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.

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.

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

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

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.

Tracking competition and cognitive control during language comprehension with multi-voxel pattern analysis

PubMed Central

Musz, Elizabeth; Thompson-Schill, Sharon L.

2017-01-01

To successfully comprehend a sentence that contains a homonym, readers must select the ambiguous word’s context-appropriate meaning. The outcome of this process is influenced both by top-down contextual support and bottom-up, word-specific characteristics. We examined how these factors jointly affect the neural signatures of lexical ambiguity resolution. We measured the similarity between multi-voxel patterns evoked by the same homonym in two distinct linguistic contexts: once after subjects read sentences that biased interpretation toward each homonym’s most frequent, dominant meaning, and again after interpretation was biased toward a weaker, subordinate meaning. We predicted that, following a subordinate-biasing context, the dominant yet inappropriate meaning would nevertheless compete for activation, manifesting in increased similarity between the neural patterns evoked by the two word meanings. In left anterior temporal lobe (ATL), degree of within-word pattern similarity was positively predicted by the association strength of each homonym’s dominant meaning. Further, within-word pattern similarity in left ATL was negatively predicted by item-specific responses in a region of left ventrolateral prefrontal cortex (VLPFC) sensitive to semantic conflict. These findings have implications for psycholinguistic models of lexical ambiguity resolution, and for the role of left VLPFC function during this process. Moreover, these findings demonstrate the utility of item-level, similarity-based analyses of fMRI data for our understanding of competition between co-activated word meanings during language comprehension. PMID:27898341

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.

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.

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

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-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.

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.

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.

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

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

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.

Distributed representations in memory: Insights from functional brain imaging

PubMed Central

Rissman, Jesse; Wagner, Anthony D.

2015-01-01

Forging new memories for facts and events, holding critical details in mind on a moment-to-moment basis, and retrieving knowledge in the service of current goals all depend on a complex interplay between neural ensembles throughout the brain. Over the past decade, researchers have increasingly leveraged powerful analytical tools (e.g., multi-voxel pattern analysis) to decode the information represented within distributed fMRI activity patterns. In this review, we discuss how these methods can sensitively index neural representations of perceptual and semantic content, and how leverage on the engagement of distributed representations provides unique insights into distinct aspects of memory-guided behavior. We emphasize that, in addition to characterizing the contents of memories, analyses of distributed patterns shed light on the processes that influence how information is encoded, maintained, or retrieved, and thus inform memory theory. We conclude by highlighting open questions about memory that can be addressed through distributed pattern analyses. PMID:21943171

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

Lexical and Syntactic Representations in the Brain: An fMRI Investigation with Multi-Voxel Pattern Analyses

ERIC Educational Resources Information Center

Fedorenko, Evelina; Nieto-Castanon, Alfonso; Kanwisher, Nancy

2012-01-01

Work in theoretical linguistics and psycholinguistics suggests that human linguistic knowledge forms a continuum between individual lexical items and abstract syntactic representations, with most linguistic representations falling between the two extremes and taking the form of lexical items stored together with the syntactic/semantic contexts in…

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.

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-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.

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

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

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.

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

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.

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.

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.

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.

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

The Effect of Spatial Smoothing on Representational Similarity in a Simple Motor Paradigm

PubMed Central

Hendriks, Michelle H. A.; Daniels, Nicky; Pegado, Felipe; Op de Beeck, Hans P.

2017-01-01

Multi-voxel pattern analyses (MVPA) are often performed on unsmoothed data, which is very different from the general practice of large smoothing extents in standard voxel-based analyses. In this report, we studied the effect of smoothing on MVPA results in a motor paradigm. Subjects pressed four buttons with two different fingers of the two hands in response to auditory commands. Overall, independent of the degree of smoothing, correlational MVPA showed distinctive patterns for the different hands in all studied regions of interest (motor cortex, prefrontal cortex, and auditory cortices). With regard to the effect of smoothing, our findings suggest that results from correlational MVPA show a minor sensitivity to smoothing. Moderate amounts of smoothing (in this case, 1−4 times the voxel size) improved MVPA correlations, from a slight improvement to large improvements depending on the region involved. None of the regions showed signs of a detrimental effect of moderate levels of smoothing. Even higher amounts of smoothing sometimes had a positive effect, most clearly in low-level auditory cortex. We conclude that smoothing seems to have a minor positive effect on MVPA results, thus researchers should be mindful about the choices they make regarding the level of smoothing. PMID:28611726

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.

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.

Multi-voxel patterns of visual category representation during episodic encoding are predictive of subsequent memory

PubMed Central

Kuhl, Brice A.; Rissman, Jesse; Wagner, Anthony D.

2012-01-01

Successful encoding of episodic memories is thought to depend on contributions from prefrontal and temporal lobe structures. Neural processes that contribute to successful encoding have been extensively explored through univariate analyses of neuroimaging data that compare mean activity levels elicited during the encoding of events that are subsequently remembered vs. those subsequently forgotten. Here, we applied pattern classification to fMRI data to assess the degree to which distributed patterns of activity within prefrontal and temporal lobe structures elicited during the encoding of word-image pairs were diagnostic of the visual category (Face or Scene) of the encoded image. We then assessed whether representation of category information was predictive of subsequent memory. Classification analyses indicated that temporal lobe structures contained information robustly diagnostic of visual category. Information in prefrontal cortex was less diagnostic of visual category, but was nonetheless associated with highly reliable classifier-based evidence for category representation. Critically, trials associated with greater classifier-based estimates of category representation in temporal and prefrontal regions were associated with a higher probability of subsequent remembering. Finally, consideration of trial-by-trial variance in classifier-based measures of category representation revealed positive correlations between prefrontal and temporal lobe representations, with the strength of these correlations varying as a function of the category of image being encoded. Together, these results indicate that multi-voxel representations of encoded information can provide unique insights into how visual experiences are transformed into episodic memories. PMID:21925190

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.

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.

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.

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

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.

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%).

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

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.

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

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.

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.

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

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).

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.

Lexical and syntactic representations in the brain: An fMRI investigation with multi-voxel pattern analyses

PubMed Central

Fedorenko, Evelina; Nieto-Castañon, Alfonso; Kanwisher, Nancy

2011-01-01

Work in theoretical linguistics and psycholinguistics suggests that human linguistic knowledge forms a continuum between individual lexical items and abstract syntactic representations, with most linguistic representations falling between the two extremes and taking the form of lexical items stored together with the syntactic/semantic contexts in which they frequently occur. Neuroimaging evidence further suggests that no brain region is selectively sensitive to only lexical information or only syntactic information. Instead, all the key brain regions that support high-level linguistic processing have been implicated in both lexical and syntactic processing, suggesting that our linguistic knowledge is plausibly represented in a distributed fashion in these brain regions. Given this distributed nature of linguistic representations, multi-voxel pattern analyses (MVPAs) can help uncover important functional properties of the language system. In the current study we use MVPAs to ask two questions: 1) Do language brain regions differ in how robustly they represent lexical vs. syntactic information?; and 2) Do any of the language bran regions distinguish between “pure” lexical information (lists of words) and “pure” abstract syntactic information (jabberwocky sentences) in the pattern of activity? We show that lexical information is represented more robustly than syntactic information across many language regions (with no language region showing the opposite pattern), as evidenced by a better discrimination between conditions that differ along the lexical dimension (sentences vs. jabberwocky, and word lists vs. nonword lists) than between conditions that differ along the syntactic dimension (sentences vs. word lists, and jabberwocky vs. nonword lists). This result suggests that lexical information may play a more critical role than syntax in the representation of linguistic meaning. We also show that several language regions reliably discriminate between “pure” lexical information and “pure” abstract syntactic information in their patterns of neural activity. PMID:21945850

Lexical and syntactic representations in the brain: an fMRI investigation with multi-voxel pattern analyses.

PubMed

Fedorenko, Evelina; Nieto-Castañon, Alfonso; Kanwisher, Nancy

2012-03-01

Work in theoretical linguistics and psycholinguistics suggests that human linguistic knowledge forms a continuum between individual lexical items and abstract syntactic representations, with most linguistic representations falling between the two extremes and taking the form of lexical items stored together with the syntactic/semantic contexts in which they frequently occur. Neuroimaging evidence further suggests that no brain region is selectively sensitive to only lexical information or only syntactic information. Instead, all the key brain regions that support high-level linguistic processing have been implicated in both lexical and syntactic processing, suggesting that our linguistic knowledge is plausibly represented in a distributed fashion in these brain regions. Given this distributed nature of linguistic representations, multi-voxel pattern analyses (MVPAs) can help uncover important functional properties of the language system. In the current study we use MVPAs to ask two questions: (1) Do language brain regions differ in how robustly they represent lexical vs. syntactic information? and (2) Do any of the language bran regions distinguish between "pure" lexical information (lists of words) and "pure" abstract syntactic information (jabberwocky sentences) in the pattern of activity? We show that lexical information is represented more robustly than syntactic information across many language regions (with no language region showing the opposite pattern), as evidenced by a better discrimination between conditions that differ along the lexical dimension (sentences vs. jabberwocky, and word lists vs. nonword lists) than between conditions that differ along the syntactic dimension (sentences vs. word lists, and jabberwocky vs. nonword lists). This result suggests that lexical information may play a more critical role than syntax in the representation of linguistic meaning. We also show that several language regions reliably discriminate between "pure" lexical information and "pure" abstract syntactic information in their patterns of neural activity. Copyright © 2011 Elsevier Ltd. All rights reserved.

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.

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.

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.

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.

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.

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.

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.

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.

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

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.

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.

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.

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.

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.

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.

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.

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.

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

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.

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.

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.

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.

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.

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

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

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.

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.

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.

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

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.

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.

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

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).

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.

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).

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.

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.

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

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

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.

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

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.

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

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

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

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

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.

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

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.

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.

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.

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

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.

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.

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.

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

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

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.

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.

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.

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.

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.

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.

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.

Regional autonomy changes in resting-state functional MRI in patients with HIV associated neurocognitive disorder

NASA Astrophysics Data System (ADS)

DSouza, Adora M.; Abidin, Anas Z.; Chockanathan, Udaysankar; Wismüller, Axel

2018-03-01

In this study, we investigate whether there are discernable changes in influence that brain regions have on themselves once patients show symptoms of HIV Associated Neurocognitive Disorder (HAND) using functional MRI (fMRI). Simple functional connectivity measures, such as correlation cannot reveal such information. To this end, we use mutual connectivity analysis (MCA) with Local Models (LM), which reveals a measure of influence in terms of predictability. Once such measures of interaction are obtained, we train two classifiers to characterize difference in patterns of regional self-influence between healthy subjects and subjects presenting with HAND symptoms. The two classifiers we use are Support Vector Machines (SVM) and Localized Generalized Matrix Learning Vector Quantization (LGMLVQ). Performing machine learning on fMRI connectivity measures is popularly known as multi-voxel pattern analysis (MVPA). By performing such an analysis, we are interested in studying the impact HIV infection has on an individual's brain. The high area under receiver operating curve (AUC) and accuracy values for 100 different train/test separations using MCA-LM self-influence measures (SVM: mean AUC=0.86, LGMLVQ: mean AUC=0.88, SVM and LGMLVQ: mean accuracy=0.78) compared with standard MVPA analysis using cross-correlation between fMRI time-series (SVM: mean AUC=0.58, LGMLVQ: mean AUC=0.57), demonstrates that self-influence features can be more discriminative than measures of interaction between time-series pairs. Furthermore, our results suggest that incorporating measures of self-influence in MVPA analysis used commonly in fMRI analysis has the potential to provide a performance boost and indicate important changes in dynamics of regions in the brain as a consequence of HIV infection.

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.

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.

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

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

Failure analysis of fuel cell electrodes using three-dimensional multi-length scale X-ray computed tomography

NASA Astrophysics Data System (ADS)

Pokhrel, A.; El Hannach, M.; Orfino, F. P.; Dutta, M.; Kjeang, E.

2016-10-01

X-ray computed tomography (XCT), a non-destructive technique, is proposed for three-dimensional, multi-length scale characterization of complex failure modes in fuel cell electrodes. Comparative tomography data sets are acquired for a conditioned beginning of life (BOL) and a degraded end of life (EOL) membrane electrode assembly subjected to cathode degradation by voltage cycling. Micro length scale analysis shows a five-fold increase in crack size and 57% thickness reduction in the EOL cathode catalyst layer, indicating widespread action of carbon corrosion. Complementary nano length scale analysis shows a significant reduction in porosity, increased pore size, and dramatically reduced effective diffusivity within the remaining porous structure of the catalyst layer at EOL. Collapsing of the structure is evident from the combination of thinning and reduced porosity, as uniquely determined by the multi-length scale approach. Additionally, a novel image processing based technique developed for nano scale segregation of pore, ionomer, and Pt/C dominated voxels shows an increase in ionomer volume fraction, Pt/C agglomerates, and severe carbon corrosion at the catalyst layer/membrane interface at EOL. In summary, XCT based multi-length scale analysis enables detailed information needed for comprehensive understanding of the complex failure modes observed in fuel cell electrodes.

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.

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.

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

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.

A decomposition model and voxel selection framework for fMRI analysis to predict neural response of visual stimuli.

PubMed

Raut, Savita V; Yadav, Dinkar M

2018-03-28

This paper presents an fMRI signal analysis methodology using geometric mean curve decomposition (GMCD) and mutual information-based voxel selection framework. Previously, the fMRI signal analysis has been conducted using empirical mean curve decomposition (EMCD) model and voxel selection on raw fMRI signal. The erstwhile methodology loses frequency component, while the latter methodology suffers from signal redundancy. Both challenges are addressed by our methodology in which the frequency component is considered by decomposing the raw fMRI signal using geometric mean rather than arithmetic mean and the voxels are selected from EMCD signal using GMCD components, rather than raw fMRI signal. The proposed methodologies are adopted for predicting the neural response. Experimentations are conducted in the openly available fMRI data of six subjects, and comparisons are made with existing decomposition models and voxel selection frameworks. Subsequently, the effect of degree of selected voxels and the selection constraints are analyzed. The comparative results and the analysis demonstrate the superiority and the reliability of the proposed methodology.

Atlas-guided volumetric diffuse optical tomography enhanced by generalized linear model analysis to image risk decision-making responses in young adults.

PubMed

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

2014-08-01

Diffuse optical tomography (DOT) is a variant of functional near infrared spectroscopy and has the capability of mapping or reconstructing three dimensional (3D) hemodynamic changes due to brain activity. Common methods used in DOT image analysis to define brain activation have limitations because the selection of activation period is relatively subjective. General linear model (GLM)-based analysis can overcome this limitation. In this study, we combine the atlas-guided 3D DOT image reconstruction with GLM-based analysis (i.e., voxel-wise GLM analysis) to investigate the brain activity that is associated with risk decision-making processes. Risk decision-making is an important cognitive process and thus is an essential topic in the field of neuroscience. The Balloon Analog Risk Task (BART) is a valid experimental model and has been commonly used to assess human risk-taking actions and tendencies while facing risks. We have used the BART paradigm with a blocked design to investigate brain activations in the prefrontal and frontal cortical areas during decision-making from 37 human participants (22 males and 15 females). Voxel-wise GLM analysis was performed after a human brain atlas template and a depth compensation algorithm were combined to form atlas-guided DOT images. In this work, we wish to demonstrate the excellence of using voxel-wise GLM analysis with DOT to image and study cognitive functions in response to risk decision-making. Results have shown significant hemodynamic changes in the dorsal lateral prefrontal cortex (DLPFC) during the active-choice mode and a different activation pattern between genders; these findings correlate well with published literature in functional magnetic resonance imaging (fMRI) and fNIRS studies. Copyright © 2014 The Authors. Human Brain Mapping Published by Wiley Periodicals, Inc.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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

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

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.

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.

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 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.

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.

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.

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

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.

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.

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.

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.

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.

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.

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.

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

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.

Characteristics of voxel prediction power in full-brain Granger causality analysis of fMRI data

NASA Astrophysics Data System (ADS)

Garg, Rahul; Cecchi, Guillermo A.; Rao, A. Ravishankar

2011-03-01

Functional neuroimaging research is moving from the study of "activations" to the study of "interactions" among brain regions. Granger causality analysis provides a powerful technique to model spatio-temporal interactions among brain regions. We apply this technique to full-brain fMRI data without aggregating any voxel data into regions of interest (ROIs). We circumvent the problem of dimensionality using sparse regression from machine learning. On a simple finger-tapping experiment we found that (1) a small number of voxels in the brain have very high prediction power, explaining the future time course of other voxels in the brain; (2) these voxels occur in small sized clusters (of size 1-4 voxels) distributed throughout the brain; (3) albeit small, these clusters overlap with most of the clusters identified with the non-temporal General Linear Model (GLM); and (4) the method identifies clusters which, while not determined by the task and not detectable by GLM, still influence brain activity.

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.

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.

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.

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.

Wavelet-based fMRI analysis: 3-D denoising, signal separation, and validation metrics

PubMed Central

Khullar, Siddharth; Michael, Andrew; Correa, Nicolle; Adali, Tulay; Baum, Stefi A.; Calhoun, Vince D.

2010-01-01

We present a novel integrated wavelet-domain based framework (w-ICA) for 3-D de-noising functional magnetic resonance imaging (fMRI) data followed by source separation analysis using independent component analysis (ICA) in the wavelet domain. We propose the idea of a 3-D wavelet-based multi-directional de-noising scheme where each volume in a 4-D fMRI data set is sub-sampled using the axial, sagittal and coronal geometries to obtain three different slice-by-slice representations of the same data. The filtered intensity value of an arbitrary voxel is computed as an expected value of the de-noised wavelet coefficients corresponding to the three viewing geometries for each sub-band. This results in a robust set of de-noised wavelet coefficients for each voxel. Given the decorrelated nature of these de-noised wavelet coefficients; it is possible to obtain more accurate source estimates using ICA in the wavelet domain. The contributions of this work can be realized as two modules. First, the analysis module where we combine a new 3-D wavelet denoising approach with better signal separation properties of ICA in the wavelet domain, to yield an activation component that corresponds closely to the true underlying signal and is maximally independent with respect to other components. Second, we propose and describe two novel shape metrics for post-ICA comparisons between activation regions obtained through different frameworks. We verified our method using simulated as well as real fMRI data and compared our results against the conventional scheme (Gaussian smoothing + spatial ICA: s-ICA). The results show significant improvements based on two important features: (1) preservation of shape of the activation region (shape metrics) and (2) receiver operating characteristic (ROC) curves. It was observed that the proposed framework was able to preserve the actual activation shape in a consistent manner even for very high noise levels in addition to significant reduction in false positives voxels. PMID:21034833

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.

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.

Effects of Hormone Therapy on Brain Volumes Changes of Postmenopausal Women Revealed by Optimally-Discriminative Voxel-Based Morphometry

PubMed Central

Zhang, Tianhao; Casanova, Ramon; Resnick, Susan M.; Manson, JoAnn E.; Baker, Laura D.; Padual, Claudia B.; Kuller, Lewis H.; Bryan, R. Nick; Espeland, Mark A.; Davatzikos, Christos

2016-01-01

Backgrounds The Women's Health Initiative Memory Study Magnetic Resonance Imaging (WHIMS-MRI) provides an opportunity to evaluate how menopausal hormone therapy (HT) affects the structure of older women’s brains. Our earlier work based on region of interest (ROI) analysis demonstrated potential structural changes underlying adverse effects of HT on cognition. However, the ROI-based analysis is limited in statistical power and precision, and cannot provide fine-grained mapping of whole-brain changes. Methods We aimed to identify local structural differences between HT and placebo groups from WHIMS-MRI in a whole-brain refined level, by using a novel method, named Optimally-Discriminative Voxel-Based Analysis (ODVBA). ODVBA is a recently proposed imaging pattern analysis approach for group comparisons utilizing a spatially adaptive analysis scheme to accurately locate areas of group differences, thereby providing superior sensitivity and specificity to detect the structural brain changes over conventional methods. Results Women assigned to HT treatments had significant Gray Matter (GM) losses compared to the placebo groups in the anterior cingulate and the adjacent medial frontal gyrus, and the orbitofrontal cortex, which persisted after multiple comparison corrections. There were no regions where HT was significantly associated with larger volumes compared to placebo, although a trend of marginal significance was found in the posterior cingulate cortical area. The CEE-Alone and CEE+MPA groups, although compared with different placebo controls, demonstrated similar effects according to the spatial patterns of structural changes. Conclusions HT had adverse effects on GM volumes and risk for cognitive impairment and dementia in older women. These findings advanced our understanding of the neurobiological underpinnings of HT effects. PMID:26974440

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

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.

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

A Corner-Point-Grid-Based Voxelization Method for Complex Geological Structure Model with Folds

NASA Astrophysics Data System (ADS)

Chen, Qiyu; Mariethoz, Gregoire; Liu, Gang

2017-04-01

3D voxelization is the foundation of geological property modeling, and is also an effective approach to realize the 3D visualization of the heterogeneous attributes in geological structures. The corner-point grid is a representative data model among all voxel models, and is a structured grid type that is widely applied at present. When carrying out subdivision for complex geological structure model with folds, we should fully consider its structural morphology and bedding features to make the generated voxels keep its original morphology. And on the basis of which, they can depict the detailed bedding features and the spatial heterogeneity of the internal attributes. In order to solve the shortage of the existing technologies, this work puts forward a corner-point-grid-based voxelization method for complex geological structure model with folds. We have realized the fast conversion from the 3D geological structure model to the fine voxel model according to the rule of isocline in Ramsay's fold classification. In addition, the voxel model conforms to the spatial features of folds, pinch-out and other complex geological structures, and the voxels of the laminas inside a fold accords with the result of geological sedimentation and tectonic movement. This will provide a carrier and model foundation for the subsequent attribute assignment as well as the quantitative analysis and evaluation based on the spatial voxels. Ultimately, we use examples and the contrastive analysis between the examples and the Ramsay's description of isoclines to discuss the effectiveness and advantages of the method proposed in this work when dealing with the voxelization of 3D geologic structural model with folds based on corner-point grids.

Data analysis in emission tomography using emission-count posteriors

NASA Astrophysics Data System (ADS)

Sitek, Arkadiusz

2012-11-01

A novel approach to the analysis of emission tomography data using the posterior probability of the number of emissions per voxel (emission count) conditioned on acquired tomographic data is explored. The posterior is derived from the prior and the Poisson likelihood of the emission-count data by marginalizing voxel activities. Based on emission-count posteriors, examples of Bayesian analysis including estimation and classification tasks in emission tomography are provided. The application of the method to computer simulations of 2D tomography is demonstrated. In particular, the minimum-mean-square-error point estimator of the emission count is demonstrated. The process of finding this estimator can be considered as a tomographic image reconstruction technique since the estimates of the number of emissions per voxel divided by voxel sensitivities and acquisition time are the estimates of the voxel activities. As an example of a classification task, a hypothesis stating that some region of interest (ROI) emitted at least or at most r-times the number of events in some other ROI is tested. The ROIs are specified by the user. The analysis described in this work provides new quantitative statistical measures that can be used in decision making in diagnostic imaging using emission tomography.

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.

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.

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.

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.

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

Applications of wavelets in morphometric analysis of medical images

NASA Astrophysics Data System (ADS)

Davatzikos, Christos; Tao, Xiaodong; Shen, Dinggang

2003-11-01

Morphometric analysis of medical images is playing an increasingly important role in understanding brain structure and function, as well as in understanding the way in which these change during development, aging and pathology. This paper presents three wavelet-based methods with related applications in morphometric analysis of magnetic resonance (MR) brain images. The first method handles cases where very limited datasets are available for the training of statistical shape models in the deformable segmentation. The method is capable of capturing a larger range of shape variability than the standard active shape models (ASMs) can, by using the elegant spatial-frequency decomposition of the shape contours provided by wavelet transforms. The second method addresses the difficulty of finding correspondences in anatomical images, which is a key step in shape analysis and deformable registration. The detection of anatomical correspondences is completed by using wavelet-based attribute vectors as morphological signatures of voxels. The third method uses wavelets to characterize the morphological measurements obtained from all voxels in a brain image, and the entire set of wavelet coefficients is further used to build a brain classifier. Since the classification scheme operates in a very-high-dimensional space, it can determine subtle population differences with complex spatial patterns. Experimental results are provided to demonstrate the performance of the proposed methods.

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

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.

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.

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.

Hand-independent representation of tool-use pantomimes in the left anterior intraparietal cortex.

PubMed

Ogawa, Kenji; Imai, Fumihito

2016-12-01

Previous neuropsychological studies of ideomotor apraxia (IMA) indicated impairments in pantomime actions for tool use for both right and left hands following lesions of parieto-premotor cortices in the left hemisphere. Using functional magnetic resonance imaging (fMRI) with multi-voxel pattern analysis (MVPA), we tested the hypothesis that the left parieto-premotor cortices are involved in the storage or retrieval of hand-independent representation of tool-use actions. In the fMRI scanner, one of three kinds of tools was displayed in pictures or letters, and the participants made pantomimes of the use of these tools using the right hand for the picture stimuli or with the left hand for the letters. We then used MVPA to classify which kind of tool the subjects were pantomiming. Whole-brain searchlight analysis revealed successful decoding using the activities largely in the contralateral primary sensorimotor region, ipsilateral cerebellum, and bilateral early visual area, which may reflect differences in low-level sensorimotor components for three types of pantomimes. Furthermore, a successful cross-classification between the right and left hands was possible using the activities of the left inferior parietal lobule (IPL) near the junction of the anterior intraparietal sulcus. Our finding indicates that the left anterior intraparietal cortex plays an important role in the production of tool-use pantomimes in a hand-independent manner, and independent of stimuli modality.

