification methods for voxel-based prediction of acute ischemic stroke outcome following intra-arterial intervention

NASA Astrophysics Data System (ADS)

Winder, Anthony J.; Siemonsen, Susanne; Flottmann, Fabian; Fiehler, Jens; Forkert, Nils D.

2017-03-01

Voxel-based tissue outcome prediction in acute ischemic stroke patients is highly relevant for both clinical routine and research. Previous research has shown that features extracted from baseline multi-parametric MRI datasets have a high predictive value and can be used for the training of classifiers, which can generate tissue outcome predictions for both intravenous and conservative treatments. However, with the recent advent and popularization of intra-arterial thrombectomy treatment, novel research specifically addressing the utility of predictive classi- fiers for thrombectomy intervention is necessary for a holistic understanding of current stroke treatment options. The aim of this work was to develop three clinically viable tissue outcome prediction models using approximate nearest-neighbor, generalized linear model, and random decision forest approaches and to evaluate the accuracy of predicting tissue outcome after intra-arterial treatment. Therefore, the three machine learning models were trained, evaluated, and compared using datasets of 42 acute ischemic stroke patients treated with intra-arterial thrombectomy. Classifier training utilized eight voxel-based features extracted from baseline MRI datasets and five global features. Evaluation of classifier-based predictions was performed via comparison to the known tissue outcome, which was determined in follow-up imaging, using the Dice coefficient and leave-on-patient-out cross validation. The random decision forest prediction model led to the best tissue outcome predictions with a mean Dice coefficient of 0.37. The approximate nearest-neighbor and generalized linear model performed equally suboptimally with average Dice coefficients of 0.28 and 0.27 respectively, suggesting that both non-linearity and machine learning are desirable properties of a classifier well-suited to the intra-arterial tissue outcome prediction problem.

Effect of sample size on multi-parametric prediction of tissue outcome in acute ischemic stroke using a random forest classifier

NASA Astrophysics Data System (ADS)

Forkert, Nils Daniel; Fiehler, Jens

2015-03-01

The tissue outcome prediction in acute ischemic stroke patients is highly relevant for clinical and research purposes. It has been shown that the combined analysis of diffusion and perfusion MRI datasets using high-level machine learning techniques leads to an improved prediction of final infarction compared to single perfusion parameter thresholding. However, most high-level classifiers require a previous training and, until now, it is ambiguous how many subjects are required for this, which is the focus of this work. 23 MRI datasets of acute stroke patients with known tissue outcome were used in this work. Relative values of diffusion and perfusion parameters as well as the binary tissue outcome were extracted on a voxel-by- voxel level for all patients and used for training of a random forest classifier. The number of patients used for training set definition was iteratively and randomly reduced from using all 22 other patients to only one other patient. Thus, 22 tissue outcome predictions were generated for each patient using the trained random forest classifiers and compared to the known tissue outcome using the Dice coefficient. Overall, a logarithmic relation between the number of patients used for training set definition and tissue outcome prediction accuracy was found. Quantitatively, a mean Dice coefficient of 0.45 was found for the prediction using the training set consisting of the voxel information from only one other patient, which increases to 0.53 if using all other patients (n=22). Based on extrapolation, 50-100 patients appear to be a reasonable tradeoff between tissue outcome prediction accuracy and effort required for data acquisition and preparation.

Simulation of spatiotemporal CT data sets using a 4D MRI-based lung motion model.

PubMed

Marx, Mirko; Ehrhardt, Jan; Werner, René; Schlemmer, Heinz-Peter; Handels, Heinz

2014-05-01

Four-dimensional CT imaging is widely used to account for motion-related effects during radiotherapy planning of lung cancer patients. However, 4D CT often contains motion artifacts, cannot be used to measure motion variability, and leads to higher dose exposure. In this article, we propose using 4D MRI to acquire motion information for the radiotherapy planning process. From the 4D MRI images, we derive a time-continuous model of the average patient-specific respiratory motion, which is then applied to simulate 4D CT data based on a static 3D CT. The idea of the motion model is to represent the average lung motion over a respiratory cycle by cyclic B-spline curves. The model generation consists of motion field estimation in the 4D MRI data by nonlinear registration, assigning respiratory phases to the motion fields, and applying a B-spline approximation on a voxel-by-voxel basis to describe the average voxel motion over a breathing cycle. To simulate a patient-specific 4D CT based on a static CT of the patient, a multi-modal registration strategy is introduced to transfer the motion model from MRI to the static CT coordinates. Differences between model-based estimated and measured motion vectors are on average 1.39 mm for amplitude-based binning of the 4D MRI data of three patients. In addition, the MRI-to-CT registration strategy is shown to be suitable for the model transformation. The application of our 4D MRI-based motion model for simulating 4D CT images provides advantages over standard 4D CT (less motion artifacts, radiation-free). This makes it interesting for radiotherapy planning.