Decoding the direction of imagined visual motion using 7 T ultra-high field fMRI

PubMed Central

Emmerling, Thomas C.; Zimmermann, Jan; Sorger, Bettina; Frost, Martin A.; Goebel, Rainer

2016-01-01

There is a long-standing debate about the neurocognitive implementation of mental imagery. One form of mental imagery is the imagery of visual motion, which is of interest due to its naturalistic and dynamic character. However, so far only the mere occurrence rather than the specific content of motion imagery was shown to be detectable. In the current study, the application of multi-voxel pattern analysis to high-resolution functional data of 12 subjects acquired with ultra-high field 7 T functional magnetic resonance imaging allowed us to show that imagery of visual motion can indeed activate the earliest levels of the visual hierarchy, but the extent thereof varies highly between subjects. Our approach enabled classification not only of complex imagery, but also of its actual contents, in that the direction of imagined motion out of four options was successfully identified in two thirds of the subjects and with accuracies of up to 91.3% in individual subjects. A searchlight analysis confirmed the local origin of decodable information in striate and extra-striate cortex. These high-accuracy findings not only shed new light on a central question in vision science on the constituents of mental imagery, but also show for the first time that the specific sub-categorical content of visual motion imagery is reliably decodable from brain imaging data on a single-subject level. PMID:26481673

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.

Multivariate pattern analysis reveals subtle brain anomalies relevant to the cognitive phenotype in neurofibromatosis type 1.

PubMed

Duarte, João V; Ribeiro, Maria J; Violante, Inês R; Cunha, Gil; Silva, Eduardo; Castelo-Branco, Miguel

2014-01-01

Neurofibromatosis Type 1 (NF1) is a common genetic condition associated with cognitive dysfunction. However, the pathophysiology of the NF1 cognitive deficits is not well understood. Abnormal brain structure, including increased total brain volume, white matter (WM) and grey matter (GM) abnormalities have been reported in the NF1 brain. These previous studies employed univariate model-driven methods preventing detection of subtle and spatially distributed differences in brain anatomy. Multivariate pattern analysis allows the combination of information from multiple spatial locations yielding a discriminative power beyond that of single voxels. Here we investigated for the first time subtle anomalies in the NF1 brain, using a multivariate data-driven classification approach. We used support vector machines (SVM) to classify whole-brain GM and WM segments of structural T1 -weighted MRI scans from 39 participants with NF1 and 60 non-affected individuals, divided in children/adolescents and adults groups. We also employed voxel-based morphometry (VBM) as a univariate gold standard to study brain structural differences. SVM classifiers correctly classified 94% of cases (sensitivity 92%; specificity 96%) revealing the existence of brain structural anomalies that discriminate NF1 individuals from controls. Accordingly, VBM analysis revealed structural differences in agreement with the SVM weight maps representing the most relevant brain regions for group discrimination. These included the hippocampus, basal ganglia, thalamus, and visual cortex. This multivariate data-driven analysis thus identified subtle anomalies in brain structure in the absence of visible pathology. Our results provide further insight into the neuroanatomical correlates of known features of the cognitive phenotype of NF1. Copyright © 2012 Wiley Periodicals, Inc.

Voxel-wise motion artifacts in population-level whole-brain connectivity analysis of resting-state FMRI.

PubMed

Spisák, Tamás; Jakab, András; Kis, Sándor A; Opposits, Gábor; Aranyi, Csaba; Berényi, Ervin; Emri, Miklós

2014-01-01

Functional Magnetic Resonance Imaging (fMRI) based brain connectivity analysis maps the functional networks of the brain by estimating the degree of synchronous neuronal activity between brain regions. Recent studies have demonstrated that "resting-state" fMRI-based brain connectivity conclusions may be erroneous when motion artifacts have a differential effect on fMRI BOLD signals for between group comparisons. A potential explanation could be that in-scanner displacement, due to rotational components, is not spatially constant in the whole brain. However, this localized nature of motion artifacts is poorly understood and is rarely considered in brain connectivity studies. In this study, we initially demonstrate the local correspondence between head displacement and the changes in the resting-state fMRI BOLD signal. Than, we investigate how connectivity strength is affected by the population-level variation in the spatial pattern of regional displacement. We introduce Regional Displacement Interaction (RDI), a new covariate parameter set for second-level connectivity analysis and demonstrate its effectiveness in reducing motion related confounds in comparisons of groups with different voxel-vise displacement pattern and preprocessed using various nuisance regression methods. The effect of using RDI as second-level covariate is than demonstrated in autism-related group comparisons. The relationship between the proposed method and some of the prevailing subject-level nuisance regression techniques is evaluated. Our results show that, depending on experimental design, treating in-scanner head motion as a global confound may not be appropriate. The degree of displacement is highly variable among various brain regions, both within and between subjects. These regional differences bias correlation-based measures of brain connectivity. The inclusion of the proposed second-level covariate into the analysis successfully reduces artifactual motion-related group differences and preserves real neuronal differences, as demonstrated by the autism-related comparisons.

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.

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

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.

Modeling Dynamic Contrast-Enhanced MRI Data with a Constrained Local AIF.

PubMed

Duan, Chong; Kallehauge, Jesper F; Pérez-Torres, Carlos J; Bretthorst, G Larry; Beeman, Scott C; Tanderup, Kari; Ackerman, Joseph J H; Garbow, Joel R

2018-02-01

This study aims to develop a constrained local arterial input function (cL-AIF) to improve quantitative analysis of dynamic contrast-enhanced (DCE)-magnetic resonance imaging (MRI) data by accounting for the contrast-agent bolus amplitude error in the voxel-specific AIF. Bayesian probability theory-based parameter estimation and model selection were used to compare tracer kinetic modeling employing either the measured remote-AIF (R-AIF, i.e., the traditional approach) or an inferred cL-AIF against both in silico DCE-MRI data and clinical, cervical cancer DCE-MRI data. When the data model included the cL-AIF, tracer kinetic parameters were correctly estimated from in silico data under contrast-to-noise conditions typical of clinical DCE-MRI experiments. Considering the clinical cervical cancer data, Bayesian model selection was performed for all tumor voxels of the 16 patients (35,602 voxels in total). Among those voxels, a tracer kinetic model that employed the voxel-specific cL-AIF was preferred (i.e., had a higher posterior probability) in 80 % of the voxels compared to the direct use of a single R-AIF. Maps of spatial variation in voxel-specific AIF bolus amplitude and arrival time for heterogeneous tissues, such as cervical cancer, are accessible with the cL-AIF approach. The cL-AIF method, which estimates unique local-AIF amplitude and arrival time for each voxel within the tissue of interest, provides better modeling of DCE-MRI data than the use of a single, measured R-AIF. The Bayesian-based data analysis described herein affords estimates of uncertainties for each model parameter, via posterior probability density functions, and voxel-wise comparison across methods/models, via model selection in data modeling.

A new leg voxel model in two different positions for simulation of the non-uniform distribution of (241)Am in leg bones.

PubMed

Khalaf, Majid; Brey, Richard R; Meldrum, Jeff

2013-01-01

A new leg voxel model in two different positions (straight and bent) has been developed for in vivo measurement calibration purposes. This voxel phantom is a representation of a human leg that may provide a substantial enhancement to Monte Carlo modeling because it more accurately models different geometric leg positions and the non-uniform distribution of Am throughout the leg bones instead of assuming a one-position geometry and a uniform distribution of radionuclides. This was accomplished by performing a radiochemical analysis on small sections of the leg bones from the U.S. Transuranium and Uranium Registries (USTUR) case 0846. USTUR case 0846 represents an individual who was repeatedly contaminated by Am via chronic inhalation. To construct the voxel model, high resolution (2 mm) computed tomography (CT) images of the USTUR case 0846 leg were obtained in different positions. Thirty-six (36) objects (universes) were segmented manually from the CT images using 3D-Doctor software. Bones were divided into 30 small sections with an assigned weight exactly equal to the weight of bone sections obtained from radiochemical analysis of the USTUR case 0846 leg. The segmented images were then converted into a boundary file, and the Human Monitoring Laboratory (HML) voxelizer was used to convert the boundary file into the leg voxel phantom. Excluding the surrounding air regions, the straight leg phantom consists of 592,023 voxels, while the bent leg consists of 337,567 voxels. The resulting leg voxel model is now ready for use as an MCNPX input file to simulate in vivo measurement of bone-seeking radionuclides.

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.

Prevalence and Patterns of Multi-Morbidity in Serbian Adults: A Cross-Sectional Study

PubMed Central

Jovic, Dragana; Vukovic, Dejana; Marinkovic, Jelena

2016-01-01

Introduction Like many developing countries, Serbia is facing a growing burden of chronic diseases. Within such public health issue, multi-morbidity requires a special attention. Aims This study investigated the prevalence of multi-morbidity in the Serbia population and assessed the co-occurrence of chronic diseases by age and gender. Methods We analyzed data from the 2013 National Health Survey, which included 13,103 individuals ≥ 20 years old. Multi-morbidity patterns were identified by exploratory factor analysis of data on self-reported chronic diseases, as well as data on measured body weight and height. The analysis was stratified by age and gender. Results Multi-morbidity was present in nearly one-third of respondents (26.9%) and existed in all age groups, with the highest prevalence among individuals aged 65 years and older (47.2% of men and 65.0% of women). Six patterns of multi-morbidity were identified: non-communicable, cardio-metabolic, respiratory, cardiovascular, aggregate, and mechanical/mental/metabolic. The non-communicable pattern was observed in both genders but only in the 20–44 years age group, while the aggregate pattern occurred only in middle-aged men. Cardio-metabolic and respiratory patterns were present in all age groups. Cardiovascular and mechanical/mental/metabolic patterns showed similar presentation in both men and women. Conclusions Multi-morbidity is a common occurrence among adults in Serbia, especially in the elderly. While several patterns may be explained by underlying pathophysiologies, some require further investigation and follow-up. Recognizing the complexity of multi-morbidity in Serbia is of great importance from both clinical and preventive perspectives given that it affects one-third of the population and may require adjustment of the healthcare system to address the needs of affected individuals. PMID:26871936

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

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

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.

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.

The influence of voxel size on atom probe tomography data.

PubMed

Torres, K L; Daniil, M; Willard, M A; Thompson, G B

2011-05-01

A methodology for determining the optimal voxel size for phase thresholding in nanostructured materials was developed using an atom simulator and a model system of a fixed two-phase composition and volume fraction. The voxel size range was banded by the atom count within each voxel. Some voxel edge lengths were found to be too large, resulting in an averaging of compositional fluctuations; others were too small with concomitant decreases in the signal-to-noise ratio for phase identification. The simulated methodology was then applied to the more complex experimentally determined data set collected from a (Co(0.95)Fe(0.05))(88)Zr(6)Hf(1)B(4)Cu(1) two-phase nanocomposite alloy to validate the approach. In this alloy, Zr and Hf segregated to an intergranular amorphous phase while Fe preferentially segregated to a crystalline phase during the isothermal annealing step that promoted primary crystallization. The atom probe data analysis of the volume fraction was compared to transmission electron microscopy (TEM) dark-field imaging analysis and a lever rule analysis of the volume fraction within the amorphous and crystalline phases of the ribbon. Copyright © 2011 Elsevier B.V. All rights reserved.

Strategies for Interactive Visualization of Large Scale Climate Simulations

NASA Astrophysics Data System (ADS)

Xie, J.; Chen, C.; Ma, K.; Parvis

2011-12-01

With the advances in computational methods and supercomputing technology, climate scientists are able to perform large-scale simulations at unprecedented resolutions. These simulations produce data that are time-varying, multivariate, and volumetric, and the data may contain thousands of time steps with each time step having billions of voxels and each voxel recording dozens of variables. Visualizing such time-varying 3D data to examine correlations between different variables thus becomes a daunting task. We have been developing strategies for interactive visualization and correlation analysis of multivariate data. The primary task is to find connection and correlation among data. Given the many complex interactions among the Earth's oceans, atmosphere, land, ice and biogeochemistry, and the sheer size of observational and climate model data sets, interactive exploration helps identify which processes matter most for a particular climate phenomenon. We may consider time-varying data as a set of samples (e.g., voxels or blocks), each of which is associated with a vector of representative or collective values over time. We refer to such a vector as a temporal curve. Correlation analysis thus operates on temporal curves of data samples. A temporal curve can be treated as a two-dimensional function where the two dimensions are time and data value. It can also be treated as a point in the high-dimensional space. In this case, to facilitate effective analysis, it is often necessary to transform temporal curve data from the original space to a space of lower dimensionality. Clustering and segmentation of temporal curve data in the original or transformed space provides us a way to categorize and visualize data of different patterns, which reveals connection or correlation of data among different variables or at different spatial locations. We have employed the power of GPU to enable interactive correlation visualization for studying the variability and correlations of a single or a pair of variables. It is desired to create a succinct volume classification that summarizes the connection among all correlation volumes with respect to various reference locations. Providing a reference location must correspond to a voxel position, the number of correlation volumes equals the total number of voxels. A brute-force solution takes all correlation volumes as the input and classifies their corresponding voxels according to their correlation volumes' distance. For large-scale time-varying multivariate data, calculating all these correlation volumes on-the-fly and analyzing the relationships among them is not feasible. We have developed a sampling-based approach for volume classification in order to reduce the computation cost of computing the correlation volumes. Users are able to employ their domain knowledge in selecting important samples. The result is a static view that captures the essence of correlation relationships; i.e., for all voxels in the same cluster, their corresponding correlation volumes are similar. This sampling-based approach enables us to obtain an approximation of correlation relations in a cost-effective manner, thus leading to a scalable solution to investigate large-scale data sets. These techniques empower climate scientists to study large data from their simulations.

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.

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.

Robust high-resolution quantification of time signals encoded by in vivo magnetic resonance spectroscopy

NASA Astrophysics Data System (ADS)

Belkić, Dževad; Belkić, Karen

2018-01-01

This paper on molecular imaging emphasizes improving specificity of magnetic resonance spectroscopy (MRS) for early cancer diagnostics by high-resolution data analysis. Sensitivity of magnetic resonance imaging (MRI) is excellent, but specificity is insufficient. Specificity is improved with MRS by going beyond morphology to assess the biochemical content of tissue. This is contingent upon accurate data quantification of diagnostically relevant biomolecules. Quantification is spectral analysis which reconstructs chemical shifts, amplitudes and relaxation times of metabolites. Chemical shifts inform on electronic shielding of resonating nuclei bound to different molecular compounds. Oscillation amplitudes in time signals retrieve the abundance of MR sensitive nuclei whose number is proportional to metabolite concentrations. Transverse relaxation times, the reciprocal of decay probabilities of resonances, arise from spin-spin coupling and reflect local field inhomogeneities. In MRS single voxels are used. For volumetric coverage, multi-voxels are employed within a hybrid of MRS and MRI called magnetic resonance spectroscopic imaging (MRSI). Common to MRS and MRSI is encoding of time signals and subsequent spectral analysis. Encoded data do not provide direct clinical information. Spectral analysis of time signals can yield the quantitative information, of which metabolite concentrations are the most clinically important. This information is equivocal with standard data analysis through the non-parametric, low-resolution fast Fourier transform and post-processing via fitting. By applying the fast Padé transform (FPT) with high-resolution, noise suppression and exact quantification via quantum mechanical signal processing, advances are made, presented herein, focusing on four areas of critical public health importance: brain, prostate, breast and ovarian cancers.

Sub-pattern based multi-manifold discriminant analysis for face recognition

NASA Astrophysics Data System (ADS)

Dai, Jiangyan; Guo, Changlu; Zhou, Wei; Shi, Yanjiao; Cong, Lin; Yi, Yugen

2018-04-01

In this paper, we present a Sub-pattern based Multi-manifold Discriminant Analysis (SpMMDA) algorithm for face recognition. Unlike existing Multi-manifold Discriminant Analysis (MMDA) approach which is based on holistic information of face image for recognition, SpMMDA operates on sub-images partitioned from the original face image and then extracts the discriminative local feature from the sub-images separately. Moreover, the structure information of different sub-images from the same face image is considered in the proposed method with the aim of further improve the recognition performance. Extensive experiments on three standard face databases (Extended YaleB, CMU PIE and AR) demonstrate that the proposed method is effective and outperforms some other sub-pattern based face recognition methods.

Inter-patient image registration algorithms to disentangle regional dose bioeffects.

PubMed

Monti, Serena; Pacelli, Roberto; Cella, Laura; Palma, Giuseppe

2018-03-20

Radiation therapy (RT) technological advances call for a comprehensive reconsideration of the definition of dose features leading to radiation induced morbidity (RIM). In this context, the voxel-based approach (VBA) to dose distribution analysis in RT offers a radically new philosophy to evaluate local dose response patterns, as an alternative to dose-volume-histograms for identifying dose sensitive regions of normal tissue. The VBA relies on mapping patient dose distributions into a single reference case anatomy which serves as anchor for local dosimetric evaluations. The inter-patient elastic image registrations (EIRs) of the planning CTs provide the deformation fields necessary for the actual warp of dose distributions. In this study we assessed the impact of EIR on the VBA results in thoracic patients by identifying two state-of-the-art EIR algorithms (Demons and B-Spline). Our analysis demonstrated that both the EIR algorithms may be successfully used to highlight subregions with dose differences associated with RIM that substantially overlap. Furthermore, the inclusion for the first time of covariates within a dosimetric statistical model that faces the multiple comparison problem expands the potential of VBA, thus paving the way to a reliable voxel-based analysis of RIM in datasets with strong correlation of the outcome with non-dosimetric variables.

Discrete Wavelet Transform-Based Whole-Spectral and Subspectral Analysis for Improved Brain Tumor Clustering Using Single Voxel MR Spectroscopy.

PubMed

Yang, Guang; Nawaz, Tahir; Barrick, Thomas R; Howe, Franklyn A; Slabaugh, Greg

2015-12-01

Many approaches have been considered for automatic grading of brain tumors by means of pattern recognition with magnetic resonance spectroscopy (MRS). Providing an improved technique which can assist clinicians in accurately identifying brain tumor grades is our main objective. The proposed technique, which is based on the discrete wavelet transform (DWT) of whole-spectral or subspectral information of key metabolites, combined with unsupervised learning, inspects the separability of the extracted wavelet features from the MRS signal to aid the clustering. In total, we included 134 short echo time single voxel MRS spectra (SV MRS) in our study that cover normal controls, low grade and high grade tumors. The combination of DWT-based whole-spectral or subspectral analysis and unsupervised clustering achieved an overall clustering accuracy of 94.8% and a balanced error rate of 7.8%. To the best of our knowledge, it is the first study using DWT combined with unsupervised learning to cluster brain SV MRS. Instead of dimensionality reduction on SV MRS or feature selection using model fitting, our study provides an alternative method of extracting features to obtain promising clustering results.

Adverse effects of metallic artifacts on voxel-wise analysis and tract-based spatial statistics in diffusion tensor imaging.

PubMed

Goto, Masami; Abe, Osamu; Hata, Junichi; Fukunaga, Issei; Shimoji, Keigo; Kunimatsu, Akira; Gomi, Tsutomu

2017-02-01

Background Diffusion tensor imaging (DTI) is a magnetic resonance imaging (MRI) technique that reflects the Brownian motion of water molecules constrained within brain tissue. Fractional anisotropy (FA) is one of the most commonly measured DTI parameters, and can be applied to quantitative analysis of white matter as tract-based spatial statistics (TBSS) and voxel-wise analysis. Purpose To show an association between metallic implants and the results of statistical analysis (voxel-wise group comparison and TBSS) for fractional anisotropy (FA) mapping, in DTI of healthy adults. Material and Methods Sixteen healthy volunteers were scanned with 3-Tesla MRI. A magnetic keeper type of dental implant was used as the metallic implant. DTI was acquired three times in each participant: (i) without a magnetic keeper (FAnon1); (ii) with a magnetic keeper (FAimp); and (iii) without a magnetic keeper (FAnon2) as reproducibility of FAnon1. Group comparisons with paired t-test were performed as FAnon1 vs. FAnon2, and as FAnon1 vs. FAimp. Results Regions of significantly reduced and increased local FA values were revealed by voxel-wise group comparison analysis (a P value of less than 0.05, corrected with family-wise error), but not by TBSS. Conclusion Metallic implants existing outside the field of view produce artifacts that affect the statistical analysis (voxel-wise group comparisons) for FA mapping. When statistical analysis for FA mapping is conducted by researchers, it is important to pay attention to any dental implants present in the mouths of the participants.

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

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

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.

A MULTI-LOCUS, MULTI-TAXA PHYLOGEOGRAPHICAL ANALYSIS OF GENETIC DIVERSITY

EPA Science Inventory

In addition to measuring spatial patterns of genetic diversity, population genetic measures of biological resources should include temporal data that indicate whether the observed patterns are the result of historical or contemporary processes. In general, genetic measures focus...

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.

4D-CT Lung registration using anatomy-based multi-level multi-resolution optical flow analysis and thin-plate splines.

PubMed

Min, Yugang; Neylon, John; Shah, Amish; Meeks, Sanford; Lee, Percy; Kupelian, Patrick; Santhanam, Anand P

2014-09-01

The accuracy of 4D-CT registration is limited by inconsistent Hounsfield unit (HU) values in the 4D-CT data from one respiratory phase to another and lower image contrast for lung substructures. This paper presents an optical flow and thin-plate spline (TPS)-based 4D-CT registration method to account for these limitations. The use of unified HU values on multiple anatomy levels (e.g., the lung contour, blood vessels, and parenchyma) accounts for registration errors by inconsistent landmark HU value. While 3D multi-resolution optical flow analysis registers each anatomical level, TPS is employed for propagating the results from one anatomical level to another ultimately leading to the 4D-CT registration. 4D-CT registration was validated using target registration error (TRE), inverse consistency error (ICE) metrics, and a statistical image comparison using Gamma criteria of 1 % intensity difference in 2 mm(3) window range. Validation results showed that the proposed method was able to register CT lung datasets with TRE and ICE values <3 mm. In addition, the average number of voxel that failed the Gamma criteria was <3 %, which supports the clinical applicability of the propose registration mechanism. The proposed 4D-CT registration computes the volumetric lung deformations within clinically viable accuracy.

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.

Merge measuring mesh for complex surface parts

NASA Astrophysics Data System (ADS)

Ye, Jianhua; Gao, Chenghui; Zeng, Shoujin; Xu, Mingsan

2018-04-01

Due to most parts self-occlude and limitation of scanner range, it is difficult to scan the entire part by one time. For modeling of part, multi measuring meshes need to be merged. In this paper, a new merge method is presented. At first, using the grid voxelization method to eliminate the most of non-overlap regions, and retrieval overlap triangles method by the topology of mesh is proposed due to its ability to improve the efficiency. Then, to remove the large deviation of overlap triangles, deleting by overlap distance is discussion. After that, this paper puts forward a new method of merger meshes by registration and combination mesh boundary point. Through experimental analysis, the suggested methods are effective.

SU-E-J-90: Lobar-Level Lung Ventilation Analysis Using 4DCT and Deformable Image Registration

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

Du, K; Bayouth, J; Patton, T

2015-06-15

Purpose: To assess regional changes in human lung ventilation and mechanics using four-dimensional computed tomography (4DCT) and deformable image registration. This work extends our prior analysis of the entire lung to a lobe-based analysis. Methods: 4DCT images acquired from 20 patients prior to radiation therapy (RT) were used for this analysis. Jacobian ventilation and motion maps were computed from the displacement field after deformable image registration between the end of expiration breathing phase and the end of inspiration breathing phase. The lobes were manually segmented on the reference phase by a medical physicist expert. The voxel-by-voxel ventilation and motion magnitudemore » for all subjects were grouped by lobes and plotted into cumulative voxel frequency curves respectively. In addition, to eliminate the effect of different breathing efforts across subjects, we applied the inter-subject equivalent lung volume (ELV) method on a subset of the cohort and reevaluated the lobar ventilation. Results: 95% of voxels in the lung are expanding during inspiration. However, some local regions of lung tissue show far more expansion than others. The greatest expansion with respiration occurs within the lower lobes; between exhale and inhale the median expansion in lower lobes is approximately 15%, while the median expansion in upper lobes is 10%. This appears to be driven by a subset of lung tissues within the lobe that have greater expansion; twice the number of voxels in the lower lobes (20%) expand by > 30% when compared to the upper lobes (10%). Conclusion: Lung ventilation and motion show significant difference on the lobar level. There are different lobar fractions of driving voxels that contribute to the major expansion of the lung. This work was supported by NIH grant CA166703.« less

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

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.

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

Group-level spatio-temporal pattern recovery in MEG decoding using multi-task joint feature learning.

PubMed

Kia, Seyed Mostafa; Pedregosa, Fabian; Blumenthal, Anna; Passerini, Andrea

2017-06-15

The use of machine learning models to discriminate between patterns of neural activity has become in recent years a standard analysis approach in neuroimaging studies. Whenever these models are linear, the estimated parameters can be visualized in the form of brain maps which can aid in understanding how brain activity in space and time underlies a cognitive function. However, the recovered brain maps often suffer from lack of interpretability, especially in group analysis of multi-subject data. To facilitate the application of brain decoding in group-level analysis, we present an application of multi-task joint feature learning for group-level multivariate pattern recovery in single-trial magnetoencephalography (MEG) decoding. The proposed method allows for recovering sparse yet consistent patterns across different subjects, and therefore enhances the interpretability of the decoding model. Our experimental results demonstrate that the mutli-task joint feature learning framework is capable of recovering more meaningful patterns of varying spatio-temporally distributed brain activity across individuals while still maintaining excellent generalization performance. We compare the performance of the multi-task joint feature learning in terms of generalization, reproducibility, and quality of pattern recovery against traditional single-subject and pooling approaches on both simulated and real MEG datasets. These results can facilitate the usage of brain decoding for the characterization of fine-level distinctive patterns in group-level inference. Considering the importance of group-level analysis, the proposed approach can provide a methodological shift towards more interpretable brain decoding models. Copyright © 2017 Elsevier B.V. All rights reserved.

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.

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.

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.

Repeatability and correlations of dynamic contrast enhanced and T2* MRI in patients with advanced pancreatic ductal adenocarcinoma.

PubMed

Klaassen, Remy; Gurney-Champion, Oliver J; Wilmink, Johanna W; Besselink, Marc G; Engelbrecht, Marc R W; Stoker, Jaap; Nederveen, Aart J; van Laarhoven, Hanneke W M

2018-07-01

In current oncological practice of pancreatic ductal adenocarcinoma (PDAC), there is a great demand for response predictors and markers for early treatment evaluation. In this study, we investigated the repeatability and the interaction of dynamic contrast enhanced (DCE) and T2* MRI in patients with advanced PDAC to enable for such evaluation using these techniques. 15 PDAC patients underwent two DCE, T2* and anatomical 3 T MRI sessions before start of treatment. Parametric maps were calculated for the transfer constant (K trans ), rate constant (k ep ), extracellular extravascular space (v e ) and perfusion fraction (v p ). Quantitative R2* (1/T2*) maps were obtained from the multi-echo T2* images. Differences between normal and cancerous pancreas were determined using a Wilcoxon matched pairs test. Repeatability was obtained using Bland-Altman analysis and relations between DCE and T2*/R2* were observed by Spearman correlation and voxel-wise binned plots of tumor voxels. PDAC K trans (p = 0.007), k ep (p < 0.001), v p (p = 0.035) were lower and v e (p < 0.001) was higher compared to normal pancreas. The coefficient of variation between sessions was 21.8% for K trans , 9.9% for k ep , 19.3% for v e , 18.2% for v p and 18.7% for R2*. Variation between patients ranged from 20.2% for k ep to 43.6% for K trans . In the tumor both K trans (r = 0.56, p = 0.030) and v e (r = 0.54, p = 0.037) showed a positive correlation with T2*. Voxel wise analysis showed a steep increase in R2* for tumor voxels with lower K trans and v e . We showed good repeatability of DCE and T2* related MRI parameters in advanced PDAC patients. Furthermore, we have illustrated the relation of DCE K trans and v e with tissue T2* and R2* indicating substantial value of these parameters for detecting tumor hypoxia in future studies. The results from our study pave the way for further response evaluation studies and patient selection based on DCE and T2* parameters. Copyright © 2018 Elsevier Inc. All rights reserved.

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.

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.

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.

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.

Gray matter alteration in isolated congenital anosmia patient: a voxel-based morphometry study.

PubMed

Yao, Linyin; Yi, Xiaoli; Wei, Yongxiang

2013-09-01

Decreased volume of gray matter (GM) was observed in olfactory loss in patients with neurodegenerative disorder. However, GM volume has not yet been investigated in isolated congenital anosmia (ICA) people. We herewith investigated the volume change of gray matter of an ICA boy by morphometric analysis of magnetic resonance images (voxel-based morphometry), and compared with that of 20 age-matched healthy controls. ICA boy presented a significant decrease in GM volume in the orbitofrontal cortex, anterior cingulate cortex, middle cingulate cortex, thalamus, insular cortex, cerebellum, precuneus, gyrus rectus, subcallosal gyrus, middle temporal gyrus, fusiform gyrus and piriform cortex. No significant GM volume increase was detected in other brain areas. The pattern of GM atrophy was similar as previous literature reported. Our results identified similar GM volume alterations regardless of the causes of olfactory impairment. Decreased GM volume was not only shown in olfactory bulbs, olfactory tracts and olfactory sulcus, also in primary olfactory cortex and the secondary cerebral olfactory areas in ICA people. This is the first study to evaluate GM volume alterations in ICA people.

Functional Connectivity Parcellation of the Human Thalamus by Independent Component Analysis.

PubMed

Zhang, Sheng; Li, Chiang-Shan R

2017-11-01

As a key structure to relay and integrate information, the thalamus supports multiple cognitive and affective functions through the connectivity between its subnuclei and cortical and subcortical regions. Although extant studies have largely described thalamic regional functions in anatomical terms, evidence accumulates to suggest a more complex picture of subareal activities and connectivities of the thalamus. In this study, we aimed to parcellate the thalamus and examine whole-brain connectivity of its functional clusters. With resting state functional magnetic resonance imaging data from 96 adults, we used independent component analysis (ICA) to parcellate the thalamus into 10 components. On the basis of the independence assumption, ICA helps to identify how subclusters overlap spatially. Whole brain functional connectivity of each subdivision was computed for independent component's time course (ICtc), which is a unique time series to represent an IC. For comparison, we computed seed-region-based functional connectivity using the averaged time course across all voxels within a thalamic subdivision. The results showed that, at p < 10 -6 , corrected, 49% of voxels on average overlapped among subdivisions. Compared with seed-region analysis, ICtc analysis revealed patterns of connectivity that were more distinguished between thalamic clusters. ICtc analysis demonstrated thalamic connectivity to the primary motor cortex, which has eluded the analysis as well as previous studies based on averaged time series, and clarified thalamic connectivity to the hippocampus, caudate nucleus, and precuneus. The new findings elucidate functional organization of the thalamus and suggest that ICA clustering in combination with ICtc rather than seed-region analysis better distinguishes whole-brain connectivities among functional clusters of a brain region.

Voxel-based population analysis for correlating local dose and rectal toxicity in prostate cancer radiotherapy

NASA Astrophysics Data System (ADS)

Acosta, Oscar; Drean, Gael; Ospina, Juan D.; Simon, Antoine; Haigron, Pascal; Lafond, Caroline; de Crevoisier, Renaud

2013-04-01

The majority of current models utilized for predicting toxicity in prostate cancer radiotherapy are based on dose-volume histograms. One of their main drawbacks is the lack of spatial accuracy, since they consider the organs as a whole volume and thus ignore the heterogeneous intra-organ radio-sensitivity. In this paper, we propose a dose-image-based framework to reveal the relationships between local dose and toxicity. In this approach, the three-dimensional (3D) planned dose distributions across a population are non-rigidly registered into a common coordinate system and compared at a voxel level, therefore enabling the identification of 3D anatomical patterns, which may be responsible for toxicity, at least to some extent. Additionally, different metrics were employed in order to assess the quality of the dose mapping. The value of this approach was demonstrated by prospectively analyzing rectal bleeding (⩾Grade 1 at 2 years) according to the CTCAE v3.0 classification in a series of 105 patients receiving 80 Gy to the prostate by intensity modulated radiation therapy (IMRT). Within the patients presenting bleeding, a significant dose excess (6 Gy on average, p < 0.01) was found in a region of the anterior rectal wall. This region, close to the prostate (1 cm), represented less than 10% of the rectum. This promising voxel-wise approach allowed subregions to be defined within the organ that may be involved in toxicity and, as such, must be considered during the inverse IMRT planning step.

Grey matter damage in progressive multiple sclerosis versus amyotrophic lateral sclerosis: a voxel-based morphometry MRI study.

PubMed

Tavazzi, Eleonora; Laganà, Maria Marcella; Bergsland, Niels; Tortorella, Paola; Pinardi, Giovanna; Lunetta, Christian; Corbo, Massimo; Rovaris, Marco

2015-03-01

Primary progressive multiple sclerosis (PPMS) and amyotrophic lateral sclerosis (ALS) seem to share some clinical and pathological features. MRI studies revealed the presence of grey matter (GM) atrophy in both diseases, but no comparative data are available. The objective was to compare the regional patterns of GM tissue loss in PPMS and ALS with voxel-based morphometry (VBM). Eighteen PPMS patients, 20 ALS patients, and 31 healthy controls (HC) were studied with a 1.5 Tesla scanner. VBM was performed to assess volumetric GM differences with age and sex as covariates. Threshold-free cluster enhancement analysis was used to obtain significant clusters. Group comparisons were tested with family-wise error correction for multiple comparisons (p < 0.05) except for HC versus MND which was tested at a level of p < 0.001 uncorrected and a cluster threshold of 20 contiguous voxels. Compared to HC, ALS patients showed GM tissue reduction in selected frontal and temporal areas, while PPMS patients showed a widespread bilateral GM volume decrease, involving both deep and cortical regions. Compared to ALS, PPMS patients showed tissue volume reductions in both deep and cortical GM areas. This preliminary study confirms that PPMS is characterized by a more diffuse cortical and subcortical GM atrophy than ALS and that, in the latter condition, brain damage is present outside the motor system. These results suggest that PPMS and ALS may share pathological features leading to GM tissue loss.

Predicting primary progressive aphasias with support vector machine approaches in structural MRI data.

PubMed

Bisenius, Sandrine; Mueller, Karsten; Diehl-Schmid, Janine; Fassbender, Klaus; Grimmer, Timo; Jessen, Frank; Kassubek, Jan; Kornhuber, Johannes; Landwehrmeyer, Bernhard; Ludolph, Albert; Schneider, Anja; Anderl-Straub, Sarah; Stuke, Katharina; Danek, Adrian; Otto, Markus; Schroeter, Matthias L

2017-01-01

Primary progressive aphasia (PPA) encompasses the three subtypes nonfluent/agrammatic variant PPA, semantic variant PPA, and the logopenic variant PPA, which are characterized by distinct patterns of language difficulties and regional brain atrophy. To validate the potential of structural magnetic resonance imaging data for early individual diagnosis, we used support vector machine classification on grey matter density maps obtained by voxel-based morphometry analysis to discriminate PPA subtypes (44 patients: 16 nonfluent/agrammatic variant PPA, 17 semantic variant PPA, 11 logopenic variant PPA) from 20 healthy controls (matched for sample size, age, and gender) in the cohort of the multi-center study of the German consortium for frontotemporal lobar degeneration. Here, we compared a whole-brain with a meta-analysis-based disease-specific regions-of-interest approach for support vector machine classification. We also used support vector machine classification to discriminate the three PPA subtypes from each other. Whole brain support vector machine classification enabled a very high accuracy between 91 and 97% for identifying specific PPA subtypes vs. healthy controls, and 78/95% for the discrimination between semantic variant vs. nonfluent/agrammatic or logopenic PPA variants. Only for the discrimination between nonfluent/agrammatic and logopenic PPA variants accuracy was low with 55%. Interestingly, the regions that contributed the most to the support vector machine classification of patients corresponded largely to the regions that were atrophic in these patients as revealed by group comparisons. Although the whole brain approach took also into account regions that were not covered in the regions-of-interest approach, both approaches showed similar accuracies due to the disease-specificity of the selected networks. Conclusion, support vector machine classification of multi-center structural magnetic resonance imaging data enables prediction of PPA subtypes with a very high accuracy paving the road for its application in clinical settings.

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.

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.

Motor Learning Induces Plasticity in the Resting Brain-Drumming Up a Connection.

PubMed

Amad, Ali; Seidman, Jade; Draper, Stephen B; Bruchhage, Muriel M K; Lowry, Ruth G; Wheeler, James; Robertson, Andrew; Williams, Steven C R; Smith, Marcus S

2017-03-01

Neuroimaging methods have recently been used to investigate plasticity-induced changes in brain structure. However, little is known about the dynamic interactions between different brain regions after extensive coordinated motor learning such as drumming. In this article, we have compared the resting-state functional connectivity (rs-FC) in 15 novice healthy participants before and after a course of drumming (30-min drumming sessions, 3 days a week for 8 weeks) and 16 age-matched novice comparison participants. To identify brain regions showing significant FC differences before and after drumming, without a priori regions of interest, a multivariate pattern analysis was performed. Drum training was associated with an increased FC between the posterior part of bilateral superior temporal gyri (pSTG) and the rest of the brain (i.e., all other voxels). These regions were then used to perform seed-to-voxel analysis. The pSTG presented an increased FC with the premotor and motor regions, the right parietal lobe and a decreased FC with the cerebellum. Perspectives and the potential for rehabilitation treatments with exercise-based intervention to overcome impairments due to brain diseases are also discussed. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Brain regions with abnormal network properties in severe epilepsy of Lennox-Gastaut phenotype: Multivariate analysis of task-free fMRI.

PubMed

Pedersen, Mangor; Curwood, Evan K; Archer, John S; Abbott, David F; Jackson, Graeme D

2015-11-01

Lennox-Gastaut syndrome, and the similar but less tightly defined Lennox-Gastaut phenotype, describe patients with severe epilepsy, generalized epileptic discharges, and variable intellectual disability. Our previous functional neuroimaging studies suggest that abnormal diffuse association network activity underlies the epileptic discharges of this clinical phenotype. Herein we use a data-driven multivariate approach to determine the spatial changes in local and global networks of patients with severe epilepsy of the Lennox-Gastaut phenotype. We studied 9 adult patients and 14 controls. In 20 min of task-free blood oxygen level-dependent functional magnetic resonance imaging data, two metrics of functional connectivity were studied: Regional homogeneity or local connectivity, a measure of concordance between each voxel to a focal cluster of adjacent voxels; and eigenvector centrality, a global connectivity estimate designed to detect important neural hubs. Multivariate pattern analysis of these data in a machine-learning framework was used to identify spatial features that classified disease subjects. Multivariate pattern analysis was 95.7% accurate in classifying subjects for both local and global connectivity measures (22/23 subjects correctly classified). Maximal discriminating features were the following: increased local connectivity in frontoinsular and intraparietal areas; increased global connectivity in posterior association areas; decreased local connectivity in sensory (visual and auditory) and medial frontal cortices; and decreased global connectivity in the cingulate cortex, striatum, hippocampus, and pons. Using a data-driven analysis method in task-free functional magnetic resonance imaging, we show increased connectivity in critical areas of association cortex and decreased connectivity in primary cortex. This supports previous findings of a critical role for these association cortical regions as a final common pathway in generating the Lennox-Gastaut phenotype. Abnormal function of these areas is likely to be important in explaining the intellectual problems characteristic of this disorder. Wiley Periodicals, Inc. © 2015 International League Against Epilepsy.

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.

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

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

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.

Magnetic resonance imaging for monitoring therapeutic response in a transgenic mouse model of Alzheimer's disease using voxel-based analysis of amyloid plaques.

PubMed

Kim, Jae-Hun; Ha, Tae Lin; Im, Geun Ho; Yang, Jehoon; Seo, Sang Won; Chung, Julius Juhyun; Chae, Sun Young; Lee, In Su; Lee, Jung Hee

2014-03-05

In this study, we have shown the potential of a voxel-based analysis for imaging amyloid plaques and its utility in monitoring therapeutic response in Alzheimer's disease (AD) mice using manganese oxide nanoparticles conjugated with an antibody of Aβ1-40 peptide (HMON-abAβ40). T1-weighted MR brain images of a drug-treated AD group (n=7), a nontreated AD group (n=7), and a wild-type group (n=7) were acquired using a 7.0 T MRI system before (D-1), 24-h (D+1) after, and 72-h (D+3) after injection with an HMON-abAβ40 contrast agent. For the treatment of AD mice, DAPT was injected intramuscularly into AD transgenic mice (50 mg/kg of body weight). For voxel-based analysis, the skull-stripped mouse brain images were spatially normalized, and these voxels' intensities were corrected to reduce voxel intensity differences across scans in different mice. Statistical analysis showed higher normalized MR signal intensity in the frontal cortex and hippocampus of AD mice over wild-type mice on D+1 and D+3 (P<0.01, uncorrected for multiple comparisons). After the treatment of AD mice, the normalized MR signal intensity in the frontal cortex and hippocampus decreased significantly in comparison with nontreated AD mice on D+1 and D+3 (P<0.01, uncorrected for multiple comparisons). These results were confirmed by histological analysis using a thioflavin staining. This unique strategy allows us to detect brain regions that are subjected to amyloid plaque deposition and has the potential for human applications in monitoring therapeutic response for drug development in AD.

Non-invasive quantification of tumour heterogeneity in water diffusivity to differentiate malignant from benign tissues of urinary bladder: a phase I study.

PubMed

Nguyen, Huyen T; Shah, Zarine K; Mortazavi, Amir; Pohar, Kamal S; Wei, Lai; Jia, Guang; Zynger, Debra L; Knopp, Michael V

2017-05-01

To quantify the heterogeneity of the tumour apparent diffusion coefficient (ADC) using voxel-based analysis to differentiate malignancy from benign wall thickening of the urinary bladder. Nineteen patients with histopathological findings of their cystectomy specimen were included. A data set of voxel-based ADC values was acquired for each patient's lesion. Histogram analysis was performed on each data set to calculate uniformity (U) and entropy (E). The k-means clustering of the voxel-wised ADC data set was implemented to measure mean intra-cluster distance (MICD) and largest inter-cluster distance (LICD). Subsequently, U, E, MICD, and LICD for malignant tumours were compared with those for benign lesions using a two-sample t-test. Eleven patients had pathological confirmation of malignancy and eight with benign wall thickening. Histogram analysis showed that malignant tumours had a significantly higher degree of ADC heterogeneity with lower U (P = 0.016) and higher E (P = 0.005) than benign lesions. In agreement with these findings, k-means clustering of voxel-wise ADC indicated that bladder malignancy presented with significantly higher MICD (P < 0.001) and higher LICD (P = 0.002) than benign wall thickening. The quantitative assessment of tumour diffusion heterogeneity using voxel-based ADC analysis has the potential to become a non-invasive tool to distinguish malignant from benign tissues of urinary bladder cancer. • Heterogeneity is an intrinsic characteristic of tumoral tissue. • Non-invasive quantification of tumour heterogeneity can provide adjunctive information to improve cancer diagnosis accuracy. • Histogram analysis and k-means clustering can quantify tumour diffusion heterogeneity. • The quantification helps differentiate malignant from benign urinary bladder tissue.

Statistical assessment of bi-exponential diffusion weighted imaging signal characteristics induced by intravoxel incoherent motion in malignant breast tumors

PubMed Central

Wong, Oi Lei; Lo, Gladys G.; Chan, Helen H. L.; Wong, Ting Ting; Cheung, Polly S. Y.

2016-01-01

Background The purpose of this study is to statistically assess whether bi-exponential intravoxel incoherent motion (IVIM) model better characterizes diffusion weighted imaging (DWI) signal of malignant breast tumor than mono-exponential Gaussian diffusion model. Methods 3 T DWI data of 29 malignant breast tumors were retrospectively included. Linear least-square mono-exponential fitting and segmented least-square bi-exponential fitting were used for apparent diffusion coefficient (ADC) and IVIM parameter quantification, respectively. F-test and Akaike Information Criterion (AIC) were used to statistically assess the preference of mono-exponential and bi-exponential model using region-of-interests (ROI)-averaged and voxel-wise analysis. Results For ROI-averaged analysis, 15 tumors were significantly better fitted by bi-exponential function and 14 tumors exhibited mono-exponential behavior. The calculated ADC, D (true diffusion coefficient) and f (pseudo-diffusion fraction) showed no significant differences between mono-exponential and bi-exponential preferable tumors. Voxel-wise analysis revealed that 27 tumors contained more voxels exhibiting mono-exponential DWI decay while only 2 tumors presented more bi-exponential decay voxels. ADC was consistently and significantly larger than D for both ROI-averaged and voxel-wise analysis. Conclusions Although the presence of IVIM effect in malignant breast tumors could be suggested, statistical assessment shows that bi-exponential fitting does not necessarily better represent the DWI signal decay in breast cancer under clinically typical acquisition protocol and signal-to-noise ratio (SNR). Our study indicates the importance to statistically examine the breast cancer DWI signal characteristics in practice. PMID:27709078

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 application of statistical parametric mapping to 123I-FP-CIT SPECT in dementia with Lewy bodies, Alzheimer's disease and Parkinson's disease.

PubMed

Colloby, Sean J; O'Brien, John T; Fenwick, John D; Firbank, Michael J; Burn, David J; McKeith, Ian G; Williams, E David

2004-11-01

Dopaminergic loss can be visualised using (123)I-FP-CIT single photon emission computed tomography (SPECT) in several disorders including Parkinson's disease (PD) and dementia with Lewy bodies (DLB). Most previous SPECT studies have adopted region of interest (ROI) methods for analysis, which are subjective and operator-dependent. The purpose of this study was to investigate differences in striatal binding of (123)I-FP-CIT SPECT using the automated technique of statistical parametric mapping (SPM99) in subjects with DLB, Alzheimer's disease (AD), PD and healthy age-matched controls. This involved spatial normalisation of each subject's image to a customised template, followed by smoothing and intensity normalisation of each image to its corresponding mean occipital count per voxel. Group differences were assessed using a two-sample t test. Applying a height threshold of P

Task-Related Deactivation and Functional Connectivity of the Subgenual Cingulate Cortex in Major Depressive Disorder

PubMed Central

Davey, Christopher G.; Yücel, Murat; Allen, Nicholas B.; Harrison, Ben J.

2012-01-01

Background: Major depressive disorder is associated with functional alterations in activity and resting-state connectivity of the extended medial frontal network. In this study we aimed to examine how task-related medial network activity and connectivity were affected in depression. Methods: 18 patients with major depressive disorder, aged 15- to 24-years-old, were matched with 19 healthy control participants. We characterized task-related activations and deactivations while participants engaged with an executive-control task (the multi-source interference task, MSIT). We used a psycho-physiological interactions approach to examine functional connectivity changes with subgenual anterior cingulate cortex. Voxel-wise statistical maps for each analysis were compared between the patient and control groups. Results: There were no differences between groups in their behavioral performances on the MSIT task, and nor in patterns of activation and deactivation. Assessment of functional connectivity with the subgenual cingulate showed that depressed patients did not demonstrate the same reduction in functional connectivity with the ventral striatum during task performance, but that they showed greater reduction in functional connectivity with adjacent ventromedial frontal cortex. The magnitude of this latter connectivity change predicted the relative activation of task-relevant executive-control regions in depressed patients. Conclusion: The study reinforces the importance of the subgenual cingulate cortex for depression, and demonstrates how dysfunctional connectivity with ventral brain regions might influence executive–attentional processes. PMID:22403553

An fMRI Investigation of Preparatory Set in the Human Cerebral Cortex and Superior Colliculus for Pro- and Anti-Saccades

PubMed Central

Furlan, Michele; Smith, Andrew T.; Walker, Robin

2016-01-01

Previous studies have identified several cortical regions that show larger BOLD responses during preparation and execution of anti-saccades than pro-saccades. We confirmed this finding with a greater BOLD response for anti-saccades than pro-saccades during the preparation phase in the FEF, IPS and DLPFC and in the FEF and IPS in the execution phase. We then applied multi-voxel pattern analysis (MVPA) to establish whether different neural populations are involved in the two types of saccade. Pro-saccades and anti-saccades were reliably decoded during saccade execution in all three cortical regions (FEF, DLPFC and IPS) and in IPS during saccade preparation. This indicates neural specialization, for programming the desired response depending on the task rule, in these regions. In a further study tailored for imaging the superior colliculus in the midbrain a similar magnitude BOLD response was observed for pro-saccades and anti-saccades and the two saccade types could not be decoded with MVPA. This was the case both for activity related to the preparation phase and also for that elicited during the execution phase. We conclude that separate cortical neural populations are involved in the task-specific programming of a saccade while in contrast, the SC has a role in response preparation but may be less involved in high-level, task-specific aspects of the control of saccades. PMID:27391390

Neural reactivation reveals mechanisms for updating memory

PubMed Central

Kuhl, Brice A.; Bainbridge, Wilma A.; Chun, Marvin M.

2012-01-01

Our ability to remember new information is often compromised by competition from prior learning, leading to many instances of forgetting. One of the challenges in studying why these lapses occur and how they can be prevented is that it is methodologically difficult to ‘see’ competition between memories as it occurs. Here, we used multi-voxel pattern analysis of human fMRI data to measure the neural reactivation of both older (competing) and newer (target) memories during individual attempts to retrieve newer memories. Of central interest was (a) whether older memories were reactivated during retrieval of newer memories, (b) how reactivation of older memories related to retrieval performance, and (c) whether neural mechanisms engaged during the encoding of newer memories were predictive of neural competition experienced during retrieval. Our results indicate that older and newer visual memories were often simultaneously reactivated in ventral temporal cortex—even when target memories were successfully retrieved. Importantly, stronger reactivation of older memories was associated with less accurate retrieval of newer memories, slower mnemonic decisions, and increased activity in anterior cingulate cortex. Finally, greater activity in the inferior frontal gyrus during the encoding of newer memories (memory updating) predicted lower competition in ventral temporal cortex during subsequent retrieval. Together, these results provide novel insight into how older memories compete with newer memories and specify neural mechanisms that allow competition to be overcome and memories to be updated. PMID:22399768

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 scale on morphological spatial pattern of forest

Treesearch

Katarzyna Ostapowicz; Peter Vogt; Kurt H. Riitters; Jacek Kozak; Christine Estreguil

2008-01-01

Assessing and monitoring landscape pattern structure from multi-scale land-cover maps can utilize morphological spatial pattern analysis (MSPA), only if various influences of scale are known and taken into account. This paper lays part of the foundation for applying MSPA analysis in landscape monitoring by quantifying scale effects on six classes of spatial patterns...

Conjoint representation of texture ensemble and location in the parahippocampal place area.

PubMed

Park, Jeongho; Park, Soojin

2017-04-01

Texture provides crucial information about the category or identity of a scene. Nonetheless, not much is known about how the texture information in a scene is represented in the brain. Previous studies have shown that the parahippocampal place area (PPA), a scene-selective part of visual cortex, responds to simple patches of texture ensemble. However, in natural scenes textures exist in spatial context within a scene. Here we tested two hypotheses that make different predictions on how textures within a scene context are represented in the PPA. The Texture-Only hypothesis suggests that the PPA represents texture ensemble (i.e., the kind of texture) as is, irrespective of its location in the scene. On the other hand, the Texture and Location hypothesis suggests that the PPA represents texture and its location within a scene (e.g., ceiling or wall) conjointly. We tested these two hypotheses across two experiments, using different but complementary methods. In experiment 1 , by using multivoxel pattern analysis (MVPA) and representational similarity analysis, we found that the representational similarity of the PPA activation patterns was significantly explained by the Texture-Only hypothesis but not by the Texture and Location hypothesis. In experiment 2 , using a repetition suppression paradigm, we found no repetition suppression for scenes that had the same texture ensemble but differed in location (supporting the Texture and Location hypothesis). On the basis of these results, we propose a framework that reconciles contrasting results from MVPA and repetition suppression and draw conclusions about how texture is represented in the PPA. NEW & NOTEWORTHY This study investigates how the parahippocampal place area (PPA) represents texture information within a scene context. We claim that texture is represented in the PPA at multiple levels: the texture ensemble information at the across-voxel level and the conjoint information of texture and its location at the within-voxel level. The study proposes a working hypothesis that reconciles contrasting results from multivoxel pattern analysis and repetition suppression, suggesting that the methods are complementary to each other but not necessarily interchangeable. Copyright © 2017 the American Physiological Society.