The impact of white matter fiber orientation in single-acquisition quantitative susceptibility mapping.

PubMed

Lancione, Marta; Tosetti, Michela; Donatelli, Graziella; Cosottini, Mirco; Costagli, Mauro

2017-11-01

The aim of this work was to assess the impact of tissue structural orientation on quantitative susceptibility mapping (QSM) reliability, and to provide a criterion to identify voxels in which measures of magnetic susceptibility (χ) are most affected by spatial orientation effects. Four healthy volunteers underwent 7-T magnetic resonance imaging (MRI). Multi-echo, gradient-echo sequences were used to obtain quantitative maps of frequency shift (FS) and χ. Information from diffusion tensor imaging (DTI) was used to investigate the relationship between tissue orientation and FS measures and QSM. After sorting voxels on the basis of their fractional anisotropy (FA), the variations in FS and χ values over tissue orientation were measured. Using a K-means clustering algorithm, voxels were separated into two groups depending on the variability of measures within each FA interval. The consistency of FS and QSM values, observed at low FA, was disrupted for FA > 0.6. The standard deviation of χ measured at high FA (0.0103 ppm) was nearly five times that at low FA (0.0022 ppm). This result was consistent through data across different head positions and for different brain regions considered separately, which confirmed that such behavior does not depend on structures with different bulk susceptibility oriented along particular angles. The reliability of single-orientation QSM anticorrelates with local FA. QSM provides replicable values with little variability in brain regions with FA < 0.6, but QSM should be interpreted cautiously in major and coherent fiber bundles, which are strongly affected by structural anisotropy and magnetic susceptibility anisotropy. Copyright © 2017 John Wiley & Sons, Ltd.

Rotationally invariant clustering of diffusion MRI data using spherical harmonics

NASA Astrophysics Data System (ADS)

Liptrot, Matthew; Lauze, François

2016-03-01

We present a simple approach to the voxelwise classification of brain tissue acquired with diffusion weighted MRI (DWI). The approach leverages the power of spherical harmonics to summarise the diffusion information, sampled at many points over a sphere, using only a handful of coefficients. We use simple features that are invariant to the rotation of the highly orientational diffusion data. This provides a way to directly classify voxels whose diffusion characteristics are similar yet whose primary diffusion orientations differ. Subsequent application of machine-learning to the spherical harmonic coefficients therefore may permit classification of DWI voxels according to their inferred underlying fibre properties, whilst ignoring the specifics of orientation. After smoothing apparent diffusion coefficients volumes, we apply a spherical harmonic transform, which models the multi-directional diffusion data as a collection of spherical basis functions. We use the derived coefficients as voxelwise feature vectors for classification. Using a simple Gaussian mixture model, we examined the classification performance for a range of sub-classes (3-20). The results were compared against existing alternatives for tissue classification e.g. fractional anisotropy (FA) or the standard model used by Camino.1 The approach was implemented on both two publicly-available datasets: an ex-vivo pig brain and in-vivo human brain from the Human Connectome Project (HCP). We have demonstrated how a robust classification of DWI data can be performed without the need for a model reconstruction step. This avoids the potential confounds and uncertainty that such models may impose, and has the benefit of being computable directly from the DWI volumes. As such, the method could prove useful in subsequent pre-processing stages, such as model fitting, where it could inform about individual voxel complexities and improve model parameter choice.

ECG strain pattern in hypertension is associated with myocardial cellular expansion and diffuse interstitial fibrosis: a multi-parametric cardiac magnetic resonance study

PubMed Central

Rodrigues, Jonathan C.L.; Amadu, Antonio Matteo; Ghosh Dastidar, Amardeep; McIntyre, Bethannie; Szantho, Gergley V.; Lyen, Stephen; Godsave, Cattleya; Ratcliffe, Laura E.K.; Burchell, Amy E.; Hart, Emma C.; Hamilton, Mark C.K.; Nightingale, Angus K.; Paton, Julian F.R.; Manghat, Nathan E.; Bucciarelli-Ducci, Chiara