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.

Effects of voxelization on dose volume histogram accuracy

NASA Astrophysics Data System (ADS)

Sunderland, Kyle; Pinter, Csaba; Lasso, Andras; Fichtinger, Gabor

2016-03-01

PURPOSE: In radiotherapy treatment planning systems, structures of interest such as targets and organs at risk are stored as 2D contours on evenly spaced planes. In order to be used in various algorithms, contours must be converted into binary labelmap volumes using voxelization. The voxelization process results in lost information, which has little effect on the volume of large structures, but has significant impact on small structures, which contain few voxels. Volume differences for segmented structures affects metrics such as dose volume histograms (DVH), which are used for treatment planning. Our goal is to evaluate the impact of voxelization on segmented structures, as well as how factors like voxel size affects metrics, such as DVH. METHODS: We create a series of implicit functions, which represent simulated structures. These structures are sampled at varying resolutions, and compared to labelmaps with high sub-millimeter resolutions. We generate DVH and evaluate voxelization error for the same structures at different resolutions by calculating the agreement acceptance percentage between the DVH. RESULTS: We implemented tools for analysis as modules in the SlicerRT toolkit based on the 3D Slicer platform. We found that there were large DVH variation from the baseline for small structures or for structures located in regions with a high dose gradient, potentially leading to the creation of suboptimal treatment plans. CONCLUSION: This work demonstrates that labelmap and dose volume voxel size is an important factor in DVH accuracy, which must be accounted for in order to ensure the development of accurate treatment plans.

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

Neuropsychological and FDG-PET profiles in VGKC autoimmune limbic encephalitis.

PubMed

Dodich, Alessandra; Cerami, Chiara; Iannaccone, Sandro; Marcone, Alessandra; Alongi, Pierpaolo; Crespi, Chiara; Canessa, Nicola; Andreetta, Francesca; Falini, Andrea; Cappa, Stefano F; Perani, Daniela

2016-10-01

Limbic encephalitis (LE) is characterized by an acute or subacute onset with memory impairments, confusional state, behavioral disorders, variably associated with seizures and dystonic movements. It is due to inflammatory processes that selectively affect the medial temporal lobe structures. Voltage-gate potassium channel (VGKC) autoantibodies are frequently observed. In this study, we assessed at the individual level FDG-PET brain metabolic dysfunctions and neuropsychological profiles in three autoimmune LE cases seropositive for neuronal VGKC-complex autoantibodies. LGI1 and CASPR2 potassium channel complex autoantibody subtyping was performed. Cognitive abilities were evaluated with an in-depth neuropsychological battery focused on episodic memory and affective recognition/processing skills. FDG-PET data were analyzed at single-subject level according to a standardized and validated voxel-based Statistical Parametric Mapping (SPM) method. Patients showed severe episodic memory and fear recognition deficits at the neuropsychological assessment. No disorder of mentalizing processing was present. Variable patterns of increases and decreases of brain glucose metabolism emerged in the limbic structures, highlighting the pathology-driven selective vulnerability of this system. Additional involvement of cortical and subcortical regions, particularly in the sensorimotor system and basal ganglia, was found. Episodic memory and fear recognition deficits characterize the cognitive profile of LE. Commonalities and differences may occur in the brain metabolic patterns. Single-subject voxel-based analysis of FDG-PET imaging could be useful in the early detection of the metabolic correlates of cognitive and non-cognitive deficits characterizing LE condition. Copyright © 2016 Elsevier Inc. All rights reserved.

Decoding conjunctions of direction-of-motion and binocular disparity from human visual cortex.

PubMed

Seymour, Kiley J; Clifford, Colin W G

2012-05-01

Motion and binocular disparity are two features in our environment that share a common correspondence problem. Decades of psychophysical research dedicated to understanding stereopsis suggest that these features interact early in human visual processing to disambiguate depth. Single-unit recordings in the monkey also provide evidence for the joint encoding of motion and disparity across much of the dorsal visual stream. Here, we used functional MRI and multivariate pattern analysis to examine where in the human brain conjunctions of motion and disparity are encoded. Subjects sequentially viewed two stimuli that could be distinguished only by their conjunctions of motion and disparity. Specifically, each stimulus contained the same feature information (leftward and rightward motion and crossed and uncrossed disparity) but differed exclusively in the way these features were paired. Our results revealed that a linear classifier could accurately decode which stimulus a subject was viewing based on voxel activation patterns throughout the dorsal visual areas and as early as V2. This decoding success was conditional on some voxels being individually sensitive to the unique conjunctions comprising each stimulus, thus a classifier could not rely on independent information about motion and binocular disparity to distinguish these conjunctions. This study expands on evidence that disparity and motion interact at many levels of human visual processing, particularly within the dorsal stream. It also lends support to the idea that stereopsis is subserved by early mechanisms also tuned to direction of motion.

Distinct Cortical Pathways for Music and Speech Revealed by Hypothesis-Free Voxel Decomposition

PubMed Central

Norman-Haignere, Sam

2015-01-01

SUMMARY The organization of human auditory cortex remains unresolved, due in part to the small stimulus sets common to fMRI studies and the overlap of neural populations within voxels. To address these challenges, we measured fMRI responses to 165 natural sounds and inferred canonical response profiles (“components”) whose weighted combinations explained voxel responses throughout auditory cortex. This analysis revealed six components, each with interpretable response characteristics despite being unconstrained by prior functional hypotheses. Four components embodied selectivity for particular acoustic features (frequency, spectrotemporal modulation, pitch). Two others exhibited pronounced selectivity for music and speech, respectively, and were not explainable by standard acoustic features. Anatomically, music and speech selectivity concentrated in distinct regions of non-primary auditory cortex. However, music selectivity was weak in raw voxel responses, and its detection required a decomposition method. Voxel decomposition identifies primary dimensions of response variation across natural sounds, revealing distinct cortical pathways for music and speech. PMID:26687225

Distinct Cortical Pathways for Music and Speech Revealed by Hypothesis-Free Voxel Decomposition.

PubMed

Norman-Haignere, Sam; Kanwisher, Nancy G; McDermott, Josh H

2015-12-16

The organization of human auditory cortex remains unresolved, due in part to the small stimulus sets common to fMRI studies and the overlap of neural populations within voxels. To address these challenges, we measured fMRI responses to 165 natural sounds and inferred canonical response profiles ("components") whose weighted combinations explained voxel responses throughout auditory cortex. This analysis revealed six components, each with interpretable response characteristics despite being unconstrained by prior functional hypotheses. Four components embodied selectivity for particular acoustic features (frequency, spectrotemporal modulation, pitch). Two others exhibited pronounced selectivity for music and speech, respectively, and were not explainable by standard acoustic features. Anatomically, music and speech selectivity concentrated in distinct regions of non-primary auditory cortex. However, music selectivity was weak in raw voxel responses, and its detection required a decomposition method. Voxel decomposition identifies primary dimensions of response variation across natural sounds, revealing distinct cortical pathways for music and speech. Copyright © 2015 Elsevier Inc. All rights reserved.

Detection of periimplant fenestration and dehiscence with the use of two scan modes and the smallest voxel sizes of a cone-beam computed tomography device.

PubMed

de-Azevedo-Vaz, Sergio Lins; Vasconcelos, Karla de Faria; Neves, Frederico Sampaio; Melo, Saulo Leonardo Sousa; Campos, Paulo Sérgio Flores; Haiter-Neto, Francisco

2013-01-01

To assess the accuracy of cone-beam computed tomography (CBCT) in periimplant fenestration and dehiscence detection, and to determine the effects of 2 voxel sizes and scan modes. One hundred titanium implants were placed in bovine ribs in which periimplant fenestration and dehiscence were simulated. CBCT images were acquired with the use of 3 protocols of the i-CAT NG unit: A) 0.2 mm voxel size half-scan (180°); B) 0.2 mm voxel size full-scan (360°); and C) 0.12 mm voxel size full scan (360°). Receiver operating characteristic curves and diagnostic values were obtained. The Az values were compared with the use of analysis of variance. The Az value for dehiscence in protocol A was significantly lower than those of B or C (P < .01). They did not statistically differ for fenestration (P > .05). Protocol B yielded the highest values. The voxel sizes did not affect fenestration and dehiscence detection, and for dehiscence full-scan performed better than half-scan. Copyright © 2013 Elsevier Inc. All rights reserved.

Neural signature of coma revealed by posteromedial cortex connection density analysis.

PubMed

Malagurski, Briguita; Péran, Patrice; Sarton, Benjamine; Riu, Beatrice; Gonzalez, Leslie; Vardon-Bounes, Fanny; Seguin, Thierry; Geeraerts, Thomas; Fourcade, Olivier; de Pasquale, Francesco; Silva, Stein

2017-01-01

Posteromedial cortex (PMC) is a highly segregated and dynamic core, which appears to play a critical role in internally/externally directed cognitive processes, including conscious awareness. Nevertheless, neuroimaging studies on acquired disorders of consciousness, have traditionally explored PMC as a homogenous and indivisible structure. We suggest that a fine-grained description of intrinsic PMC topology during coma, could expand our understanding about how this cortical hub contributes to consciousness generation and maintain, and could permit the identification of specific markers related to brain injury mechanism and useful for neurological prognostication. To explore this, we used a recently developed voxel-based unbiased approach, named functional connectivity density (CD). We compared 27 comatose patients (15 traumatic and 12 anoxic), to 14 age-matched healthy controls. The patients' outcome was assessed 3 months later using Coma Recovery Scale-Revised (CRS-R). A complex pattern of decreased and increased connections was observed, suggesting a network imbalance between internal/external processing systems, within PMC during coma. The number of PMC voxels with hypo-CD positive correlation showed a significant negative association with the CRS-R score, notwithstanding aetiology. Traumatic injury specifically appeared to be associated with a greater prevalence of hyper-connected (negative correlation) voxels, which was inversely associated with patient neurological outcome. A logistic regression model using the number of hypo-CD positive and hyper-CD negative correlations, accurately permitted patient's outcome prediction (AUC = 0.906, 95%IC = 0.795-1). These points might reflect adaptive plasticity mechanism and pave the way for innovative prognosis and therapeutics methods.

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.

Voluntary Enhancement of Neural Signatures of Affiliative Emotion Using fMRI Neurofeedback

PubMed Central

Moll, Jorge; Weingartner, Julie H.; Bado, Patricia; Basilio, Rodrigo; Sato, João R.; Melo, Bruno R.; Bramati, Ivanei E.; de Oliveira-Souza, Ricardo; Zahn, Roland

2014-01-01

In Ridley Scott’s film “Blade Runner”, empathy-detection devices are employed to measure affiliative emotions. Despite recent neurocomputational advances, it is unknown whether brain signatures of affiliative emotions, such as tenderness/affection, can be decoded and voluntarily modulated. Here, we employed multivariate voxel pattern analysis and real-time fMRI to address this question. We found that participants were able to use visual feedback based on decoded fMRI patterns as a neurofeedback signal to increase brain activation characteristic of tenderness/affection relative to pride, an equally complex control emotion. Such improvement was not observed in a control group performing the same fMRI task without neurofeedback. Furthermore, the neurofeedback-driven enhancement of tenderness/affection-related distributed patterns was associated with local fMRI responses in the septohypothalamic area and frontopolar cortex, regions previously implicated in affiliative emotion. This demonstrates that humans can voluntarily enhance brain signatures of tenderness/affection, unlocking new possibilities for promoting prosocial emotions and countering antisocial behavior. PMID:24847819

Real-Time Analysis of a Sensor's Data for Automated Decision Making in an IoT-Based Smart Home.

PubMed

Khan, Nida Saddaf; Ghani, Sayeed; Haider, Sajjad

2018-05-25

IoT devices frequently generate large volumes of streaming data and in order to take advantage of this data, their temporal patterns must be learned and identified. Streaming data analysis has become popular after being successfully used in many applications including forecasting electricity load, stock market prices, weather conditions, etc. Artificial Neural Networks (ANNs) have been successfully utilized in understanding the embedded interesting patterns/behaviors in the data and forecasting the future values based on it. One such pattern is modelled and learned in the present study to identify the occurrence of a specific pattern in a Water Management System (WMS). This prediction aids in making an automatic decision support system, to switch OFF a hydraulic suction pump at the appropriate time. Three types of ANN, namely Multi-Input Multi-Output (MIMO), Multi-Input Single-Output (MISO), and Recurrent Neural Network (RNN) have been compared, for multi-step-ahead forecasting, on a sensor's streaming data. Experiments have shown that RNN has the best performance among three models and based on its prediction, a system can be implemented to make the best decision with 86% accuracy.

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.

Individual Patient Diagnosis of AD and FTD via High-Dimensional Pattern Classification of MRI

PubMed Central

Davatzikos, C.; Resnick, S. M.; Wu, X.; Parmpi, P.; Clark, C. M.

2008-01-01

The purpose of this study is to determine the diagnostic accuracy of MRI-based high-dimensional pattern classification in differentiating between patients with Alzheimer’s Disease (AD), Frontotemporal Dementia (FTD), and healthy controls, on an individual patient basis. MRI scans of 37 patients with AD and 37 age-matched cognitively normal elderly individuals, as well as 12 patients with FTD and 12 age-matched cognitively normal elderly individuals, were analyzed using voxel-based analysis and high-dimensional pattern classification. Diagnostic sensitivity and specificity of spatial patterns of regional brain atrophy found to be characteristic of AD and FTD were determined via cross-validation and via split-sample methods. Complex spatial patterns of relatively reduced brain volumes were identified, including temporal, orbitofrontal, parietal and cingulate regions, which were predominantly characteristic of either AD or FTD. These patterns provided 100% diagnostic accuracy, when used to separate AD or FTD from healthy controls. The ability to correctly distinguish AD from FTD averaged 84.3%. All estimates of diagnostic accuracy were determined via cross-validation. In conclusion, AD- and FTD-specific patterns of brain atrophy can be detected with high accuracy using high-dimensional pattern classification of MRI scans obtained in a typical clinical setting. PMID:18474436

Therapeutic high-frequency stimulation of the subthalamic nucleus in Parkinson's disease produces global increases in cerebral blood flow.

PubMed

Sidtis, John J; Tagliati, Michele; Alterman, Ron; Sidtis, Diana; Dhawan, Vijay; Eidelberg, David

2012-01-01

Chronic, high-frequency electrical stimulation of the subthalamic nuclei (STNs) has become an effective and widely used therapy in Parkinson's disease (PD), but the therapeutic mechanism is not understood. Stimulation of the STN is believed to reorganize neurophysiological activity patterns within the basal ganglia, whereas local field effects extending to tracts adjacent to the STN are viewed as sources of nontherapeutic side effects. This study is part of a larger project investigating the effects of STN stimulation on speech and regional cerebral blood flow (CBF) in human subjects with PD. While generating measures of global CBF (gCBF) to normalize regional CBF values for a subsequent combined analysis of regional CBF and speech data, we observed a third effect of this therapy: a gCBF increase. This effect was present across three estimates of gCBF ranging from values based on the highest activity voxels to those based on all voxels. The magnitude of the gCBF increase was related to the subject's duration of PD. It is not clear whether this CBF effect has a therapeutic role, but the impact of deep brain stimulation on cerebrovascular control warrants study from neuroscience, pathophysiological, and therapeutic perspectives.

Detection of white matter lesions in cerebral small vessel disease

NASA Astrophysics Data System (ADS)

Riad, Medhat M.; Platel, Bram; de Leeuw, Frank-Erik; Karssemeijer, Nico

2013-02-01

White matter lesions (WML) are diffuse white matter abnormalities commonly found in older subjects and are important indicators of stroke, multiple sclerosis, dementia and other disorders. We present an automated WML detection method and evaluate it on a dataset of small vessel disease (SVD) patients. In early SVD, small WMLs are expected to be of importance for the prediction of disease progression. Commonly used WML segmentation methods tend to ignore small WMLs and are mostly validated on the basis of total lesion load or a Dice coefficient for all detected WMLs. Therefore, in this paper, we present a method that is designed to detect individual lesions, large or small, and we validate the detection performance of our system with FROC (free-response ROC) analysis. For the automated detection, we use supervised classification making use of multimodal voxel based features from different magnetic resonance imaging (MRI) sequences, including intensities, tissue probabilities, voxel locations and distances, neighborhood textures and others. After preprocessing, including co-registration, brain extraction, bias correction, intensity normalization, and nonlinear registration, ventricle segmentation is performed and features are calculated for each brain voxel. A gentle-boost classifier is trained using these features from 50 manually annotated subjects to give each voxel a probability of being a lesion voxel. We perform ROC analysis to illustrate the benefits of using additional features to the commonly used voxel intensities; significantly increasing the area under the curve (Az) from 0.81 to 0.96 (p<0.05). We perform the FROC analysis by testing our classifier on 50 previously unseen subjects and compare the results with manual annotations performed by two experts. Using the first annotator results as our reference, the second annotator performs at a sensitivity of 0.90 with an average of 41 false positives per subject while our automated method reached the same level of sensitivity at approximately 180 false positives per subject.

Efficient Blockwise Permutation Tests Preserving Exchangeability

PubMed Central

Zhou, Chunxiao; Zwilling, Chris E.; Calhoun, Vince D.; Wang, Michelle Y.

2014-01-01

In this paper, we present a new blockwise permutation test approach based on the moments of the test statistic. The method is of importance to neuroimaging studies. In order to preserve the exchangeability condition required in permutation tests, we divide the entire set of data into certain exchangeability blocks. In addition, computationally efficient moments-based permutation tests are performed by approximating the permutation distribution of the test statistic with the Pearson distribution series. This involves the calculation of the first four moments of the permutation distribution within each block and then over the entire set of data. The accuracy and efficiency of the proposed method are demonstrated through simulated experiment on the magnetic resonance imaging (MRI) brain data, specifically the multi-site voxel-based morphometry analysis from structural MRI (sMRI). PMID:25289113

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.

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.

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.

WE-FG-202-12: Investigation of Longitudinal Salivary Gland DCE-MRI Changes

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

Ger, R; Howell, R; Li, H

Purpose: To determine the correlation between dose and changes through treatment in dynamic contrast enhanced (DCE) MRI voxel parameters (Ktrans, kep, Ve, and Vp) within salivary glands of head and neck oropharyngeal squamous cell carcinoma (HNSCC) patients. Methods: 17 HNSCC patients treated with definitive radiation therapy completed DCE-MRI scans on a 3T scanner at pre-treatment, mid-treatment, and post-treatment time points. Mid-treatment and post-treatment DCE images were deformably registered to pre-treatment DCE images (Velocity software package). Pharmacokinetic analysis of the DCE images used a modified Tofts model to produce parameter maps with an arterial input function selected from each patient’s perivertebralmore » space on the image (NordicICE software package). In-house software was developed for voxel-by-voxel longitudinal analysis of the salivary glands within the registered images. The planning CT was rigidly registered to the pre-treatment DCE image to obtain dose values in each voxel. Voxels within the lower and upper dose quartiles for each gland were averaged for each patient, then an average of the patients’ means for the two quartiles were compared. Dose-relationships were also assessed by Spearman correlations between dose and voxel parameter changes for each patient’s gland. Results: Changes in parameters’ means between time points were observed, but inter-patient variability was high. Ve of the parotid was the only parameter that had a consistently significant longitudinal difference between dose quartiles. The highest Spearman correlation was Vp of the sublingual gland for the change in the pre-treatment to mid-treatment values with only a ρ=0.29. Conclusion: In this preliminary study, there was large inter-patient variability in the changes of DCE voxel parameters with no clear relationship with dose. Additional patients may reduce the uncertainties and allow for the determination of the existence of parameter and dose relationships.« less

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.

An exploratory study of volumetric analysis for assessing tumor response with (18)F-FAZA PET/CT in patients with advanced non-small-cell lung cancer (NSCLC).

PubMed

Kerner, Gerald S M A; Bollineni, Vikram R; Hiltermann, Thijo J N; Sijtsema, Nanna M; Fischer, Alexander; Bongaerts, Alphons H H; Pruim, Jan; Groen, Harry J M

2016-12-01

Hypoxia is associated with resistance to chemotherapy and radiotherapy and is randomly distributed within malignancies. Characterization of changes in intratumoral hypoxic regions is possible with specially developed PET tracers such as (18)F-fluoroazomycin arabinoside ((18)F-FAZA) while tumor metabolism can be measured with 2-deoxy-2-[(18)F]fluoro-D-glucose ((18)F-FDG). The purpose of this study was to study the effects of chemotherapy on (18)F-FAZA and (18)F-FDG uptake simultaneously in non-small-cell lung cancer (NSCLC) patients At baseline and after the second chemotherapy cycle, both PET/CT with (18)F-FDG and (18)F-FAZA was performed in seven patients with metastasized NSCLC. (18)F-FAZA and (18)F-FDG scans were aligned with deformable image registration using Mirada DBx. The primary tumors were contoured, and on the (18)F-FDG scan, volumes of interest (VOI) were drawn using a 41 % adaptive threshold technique. Subsequently, the resulting VOI was transferred to the (18)F-FAZA scan. (18)F-FAZA maximum tumor-to-background (T/Bgmax) ratio and the fractional hypoxic volume (FHV) were assessed. Measurements were corrected for partial volume effects. Finally, a voxel-by-voxel analysis of the primary tumor was performed to assess regional uptake differences. In the primary tumor of all seven patients, median (18)F-FDG standard uptake value (SUVmax) decreased significantly (p = 0.03). There was no significant decrease in (18)F-FAZA uptake as measured with T/Bgmax (p = 0.24) or the FHV (p = 0.35). Additionally, volumetric voxel-by-voxel analysis showed that low hypoxic tumors did not significantly change in hypoxic status between baseline and two cycles of chemotherapy, whereas highly hypoxic tumors did. Individualized volumetric voxel-by-voxel analysis revealed that hypoxia and metabolism were not associated before and after 2 cycles of chemotherapy. Tumor hypoxia and metabolism are independent dynamic events as measured by (18)F-FAZA PET and (18)F-FDG PET, both prior to and after treatment with chemotherapy in NSCLC patients.

Connectivity-based fixel enhancement: Whole-brain statistical analysis of diffusion MRI measures in the presence of crossing fibres

PubMed Central

Raffelt, David A.; Smith, Robert E.; Ridgway, Gerard R.; Tournier, J-Donald; Vaughan, David N.; Rose, Stephen; Henderson, Robert; Connelly, Alan

2015-01-01

In brain regions containing crossing fibre bundles, voxel-average diffusion MRI measures such as fractional anisotropy (FA) are difficult to interpret, and lack within-voxel single fibre population specificity. Recent work has focused on the development of more interpretable quantitative measures that can be associated with a specific fibre population within a voxel containing crossing fibres (herein we use fixel to refer to a specific fibre population within a single voxel). Unfortunately, traditional 3D methods for smoothing and cluster-based statistical inference cannot be used for voxel-based analysis of these measures, since the local neighbourhood for smoothing and cluster formation can be ambiguous when adjacent voxels may have different numbers of fixels, or ill-defined when they belong to different tracts. Here we introduce a novel statistical method to perform whole-brain fixel-based analysis called connectivity-based fixel enhancement (CFE). CFE uses probabilistic tractography to identify structurally connected fixels that are likely to share underlying anatomy and pathology. Probabilistic connectivity information is then used for tract-specific smoothing (prior to the statistical analysis) and enhancement of the statistical map (using a threshold-free cluster enhancement-like approach). To investigate the characteristics of the CFE method, we assessed sensitivity and specificity using a large number of combinations of CFE enhancement parameters and smoothing extents, using simulated pathology generated with a range of test-statistic signal-to-noise ratios in five different white matter regions (chosen to cover a broad range of fibre bundle features). The results suggest that CFE input parameters are relatively insensitive to the characteristics of the simulated pathology. We therefore recommend a single set of CFE parameters that should give near optimal results in future studies where the group effect is unknown. We then demonstrate the proposed method by comparing apparent fibre density between motor neurone disease (MND) patients with control subjects. The MND results illustrate the benefit of fixel-specific statistical inference in white matter regions that contain crossing fibres. PMID:26004503

APEX_SCOPE: A graphical user interface for visualization of multi-modal data in inter-disciplinary studies.

PubMed

Kanbar, Lara J; Shalish, Wissam; Precup, Doina; Brown, Karen; Sant'Anna, Guilherme M; Kearney, Robert E

2017-07-01

In multi-disciplinary studies, different forms of data are often collected for analysis. For example, APEX, a study on the automated prediction of extubation readiness in extremely preterm infants, collects clinical parameters and cardiorespiratory signals. A variety of cardiorespiratory metrics are computed from these signals and used to assign a cardiorespiratory pattern at each time. In such a situation, exploratory analysis requires a visualization tool capable of displaying these different types of acquired and computed signals in an integrated environment. Thus, we developed APEX_SCOPE, a graphical tool for the visualization of multi-modal data comprising cardiorespiratory signals, automated cardiorespiratory metrics, automated respiratory patterns, manually classified respiratory patterns, and manual annotations by clinicians during data acquisition. This MATLAB-based application provides a means for collaborators to view combinations of signals to promote discussion, generate hypotheses and develop features.

Automated voxel classification used with atlas-guided diffuse optical tomography for assessment of functional brain networks in young and older adults.