2017-01-01

Aims In hypertension, the presence of left ventricular (LV) strain pattern on 12-lead electrocardiogram (ECG) carries adverse cardiovascular prognosis. The underlying mechanisms are poorly understood. We investigated whether hypertensive ECG strain is associated with myocardial interstitial fibrosis and impaired myocardial strain, assessed by multi-parametric cardiac magnetic resonance (CMR). Methods and results A total of 100 hypertensive patients [50 ± 14 years, male: 58%, office systolic blood pressure (SBP): 170 ± 30 mmHg, office diastolic blood pressure (DBP): 97 ± 14 mmHg) underwent ECG and 1.5T CMR and were compared with 25 normotensive controls (46 ± 14 years, 60% male, SBP: 124 ± 8 mmHg, DBP: 76 ± 7 mmHg). Native T1 and extracellular volume fraction (ECV) were calculated with the modified look-locker inversion-recovery sequence. Myocardial strain values were estimated with voxel-tracking software. ECG strain (n = 20) was associated with significantly higher indexed LV mass (LVM) (119 ± 32 vs. 80 ± 17 g/m2, P < 0.05) and ECV (30 ± 4 vs. 27 ± 3%, P < 0.05) compared with hypertensive subjects without ECG strain (n = 80). ECG strain subjects had significantly impaired circumferential strain compared with hypertensive subjects without ECG strain and controls (−15.2 ± 4.7 vs. −17.0 ± 3.3 vs. −17.3 ± 2.4%, P < 0.05, respectively). In subgroup analysis, comparing ECG strain subjects to hypertensive subjects with elevated LVM but no ECG strain, a significantly higher ECV (30 ± 4 vs. 28 ± 3%, P < 0.05) was still observed. Indexed LVM was the only variable independently associated with ECG strain in multivariate logistic regression analysis [odds ratio (95th confidence interval): 1.07 (1.02–1.12), P < 0.05). Conclusion In hypertension, ECG strain is a marker of advanced LVH associated with increased interstitial fibrosis and associated with significant myocardial circumferential strain impairment. PMID:27334442

ECG strain pattern in hypertension is associated with myocardial cellular expansion and diffuse interstitial fibrosis: a multi-parametric cardiac magnetic resonance study.

PubMed

Rodrigues, Jonathan C L; Amadu, Antonio Matteo; Ghosh Dastidar, Amardeep; McIntyre, Bethannie; Szantho, Gergley V; Lyen, Stephen; Godsave, Cattleya; Ratcliffe, Laura E K; Burchell, Amy E; Hart, Emma C; Hamilton, Mark C K; Nightingale, Angus K; Paton, Julian F R; Manghat, Nathan E; Bucciarelli-Ducci, Chiara

2017-04-01

In hypertension, the presence of left ventricular (LV) strain pattern on 12-lead electrocardiogram (ECG) carries adverse cardiovascular prognosis. The underlying mechanisms are poorly understood. We investigated whether hypertensive ECG strain is associated with myocardial interstitial fibrosis and impaired myocardial strain, assessed by multi-parametric cardiac magnetic resonance (CMR). A total of 100 hypertensive patients [50 ± 14 years, male: 58%, office systolic blood pressure (SBP): 170 ± 30 mmHg, office diastolic blood pressure (DBP): 97 ± 14 mmHg) underwent ECG and 1.5T CMR and were compared with 25 normotensive controls (46 ± 14 years, 60% male, SBP: 124 ± 8 mmHg, DBP: 76 ± 7 mmHg). Native T1 and extracellular volume fraction (ECV) were calculated with the modified look-locker inversion-recovery sequence. Myocardial strain values were estimated with voxel-tracking software. ECG strain (n = 20) was associated with significantly higher indexed LV mass (LVM) (119 ± 32 vs. 80 ± 17 g/m2, P < 0.05) and ECV (30 ± 4 vs. 27 ± 3%, P < 0.05) compared with hypertensive subjects without ECG strain (n = 80). ECG strain subjects had significantly impaired circumferential strain compared with hypertensive subjects without ECG strain and controls (-15.2 ± 4.7 vs. -17.0 ± 3.3 vs. -17.3 ± 2.4%, P < 0.05, respectively). In subgroup analysis, comparing ECG strain subjects to hypertensive subjects with elevated LVM but no ECG strain, a significantly higher ECV (30 ± 4 vs. 28 ± 3%, P < 0.05) was still observed. Indexed LVM was the only variable independently associated with ECG strain in multivariate logistic regression analysis [odds ratio (95th confidence interval): 1.07 (1.02-1.12), P < 0.05). In hypertension, ECG strain is a marker of advanced LVH associated with increased interstitial fibrosis and associated with significant myocardial circumferential strain impairment. © The Author 2016. Published by Oxford University Press on behalf of the European Society of Cardiology.

Voxel-Based 3-D Tree Modeling from Lidar Images for Extracting Tree Structual Information

NASA Astrophysics Data System (ADS)

Hosoi, F.

2014-12-01

Recently, lidar (light detection and ranging) has been used to extracting tree structural information. Portable scanning lidar systems can capture the complex shape of individual trees as a 3-D point-cloud image. 3-D tree models reproduced from the lidar-derived 3-D image can be used to estimate tree structural parameters. We have proposed the voxel-based 3-D modeling for extracting tree structural parameters. One of the tree parameters derived from the voxel modeling is leaf area density (LAD). We refer to the method as the voxel-based canopy profiling (VCP) method. In this method, several measurement points surrounding the canopy and optimally inclined laser beams are adopted for full laser beam illumination of whole canopy up to the internal. From obtained lidar image, the 3-D information is reproduced as the voxel attributes in the 3-D voxel array. Based on the voxel attributes, contact frequency of laser beams on leaves is computed and LAD in each horizontal layer is obtained. This method offered accurate LAD estimation for individual trees and woody canopy trees. For more accurate LAD estimation, the voxel model was constructed by combining airborne and portable ground-based lidar data. The profiles obtained by the two types of lidar complemented each other, thus eliminating blind regions and yielding more accurate LAD profiles than could be obtained by using each type of lidar alone. Based on the estimation results, we proposed an index named laser beam coverage index, Ω, which relates to the lidar's laser beam settings and a laser beam attenuation factor. It was shown that this index can be used for adjusting measurement set-up of lidar systems and also used for explaining the LAD estimation error using different types of lidar systems. Moreover, we proposed a method to estimate woody material volume as another application of the voxel tree modeling. In this method, voxel solid model of a target tree was produced from the lidar image, which is composed of consecutive voxels that filled the outer surface and the interior of the stem and large branches. From the model, the woody material volume of any part of the target tree can be directly calculated easily by counting the number of corresponding voxels and multiplying the result by the per-voxel volume.

TH-CD-202-10: Tumor Metabolic Control Probability & Dose Response Mapping for Adaptive Dose Painting by Number

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chen, S; Wilson, G; Krauss, D

Purpose: Adaptive dose-painting-by-number (DPbN) requires a dose-response-mapping (DRM) obtained early in the treatment course. To obtain DRM, voxel-by-voxel tumor dose response needs to be quantified. Our recent study has demonstrated that voxel-by-voxel radio-sensitivity of patient tumor can be determined using tumor-metabolic-ratio measured early during the treatment using FDG-PET images. In this study, the measurements were utilized to construct tumor metabolic control probability (TMCP) and DRM for DPbN. Methods: FDG-PET/CT images of 18 HN cancer patients obtained pre- and weekly during the treatment were used. Spatial parametric images of tumor-metabolic-ratio (dSUV) were constructed following voxel-by-voxel deformable image registration. Each voxel valuemore » in dSUV was a function of baseline SUV and delivered dose. Utilizing all values of dSUV in the controlled tumor group at the last treatment week, a cut-off function between the baseline SUV and dSUV was formed, and applied in early treatment days on dSUV of all tumors to model the TMCP. At the treatment week k, TMCP was constructed with respect to the tumor voxel dSUV appeared at the week using the maximum likelihood estimation for all dose levels, and used for DRM construction. Results: TMCPs estimated in the week 2 & 3 have D{sub 50}=11.1∼47.6Gy; γ{sub 50}=0.55∼0.92 respectively with respect to dSUV=0.3∼1.2. The corresponding DRM between tumor voxel dSUV and the expected treatment dose has sigmoid shape. The expected treatment dose are 26∼40Gy (for 95% TMCP) for high sensitive tumor voxels with dSUV=0.3∼0.5; and 65∼110Gy for low sensitive tumor voxels with the dSUV>1.0 depending on the time of the estimation. Conclusion: TMCP can be constructed voxel-by-voxel in human tumor using multiple FDG-PET imaging obtained in early treatment days. TMCP provides a potential quantitative objective of tumor DRM for DPbN to plan the best dose, escalate or de-escalate, in tumor adaptively based on its own radio-sensitivity.« less

Identifying Voxels at Risk for Progression in Glioblastoma Based on Dosimetry, Physiologic and Metabolic MRI.