PubMed

Li, Lin; Cazzell, Mary; Babawale, Olajide; Liu, Hanli

2016-10-01

Atlas-guided diffuse optical tomography (atlas-DOT) is a computational means to image changes in cortical hemodynamic signals during human brain activities. Graph theory analysis (GTA) is a network analysis tool commonly used in functional neuroimaging to study brain networks. Atlas-DOT has not been analyzed with GTA to derive large-scale brain connectivity/networks based on near-infrared spectroscopy (NIRS) measurements. We introduced an automated voxel classification (AVC) method that facilitated the use of GTA with atlas-DOT images by grouping unequal-sized finite element voxels into anatomically meaningful regions of interest within the human brain. The overall approach included volume segmentation, AVC, and cross-correlation. To demonstrate the usefulness of AVC, we applied reproducibility analysis to resting-state functional connectivity measurements conducted from 15 young adults in a two-week period. We also quantified and compared changes in several brain network metrics between young and older adults, which were in agreement with those reported by a previous positron emission tomography study. Overall, this study demonstrated that AVC is a useful means for facilitating integration or combination of atlas-DOT with GTA and thus for quantifying NIRS-based, voxel-wise resting-state functional brain networks.

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.

Imaging predictors of poststroke depression: methodological factors in voxel-based analysis

PubMed Central

Gozzi, Sophia A; Wood, Amanda G; Chen, Jian; Vaddadi, Krishnarao; Phan, Thanh G

2014-01-01

Objective The purpose of this study was to explore the relationship between lesion location and poststroke depression using statistical parametric mapping. Methods First episode patients with stroke were assessed within 12 days and at 1-month poststroke. Patients with an a priori defined cut-off score of 11 on the Hospital Anxiety and Depression Scale (HADS) at follow-up were further assessed using the Mini-International Neuropsychiatric Interview (MINI) to confirm a clinical diagnosis of major or minor depression in accordance with Diagnostic and Statistical Manual-IV (DSM-IV) inclusion criteria. Participants were included if they were aged 18–85 years, proficient in English and eligible for MRI. Patients were excluded if they had a confounding diagnosis such as major depressive disorder at the time of admission, a neurodegenerative disease, epilepsy or an imminently life-threatening comorbid illness, subarachnoid or subdural stroke, a second episode of stroke before follow-up and/or a serious impairment of consciousness or language. Infarcts observed on MRI scans were manually segmented into binary images, linearly registered into a common stereotaxic coordinate space. Using statistical parametric mapping, we compared infarct patterns in patients with stroke with and without depression. Results 27% (15/55 patients) met criteria for depression at follow-up. Mean infarct volume was 19±53 mL and National Institute of Health Stroke Scale (NIHSS) at Time 1 (within 12 days of stroke) was 4±4, indicating a sample of mild strokes. No voxels or clusters were significant after a multiple comparison correction was applied (p>0.05). Examination of infarct maps showed that there was minimal overlap of infarct location between patients, thus invalidating the voxel comparison analysis. Conclusions This study provided inconclusive evidence for the association between infarcts in a specific region and poststroke depression. PMID:25001395

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.

Complex network analysis of brain functional connectivity under a multi-step cognitive task

NASA Astrophysics Data System (ADS)

Cai, Shi-Min; Chen, Wei; Liu, Dong-Bai; Tang, Ming; Chen, Xun

2017-01-01

Functional brain network has been widely studied to understand the relationship between brain organization and behavior. In this paper, we aim to explore the functional connectivity of brain network under a multi-step cognitive task involving consecutive behaviors, and further understand the effect of behaviors on the brain organization. The functional brain networks are constructed based on a high spatial and temporal resolution fMRI dataset and analyzed via complex network based approach. We find that at voxel level the functional brain network shows robust small-worldness and scale-free characteristics, while its assortativity and rich-club organization are slightly restricted to the order of behaviors performed. More interestingly, the functional connectivity of brain network in activated ROIs strongly correlates with behaviors and is obviously restricted to the order of behaviors performed. These empirical results suggest that the brain organization has the generic properties of small-worldness and scale-free characteristics, and its diverse functional connectivity emerging from activated ROIs is strongly driven by these behavioral activities via the plasticity of brain.

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

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.

Assortativity Patterns in Multi-dimensional Inter-organizational Networks: A Case Study of the Humanitarian Relief Sector

NASA Astrophysics Data System (ADS)

Zhao, Kang; Ngamassi, Louis-Marie; Yen, John; Maitland, Carleen; Tapia, Andrea

We use computational tools to study assortativity patterns in multi-dimensional inter-organizational networks on the basis of different node attributes. In the case study of an inter-organizational network in the humanitarian relief sector, we consider not only macro-level topological patterns, but also assortativity on the basis of micro-level organizational attributes. Unlike assortative social networks, this inter-organizational network exhibits disassortative or random patterns on three node attributes. We believe organizations' seek of complementarity is one of the main reasons for the special patterns. Our analysis also provides insights on how to promote collaborations among the humanitarian relief organizations.

Fast and robust multimodal image registration using a local derivative pattern.

PubMed

Jiang, Dongsheng; Shi, Yonghong; Chen, Xinrong; Wang, Manning; Song, Zhijian

2017-02-01

Deformable multimodal image registration, which can benefit radiotherapy and image guided surgery by providing complementary information, remains a challenging task in the medical image analysis field due to the difficulty of defining a proper similarity measure. This article presents a novel, robust and fast binary descriptor, the discriminative local derivative pattern (dLDP), which is able to encode images of different modalities into similar image representations. dLDP calculates a binary string for each voxel according to the pattern of intensity derivatives in its neighborhood. The descriptor similarity is evaluated using the Hamming distance, which can be efficiently computed, instead of conventional L1 or L2 norms. For the first time, we validated the effectiveness and feasibility of the local derivative pattern for multimodal deformable image registration with several multi-modal registration applications. dLDP was compared with three state-of-the-art methods in artificial image and clinical settings. In the experiments of deformable registration between different magnetic resonance imaging (MRI) modalities from BrainWeb, between computed tomography and MRI images from patient data, and between MRI and ultrasound images from BITE database, we show our method outperforms localized mutual information and entropy images in terms of both accuracy and time efficiency. We have further validated dLDP for the deformable registration of preoperative MRI and three-dimensional intraoperative ultrasound images. Our results indicate that dLDP reduces the average mean target registration error from 4.12 mm to 2.30 mm. This accuracy is statistically equivalent to the accuracy of the state-of-the-art methods in the study; however, in terms of computational complexity, our method significantly outperforms other methods and is even comparable to the sum of the absolute difference. The results reveal that dLDP can achieve superior performance regarding both accuracy and time efficiency in general multimodal image registration. In addition, dLDP also indicates the potential for clinical ultrasound guided intervention. © 2016 The Authors. Medical Physics published by Wiley Periodicals, Inc. on behalf of American Association of Physicists in Medicine.

Comparing cerebral perfusion in Alzheimer's disease and Parkinson's disease dementia: an ASL-MRI study.

PubMed

Le Heron, Campbell J; Wright, Sarah L; Melzer, Tracy R; Myall, Daniel J; MacAskill, Michael R; Livingston, Leslie; Keenan, Ross J; Watts, Richard; Dalrymple-Alford, John C; Anderson, Tim J

2014-06-01

Emerging evidence suggests that Alzheimer's disease (AD) and Parkinson's disease dementia (PDD) share neurodegenerative mechanisms. We sought to directly compare cerebral perfusion in these two conditions using arterial spin labeling magnetic resonance imaging (ASL-MRI). In total, 17 AD, 20 PDD, and 37 matched healthy controls completed ASL and structural MRI, and comprehensive neuropsychological testing. Alzheimer's disease and PDD perfusion was analyzed by whole-brain voxel-based analysis (to assess absolute blood flow), a priori specified region of interest analysis, and principal component analysis (to generate a network differentiating the two groups). Corrections were made for cerebral atrophy, age, sex, education, and MRI scanner software version. Analysis of absolute blood flow showed no significant differences between AD and PDD. Comparing each group with controls revealed an overlapping, posterior pattern of hypoperfusion, including posterior cingulate gyrus, precuneus, and occipital regions. The perfusion network that differentiated AD and PDD groups identified relative differences in medial temporal lobes (AD

A scalable method to improve gray matter segmentation at ultra high field MRI.

PubMed

Gulban, Omer Faruk; Schneider, Marian; Marquardt, Ingo; Haast, Roy A M; De Martino, Federico

2018-01-01

High-resolution (functional) magnetic resonance imaging (MRI) at ultra high magnetic fields (7 Tesla and above) enables researchers to study how anatomical and functional properties change within the cortical ribbon, along surfaces and across cortical depths. These studies require an accurate delineation of the gray matter ribbon, which often suffers from inclusion of blood vessels, dura mater and other non-brain tissue. Residual segmentation errors are commonly corrected by browsing the data slice-by-slice and manually changing labels. This task becomes increasingly laborious and prone to error at higher resolutions since both work and error scale with the number of voxels. Here we show that many mislabeled, non-brain voxels can be corrected more efficiently and semi-automatically by representing three-dimensional anatomical images using two-dimensional histograms. We propose both a uni-modal (based on first spatial derivative) and multi-modal (based on compositional data analysis) approach to this representation and quantify the benefits in 7 Tesla MRI data of nine volunteers. We present an openly accessible Python implementation of these approaches and demonstrate that editing cortical segmentations using two-dimensional histogram representations as an additional post-processing step aids existing algorithms and yields improved gray matter borders. By making our data and corresponding expert (ground truth) segmentations openly available, we facilitate future efforts to develop and test segmentation algorithms on this challenging type of data.

Reproducibility and reliability of short-TE whole-brain MR spectroscopic imaging of human brain at 3T.

PubMed

Ding, Xiao-Qi; Maudsley, Andrew A; Sabati, Mohammad; Sheriff, Sulaiman; Dellani, Paulo R; Lanfermann, Heinrich

2015-03-01

A feasibility study of an echo-planar spectroscopic imaging (EPSI) using a short echo time (TE) that trades off sensitivity, compared with other short-TE methods, to achieve whole brain coverage using inversion recovery and spatial oversampling to control lipid bleeding. Twenty subjects were scanned to examine intersubject variance. One subject was scanned five times to examine intrasubject reproducibility. Data were analyzed to determine coefficients of variance (COV) and intraclass correlation coefficient (ICC) for N-acetylaspartate (NAA), total creatine (tCr), total choline (tCho), glutamine/glutamate (Glx), and myo-inositol (mI). Regional metabolite concentrations were derived by using multi-voxel analysis based on lobar-level anatomic regions. For whole-brain mean values, the intrasubject COVs were 14%, 15%, and 20% for NAA, tCr, and tCho, respectively, and 31% for Glx and mI. The intersubject COVs were up to 6% higher. For regional distributions, the intrasubject COVs were ≤ 5% for NAA, tCr, and tCho; ≤ 9% for Glx; and ≤15% for mI, with about 6% higher intersubject COVs. The ICCs of 5 metabolites were ≥ 0.7, indicating the reliability of the measurements. The present EPSI method enables estimation of the whole-brain metabolite distributions, including Glx and mI with small voxel size, and a reasonable scan time and reproducibility. © 2014 Wiley Periodicals, Inc.

A scalable method to improve gray matter segmentation at ultra high field MRI

PubMed Central

De Martino, Federico

2018-01-01

High-resolution (functional) magnetic resonance imaging (MRI) at ultra high magnetic fields (7 Tesla and above) enables researchers to study how anatomical and functional properties change within the cortical ribbon, along surfaces and across cortical depths. These studies require an accurate delineation of the gray matter ribbon, which often suffers from inclusion of blood vessels, dura mater and other non-brain tissue. Residual segmentation errors are commonly corrected by browsing the data slice-by-slice and manually changing labels. This task becomes increasingly laborious and prone to error at higher resolutions since both work and error scale with the number of voxels. Here we show that many mislabeled, non-brain voxels can be corrected more efficiently and semi-automatically by representing three-dimensional anatomical images using two-dimensional histograms. We propose both a uni-modal (based on first spatial derivative) and multi-modal (based on compositional data analysis) approach to this representation and quantify the benefits in 7 Tesla MRI data of nine volunteers. We present an openly accessible Python implementation of these approaches and demonstrate that editing cortical segmentations using two-dimensional histogram representations as an additional post-processing step aids existing algorithms and yields improved gray matter borders. By making our data and corresponding expert (ground truth) segmentations openly available, we facilitate future efforts to develop and test segmentation algorithms on this challenging type of data. PMID:29874295

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.

Cluster analysis of dynamic contrast enhanced MRI reveals tumor subregions related to locoregional relapse for cervical cancer patients.

PubMed

Torheim, Turid; Groendahl, Aurora R; Andersen, Erlend K F; Lyng, Heidi; Malinen, Eirik; Kvaal, Knut; Futsaether, Cecilia M

2016-11-01

Solid tumors are known to be spatially heterogeneous. Detection of treatment-resistant tumor regions can improve clinical outcome, by enabling implementation of strategies targeting such regions. In this study, K-means clustering was used to group voxels in dynamic contrast enhanced magnetic resonance images (DCE-MRI) of cervical cancers. The aim was to identify clusters reflecting treatment resistance that could be used for targeted radiotherapy with a dose-painting approach. Eighty-one patients with locally advanced cervical cancer underwent DCE-MRI prior to chemoradiotherapy. The resulting image time series were fitted to two pharmacokinetic models, the Tofts model (yielding parameters K trans and ν e ) and the Brix model (A Brix , k ep and k el ). K-means clustering was used to group similar voxels based on either the pharmacokinetic parameter maps or the relative signal increase (RSI) time series. The associations between voxel clusters and treatment outcome (measured as locoregional control) were evaluated using the volume fraction or the spatial distribution of each cluster. One voxel cluster based on the RSI time series was significantly related to locoregional control (adjusted p-value 0.048). This cluster consisted of low-enhancing voxels. We found that tumors with poor prognosis had this RSI-based cluster gathered into few patches, making this cluster a potential candidate for targeted radiotherapy. None of the voxels clusters based on Tofts or Brix parameter maps were significantly related to treatment outcome. We identified one group of tumor voxels significantly associated with locoregional relapse that could potentially be used for dose painting. This tumor voxel cluster was identified using the raw MRI time series rather than the pharmacokinetic maps.

TH-CD-206-01: Expectation-Maximization Algorithm-Based Tissue Mixture Quantification for Perfusion MRI

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

Han, H; Xing, L; Liang, Z

Purpose: To investigate the feasibility of estimating the tissue mixture perfusions and quantifying cerebral blood flow change in arterial spin labeled (ASL) perfusion MR images. Methods: The proposed perfusion MR image analysis framework consists of 5 steps: (1) Inhomogeneity correction was performed on the T1- and T2-weighted images, which are available for each studied perfusion MR dataset. (2) We used the publicly available FSL toolbox to strip off the non-brain structures from the T1- and T2-weighted MR images. (3) We applied a multi-spectral tissue-mixture segmentation algorithm on both T1- and T2-structural MR images to roughly estimate the fraction of eachmore » tissue type - white matter, grey matter and cerebral spinal fluid inside each image voxel. (4) The distributions of the three tissue types or tissue mixture across the structural image array are down-sampled and mapped onto the ASL voxel array via a co-registration operation. (5) The presented 4-dimensional expectation-maximization (4D-EM) algorithm takes the down-sampled three tissue type distributions on perfusion image data to generate the perfusion mean, variance and percentage images for each tissue type of interest. Results: Experimental results on three volunteer datasets demonstrated that the multi-spectral tissue-mixture segmentation algorithm was effective to initialize tissue mixtures from T1- and T2-weighted MR images. Compared with the conventional ASL image processing toolbox, the proposed 4D-EM algorithm not only generated comparable perfusion mean images, but also produced perfusion variance and percentage images, which the ASL toolbox cannot obtain. It is observed that the perfusion contribution percentages may not be the same as the corresponding tissue mixture volume fractions estimated in the structural images. Conclusion: A specific application to brain ASL images showed that the presented perfusion image analysis method is promising for detecting subtle changes in tissue perfusions, which is valuable for the early diagnosis of certain brain diseases, e.g. multiple sclerosis.« less

Anatomy assisted PET image reconstruction incorporating multi-resolution joint entropy

NASA Astrophysics Data System (ADS)

Tang, Jing; Rahmim, Arman

2015-01-01

A promising approach in PET image reconstruction is to incorporate high resolution anatomical information (measured from MR or CT) taking the anato-functional similarity measures such as mutual information or joint entropy (JE) as the prior. These similarity measures only classify voxels based on intensity values, while neglecting structural spatial information. In this work, we developed an anatomy-assisted maximum a posteriori (MAP) reconstruction algorithm wherein the JE measure is supplied by spatial information generated using wavelet multi-resolution analysis. The proposed wavelet-based JE (WJE) MAP algorithm involves calculation of derivatives of the subband JE measures with respect to individual PET image voxel intensities, which we have shown can be computed very similarly to how the inverse wavelet transform is implemented. We performed a simulation study with the BrainWeb phantom creating PET data corresponding to different noise levels. Realistically simulated T1-weighted MR images provided by BrainWeb modeling were applied in the anatomy-assisted reconstruction with the WJE-MAP algorithm and the intensity-only JE-MAP algorithm. Quantitative analysis showed that the WJE-MAP algorithm performed similarly to the JE-MAP algorithm at low noise level in the gray matter (GM) and white matter (WM) regions in terms of noise versus bias tradeoff. When noise increased to medium level in the simulated data, the WJE-MAP algorithm started to surpass the JE-MAP algorithm in the GM region, which is less uniform with smaller isolated structures compared to the WM region. In the high noise level simulation, the WJE-MAP algorithm presented clear improvement over the JE-MAP algorithm in both the GM and WM regions. In addition to the simulation study, we applied the reconstruction algorithms to real patient studies involving DPA-173 PET data and Florbetapir PET data with corresponding T1-MPRAGE MRI images. Compared to the intensity-only JE-MAP algorithm, the WJE-MAP algorithm resulted in comparable regional mean values to those from the maximum likelihood algorithm while reducing noise. Achieving robust performance in various noise-level simulation and patient studies, the WJE-MAP algorithm demonstrates its potential in clinical quantitative PET imaging.

The SENSE-Isomorphism Theoretical Image Voxel Estimation (SENSE-ITIVE) Model for Reconstruction and Observing Statistical Properties of Reconstruction Operators

PubMed Central

Bruce, Iain P.; Karaman, M. Muge; Rowe, Daniel B.

2012-01-01

The acquisition of sub-sampled data from an array of receiver coils has become a common means of reducing data acquisition time in MRI. Of the various techniques used in parallel MRI, SENSitivity Encoding (SENSE) is one of the most common, making use of a complex-valued weighted least squares estimation to unfold the aliased images. It was recently shown in Bruce et al. [Magn. Reson. Imag. 29(2011):1267–1287] that when the SENSE model is represented in terms of a real-valued isomorphism, it assumes a skew-symmetric covariance between receiver coils, as well as an identity covariance structure between voxels. In this manuscript, we show that not only is the skew-symmetric coil covariance unlike that of real data, but the estimated covariance structure between voxels over a time series of experimental data is not an identity matrix. As such, a new model, entitled SENSE-ITIVE, is described with both revised coil and voxel covariance structures. Both the SENSE and SENSE-ITIVE models are represented in terms of real-valued isomorphisms, allowing for a statistical analysis of reconstructed voxel means, variances, and correlations resulting from the use of different coil and voxel covariance structures used in the reconstruction processes to be conducted. It is shown through both theoretical and experimental illustrations that the miss-specification of the coil and voxel covariance structures in the SENSE model results in a lower standard deviation in each voxel of the reconstructed images, and thus an artificial increase in SNR, compared to the standard deviation and SNR of the SENSE-ITIVE model where both the coil and voxel covariances are appropriately accounted for. It is also shown that there are differences in the correlations induced by the reconstruction operations of both models, and consequently there are differences in the correlations estimated throughout the course of reconstructed time series. These differences in correlations could result in meaningful differences in interpretation of results. PMID:22617147

Rotating and translating anthropomorphic head voxel models to establish an horizontal Frankfort plane for dental CBCT Monte Carlo simulations: a dose comparison study

NASA Astrophysics Data System (ADS)

Stratis, A.; Zhang, G.; Jacobs, R.; Bogaerts, R.; Bosmans, H.

2016-12-01

In order to carry out Monte Carlo (MC) dosimetry studies, voxel phantoms, modeling human anatomy, and organ-based segmentation of CT image data sets are applied to simulation frameworks. The resulting voxel phantoms preserve patient CT acquisition geometry; in the case of head voxel models built upon head CT images, the head support with which CT scanners are equipped introduces an inclination to the head, and hence to the head voxel model. In dental cone beam CT (CBCT) imaging, patients are always positioned in such a way that the Frankfort line is horizontal, implying that there is no head inclination. The orientation of the head is important, as it influences the distance of critical radiosensitive organs like the thyroid and the esophagus from the x-ray tube. This work aims to propose a procedure to adjust head voxel phantom orientation, and to investigate the impact of head inclination on organ doses in dental CBCT MC dosimetry studies. The female adult ICRP, and three in-house-built paediatric voxel phantoms were in this study. An EGSnrc MC framework was employed to simulate two commonly used protocols; a Morita Accuitomo 170 dental CBCT scanner (FOVs: 60  ×  60 mm2 and 80  ×  80 mm2, standard resolution), and a 3D Teeth protocol (FOV: 100  ×  90 mm2) in a Planmeca Promax 3D MAX scanner. Result analysis revealed large absorbed organ dose differences in radiosensitive organs between the original and the geometrically corrected voxel models of this study, ranging from  -45.6% to 39.3%. Therefore, accurate dental CBCT MC dose calculations require geometrical adjustments to be applied to head voxel models.

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.

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.

A Statistical Analysis of Brain Morphology Using Wild Bootstrapping

PubMed Central

Ibrahim, Joseph G.; Tang, Niansheng; Rowe, Daniel B.; Hao, Xuejun; Bansal, Ravi; Peterson, Bradley S.

2008-01-01

Methods for the analysis of brain morphology, including voxel-based morphology and surface-based morphometries, have been used to detect associations between brain structure and covariates of interest, such as diagnosis, severity of disease, age, IQ, and genotype. The statistical analysis of morphometric measures usually involves two statistical procedures: 1) invoking a statistical model at each voxel (or point) on the surface of the brain or brain subregion, followed by mapping test statistics (e.g., t test) or their associated p values at each of those voxels; 2) correction for the multiple statistical tests conducted across all voxels on the surface of the brain region under investigation. We propose the use of new statistical methods for each of these procedures. We first use a heteroscedastic linear model to test the associations between the morphological measures at each voxel on the surface of the specified subregion (e.g., cortical or subcortical surfaces) and the covariates of interest. Moreover, we develop a robust test procedure that is based on a resampling method, called wild bootstrapping. This procedure assesses the statistical significance of the associations between a measure of given brain structure and the covariates of interest. The value of this robust test procedure lies in its computationally simplicity and in its applicability to a wide range of imaging data, including data from both anatomical and functional magnetic resonance imaging (fMRI). Simulation studies demonstrate that this robust test procedure can accurately control the family-wise error rate. We demonstrate the application of this robust test procedure to the detection of statistically significant differences in the morphology of the hippocampus over time across gender groups in a large sample of healthy subjects. PMID:17649909

- 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.

Quantitative dynamic ¹⁸FDG-PET and tracer kinetic analysis of soft tissue sarcomas.

PubMed

Rusten, Espen; Rødal, Jan; Revheim, Mona E; Skretting, Arne; Bruland, Oyvind S; Malinen, Eirik

2013-08-01

To study soft tissue sarcomas using dynamic positron emission tomography (PET) with the glucose analog tracer [(18)F]fluoro-2-deoxy-D-glucose ((18)FDG), to investigate correlations between derived PET image parameters and clinical characteristics, and to discuss implications of dynamic PET acquisition (D-PET). D-PET images of 11 patients with soft tissue sarcomas were analyzed voxel-by-voxel using a compartment tracer kinetic model providing estimates of transfer rates between the vascular, non-metabolized, and metabolized compartments. Furthermore, standard uptake values (SUVs) in the early (2 min p.i.; SUVE) and late (45 min p.i.; SUVL) phases of the PET acquisition were obtained. The derived transfer rates K1, k2 and k3, along with the metabolic rate of (18)FDG (MRFDG) and the vascular fraction νp, was fused with the computed tomography (CT) images for visual interpretation. Correlations between D-PET imaging parameters and clinical parameters, i.e. tumor size, grade and clinical status, were calculated with a significance level of 0.05. The temporal uptake pattern of (18)FDG in the tumor varied considerably from patient to patient. SUVE peak was higher than SUVL peak for four patients. The images of the rate constants showed a systematic pattern, often with elevated intensity in the tumors compared to surrounding tissue. Significant correlations were found between SUVE/L and some of the rate parameters. Dynamic (18)FDG-PET may provide additional valuable information on soft tissue sarcomas not obtainable from conventional (18)FDG-PET. The prognostic role of dynamic imaging should be investigated.

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

Voxel-Based Morphometry ALE meta-analysis of Bipolar Disorder

NASA Astrophysics Data System (ADS)

Magana, Omar; Laird, Robert

2012-03-01

A meta-analysis was performed independently to view the changes in gray matter (GM) on patients with Bipolar disorder (BP). The meta-analysis was conducted on a Talairach Space using GingerALE to determine the voxels and their permutation. In order to achieve the data acquisition, published experiments and similar research studies were uploaded onto the online Voxel-Based Morphometry database (VBM). By doing so, coordinates of activation locations were extracted from Bipolar disorder related journals utilizing Sleuth. Once the coordinates of given experiments were selected and imported to GingerALE, a Gaussian was performed on all foci points to create the concentration points of GM on BP patients. The results included volume reductions and variations of GM between Normal Healthy controls and Patients with Bipolar disorder. A significant amount of GM clusters were obtained in Normal Healthy controls over BP patients on the right precentral gyrus, right anterior cingulate, and the left inferior frontal gyrus. In future research, more published journals could be uploaded onto the database and another VBM meta-analysis could be performed including more activation coordinates or a variation of age groups.