PubMed

Anwar, Mekhail; Molinaro, Annette M; Morin, Olivier; Chang, Susan M; Haas-Kogan, Daphne A; Nelson, Sarah J; Lupo, Janine M

2017-09-01

Despite the longstanding role of radiation in cancer treatment and the presence of advanced, high-resolution imaging techniques, delineation of voxels at-risk for progression remains purely a geometric expansion of anatomic images, missing subclinical disease at risk for recurrence while treating potentially uninvolved tissue and increasing toxicity. This remains despite the modern ability to precisely shape radiation fields. A striking example of this is the treatment of glioblastoma, a highly infiltrative tumor that may benefit from accurate identification of subclinical disease. In this study, we hypothesize that parameters from physiologic and metabolic magnetic resonance imaging (MRI) at diagnosis could predict the likelihood of voxel progression at radiographic recurrence in glioblastoma by identifying voxel characteristics that indicate subclinical disease. Integrating dosimetry can reveal its effect on voxel outcome, enabling risk-adapted voxel dosing. As a system example, 24 patients with glioblastoma treated with radiotherapy, temozolomide and an anti-angiogenic agent were analyzed. Pretreatment median apparent diffusion coefficient (ADC), fractional anisotropy (FA), relative cerebral blood volume (rCBV), vessel leakage (percentage recovery), choline-to-NAA index (CNI) and dose of voxels in the T2 nonenhancing lesion (NEL), T1 post-contrast enhancing lesion (CEL) or normal-appearing volume (NAV) of brain, were calculated for voxels that progressed [NAV→NEL, CEL (N = 8,765)] and compared against those that remained stable [NAV→NAV (N = 98,665)]. Voxels that progressed (NAV→NEL) had significantly different (P < 0.01) ADC (860), FA (0.36) and CNI (0.67) versus stable voxels (804, 0.43 and 0.05, respectively), indicating increased cell turnover, edema and decreased directionality, consistent with subclinical disease. NAV→CEL voxels were more abnormal (1,014, 0.28, 2.67, respectively) and leakier (percentage recovery = 70). A predictive model identified areas of recurrence, demonstrating that elevated CNI potentiates abnormal diffusion, even far (>2 cm) from the tumor and dose escalation >45 Gy has diminishing benefits. Integrating advanced MRI with dosimetry can identify at voxels at risk for progression and may allow voxel-level risk-adapted dose escalation to subclinical disease while sparing normal tissue. When combined with modern planning software, this technique may enable risk-adapted radiotherapy in any disease site with multimodal imaging.

An analytical method for computing voxel S values for electrons and photons.

PubMed

Amato, Ernesto; Minutoli, Fabio; Pacilio, Massimiliano; Campenni, Alfredo; Baldari, Sergio

2012-11-01

The use of voxel S values (VSVs) is perhaps the most common approach to radiation dosimetry for nonuniform distributions of activity within organs or tumors. However, VSVs are currently available only for a limited number of voxel sizes and radionuclides. The objective of this study was to develop a general method to evaluate them for any spectrum of electrons and photons in any cubic voxel dimension of practical interest for clinical dosimetry in targeted radionuclide therapy. The authors developed a Monte Carlo simulation in Geant4 in order to evaluate the energy deposited per disintegration (E(dep)) in a voxelized region of soft tissue from monoenergetic electrons (10-2000 keV) or photons (10-1000 keV) homogeneously distributed in the central voxel, considering voxel dimensions ranging from 3 mm to 10 mm. E(dep) was represented as a function of a dimensionless quantity termed the "normalized radius," R(n) = R∕l, where l is the voxel size and R is the distance from the origin. The authors introduced two parametric functions in order to fit the electron and photon results, and they interpolated the parameters to derive VSVs for any energy and voxel side within the ranges mentioned above. In order to validate the results, the authors determined VSV for two radionuclides ((131)I and (89)Sr) and two voxel dimensions and they compared them with reference data. A validation study in a simple sphere model, accounting for tissue inhomogeneities, is presented. The E(dep)(R(n)) for both monoenergetic electrons and photons exhibit a smooth variation with energy and voxel size, implying that VSVs for monoenergetic electrons or photons may be derived by interpolation over the range of energies and dimensions considered. By integration, S values for continuous emission spectra from β(-) decay may be derived as well. The approach allows the determination of VSVs for monoenergetic (Auger or conversion) electrons and (x-ray or gamma-ray) photons by means of two functions whose parameters can be interpolated from tabular data provided. Through integration, it is possible to generalize the method to any continuous (beta) spectrum, allowing to calculate VSVs for any electron and photon emitter in a voxelized structure.

DeepNAT: Deep convolutional neural network for segmenting neuroanatomy.