A multi-pattern hash-binary hybrid algorithm for URL matching in the HTTP protocol.

PubMed

Zeng, Ping; Tan, Qingping; Meng, Xiankai; Shao, Zeming; Xie, Qinzheng; Yan, Ying; Cao, Wei; Xu, Jianjun

2017-01-01

In this paper, based on our previous multi-pattern uniform resource locator (URL) binary-matching algorithm called HEM, we propose an improved multi-pattern matching algorithm called MH that is based on hash tables and binary tables. The MH algorithm can be applied to the fields of network security, data analysis, load balancing, cloud robotic communications, and so on-all of which require string matching from a fixed starting position. Our approach effectively solves the performance problems of the classical multi-pattern matching algorithms. This paper explores ways to improve string matching performance under the HTTP protocol by using a hash method combined with a binary method that transforms the symbol-space matching problem into a digital-space numerical-size comparison and hashing problem. The MH approach has a fast matching speed, requires little memory, performs better than both the classical algorithms and HEM for matching fields in an HTTP stream, and it has great promise for use in real-world applications.

A multi-pattern hash-binary hybrid algorithm for URL matching in the HTTP protocol

PubMed Central

Tan, Qingping; Meng, Xiankai; Shao, Zeming; Xie, Qinzheng; Yan, Ying; Cao, Wei; Xu, Jianjun

2017-01-01

In this paper, based on our previous multi-pattern uniform resource locator (URL) binary-matching algorithm called HEM, we propose an improved multi-pattern matching algorithm called MH that is based on hash tables and binary tables. The MH algorithm can be applied to the fields of network security, data analysis, load balancing, cloud robotic communications, and so on—all of which require string matching from a fixed starting position. Our approach effectively solves the performance problems of the classical multi-pattern matching algorithms. This paper explores ways to improve string matching performance under the HTTP protocol by using a hash method combined with a binary method that transforms the symbol-space matching problem into a digital-space numerical-size comparison and hashing problem. The MH approach has a fast matching speed, requires little memory, performs better than both the classical algorithms and HEM for matching fields in an HTTP stream, and it has great promise for use in real-world applications. PMID:28399157

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

Mentalizing regions represent distributed, continuous, and abstract dimensions of others' beliefs.

PubMed

Koster-Hale, Jorie; Richardson, Hilary; Velez, Natalia; Asaba, Mika; Young, Liane; Saxe, Rebecca

2017-11-01

The human capacity to reason about others' minds includes making causal inferences about intentions, beliefs, values, and goals. Previous fMRI research has suggested that a network of brain regions, including bilateral temporo-parietal junction (TPJ), superior temporal sulcus (STS), and medial prefrontal-cortex (MPFC), are reliably recruited for mental state reasoning. Here, in two fMRI experiments, we investigate the representational content of these regions. Building on existing computational and neural evidence, we hypothesized that social brain regions contain at least two functionally and spatially distinct components: one that represents information related to others' motivations and values, and another that represents information about others' beliefs and knowledge. Using multi-voxel pattern analysis, we find evidence that motivational versus epistemic features are independently represented by theory of mind (ToM) regions: RTPJ contains information about the justification of the belief, bilateral TPJ represents the modality of the source of knowledge, and VMPFC represents the valence of the resulting emotion. These representations are found only in regions implicated in social cognition and predict behavioral responses at the level of single items. We argue that cortical regions implicated in mental state inference contain complementary, but distinct, representations of epistemic and motivational features of others' beliefs, and that, mirroring the processes observed in sensory systems, social stimuli are represented in distinct and distributed formats across the human brain. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

"Neural overlap of L1 and L2 semantic representations across visual and auditory modalities: a decoding approach".

PubMed

Van de Putte, Eowyn; De Baene, Wouter; Price, Cathy J; Duyck, Wouter

2018-05-01

This study investigated whether brain activity in Dutch-French bilinguals during semantic access to concepts from one language could be used to predict neural activation during access to the same concepts from another language, in different language modalities/tasks. This was tested using multi-voxel pattern analysis (MVPA), within and across language comprehension (word listening and word reading) and production (picture naming). It was possible to identify the picture or word named, read or heard in one language (e.g. maan, meaning moon) based on the brain activity in a distributed bilateral brain network while, respectively, naming, reading or listening to the picture or word in the other language (e.g. lune). The brain regions identified differed across tasks. During picture naming, brain activation in the occipital and temporal regions allowed concepts to be predicted across languages. During word listening and word reading, across-language predictions were observed in the rolandic operculum and several motor-related areas (pre- and postcentral, the cerebellum). In addition, across-language predictions during reading were identified in regions typically associated with semantic processing (left inferior frontal, middle temporal cortex, right cerebellum and precuneus) and visual processing (inferior and middle occipital regions and calcarine sulcus). Furthermore, across modalities and languages, the left lingual gyrus showed semantic overlap across production and word reading. These findings support the idea of at least partially language- and modality-independent semantic neural representations. Copyright © 2018 The Authors. Published by Elsevier Ltd.. All rights reserved.

Voxel-based morphometric brain comparison between healthy subjects and major depressive disorder patients in Japanese with the s/s genotype of 5-HTTLPR.

PubMed

Igata, Natsuki; Kakeda, Shingo; Watanabe, Keita; Ide, Satoru; Kishi, Taro; Abe, Osamu; Igata, Ryouhei; Katsuki, Asuka; Iwata, Nakao; Yoshimura, Reiji; Korogi, Yukunori

2017-06-21

Individuals with s/s genotype of serotonin transporter gene-linked promotor region (5-HTTLPR), which appear with a high frequency in Japanese, exhibit more diagnosable depression in relation to stressful life events than those with the s/l or l/l genotype. We prospectively investigated the brain volume changes in first-episode and medication naïve major depression disorder patients (MDD) with the s/s genotype in Japanese. We assessed the differences between 27 MDD with the s/s genotype and 44 healthy subjects (HS) with the same genotype using a whole-brain voxel-by-voxel statistical analysis of MRI. Gray matter volume in a brain region with significant clusters obtained via voxel-based morphometry analysis were measured and, as an exploratory analysis, evaluated for relationships to the subcategory scores (core, sleep, activity, psychic, somatic anxiety, delusion) of the Hamilton Depression Rating Scale (HAM-D) and the Social Readjustment Rating Scale (SRRS). The brain volume in the left insula lobe was significantly smaller in the MDD than in the HS. The left insula lobe volume correlated negatively with the "psychic" score of HAM-D and the SRRS. In a Japanese population with the s/s genotype, we found an atrophy of the insula in the MDD, which might be associated with "psychic" symptom and stress events.

Voxel-by-voxel analysis of brain SPECT perfusion in Fibromyalgia

NASA Astrophysics Data System (ADS)

Guedj, Eric; Taïeb, David; Cammilleri, Serge; Lussato, David; de Laforte, Catherine; Niboyet, Jean; Mundler, Olivier

2007-02-01

We evaluated brain perfusion SPECT at rest, without noxious stiumuli, in a homogeneous group of hyperalgesic FM patients. We performed a voxel-based analysis in comparison to a control group, matched for age and gender. Under such conditions, we made the assumption that significant cerebral perfusion abnormalities could be demonstrated, evidencing altered cerebral processing associated with spontaneous pain in FM patients. The secondary objective was to study the reversibility and the prognostic value of such possible perfusion abnormalities under specific treatment. Eighteen hyperalgesic FM women (mean age 48 yr; range 25-63 yr; ACR criteria) and 10 healthy women matched for age were enrolled in the study. A voxel-by-voxel group analysis was performed using SPM2 ( p<0.05, corrected for multiple comparisons). All brain SPECT were performed before any change was made in therapy in the pain care unit. A second SPECT was performed a month later after specific treatment by Ketamine. Compared to control subjects, we observed individual brain SPECT abnormalities in FM patients, confirmed by SPM2 analysis with hyperperfusion of the somatosensory cortex and hypoperfusion of the frontal, cingulate, medial temporal and cerebellar cortices. We also found that a medial frontal and anterior cingulate hypoperfusions were highly predictive (PPV=83%; NPV=91%) of non-response on Ketamine, and that only responders showed significant modification of brain perfusion, after treatment. In the present study performed without noxious stimuli in hyperalgesic FM patients, we found significant hyperperfusion in regions of the brain known to be involved in sensory dimension of pain processing and significant hypoperfusion in areas assumed to be associated with the affective dimension. As current pharmacological and non-pharmacological therapies act differently on both components of pain, we hypothesize that SPECT could be a valuable and readily available tool to guide individual therapeutic strategy and provide objective follow-up of pain-processing recovery under treatment.

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.

Multi-segmental movement patterns reflect juggling complexity and skill level.

PubMed

Zago, Matteo; Pacifici, Ilaria; Lovecchio, Nicola; Galli, Manuela; Federolf, Peter Andreas; Sforza, Chiarella

2017-08-01

The juggling action of six experts and six intermediates jugglers was recorded with a motion capture system and decomposed into its fundamental components through Principal Component Analysis. The aim was to quantify trends in movement dimensionality, multi-segmental patterns and rhythmicity as a function of proficiency level and task complexity. Dimensionality was quantified in terms of Residual Variance, while the Relative Amplitude was introduced to account for individual differences in movement components. We observed that: experience-related modifications in multi-segmental actions exist, such as the progressive reduction of error-correction movements, especially in complex task condition. The systematic identification of motor patterns sensitive to the acquisition of specific experience could accelerate the learning process. Copyright © 2017 Elsevier B.V. All rights reserved.

Using Multi-Group Confirmatory Factor Analysis to Evaluate Cross-Cultural Research: Identifying and Understanding Non-Invariance

ERIC Educational Resources Information Center

Brown, Gavin T. L.; Harris, Lois R.; O'Quin, Chrissie; Lane, Kenneth E.

2017-01-01

Multi-group confirmatory factor analysis (MGCFA) allows researchers to determine whether a research inventory elicits similar response patterns across samples. If statistical equivalence in responding is found, then scale score comparisons become possible and samples can be said to be from the same population. This paper illustrates the use of…

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.

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.

Coarse Point Cloud Registration by Egi Matching of Voxel Clusters

NASA Astrophysics Data System (ADS)

Wang, Jinhu; Lindenbergh, Roderik; Shen, Yueqian; Menenti, Massimo

2016-06-01

Laser scanning samples the surface geometry of objects efficiently and records versatile information as point clouds. However, often more scans are required to fully cover a scene. Therefore, a registration step is required that transforms the different scans into a common coordinate system. The registration of point clouds is usually conducted in two steps, i.e. coarse registration followed by fine registration. In this study an automatic marker-free coarse registration method for pair-wise scans is presented. First the two input point clouds are re-sampled as voxels and dimensionality features of the voxels are determined by principal component analysis (PCA). Then voxel cells with the same dimensionality are clustered. Next, the Extended Gaussian Image (EGI) descriptor of those voxel clusters are constructed using significant eigenvectors of each voxel in the cluster. Correspondences between clusters in source and target data are obtained according to the similarity between their EGI descriptors. The random sampling consensus (RANSAC) algorithm is employed to remove outlying correspondences until a coarse alignment is obtained. If necessary, a fine registration is performed in a final step. This new method is illustrated on scan data sampling two indoor scenarios. The results of the tests are evaluated by computing the point to point distance between the two input point clouds. The presented two tests resulted in mean distances of 7.6 mm and 9.5 mm respectively, which are adequate for fine registration.

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.

Pattern classification of fMRI data: applications for analysis of spatially distributed cortical networks.

PubMed

Yourganov, Grigori; Schmah, Tanya; Churchill, Nathan W; Berman, Marc G; Grady, Cheryl L; Strother, Stephen C

2014-08-01

The field of fMRI data analysis is rapidly growing in sophistication, particularly in the domain of multivariate pattern classification. However, the interaction between the properties of the analytical model and the parameters of the BOLD signal (e.g. signal magnitude, temporal variance and functional connectivity) is still an open problem. We addressed this problem by evaluating a set of pattern classification algorithms on simulated and experimental block-design fMRI data. The set of classifiers consisted of linear and quadratic discriminants, linear support vector machine, and linear and nonlinear Gaussian naive Bayes classifiers. For linear discriminant, we used two methods of regularization: principal component analysis, and ridge regularization. The classifiers were used (1) to classify the volumes according to the behavioral task that was performed by the subject, and (2) to construct spatial maps that indicated the relative contribution of each voxel to classification. Our evaluation metrics were: (1) accuracy of out-of-sample classification and (2) reproducibility of spatial maps. In simulated data sets, we performed an additional evaluation of spatial maps with ROC analysis. We varied the magnitude, temporal variance and connectivity of simulated fMRI signal and identified the optimal classifier for each simulated environment. Overall, the best performers were linear and quadratic discriminants (operating on principal components of the data matrix) and, in some rare situations, a nonlinear Gaussian naïve Bayes classifier. The results from the simulated data were supported by within-subject analysis of experimental fMRI data, collected in a study of aging. This is the first study that systematically characterizes interactions between analysis model and signal parameters (such as magnitude, variance and correlation) on the performance of pattern classifiers for fMRI. Copyright © 2014 Elsevier Inc. All rights reserved.

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.

WE-FG-202-09: Voxel-Level Analysis of Adverse Treatment Response in Pediatric Patients Treated for Ependymoma with Passive Scattering Proton Therapy

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

Peeler, C; The University of Texas Graduate School of Biomedical Sciences at Houston, Houston, TX; Mirkovic, D

2016-06-15

Purpose: We identified patients treated for ependymoma with passive scattering proton therapy who subsequently developed treatment-related imaging changes on MRI. We sought to determine if there is any spatial correlation between imaged response, dose, and LET. Methods: A group of 14 patients treated for ependymoma were identified as having post-treatment MR imaging changes observable as T2-FLAIR hyperintensity with or without enhancement on T1 post-contrast sequences. MR images were registered with treatment planning CT images and regions of treatment-related change contoured by a practicing radiation oncologist. The contoured regions were identified as response with voxels represented as 1 while voxels withinmore » the brain outside of the response region were represented as 0. An in-house Monte Carlo system was used to recalculate treatment plans to obtain dose and LET information. Voxels were binned according to LET values in 0.3 keV µm{sup −1} bins. Dose and corresponding response value (0 or 1) for each voxel for a given LET bin were then plotted and fit with the Lyman-Kutcher-Burman dose response model to determine TD{sub 50} and m parameters for each LET value. Response parameters from all patients were then collated, and linear fits of the data were performed. Results: The response parameters TD50 and m both show trends with LET. Outliers were observed due to low numbers of response voxels in some cases. TD{sub 50} values decreased with LET while m increased with LET. The former result would indicate that for higher LET values, the dose is more effective, which is consistent with relative biological effectiveness (RBE) models for proton therapy. Conclusion: A novel method of voxel-level analysis of image biomarker-based adverse patient treatment response in proton therapy according to dose and LET has been presented. Fitted TD{sub 50} values show a decreasing trend with LET supporting the typical models of proton RBE. Funding provided by NIH Program Project Grant 2U19CA021239-35.« less

Stereoscopic processing of crossed and uncrossed disparities in the human visual cortex.

PubMed

Li, Yuan; Zhang, Chuncheng; Hou, Chunping; Yao, Li; Zhang, Jiacai; Long, Zhiying

2017-12-21

Binocular disparity provides a powerful cue for depth perception in a stereoscopic environment. Despite increasing knowledge of the cortical areas that process disparity from neuroimaging studies, the neural mechanism underlying disparity sign processing [crossed disparity (CD)/uncrossed disparity (UD)] is still poorly understood. In the present study, functional magnetic resonance imaging (fMRI) was used to explore different neural features that are relevant to disparity-sign processing. We performed an fMRI experiment on 27 right-handed healthy human volunteers by using both general linear model (GLM) and multi-voxel pattern analysis (MVPA) methods. First, GLM was used to determine the cortical areas that displayed different responses to different disparity signs. Second, MVPA was used to determine how the cortical areas discriminate different disparity signs. The GLM analysis results indicated that shapes with UD induced significantly stronger activity in the sub-region (LO) of the lateral occipital cortex (LOC) than those with CD. The results of MVPA based on region of interest indicated that areas V3d and V3A displayed higher accuracy in the discrimination of crossed and uncrossed disparities than LOC. The results of searchlight-based MVPA indicated that the dorsal visual cortex showed significantly higher prediction accuracy than the ventral visual cortex and the sub-region LO of LOC showed high accuracy in the discrimination of crossed and uncrossed disparities. The results may suggest the dorsal visual areas are more discriminative to the disparity signs than the ventral visual areas although they are not sensitive to the disparity sign processing. Moreover, the LO in the ventral visual cortex is relevant to the recognition of shapes with different disparity signs and discriminative to the disparity sign.

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.

Spinal focal lesion detection in multiple myeloma using multimodal image features

NASA Astrophysics Data System (ADS)

Fränzle, Andrea; Hillengass, Jens; Bendl, Rolf

2015-03-01

Multiple myeloma is a tumor disease in the bone marrow that affects the skeleton systemically, i.e. multiple lesions can occur in different sites in the skeleton. To quantify overall tumor mass for determining degree of disease and for analysis of therapy response, volumetry of all lesions is needed. Since the large amount of lesions in one patient impedes manual segmentation of all lesions, quantification of overall tumor volume is not possible until now. Therefore development of automatic lesion detection and segmentation methods is necessary. Since focal tumors in multiple myeloma show different characteristics in different modalities (changes in bone structure in CT images, hypointensity in T1 weighted MR images and hyperintensity in T2 weighted MR images), multimodal image analysis is necessary for the detection of focal tumors. In this paper a pattern recognition approach is presented that identifies focal lesions in lumbar vertebrae based on features from T1 and T2 weighted MR images. Image voxels within bone are classified using random forests based on plain intensities and intensity value derived features (maximum, minimum, mean, median) in a 5 x 5 neighborhood around a voxel from both T1 and T2 weighted MR images. A test data sample of lesions in 8 lumbar vertebrae from 4 multiple myeloma patients can be classified at an accuracy of 95% (using a leave-one-patient-out test). The approach provides a reasonable delineation of the example lesions. This is an important step towards automatic tumor volume quantification in multiple myeloma.

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.

Early Development of Functional Network Segregation Revealed by Connectomic Analysis of the Preterm Human Brain.

PubMed

Cao, Miao; He, Yong; Dai, Zhengjia; Liao, Xuhong; Jeon, Tina; Ouyang, Minhui; Chalak, Lina; Bi, Yanchao; Rollins, Nancy; Dong, Qi; Huang, Hao

2017-03-01

Human brain functional networks are topologically organized with nontrivial connectivity characteristics such as small-worldness and densely linked hubs to support highly segregated and integrated information processing. However, how they emerge and change at very early developmental phases remains poorly understood. Here, we used resting-state functional MRI and voxel-based graph theory analysis to systematically investigate the topological organization of whole-brain networks in 40 infants aged around 31 to 42 postmenstrual weeks. The functional connectivity strength and heterogeneity increased significantly in primary motor, somatosensory, visual, and auditory regions, but much less in high-order default-mode and executive-control regions. The hub and rich-club structures in primary regions were already present at around 31 postmenstrual weeks and exhibited remarkable expansions with age, accompanied by increased local clustering and shortest path length, indicating a transition from a relatively random to a more organized configuration. Moreover, multivariate pattern analysis using support vector regression revealed that individual brain maturity of preterm babies could be predicted by the network connectivity patterns. Collectively, we highlighted a gradually enhanced functional network segregation manner in the third trimester, which is primarily driven by the rapid increases of functional connectivity of the primary regions, providing crucial insights into the topological development patterns prior to birth. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

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.

A multi-scale analysis of landscape statistics

Treesearch

Douglas H. Cain; Kurt H. Riitters; Kenneth Orvis

1997-01-01

It is now feasible to monitor some aspects of landscape ecological condition nationwide using remotely- sensed imagery and indicators of land cover pattern. Previous research showed redundancies among many reported pattern indicators and identified six unique dimensions of land cover pattern. This study tested the stability of those dimensions and representative...

Poly-Pattern Compressive Segmentation of ASTER Data for GIS

NASA Technical Reports Server (NTRS)

Myers, Wayne; Warner, Eric; Tutwiler, Richard

2007-01-01

Pattern-based segmentation of multi-band image data, such as ASTER, produces one-byte and two-byte approximate compressions. This is a dual segmentation consisting of nested coarser and finer level pattern mappings called poly-patterns. The coarser A-level version is structured for direct incorporation into geographic information systems in the manner of a raster map. GIs renderings of this A-level approximation are called pattern pictures which have the appearance of color enhanced images. The two-byte version consisting of thousands of B-level segments provides a capability for approximate restoration of the multi-band data in selected areas or entire scenes. Poly-patterns are especially useful for purposes of change detection and landscape analysis at multiple scales. The primary author has implemented the segmentation methodology in a public domain software suite.

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.

Dynamic whole body PET parametric imaging: II. Task-oriented statistical estimation

PubMed Central

Karakatsanis, Nicolas A.; Lodge, Martin A.; Zhou, Y.; Wahl, Richard L.; Rahmim, Arman

2013-01-01

In the context of oncology, dynamic PET imaging coupled with standard graphical linear analysis has been previously employed to enable quantitative estimation of tracer kinetic parameters of physiological interest at the voxel level, thus, enabling quantitative PET parametric imaging. However, dynamic PET acquisition protocols have been confined to the limited axial field-of-view (~15–20cm) of a single bed position and have not been translated to the whole-body clinical imaging domain. On the contrary, standardized uptake value (SUV) PET imaging, considered as the routine approach in clinical oncology, commonly involves multi-bed acquisitions, but is performed statically, thus not allowing for dynamic tracking of the tracer distribution. Here, we pursue a transition to dynamic whole body PET parametric imaging, by presenting, within a unified framework, clinically feasible multi-bed dynamic PET acquisition protocols and parametric imaging methods. In a companion study, we presented a novel clinically feasible dynamic (4D) multi-bed PET acquisition protocol as well as the concept of whole body PET parametric imaging employing Patlak ordinary least squares (OLS) regression to estimate the quantitative parameters of tracer uptake rate Ki and total blood distribution volume V. In the present study, we propose an advanced hybrid linear regression framework, driven by Patlak kinetic voxel correlations, to achieve superior trade-off between contrast-to-noise ratio (CNR) and mean squared error (MSE) than provided by OLS for the final Ki parametric images, enabling task-based performance optimization. Overall, whether the observer's task is to detect a tumor or quantitatively assess treatment response, the proposed statistical estimation framework can be adapted to satisfy the specific task performance criteria, by adjusting the Patlak correlation-coefficient (WR) reference value. The multi-bed dynamic acquisition protocol, as optimized in the preceding companion study, was employed along with extensive Monte Carlo simulations and an initial clinical FDG patient dataset to validate and demonstrate the potential of the proposed statistical estimation methods. Both simulated and clinical results suggest that hybrid regression in the context of whole-body Patlak Ki imaging considerably reduces MSE without compromising high CNR. Alternatively, for a given CNR, hybrid regression enables larger reductions than OLS in the number of dynamic frames per bed, allowing for even shorter acquisitions of ~30min, thus further contributing to the clinical adoption of the proposed framework. Compared to the SUV approach, whole body parametric imaging can provide better tumor quantification, and can act as a complement to SUV, for the task of tumor detection. PMID:24080994

Dynamic whole-body PET parametric imaging: II. Task-oriented statistical estimation.

PubMed

Karakatsanis, Nicolas A; Lodge, Martin A; Zhou, Y; Wahl, Richard L; Rahmim, Arman

2013-10-21

In the context of oncology, dynamic PET imaging coupled with standard graphical linear analysis has been previously employed to enable quantitative estimation of tracer kinetic parameters of physiological interest at the voxel level, thus, enabling quantitative PET parametric imaging. However, dynamic PET acquisition protocols have been confined to the limited axial field-of-view (~15-20 cm) of a single-bed position and have not been translated to the whole-body clinical imaging domain. On the contrary, standardized uptake value (SUV) PET imaging, considered as the routine approach in clinical oncology, commonly involves multi-bed acquisitions, but is performed statically, thus not allowing for dynamic tracking of the tracer distribution. Here, we pursue a transition to dynamic whole-body PET parametric imaging, by presenting, within a unified framework, clinically feasible multi-bed dynamic PET acquisition protocols and parametric imaging methods. In a companion study, we presented a novel clinically feasible dynamic (4D) multi-bed PET acquisition protocol as well as the concept of whole-body PET parametric imaging employing Patlak ordinary least squares (OLS) regression to estimate the quantitative parameters of tracer uptake rate Ki and total blood distribution volume V. In the present study, we propose an advanced hybrid linear regression framework, driven by Patlak kinetic voxel correlations, to achieve superior trade-off between contrast-to-noise ratio (CNR) and mean squared error (MSE) than provided by OLS for the final Ki parametric images, enabling task-based performance optimization. Overall, whether the observer's task is to detect a tumor or quantitatively assess treatment response, the proposed statistical estimation framework can be adapted to satisfy the specific task performance criteria, by adjusting the Patlak correlation-coefficient (WR) reference value. The multi-bed dynamic acquisition protocol, as optimized in the preceding companion study, was employed along with extensive Monte Carlo simulations and an initial clinical (18)F-deoxyglucose patient dataset to validate and demonstrate the potential of the proposed statistical estimation methods. Both simulated and clinical results suggest that hybrid regression in the context of whole-body Patlak Ki imaging considerably reduces MSE without compromising high CNR. Alternatively, for a given CNR, hybrid regression enables larger reductions than OLS in the number of dynamic frames per bed, allowing for even shorter acquisitions of ~30 min, thus further contributing to the clinical adoption of the proposed framework. Compared to the SUV approach, whole-body parametric imaging can provide better tumor quantification, and can act as a complement to SUV, for the task of tumor detection.