PubMed

Wachinger, Christian; Reuter, Martin; Klein, Tassilo

2018-04-15

We introduce DeepNAT, a 3D Deep convolutional neural network for the automatic segmentation of NeuroAnaTomy in T1-weighted magnetic resonance images. DeepNAT is an end-to-end learning-based approach to brain segmentation that jointly learns an abstract feature representation and a multi-class classification. We propose a 3D patch-based approach, where we do not only predict the center voxel of the patch but also neighbors, which is formulated as multi-task learning. To address a class imbalance problem, we arrange two networks hierarchically, where the first one separates foreground from background, and the second one identifies 25 brain structures on the foreground. Since patches lack spatial context, we augment them with coordinates. To this end, we introduce a novel intrinsic parameterization of the brain volume, formed by eigenfunctions of the Laplace-Beltrami operator. As network architecture, we use three convolutional layers with pooling, batch normalization, and non-linearities, followed by fully connected layers with dropout. The final segmentation is inferred from the probabilistic output of the network with a 3D fully connected conditional random field, which ensures label agreement between close voxels. The roughly 2.7million parameters in the network are learned with stochastic gradient descent. Our results show that DeepNAT compares favorably to state-of-the-art methods. Finally, the purely learning-based method may have a high potential for the adaptation to young, old, or diseased brains by fine-tuning the pre-trained network with a small training sample on the target application, where the availability of larger datasets with manual annotations may boost the overall segmentation accuracy in the future. Copyright © 2017 Elsevier Inc. All rights reserved.

Regional gray matter atrophy in relapsing remitting multiple sclerosis: baseline analysis of multi-center data.

PubMed

Datta, Sushmita; Staewen, Terrell D; Cofield, Stacy S; Cutter, Gary R; Lublin, Fred D; Wolinsky, Jerry S; Narayana, Ponnada A

2015-03-01

Regional gray matter (GM) atrophy in multiple sclerosis (MS) at disease onset and its temporal variation can provide objective information regarding disease evolution. An automated pipeline for estimating atrophy of various GM structures was developed using tensor based morphometry (TBM) and implemented on a multi-center sub-cohort of 1008 relapsing remitting MS (RRMS) patients enrolled in a Phase 3 clinical trial. Four hundred age and gender matched healthy controls were used for comparison. Using the analysis of covariance, atrophy differences between MS patients and healthy controls were assessed on a voxel-by-voxel analysis. Regional GM atrophy was observed in a number of deep GM structures that included thalamus, caudate nucleus, putamen, and cortical GM regions. General linear regression analysis was performed to analyze the effects of age, gender, and scanner field strength, and imaging sequence on the regional atrophy. Correlations between regional GM volumes and expanded disability status scale (EDSS) scores, disease duration (DD), T2 lesion load (T2 LL), T1 lesion load (T1 LL), and normalized cerebrospinal fluid (nCSF) were analyzed using Pearson׳s correlation coefficient. Thalamic atrophy observed in MS patients compared to healthy controls remained consistent within subgroups based on gender and scanner field strength. Weak correlations between thalamic volume and EDSS (r=-0.133; p<0.001) and DD (r=-0.098; p=0.003) were observed. Of all the structures, thalamic volume moderately correlated with T2 LL (r=-0.492; P-value<0.001), T1 LL (r=-0.473; P-value<0.001) and nCSF (r=-0.367; P-value<0.001). Copyright © 2015 Elsevier B.V. All rights reserved.

Scanning linear estimation: improvements over region of interest (ROI) methods

NASA Astrophysics Data System (ADS)

Kupinski, Meredith K.; Clarkson, Eric W.; Barrett, Harrison H.

2013-03-01

In tomographic medical imaging, a signal activity is typically estimated by summing voxels from a reconstructed image. We introduce an alternative estimation scheme that operates on the raw projection data and offers a substantial improvement, as measured by the ensemble mean-square error (EMSE), when compared to using voxel values from a maximum-likelihood expectation-maximization (MLEM) reconstruction. The scanning-linear (SL) estimator operates on the raw projection data and is derived as a special case of maximum-likelihood estimation with a series of approximations to make the calculation tractable. The approximated likelihood accounts for background randomness, measurement noise and variability in the parameters to be estimated. When signal size and location are known, the SL estimate of signal activity is unbiased, i.e. the average estimate equals the true value. By contrast, unpredictable bias arising from the null functions of the imaging system affect standard algorithms that operate on reconstructed data. The SL method is demonstrated for two different tasks: (1) simultaneously estimating a signal’s size, location and activity; (2) for a fixed signal size and location, estimating activity. Noisy projection data are realistically simulated using measured calibration data from the multi-module multi-resolution small-animal SPECT imaging system. For both tasks, the same set of images is reconstructed using the MLEM algorithm (80 iterations), and the average and maximum values within the region of interest (ROI) are calculated for comparison. This comparison shows dramatic improvements in EMSE for the SL estimates. To show that the bias in ROI estimates affects not only absolute values but also relative differences, such as those used to monitor the response to therapy, the activity estimation task is repeated for three different signal sizes.

Evaluation of a 3D local multiresolution algorithm for the correction of partial volume effects in positron emission tomography

PubMed Central

Le Pogam, Adrien; Hatt, Mathieu; Descourt, Patrice; Boussion, Nicolas; Tsoumpas, Charalampos; Turkheimer, Federico E.; Prunier-Aesch, Caroline; Baulieu, Jean-Louis; Guilloteau, Denis; Visvikis, Dimitris

2011-01-01

Purpose Partial volume effects (PVE) are consequences of the limited spatial resolution in emission tomography leading to under-estimation of uptake in tissues of size similar to the point spread function (PSF) of the scanner as well as activity spillover between adjacent structures. Among PVE correction methodologies, a voxel-wise mutual multi-resolution analysis (MMA) was recently introduced. MMA is based on the extraction and transformation of high resolution details from an anatomical image (MR/CT) and their subsequent incorporation into a low resolution PET image using wavelet decompositions. Although this method allows creating PVE corrected images, it is based on a 2D global correlation model which may introduce artefacts in regions where no significant correlation exists between anatomical and functional details. Methods A new model was designed to overcome these two issues (2D only and global correlation) using a 3D wavelet decomposition process combined with a local analysis. The algorithm was evaluated on synthetic, simulated and patient images, and its performance was compared to the original approach as well as the geometric transfer matrix (GTM) method. Results Quantitative performance was similar to the 2D global model and GTM in correlated cases. In cases where mismatches between anatomical and functional information were present the new model outperformed the 2D global approach, avoiding artefacts and significantly improving quality of the corrected images and their quantitative accuracy. Conclusions A new 3D local model was proposed for a voxel-wise PVE correction based on the original mutual multi-resolution analysis approach. Its evaluation demonstrated an improved and more robust qualitative and quantitative accuracy compared to the original MMA methodology, particularly in the absence of full correlation between anatomical and functional information. PMID:21978037

Clustering of temperamental and cognitive risk factors for anxiety in a college sample of late adolescents.

PubMed

Viana, Andres G; Gratz, Kim L; Bierman, Karen L

2013-01-01

Temperamental vulnerabilities (e.g., behavioral inhibition, anxiety sensitivity) and cognitive biases (e.g., interpretive and judgment biases) may exacerbate feelings of stress and anxiety, particularly among late adolescents during the early years of college. The goal of the present study was to apply person-centered analyses to explore possible heterogeneity in the patterns of these four risk factors in late adolescence, and to examine associations with several anxiety outcomes (i.e., worry, anxiety symptoms, and trait anxiety). Cluster analyses in a college sample of 855 late adolescents revealed a Low-Risk group, along with four reliable clusters with distinct profiles of risk factors and anxiety outcomes (Inhibited, Sensitive, Cognitively-Biased, and Multi-Risk). Of the risk profiles, Multi-Risk youth experienced the highest levels of anxiety outcomes, whereas Inhibited youth experienced the lowest levels of anxiety outcomes. Sensitive and Cognitively-Biased youth experienced comparable levels of anxiety-related outcomes, despite different constellations of risk factors. Implications for interventions and future research are discussed.

PET image reconstruction using multi-parametric anato-functional priors

NASA Astrophysics Data System (ADS)

Mehranian, Abolfazl; Belzunce, Martin A.; Niccolini, Flavia; Politis, Marios; Prieto, Claudia; Turkheimer, Federico; Hammers, Alexander; Reader, Andrew J.

2017-08-01

In this study, we investigate the application of multi-parametric anato-functional (MR-PET) priors for the maximum a posteriori (MAP) reconstruction of brain PET data in order to address the limitations of the conventional anatomical priors in the presence of PET-MR mismatches. In addition to partial volume correction benefits, the suitability of these priors for reconstruction of low-count PET data is also introduced and demonstrated, comparing to standard maximum-likelihood (ML) reconstruction of high-count data. The conventional local Tikhonov and total variation (TV) priors and current state-of-the-art anatomical priors including the Kaipio, non-local Tikhonov prior with Bowsher and Gaussian similarity kernels are investigated and presented in a unified framework. The Gaussian kernels are calculated using both voxel- and patch-based feature vectors. To cope with PET and MR mismatches, the Bowsher and Gaussian priors are extended to multi-parametric priors. In addition, we propose a modified joint Burg entropy prior that by definition exploits all parametric information in the MAP reconstruction of PET data. The performance of the priors was extensively evaluated using 3D simulations and two clinical brain datasets of [18F]florbetaben and [18F]FDG radiotracers. For simulations, several anato-functional mismatches were intentionally introduced between the PET and MR images, and furthermore, for the FDG clinical dataset, two PET-unique active tumours were embedded in the PET data. Our simulation results showed that the joint Burg entropy prior far outperformed the conventional anatomical priors in terms of preserving PET unique lesions, while still reconstructing functional boundaries with corresponding MR boundaries. In addition, the multi-parametric extension of the Gaussian and Bowsher priors led to enhanced preservation of edge and PET unique features and also an improved bias-variance performance. In agreement with the simulation results, the clinical results also showed that the Gaussian prior with voxel-based feature vectors, the Bowsher and the joint Burg entropy priors were the best performing priors. However, for the FDG dataset with simulated tumours, the TV and proposed priors were capable of preserving the PET-unique tumours. Finally, an important outcome was the demonstration that the MAP reconstruction of a low-count FDG PET dataset using the proposed joint entropy prior can lead to comparable image quality to a conventional ML reconstruction with up to 5 times more counts. In conclusion, multi-parametric anato-functional priors provide a solution to address the pitfalls of the conventional priors and are therefore likely to increase the diagnostic confidence in MR-guided PET image reconstructions.