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.

Development of a Multi-Centre Clinical Trial Data Archiving and Analysis Platform for Functional Imaging

NASA Astrophysics Data System (ADS)

Driscoll, Brandon; Jaffray, David; Coolens, Catherine

2014-03-01

Purpose: To provide clinicians & researchers participating in multi-centre clinical trials with a central repository for large volume dynamic imaging data as well as a set of tools for providing end-to-end testing and image analysis standards of practice. Methods: There are three main pieces to the data archiving and analysis system; the PACS server, the data analysis computer(s) and the high-speed networks that connect them. Each clinical trial is anonymized using a customizable anonymizer and is stored on a PACS only accessible by AE title access control. The remote analysis station consists of a single virtual machine per trial running on a powerful PC supporting multiple simultaneous instances. Imaging data management and analysis is performed within ClearCanvas Workstation® using custom designed plug-ins for kinetic modelling (The DCE-Tool®), quality assurance (The DCE-QA Tool) and RECIST. Results: A framework has been set up currently serving seven clinical trials spanning five hospitals with three more trials to be added over the next six months. After initial rapid image transfer (+ 2 MB/s), all data analysis is done server side making it robust and rapid. This has provided the ability to perform computationally expensive operations such as voxel-wise kinetic modelling on very large data archives (+20 GB/50k images/patient) remotely with minimal end-user hardware. Conclusions: This system is currently in its proof of concept stage but has been used successfully to send and analyze data from remote hospitals. Next steps will involve scaling up the system with a more powerful PACS and multiple high powered analysis machines as well as adding real-time review capabilities.

A Voxel-by-Voxel Comparison of Deformable Vector Fields Obtained by Three Deformable Image Registration Algorithms Applied to 4DCT Lung Studies.

PubMed

Fatyga, Mirek; Dogan, Nesrin; Weiss, Elizabeth; Sleeman, William C; Zhang, Baoshe; Lehman, William J; Williamson, Jeffrey F; Wijesooriya, Krishni; Christensen, Gary E

2015-01-01

Commonly used methods of assessing the accuracy of deformable image registration (DIR) rely on image segmentation or landmark selection. These methods are very labor intensive and thus limited to relatively small number of image pairs. The direct voxel-by-voxel comparison can be automated to examine fluctuations in DIR quality on a long series of image pairs. A voxel-by-voxel comparison of three DIR algorithms applied to lung patients is presented. Registrations are compared by comparing volume histograms formed both with individual DIR maps and with a voxel-by-voxel subtraction of the two maps. When two DIR maps agree one concludes that both maps are interchangeable in treatment planning applications, though one cannot conclude that either one agrees with the ground truth. If two DIR maps significantly disagree one concludes that at least one of the maps deviates from the ground truth. We use the method to compare 3 DIR algorithms applied to peak inhale-peak exhale registrations of 4DFBCT data obtained from 13 patients. All three algorithms appear to be nearly equivalent when compared using DICE similarity coefficients. A comparison based on Jacobian volume histograms shows that all three algorithms measure changes in total volume of the lungs with reasonable accuracy, but show large differences in the variance of Jacobian distribution on contoured structures. Analysis of voxel-by-voxel subtraction of DIR maps shows differences between algorithms that exceed a centimeter for some registrations. Deformation maps produced by DIR algorithms must be treated as mathematical approximations of physical tissue deformation that are not self-consistent and may thus be useful only in applications for which they have been specifically validated. The three algorithms tested in this work perform fairly robustly for the task of contour propagation, but produce potentially unreliable results for the task of DVH accumulation or measurement of local volume change. Performance of DIR algorithms varies significantly from one image pair to the next hence validation efforts, which are exhaustive but performed on a small number of image pairs may not reflect the performance of the same algorithm in practical clinical situations. Such efforts should be supplemented by validation based on a longer series of images of clinical quality.

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.

Regional flow in a complex coastal aquifer system: Combining voxel geological modelling with regularized calibration

NASA Astrophysics Data System (ADS)

Meyer, Rena; Engesgaard, Peter; Høyer, Anne-Sophie; Jørgensen, Flemming; Vignoli, Giulio; Sonnenborg, Torben O.

2018-07-01

Low-lying coastal regions are often highly populated, constitute sensitive habitats and are at the same time exposed to challenging hydrological environments due to surface flooding from storm events and saltwater intrusion, which both may affect drinking water supply from shallow and deeper aquifers. Near the Wadden Sea at the border of Southern Denmark and Northern Germany, the hydraulic system (connecting groundwater, river water, and the sea) was altered over centuries (until the 19th century) by e.g. the construction of dikes and drains to prevent flooding and allow agricultural use. Today, massive saltwater intrusions extend up to 20 km inland. In order to understand the regional flow, a methodological approach was developed that combined: (1) a highly-resolved voxel geological model, (2) a ∼1 million node groundwater model with 46 hydrofacies coupled to rivers, drains and the sea, (3) Tikhonov regularization calibration using hydraulic heads and average stream discharges as targets and (4) parameter uncertainty analysis. It is relatively new to use voxel models for constructing geological models that often have been simplified to stacked, pseudo-3D layer geology. The study is therefore one of the first to combine a voxel geological model with state-of-the-art flow calibration techniques. The results show that voxel geological modelling, where lithofacies information are transferred to each volumetric element, is a useful method to preserve 3D geological heterogeneity on a local scale, which is important when distinct geological features such as buried valleys are abundant. Furthermore, it is demonstrated that simpler geological models and simpler calibration methods do not perform as well. The proposed approach is applicable to many other systems, because it combines advanced and flexible geological modelling and flow calibration techniques. This has led to new insights in the regional flow patterns and especially about water cycling in the marsh area near the coast based on the ability to define six predictive scenarios from the linear analysis of parameter uncertainty. The results show that the coastal system near the Danish-German border is mainly controlled by flow in the two aquifers separated by a thick clay layer, and several deep high-permeable buried valleys that connect the sea with the interior and the two aquifers. The drained marsh area acts like a huge regional sink limiting submarine groundwater discharge. With respect to water balance, the greatest sensitivity to parameter uncertainty was observed in the drained marsh area, where some scenarios showed increased flow of sea water into the interior and increased drainage. We speculate that the massive salt water intrusion may be caused by a combination of the preferential pathways provided by the buried valleys, the marsh drainage and relatively high hydraulic conductivities in the two main aquifers as described by one of the scenarios. This is currently under investigation by using a salt water transport model.

Comparison of Monoenergetic Photon Organ Dose Rate Coefficients for the Female Stylized and Voxel Phantoms Submerged in Air

DOE PAGES

Hiller, Mauritius; Dewji, Shaheen Azim

2017-02-16

Dose rate coefficients computed using the International Commission on Radiological Protection (ICRP) reference adult female voxel phantom were compared with values computed using the Oak Ridge National Laboratory (ORNL) adult female stylized phantom in an air submersion exposure geometry. This is a continuation of previous work comparing monoenergetic organ dose rate coefficients for the male adult phantoms. With both the male and female data computed, effective dose rate as defined by ICRP Publication 103 was compared for both phantoms. Organ dose rate coefficients for the female phantom and ratios of organ dose rates for the voxel and stylized phantoms aremore » provided in the energy range from 30 to 5 MeV. Analysis of the contribution of the organs to effective dose is also provided. Lastly, comparison of effective dose rates between the voxel and stylized phantoms was within 8% at 100 keV and is <5% between 200 and 5000 keV.« less

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.

Comparison of Monoenergetic Photon Organ Dose Rate Coefficients for the Female Stylized and Voxel Phantoms Submerged in Air

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

Hiller, Mauritius; Dewji, Shaheen Azim

Dose rate coefficients computed using the International Commission on Radiological Protection (ICRP) reference adult female voxel phantom were compared with values computed using the Oak Ridge National Laboratory (ORNL) adult female stylized phantom in an air submersion exposure geometry. This is a continuation of previous work comparing monoenergetic organ dose rate coefficients for the male adult phantoms. With both the male and female data computed, effective dose rate as defined by ICRP Publication 103 was compared for both phantoms. Organ dose rate coefficients for the female phantom and ratios of organ dose rates for the voxel and stylized phantoms aremore » provided in the energy range from 30 to 5 MeV. Analysis of the contribution of the organs to effective dose is also provided. Lastly, comparison of effective dose rates between the voxel and stylized phantoms was within 8% at 100 keV and is <5% between 200 and 5000 keV.« less

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

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.

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.

Local variance for multi-scale analysis in geomorphometry.

PubMed

Drăguţ, Lucian; Eisank, Clemens; Strasser, Thomas

2011-07-15

Increasing availability of high resolution Digital Elevation Models (DEMs) is leading to a paradigm shift regarding scale issues in geomorphometry, prompting new solutions to cope with multi-scale analysis and detection of characteristic scales. We tested the suitability of the local variance (LV) method, originally developed for image analysis, for multi-scale analysis in geomorphometry. The method consists of: 1) up-scaling land-surface parameters derived from a DEM; 2) calculating LV as the average standard deviation (SD) within a 3 × 3 moving window for each scale level; 3) calculating the rate of change of LV (ROC-LV) from one level to another, and 4) plotting values so obtained against scale levels. We interpreted peaks in the ROC-LV graphs as markers of scale levels where cells or segments match types of pattern elements characterized by (relatively) equal degrees of homogeneity. The proposed method has been applied to LiDAR DEMs in two test areas different in terms of roughness: low relief and mountainous, respectively. For each test area, scale levels for slope gradient, plan, and profile curvatures were produced at constant increments with either resampling (cell-based) or image segmentation (object-based). Visual assessment revealed homogeneous areas that convincingly associate into patterns of land-surface parameters well differentiated across scales. We found that the LV method performed better on scale levels generated through segmentation as compared to up-scaling through resampling. The results indicate that coupling multi-scale pattern analysis with delineation of morphometric primitives is possible. This approach could be further used for developing hierarchical classifications of landform elements.

Local variance for multi-scale analysis in geomorphometry

PubMed Central

Drăguţ, Lucian; Eisank, Clemens; Strasser, Thomas

2011-01-01

Increasing availability of high resolution Digital Elevation Models (DEMs) is leading to a paradigm shift regarding scale issues in geomorphometry, prompting new solutions to cope with multi-scale analysis and detection of characteristic scales. We tested the suitability of the local variance (LV) method, originally developed for image analysis, for multi-scale analysis in geomorphometry. The method consists of: 1) up-scaling land-surface parameters derived from a DEM; 2) calculating LV as the average standard deviation (SD) within a 3 × 3 moving window for each scale level; 3) calculating the rate of change of LV (ROC-LV) from one level to another, and 4) plotting values so obtained against scale levels. We interpreted peaks in the ROC-LV graphs as markers of scale levels where cells or segments match types of pattern elements characterized by (relatively) equal degrees of homogeneity. The proposed method has been applied to LiDAR DEMs in two test areas different in terms of roughness: low relief and mountainous, respectively. For each test area, scale levels for slope gradient, plan, and profile curvatures were produced at constant increments with either resampling (cell-based) or image segmentation (object-based). Visual assessment revealed homogeneous areas that convincingly associate into patterns of land-surface parameters well differentiated across scales. We found that the LV method performed better on scale levels generated through segmentation as compared to up-scaling through resampling. The results indicate that coupling multi-scale pattern analysis with delineation of morphometric primitives is possible. This approach could be further used for developing hierarchical classifications of landform elements. PMID:21779138

Speeding up 3D speckle tracking using PatchMatch

NASA Astrophysics Data System (ADS)

Zontak, Maria; O'Donnell, Matthew

2016-03-01

Echocardiography provides valuable information to diagnose heart dysfunction. A typical exam records several minutes of real-time cardiac images. To enable complete analysis of 3D cardiac strains, 4-D (3-D+t) echocardiography is used. This results in a huge dataset and requires effective automated analysis. Ultrasound speckle tracking is an effective method for tissue motion analysis. It involves correlation of a 3D kernel (block) around a voxel with kernels in later frames. The search region is usually confined to a local neighborhood, due to biomechanical and computational constraints. For high strains and moderate frame-rates, however, this search region will remain large, leading to a considerable computational burden. Moreover, speckle decorrelation (due to high strains) leads to errors in tracking. To solve this, spatial motion coherency between adjacent voxels should be imposed, e.g., by averaging their correlation functions.1 This requires storing correlation functions for neighboring voxels, thus increasing memory demands. In this work, we propose an efficient search using PatchMatch, 2 a powerful method to find correspondences between images. Here we adopt PatchMatch for 3D volumes and radio-frequency signals. As opposed to an exact search, PatchMatch performs random sampling of the search region and propagates successive matches among neighboring voxels. We show that: 1) Inherently smooth offset propagation in PatchMatch contributes to spatial motion coherence without any additional processing or memory demand. 2) For typical scenarios, PatchMatch is at least 20 times faster than the exact search, while maintaining comparable tracking accuracy.

Imaging Lung Function in Mice Using SPECT/CT and Per-Voxel Analysis

PubMed Central

Jobse, Brian N.; Rhem, Rod G.; McCurry, Cory A. J. R.; Wang, Iris Q.; Labiris, N. Renée

2012-01-01

Chronic lung disease is a major worldwide health concern but better tools are required to understand the underlying pathologies. Ventilation/perfusion (V/Q) single photon emission computed tomography (SPECT) with per-voxel analysis allows for non-invasive measurement of regional lung function. A clinically adapted V/Q methodology was used in healthy mice to investigate V/Q relationships. Twelve week-old mice were imaged to describe normal lung function while 36 week-old mice were imaged to determine how age affects V/Q. Mice were ventilated with Technegas™ and injected with 99mTc-macroaggregated albumin to trace ventilation and perfusion, respectively. For both processes, SPECT and CT images were acquired, co-registered, and quantitatively analyzed. On a per-voxel basis, ventilation and perfusion were moderately correlated (R = 0.58±0.03) in 12 week old animals and a mean log(V/Q) ratio of −0.07±0.01 and standard deviation of 0.36±0.02 were found, defining the extent of V/Q matching. In contrast, 36 week old animals had significantly increased levels of V/Q mismatching throughout the periphery of the lung. Measures of V/Q were consistent across healthy animals and differences were observed with age demonstrating the capability of this technique in quantifying lung function. Per-voxel analysis and the ability to non-invasively assess lung function will aid in the investigation of chronic lung disease models and drug efficacy studies. PMID:22870297

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.

Classification of pulmonary emphysema from chest CT scans using integral geometry descriptors

NASA Astrophysics Data System (ADS)

van Rikxoort, E. M.; Goldin, J. G.; Galperin-Aizenberg, M.; Brown, M. S.

2011-03-01

To gain insight into the underlying pathways of emphysema and monitor the effect of treatment, methods to quantify and phenotype the different types of emphysema from chest CT scans are of crucial importance. Current standard measures rely on density thresholds for individual voxels, which is influenced by inspiration level and does not take into account the spatial relationship between voxels. Measures based on texture analysis do take the interrelation between voxels into account and therefore might be useful for distinguishing different types of emphysema. In this study, we propose to use Minkowski functionals combined with rotation invariant Gaussian features to distinguish between healthy and emphysematous tissue and classify three different types of emphysema. Minkowski functionals characterize binary images in terms of geometry and topology. In 3D, four Minkowski functionals are defined. By varying the threshold and size of neighborhood around a voxel, a set of Minkowski functionals can be defined for each voxel. Ten chest CT scans with 1810 annotated regions were used to train the method. A set of 108 features was calculated for each training sample from which 10 features were selected to be most informative. A linear discriminant classifier was trained to classify each voxel in the lungs into a subtype of emphysema or normal lung. The method was applied to an independent test set of 30 chest CT scans with varying amounts and types of emphysema with 4347 annotated regions of interest. The method is shown to perform well, with an overall accuracy of 95%.

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.

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

Sensitivity analysis of multi-objective optimization of CPG parameters for quadruped robot locomotion

NASA Astrophysics Data System (ADS)

Oliveira, Miguel; Santos, Cristina P.; Costa, Lino

2012-09-01

In this paper, a study based on sensitivity analysis is performed for a gait multi-objective optimization system that combines bio-inspired Central Patterns Generators (CPGs) and a multi-objective evolutionary algorithm based on NSGA-II. In this system, CPGs are modeled as autonomous differential equations, that generate the necessary limb movement to perform the required walking gait. In order to optimize the walking gait, a multi-objective problem with three conflicting objectives is formulated: maximization of the velocity, the wide stability margin and the behavioral diversity. The experimental results highlight the effectiveness of this multi-objective approach and the importance of the objectives to find different walking gait solutions for the quadruped robot.

Multi-region statistical shape model for cochlear implantation

NASA Astrophysics Data System (ADS)

Romera, Jordi; Kjer, H. Martin; Piella, Gemma; Ceresa, Mario; González Ballester, Miguel A.

2016-03-01

Statistical shape models are commonly used to analyze the variability between similar anatomical structures and their use is established as a tool for analysis and segmentation of medical images. However, using a global model to capture the variability of complex structures is not enough to achieve the best results. The complexity of a proper global model increases even more when the amount of data available is limited to a small number of datasets. Typically, the anatomical variability between structures is associated to the variability of their physiological regions. In this paper, a complete pipeline is proposed for building a multi-region statistical shape model to study the entire variability from locally identified physiological regions of the inner ear. The proposed model, which is based on an extension of the Point Distribution Model (PDM), is built for a training set of 17 high-resolution images (24.5 μm voxels) of the inner ear. The model is evaluated according to its generalization ability and specificity. The results are compared with the ones of a global model built directly using the standard PDM approach. The evaluation results suggest that better accuracy can be achieved using a regional modeling of the inner ear.

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…

A method to estimate the effect of deformable image registration uncertainties on daily dose mapping

PubMed Central

Murphy, Martin J.; Salguero, Francisco J.; Siebers, Jeffrey V.; Staub, David; Vaman, Constantin

2012-01-01

Purpose: To develop a statistical sampling procedure for spatially-correlated uncertainties in deformable image registration and then use it to demonstrate their effect on daily dose mapping. Methods: Sequential daily CT studies are acquired to map anatomical variations prior to fractionated external beam radiotherapy. The CTs are deformably registered to the planning CT to obtain displacement vector fields (DVFs). The DVFs are used to accumulate the dose delivered each day onto the planning CT. Each DVF has spatially-correlated uncertainties associated with it. Principal components analysis (PCA) is applied to measured DVF error maps to produce decorrelated principal component modes of the errors. The modes are sampled independently and reconstructed to produce synthetic registration error maps. The synthetic error maps are convolved with dose mapped via deformable registration to model the resulting uncertainty in the dose mapping. The results are compared to the dose mapping uncertainty that would result from uncorrelated DVF errors that vary randomly from voxel to voxel. Results: The error sampling method is shown to produce synthetic DVF error maps that are statistically indistinguishable from the observed error maps. Spatially-correlated DVF uncertainties modeled by our procedure produce patterns of dose mapping error that are different from that due to randomly distributed uncertainties. Conclusions: Deformable image registration uncertainties have complex spatial distributions. The authors have developed and tested a method to decorrelate the spatial uncertainties and make statistical samples of highly correlated error maps. The sample error maps can be used to investigate the effect of DVF uncertainties on daily dose mapping via deformable image registration. An initial demonstration of this methodology shows that dose mapping uncertainties can be sensitive to spatial patterns in the DVF uncertainties. PMID:22320766

Classification of autistic individuals and controls using cross-task characterization of fMRI activity

PubMed Central

Chanel, Guillaume; Pichon, Swann; Conty, Laurence; Berthoz, Sylvie; Chevallier, Coralie; Grèzes, Julie

2015-01-01

Multivariate pattern analysis (MVPA) has been applied successfully to task-based and resting-based fMRI recordings to investigate which neural markers distinguish individuals with autistic spectrum disorders (ASD) from controls. While most studies have focused on brain connectivity during resting state episodes and regions of interest approaches (ROI), a wealth of task-based fMRI datasets have been acquired in these populations in the last decade. This calls for techniques that can leverage information not only from a single dataset, but from several existing datasets that might share some common features and biomarkers. We propose a fully data-driven (voxel-based) approach that we apply to two different fMRI experiments with social stimuli (faces and bodies). The method, based on Support Vector Machines (SVMs) and Recursive Feature Elimination (RFE), is first trained for each experiment independently and each output is then combined to obtain a final classification output. Second, this RFE output is used to determine which voxels are most often selected for classification to generate maps of significant discriminative activity. Finally, to further explore the clinical validity of the approach, we correlate phenotypic information with obtained classifier scores. The results reveal good classification accuracy (range between 69% and 92.3%). Moreover, we were able to identify discriminative activity patterns pertaining to the social brain without relying on a priori ROI definitions. Finally, social motivation was the only dimension which correlated with classifier scores, suggesting that it is the main dimension captured by the classifiers. Altogether, we believe that the present RFE method proves to be efficient and may help identifying relevant biomarkers by taking advantage of acquired task-based fMRI datasets in psychiatric populations. PMID:26793434

Effect of SOHAM meditation on human brain: a voxel-based morphometry study.

PubMed

Kumar, Uttam; Guleria, Anupam; Kishan, Sadguru Sri Kunal; Khetrapal, C L

2014-01-01

The anatomical correlates of long-term meditators involved in practice of "SOHAM" meditation have been studied using voxel-based morphometry (VBM). The VBM analysis indicates significantly higher gray matter density in brain stem, ventral pallidum, and supplementary motor area in the meditators as compared with age-matched nonmeditators. The observed changes in brain structure are compared with other forms of meditation. Copyright © 2013 by the American Society of Neuroimaging.

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.

Towards a voxel-based geographic automata for the simulation of geospatial processes

NASA Astrophysics Data System (ADS)

Jjumba, Anthony; Dragićević, Suzana

2016-07-01

Many geographic processes evolve in a three dimensional space and time continuum. However, when they are represented with the aid of geographic information systems (GIS) or geosimulation models they are modelled in a framework of two-dimensional space with an added temporal component. The objective of this study is to propose the design and implementation of voxel-based automata as a methodological approach for representing spatial processes evolving in the four-dimensional (4D) space-time domain. Similar to geographic automata models which are developed to capture and forecast geospatial processes that change in a two-dimensional spatial framework using cells (raster geospatial data), voxel automata rely on the automata theory and use three-dimensional volumetric units (voxels). Transition rules have been developed to represent various spatial processes which range from the movement of an object in 3D to the diffusion of airborne particles and landslide simulation. In addition, the proposed 4D models demonstrate that complex processes can be readily reproduced from simple transition functions without complex methodological approaches. The voxel-based automata approach provides a unique basis to model geospatial processes in 4D for the purpose of improving representation, analysis and understanding their spatiotemporal dynamics. This study contributes to the advancement of the concepts and framework of 4D GIS.

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

Three-Dimensions Segmentation of Pulmonary Vascular Trees for Low Dose CT Scans

NASA Astrophysics Data System (ADS)

Lai, Jun; Huang, Ying; Wang, Ying; Wang, Jun

2016-12-01

Due to the low contrast and the partial volume effects, providing an accurate and in vivo analysis for pulmonary vascular trees from low dose CT scans is a challenging task. This paper proposes an automatic integration segmentation approach for the vascular trees in low dose CT scans. It consists of the following steps: firstly, lung volumes are acquired by the knowledge based method from the CT scans, and then the data are smoothed by the 3D Gaussian filter; secondly, two or three seeds are gotten by the adaptive 2D segmentation and the maximum area selecting from different position scans; thirdly, each seed as the start voxel is inputted for a quick multi-seeds 3D region growing to get vascular trees; finally, the trees are refined by the smooth filter. Through skeleton analyzing for the vascular trees, the results show that the proposed method can provide much better and lower level vascular branches.

3D Gabor wavelet based vessel filtering of photoacoustic images.

PubMed

Haq, Israr Ul; Nagoaka, Ryo; Makino, Takahiro; Tabata, Takuya; Saijo, Yoshifumi

2016-08-01

Filtering and segmentation of vasculature is an important issue in medical imaging. The visualization of vasculature is crucial for the early diagnosis and therapy in numerous medical applications. This paper investigates the use of Gabor wavelet to enhance the effect of vasculature while eliminating the noise due to size, sensitivity and aperture of the detector in 3D Optical Resolution Photoacoustic Microscopy (OR-PAM). A detailed multi-scale analysis of wavelet filtering and Hessian based method is analyzed for extracting vessels of different sizes since the blood vessels usually vary with in a range of radii. The proposed algorithm first enhances the vasculature in the image and then tubular structures are classified by eigenvalue decomposition of the local Hessian matrix at each voxel in the image. The algorithm is tested on non-invasive experiments, which shows appreciable results to enhance vasculature in photo-acoustic images.

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.

Large-scale changes in network interactions as a physiological signature of spatial neglect

PubMed Central

Baldassarre, Antonello; Ramsey, Lenny; Hacker, Carl L.; Callejas, Alicia; Astafiev, Serguei V.; Metcalf, Nicholas V.; Zinn, Kristi; Rengachary, Jennifer; Snyder, Abraham Z.; Carter, Alex R.; Shulman, Gordon L.