Automated modification and fusion of voxel models to construct body phantoms with heterogeneous breast tissue: Application to MRI simulations.

PubMed

Rispoli, Joseph V; Wright, Steven M; Malloy, Craig R; McDougall, Mary P

2017-01-01

Human voxel models incorporating detailed anatomical features are vital tools for the computational evaluation of electromagnetic (EM) fields within the body. Besides whole-body human voxel models, phantoms representing smaller heterogeneous anatomical features are often employed; for example, localized breast voxel models incorporating fatty and fibroglandular tissues have been developed for a variety of EM applications including mammography simulation and dosimetry, magnetic resonance imaging (MRI), and ultra-wideband microwave imaging. However, considering wavelength effects, electromagnetic modeling of the breast at sub-microwave frequencies necessitates detailed breast phantoms in conjunction with whole-body voxel models. Heterogeneous breast phantoms are sized to fit within radiofrequency coil hardware, modified by voxel-wise extrusion, and fused to whole-body models using voxel-wise, tissue-dependent logical operators. To illustrate the utility of this method, finite-difference time-domain simulations are performed using a whole-body model integrated with a variety of available breast phantoms spanning the standard four tissue density classifications representing the majority of the population. The software library uses a combination of voxel operations to seamlessly size, modify, and fuse eleven breast phantoms to whole-body voxel models. The software is publicly available on GitHub and is linked to the file exchange at MATLAB ® Central. Simulations confirm the proportions of fatty and fibroglandular tissues in breast phantoms have significant yet predictable implications on projected power deposition in tissue. Breast phantoms may be modified and fused to whole-body voxel models using the software presented in this work; user considerations for the open-source software and resultant phantoms are discussed. Furthermore, results indicate simulating breast models as predominantly fatty tissue can considerably underestimate the potential for tissue heating in women with substantial fibroglandular tissue.

Automated modification and fusion of voxel models to construct body phantoms with heterogeneous breast tissue: Application to MRI simulations

PubMed Central

Rispoli, Joseph V.; Wright, Steven M.; Malloy, Craig R.; McDougall, Mary P.

2017-01-01

Background Human voxel models incorporating detailed anatomical features are vital tools for the computational evaluation of electromagnetic (EM) fields within the body. Besides whole-body human voxel models, phantoms representing smaller heterogeneous anatomical features are often employed; for example, localized breast voxel models incorporating fatty and fibroglandular tissues have been developed for a variety of EM applications including mammography simulation and dosimetry, magnetic resonance imaging (MRI), and ultra-wideband microwave imaging. However, considering wavelength effects, electromagnetic modeling of the breast at sub-microwave frequencies necessitates detailed breast phantoms in conjunction with whole-body voxel models. Methods Heterogeneous breast phantoms are sized to fit within radiofrequency coil hardware, modified by voxel-wise extrusion, and fused to whole-body models using voxel-wise, tissue-dependent logical operators. To illustrate the utility of this method, finite-difference time-domain simulations are performed using a whole-body model integrated with a variety of available breast phantoms spanning the standard four tissue density classifications representing the majority of the population. Results The software library uses a combination of voxel operations to seamlessly size, modify, and fuse eleven breast phantoms to whole-body voxel models. The software is publicly available on GitHub and is linked to the file exchange at MATLAB® Central. Simulations confirm the proportions of fatty and fibroglandular tissues in breast phantoms have significant yet predictable implications on projected power deposition in tissue. Conclusions Breast phantoms may be modified and fused to whole-body voxel models using the software presented in this work; user considerations for the open-source software and resultant phantoms are discussed. Furthermore, results indicate simulating breast models as predominantly fatty tissue can considerably underestimate the potential for tissue heating in women with substantial fibroglandular tissue. PMID:28798837