2014-01-01

The relationship between spontaneous brain activity and behaviour following focal injury is not well understood. Here, we report a large-scale study of resting state functional connectivity MRI and spatial neglect following stroke in a large (n = 84) heterogeneous sample of first-ever stroke patients (within 1–2 weeks). Spatial neglect, which is typically more severe after right than left hemisphere injury, includes deficits of spatial attention and motor actions contralateral to the lesion, and low general attention due to impaired vigilance/arousal. Patients underwent structural and resting state functional MRI scans, and spatial neglect was measured using the Posner spatial cueing task, and Mesulam and Behavioural Inattention Test cancellation tests. A principal component analysis of the behavioural tests revealed a main factor accounting for 34% of variance that captured three correlated behavioural deficits: visual neglect of the contralesional visual field, visuomotor neglect of the contralesional field, and low overall performance. In an independent sample (21 healthy subjects), we defined 10 resting state networks consisting of 169 brain regions: visual-fovea and visual-periphery, sensory-motor, auditory, dorsal attention, ventral attention, language, fronto-parietal control, cingulo-opercular control, and default mode. We correlated the neglect factor score with the strength of resting state functional connectivity within and across the 10 resting state networks. All damaged brain voxels were removed from the functional connectivity:behaviour correlational analysis. We found that the correlated behavioural deficits summarized by the factor score were associated with correlated multi-network patterns of abnormal functional connectivity involving large swaths of cortex. Specifically, dorsal attention and sensory-motor networks showed: (i) reduced interhemispheric functional connectivity; (ii) reduced anti-correlation with fronto-parietal and default mode networks in the right hemisphere; and (iii) increased intrahemispheric connectivity with the basal ganglia. These patterns of functional connectivity:behaviour correlations were stronger in patients with right- as compared to left-hemisphere damage and were independent of lesion volume. Our findings identify large-scale changes in resting state network interactions that are a physiological signature of spatial neglect and may relate to its right hemisphere lateralization. PMID:25367028

TWave: High-Order Analysis of Functional MRI

PubMed Central

Barnathan, Michael; Megalooikonomou, Vasileios; Faloutsos, Christos; Faro, Scott; Mohamed, Feroze B.

2011-01-01

The traditional approach to functional image analysis models images as matrices of raw voxel intensity values. Although such a representation is widely utilized and heavily entrenched both within neuroimaging and in the wider data mining community, the strong interactions among space, time, and categorical modes such as subject and experimental task inherent in functional imaging yield a dataset with “high-order” structure, which matrix models are incapable of exploiting. Reasoning across all of these modes of data concurrently requires a high-order model capable of representing relationships between all modes of the data in tandem. We thus propose to model functional MRI data using tensors, which are high-order generalizations of matrices equivalent to multidimensional arrays or data cubes. However, several unique challenges exist in the high-order analysis of functional medical data: naïve tensor models are incapable of exploiting spatiotemporal locality patterns, standard tensor analysis techniques exhibit poor efficiency, and mixtures of numeric and categorical modes of data are very often present in neuroimaging experiments. Formulating the problem of image clustering as a form of Latent Semantic Analysis and using the WaveCluster algorithm as a baseline, we propose a comprehensive hybrid tensor and wavelet framework for clustering, concept discovery, and compression of functional medical images which successfully addresses these challenges. Our approach reduced runtime and dataset size on a 9.3 GB finger opposition motor task fMRI dataset by up to 98% while exhibiting improved spatiotemporal coherence relative to standard tensor, wavelet, and voxel-based approaches. Our clustering technique was capable of automatically differentiating between the frontal areas of the brain responsible for task-related habituation and the motor regions responsible for executing the motor task, in contrast to a widely used fMRI analysis program, SPM, which only detected the latter region. Furthermore, our approach discovered latent concepts suggestive of subject handedness nearly 100x faster than standard approaches. These results suggest that a high-order model is an integral component to accurate scalable functional neuroimaging. PMID:21729758

Multivariate representation of food preferences in the human brain.

PubMed

Pogoda, Luca; Holzer, Matthias; Mormann, Florian; Weber, Bernd

2016-12-01

One major goal in decision neuroscience is to investigate the neuronal mechanisms being responsible for the computation of product preferences. The aim of the present fMRI study was to investigate whether similar patterns of brain activity, reflecting category dependent and category independent preference signals, can be observed in case of different food product categories (i.e. chocolate bars and salty snacks). To that end we used a multivariate searchlight approach in which a linear support vector machine (l-SVM) was trained to distinguish preferred from non-preferred chocolate bars and subsequently tested its predictive power in case of chocolate bars (within category prediction) and salty snacks (across category prediction). Preferences were measured by a binary forced choice decision paradigm before the fMRI task. In the scanner, subjects saw only one product per trial which they had to rate after presentation. Consistent with previous multi voxel pattern analysis (MVPA) studies, we found category dependent preference signals in the ventral parts of medial prefrontal cortex (mPFC), but also in dorsal anterior cingulate cortex (dACC) and dorsolateral prefrontal cortex (dlPFC). Category independent preference signals were observed in the dorsal parts of mPFC, dACC, and dlPFC. While the first two results have also been reported in a closely related study, the activation in dlPFC is new in this context. We propose that the dlPFC activity does not reflect the products' value computation per se, but rather a modulatory signal which is computed in anticipation of the forthcoming product rating after stimulus presentation. Furthermore we postulate that this kind of dlPFC activation emerges only if the anticipated choices fall into the domain of primary rewards, such as foods. Thus, in contrast to previous studies which investigated preference decoding for stimuli from utterly different categories, the present study revealed some food domain specific aspects of preference processing in the human brain. Copyright © 2016 Elsevier Inc. All rights reserved.

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.

Neurofunctional maps of the 'maternal brain' and the effects of oxytocin: a multimodal voxel-based meta-analysis.

PubMed

Rocchetti, Matteo; Radua, Joaquim; Paloyelis, Yannis; Xenaki, Lida-Alkisti; Frascarelli, Marianna; Caverzasi, Edgardo; Politi, Pierluigi; Fusar-Poli, Paolo

2014-10-01

Several studies have tried to understand the possible neurobiological basis of mothering. The putative involvement of oxytocin, in this regard, has been deeply investigated. Performing a voxel-based meta-analysis, we aimed at testing the hypothesis of overlapping brain activation in functional magnetic resonance imaging (fMRI) studies investigating the mother-infant interaction and the oxytocin modulation of emotional stimuli in humans. We performed two systematic literature searches: fMRI studies investigating the neurofunctional correlates of the 'maternal brain' by employing mother-infant paradigms; and fMRI studies employing oxytocin during emotional tasks. A unimodal voxel-based meta-analysis was performed on each database, whereas a multimodal voxel-based meta-analytical tool was adopted to assess the hypothesis that the neurofunctional effects of oxytocin are detected in brain areas implicated in the 'maternal brain.' We found greater activation in the bilateral insula extending to the inferior frontal gyrus, basal ganglia and thalamus during mother-infant interaction and greater left insular activation associated with oxytocin administration versus placebo. Left insula extending to basal ganglia and frontotemporal gyri as well as bilateral thalamus and amygdala showed consistent activation across the two paradigms. Right insula also showed activation across the two paradigms, and dorsomedial frontal cortex activation in mothers but deactivation with oxytocin. Significant activation in areas involved in empathy, emotion regulation, motivation, social cognition and theory of mind emerged from our multimodal meta-analysis, supporting the need for further studies directly investigating the neurobiology of oxytocin in the mother-infant relationship. © 2014 The Authors. Psychiatry and Clinical Neurosciences © 2014 Japanese Society of Psychiatry and Neurology.

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

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

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

Discrimination of geographical origin and detection of adulteration of kudzu root by fluorescence spectroscopy coupled with multi-way pattern recognition

NASA Astrophysics Data System (ADS)

Hu, Leqian; Ma, Shuai; Yin, Chunling

2018-03-01

In this work, fluorescence spectroscopy combined with multi-way pattern recognition techniques were developed for determining the geographical origin of kudzu root and detection and quantification of adulterants in kudzu root. Excitation-emission (EEM) spectra were obtained for 150 pure kudzu root samples of different geographical origins and 150 fake kudzu roots with different adulteration proportions by recording emission from 330 to 570 nm with excitation in the range of 320-480 nm, respectively. Multi-way principal components analysis (M-PCA) and multilinear partial least squares discriminant analysis (N-PLS-DA) methods were used to decompose the excitation-emission matrices datasets. 150 pure kudzu root samples could be differentiated exactly from each other according to their geographical origins by M-PCA and N-PLS-DA models. For the adulteration kudzu root samples, N-PLS-DA got better and more reliable classification result comparing with the M-PCA model. The results obtained in this study indicated that EEM spectroscopy coupling with multi-way pattern recognition could be used as an easy, rapid and novel tool to distinguish the geographical origin of kudzu root and detect adulterated kudzu root. Besides, this method was also suitable for determining the geographic origin and detection the adulteration of the other foodstuffs which can produce fluorescence.

Multi-modal magnetic resonance imaging in the acute and sub-acute phase of mild traumatic brain injury: can we see the difference?

PubMed

Toth, Arnold; Kovacs, Noemi; Perlaki, Gabor; Orsi, Gergely; Aradi, Mihaly; Komaromy, Hedvig; Ezer, Erzsebet; Bukovics, Peter; Farkas, Orsolya; Janszky, Jozsef; Doczi, Tamas; Buki, Andras; Schwarcz, Attila

2013-01-01

Advanced magnetic resonance imaging (MRI) methods were shown to be able to detect the subtle structural consequences of mild traumatic brain injury (mTBI). The objective of this study was to investigate the acute structural alterations and recovery after mTBI, using diffusion tensor imaging (DTI) to reveal axonal pathology, volumetric analysis, and susceptibility weighted imaging (SWI) to detect microhemorrhage. Fourteen patients with mTBI who had computed tomography with negative results underwent MRI within 3 days and 1 month after injury. High resolution T1-weighted imaging, DTI, and SWI, were performed at both time points. A control group of 14 matched volunteers were also examined following the same imaging protocol and time interval. Tract-Based Spatial Statistics (TBSS) were performed on DTI data to reveal group differences. T1-weighted images were fed into Freesurfer volumetric analysis. TBSS showed fractional anisotropy (FA) to be significantly (corrected p<0.05) lower, and mean diffusivity (MD) to be higher in the mTBI group in several white matter tracts (FA=40,737; MD=39,078 voxels) compared with controls at 72 hours after injury and still 1month later for FA. Longitudinal analysis revealed significant change (i.e., normalization) of FA and MD over 1 month dominantly in the left hemisphere (FA=3408; MD=7450 voxels). A significant (p<0.05) decrease in cortical volumes (mean 1%) and increase in ventricular volumes (mean 3.4%) appeared at 1 month after injury in the mTBI group. SWI did not reveal microhemorrhage in our patients. Our findings present dynamic micro- and macrostructural changes occurring in the acute to sub-acute phase in mTBI, in very mildly injured patients lacking microhemorrhage detectable by SWI. These results underscore the importance of strictly defined image acquisition time points when performing MRI studies on patients with mTBI.

CAS-viewer: web-based tool for splicing-guided integrative analysis of multi-omics cancer data.

PubMed

Han, Seonggyun; Kim, Dongwook; Kim, Youngjun; Choi, Kanghoon; Miller, Jason E; Kim, Dokyoon; Lee, Younghee

2018-04-20

The Cancer Genome Atlas (TCGA) project is a public resource that provides transcriptomic, DNA sequence, methylation, and clinical data for 33 cancer types. Transforming the large size and high complexity of TCGA cancer genome data into integrated knowledge can be useful to promote cancer research. Alternative splicing (AS) is a key regulatory mechanism of genes in human cancer development and in the interaction with epigenetic factors. Therefore, AS-guided integration of existing TCGA data sets will make it easier to gain insight into the genetic architecture of cancer risk and related outcomes. There are already existing tools analyzing and visualizing alternative mRNA splicing patterns for large-scale RNA-seq experiments. However, these existing web-based tools are limited to the analysis of individual TCGA data sets at a time, such as only transcriptomic information. We implemented CAS-viewer (integrative analysis of Cancer genome data based on Alternative Splicing), a web-based tool leveraging multi-cancer omics data from TCGA. It illustrates alternative mRNA splicing patterns along with methylation, miRNAs, and SNPs, and then provides an analysis tool to link differential transcript expression ratio to methylation, miRNA, and splicing regulatory elements for 33 cancer types. Moreover, one can analyze AS patterns with clinical data to identify potential transcripts associated with different survival outcome for each cancer. CAS-viewer is a web-based application for transcript isoform-driven integration of multi-omics data in multiple cancer types and will aid in the visualization and possible discovery of biomarkers for cancer by integrating multi-omics data from TCGA.

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.

Material Identification and Quantification in Spectral X-ray Micro-CT

NASA Astrophysics Data System (ADS)

Holmes, Thomas Wesley

The identification and quantification of all the voxels within a reconstructed microCT image was possible through making comparisons of the attenuation profile from an unknown voxel with precalculated signatures of known materials. This was accomplished through simulations with the MCNP6 general-purpose radiation-transport package that modeled a CdTe detector array consisting of 200 elements which were able to differentiate between 100 separate energy bins over the entire range of the emitted 110 kVp tungsten x-ray spectra. The information from each of the separate energy bins was then used to create a single reconstructed image that was then grouped back together to produce a final image where each voxel had a corresponding attenuation pro le. A library of known attenuation profiles was created for each of the materials expected to be within an object with otherwise unknown parameters. A least squares analysis was performed, and comparisons were then made for each voxel's attenuation profile in the unknown object and combinations of each possible library combination of attenuation profiles. Based on predetermined thresholds that the results must meet, some of the combinations were then removed. Of the remaining combinations, a voting system based on statistical evaluations of the fits was designed to select the most appropriate material combination to the input unknown voxel. This was performed over all of the voxels in the reconstructed image and a final resulting material map was produced. These material locations were then quantified by creating an equation of the response from several different densities of the same material and recording the response of the base library. This entire process was called the All Combinations Library Least Squares (ACLLS)analysis and was used to test several Different models. These models investigated a range of densities for the x-ray contrast agents of gold and gadolinium that can be used in many medical applications, as well as a range of densities of bone to test the ACLLS ability to be used with bone density estimation. A final test used a model with five different materials present within the object and consisted of two separate features with mixtures of three materials as gold, iodine and water, and another feature with gadolinium, iodine and water. The remaining four features were all mixtures of water with bone, gold, gadolinium, and iodine. All of the various material mixtures were successfully identified and quantified using the ACLLS analysis package within an acceptable statistical range. The ACLLS method has proven itself as a viable analysis tool for determining both the physical locations and the amount of all the materials present within a given object. This tool could be implemented in the future so as to further assist a team of medical practitioners in diagnosing a subject through reducing ambiguities in an image and providing a quantifiable solution to all of the voxels.

Detection of breast cancer in automated 3D breast ultrasound

NASA Astrophysics Data System (ADS)

Tan, Tao; Platel, Bram; Mus, Roel; Karssemeijer, Nico

2012-03-01

Automated 3D breast ultrasound (ABUS) is a novel imaging modality, in which motorized scans of the breasts are made with a wide transducer through a membrane under modest compression. The technology has gained high interest and may become widely used in screening of dense breasts, where sensitivity of mammography is poor. ABUS has a high sensitivity for detecting solid breast lesions. However, reading ABUS images is time consuming, and subtle abnormalities may be missed. Therefore, we are developing a computer aided detection (CAD) system to help reduce reading time and errors. In the multi-stage system we propose, segmentations of the breast and nipple are performed, providing landmarks for the detection algorithm. Subsequently, voxel features characterizing coronal spiculation patterns, blobness, contrast, and locations with respect to landmarks are extracted. Using an ensemble of classifiers, a likelihood map indicating potential malignancies is computed. Local maxima in the likelihood map are determined using a local maxima detector and form a set of candidate lesions in each view. These candidates are further processed in a second detection stage, which includes region segmentation, feature extraction and a final classification. Region segmentation is performed using a 3D spiral-scanning dynamic programming method. Region features include descriptors of shape, acoustic behavior and texture. Performance was determined using a 78-patient dataset with 93 images, including 50 malignant lesions. We used 10-fold cross-validation. Using FROC analysis we found that the system obtains a lesion sensitivity of 60% and 70% at 2 and 4 false positives per image respectively.

Decoding facial expressions based on face-selective and motion-sensitive areas.

PubMed

Liang, Yin; Liu, Baolin; Xu, Junhai; Zhang, Gaoyan; Li, Xianglin; Wang, Peiyuan; Wang, Bin

2017-06-01

Humans can easily recognize others' facial expressions. Among the brain substrates that enable this ability, considerable attention has been paid to face-selective areas; in contrast, whether motion-sensitive areas, which clearly exhibit sensitivity to facial movements, are involved in facial expression recognition remained unclear. The present functional magnetic resonance imaging (fMRI) study used multi-voxel pattern analysis (MVPA) to explore facial expression decoding in both face-selective and motion-sensitive areas. In a block design experiment, participants viewed facial expressions of six basic emotions (anger, disgust, fear, joy, sadness, and surprise) in images, videos, and eyes-obscured videos. Due to the use of multiple stimulus types, the impacts of facial motion and eye-related information on facial expression decoding were also examined. It was found that motion-sensitive areas showed significant responses to emotional expressions and that dynamic expressions could be successfully decoded in both face-selective and motion-sensitive areas. Compared with static stimuli, dynamic expressions elicited consistently higher neural responses and decoding performance in all regions. A significant decrease in both activation and decoding accuracy due to the absence of eye-related information was also observed. Overall, the findings showed that emotional expressions are represented in motion-sensitive areas in addition to conventional face-selective areas, suggesting that motion-sensitive regions may also effectively contribute to facial expression recognition. The results also suggested that facial motion and eye-related information played important roles by carrying considerable expression information that could facilitate facial expression recognition. Hum Brain Mapp 38:3113-3125, 2017. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

Distinct regions of the hippocampus are associated with memory for different spatial locations.

PubMed

Jeye, Brittany M; MacEvoy, Sean P; Karanian, Jessica M; Slotnick, Scott D

2018-05-15

In the present functional magnetic resonance imaging (fMRI) study, we aimed to evaluate whether distinct regions of the hippocampus were associated with spatial memory for items presented in different locations of the visual field. In Experiment 1, during the study phase, participants viewed abstract shapes in the left or right visual field while maintaining central fixation. At test, old shapes were presented at fixation and participants classified each shape as previously in the "left" or "right" visual field followed by an "unsure"-"sure"-"very sure" confidence rating. Accurate spatial memory for shapes in the left visual field was isolated by contrasting accurate versus inaccurate spatial location responses. This contrast produced one hippocampal activation in which the interaction between item type and accuracy was significant. The analogous contrast for right visual field shapes did not produce activity in the hippocampus; however, the contrast of high confidence versus low confidence right-hits produced one hippocampal activation in which the interaction between item type and confidence was significant. In Experiment 2, the same paradigm was used but shapes were presented in each quadrant of the visual field during the study phase. Accurate memory for shapes in each quadrant, exclusively masked by accurate memory for shapes in the other quadrants, produced a distinct activation in the hippocampus. A multi-voxel pattern analysis (MVPA) of hippocampal activity revealed a significant correlation between behavioral spatial location accuracy and hippocampal MVPA accuracy across participants. The findings of both experiments indicate that distinct hippocampal regions are associated with memory for different visual field locations. Copyright © 2018 Elsevier B.V. All rights reserved.

Differences in neural responses to ipsilateral stimuli in wide-view fields between face- and house-selective areas

PubMed Central

Li, Ting; Niu, Yan; Xiang, Jie; Cheng, Junjie; Liu, Bo; Zhang, Hui; Yan, Tianyi; Kanazawa, Susumu; Wu, Jinglong

2018-01-01

Category-selective brain areas exhibit varying levels of neural activity to ipsilaterally presented stimuli. However, in face- and house-selective areas, the neural responses evoked by ipsilateral stimuli in the peripheral visual field remain unclear. In this study, we displayed face and house images using a wide-view visual presentation system while performing functional magnetic resonance imaging (fMRI). The face-selective areas (fusiform face area (FFA) and occipital face area (OFA)) exhibited intense neural responses to ipsilaterally presented images, whereas the house-selective areas (parahippocampal place area (PPA) and transverse occipital sulcus (TOS)) exhibited substantially smaller and even negative neural responses to the ipsilaterally presented images. We also found that the category preferences of the contralateral and ipsilateral neural responses were similar. Interestingly, the face- and house-selective areas exhibited neural responses to ipsilateral images that were smaller than the responses to the contralateral images. Multi-voxel pattern analysis (MVPA) was implemented to evaluate the difference between the contralateral and ipsilateral responses. The classification accuracies were much greater than those expected by chance. The classification accuracies in the FFA were smaller than those in the PPA and TOS. The closer eccentricities elicited greater classification accuracies in the PPA and TOS. We propose that these ipsilateral neural responses might be interpreted by interhemispheric communication through intrahemispheric connectivity of white matter connection and interhemispheric connectivity via the corpus callosum and occipital white matter connection. Furthermore, the PPA and TOS likely have weaker interhemispheric communication than the FFA and OFA, particularly in the peripheral visual field. PMID:29451872

Cluster Analysis of Time-Dependent Crystallographic Data: Direct Identification of Time-Independent Structural Intermediates

PubMed Central

Kostov, Konstantin S.; Moffat, Keith

2011-01-01

The initial output of a time-resolved macromolecular crystallography experiment is a time-dependent series of difference electron density maps that displays the time-dependent changes in underlying structure as a reaction progresses. The goal is to interpret such data in terms of a small number of crystallographically refinable, time-independent structures, each associated with a reaction intermediate; to establish the pathways and rate coefficients by which these intermediates interconvert; and thereby to elucidate a chemical kinetic mechanism. One strategy toward achieving this goal is to use cluster analysis, a statistical method that groups objects based on their similarity. If the difference electron density at a particular voxel in the time-dependent difference electron density (TDED) maps is sensitive to the presence of one and only one intermediate, then its temporal evolution will exactly parallel the concentration profile of that intermediate with time. The rationale is therefore to cluster voxels with respect to the shapes of their TDEDs, so that each group or cluster of voxels corresponds to one structural intermediate. Clusters of voxels whose TDEDs reflect the presence of two or more specific intermediates can also be identified. From such groupings one can then infer the number of intermediates, obtain their time-independent difference density characteristics, and refine the structure of each intermediate. We review the principles of cluster analysis and clustering algorithms in a crystallographic context, and describe the application of the method to simulated and experimental time-resolved crystallographic data for the photocycle of photoactive yellow protein. PMID:21244840

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.

White matter volume loss in amyotrophic lateral sclerosis: A meta-analysis of voxel-based morphometry studies.

PubMed

Chen, Guangxiang; Zhou, Baiwan; Zhu, Hongyan; Kuang, Weihong; Bi, Feng; Ai, Hua; Gu, Zhongwei; Huang, Xiaoqi; Lui, Su; Gong, Qiyong

2018-04-20

Structural neuroimaging studies of white matter (WM) volume in amyotrophic lateral sclerosis (ALS) using voxel-based morphometry (VBM) have yielded inconsistent findings. This study aimed to perform a quantitative voxel-based meta-analysis using effect-size signed differential mapping (ES-SDM) to establish a statistical consensus between published studies for WM volume alterations in ALS. The pooled meta-analysis revealed significant WM volume losses in the bilateral supplementary motor areas (SMAs), bilateral precentral gyri (PGs), left middle cerebellar peduncle and right cerebellum in patients with ALS, involving the corticospinal tract (CST), interhemispheric fibers, subcortical arcuate fibers, projection fibers to the striatum and cortico-ponto-cerebellar tract. The meta-regression showed that the ALS functional rating scale-revised (ALSFRS-R) was positively correlated with decreased WM volume in the bilateral SMAs, whereas illness duration was negatively correlated with WM volume reduction in the right SMA. This study provides a thorough profile of WM volume loss in ALS and robust evidence that ALS is a multisystem neurodegenerative disease that involves a variety of subcortical WM tracts extending beyond motor cortex involvement. Copyright © 2018 Elsevier Inc. All rights reserved.

Supratentorial lesions contribute to trigeminal neuralgia in multiple sclerosis.

PubMed

Fröhlich, Kilian; Winder, Klemens; Linker, Ralf A; Engelhorn, Tobias; Dörfler, Arnd; Lee, De-Hyung; Hilz, Max J; Schwab, Stefan; Seifert, Frank

2018-06-01

Background It has been proposed that multiple sclerosis lesions afflicting the pontine trigeminal afferents contribute to trigeminal neuralgia in multiple sclerosis. So far, there are no imaging studies that have evaluated interactions between supratentorial lesions and trigeminal neuralgia in multiple sclerosis patients. Methods We conducted a retrospective study and sought multiple sclerosis patients with trigeminal neuralgia and controls in a local database. Multiple sclerosis lesions were manually outlined and transformed into stereotaxic space. We determined the lesion overlap and performed a voxel-wise subtraction analysis. Secondly, we conducted a voxel-wise non-parametric analysis using the Liebermeister test. Results From 12,210 multiple sclerosis patient records screened, we identified 41 patients with trigeminal neuralgia. The voxel-wise subtraction analysis yielded associations between trigeminal neuralgia and multiple sclerosis lesions in the pontine trigeminal afferents, as well as larger supratentorial lesion clusters in the contralateral insula and hippocampus. The non-parametric statistical analysis using the Liebermeister test yielded similar areas to be associated with multiple sclerosis-related trigeminal neuralgia. Conclusions Our study confirms previous data on associations between multiple sclerosis-related trigeminal neuralgia and pontine lesions, and showed for the first time an association with lesions in the insular region, a region involved in pain processing and endogenous pain modulation.

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

SU-F-T-118: Characterization of Change in Fractional Anisotropy After Radiation Therapy: Does Nearby Disruption Predict for White Matter Damage?

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

Pettersson, N; Karunamuni, R; Connor, M

Purpose: We investigated predictors of fractional anisotropy (FA) change in the corticospinal white matter tract (CST) following radiation therapy (RT). Methods: Diffusion tensor imaging (DTI) is a non-invasive modality which models water diffusion properties. FA quantifies the extent of directional bias—a decrease indicates disrupted white matter integrity. Fifteen patients with high-grade glioma underwent DTI scans before, and ten months after RT to 59.4–60 Gy. The CST was segmented using an automated atlas-based algorithm on all DTI images. Treatment planning CT and DTI images were aligned using non-linear registration allowing for baseline FA, follow-up FA, and absorbed dose to be determinedmore » in each voxel. Relative FA change was dichotomized into a binary outcome using 25% decrease as cutoff. Three metrics were assessed as predictors: voxel dose, distance from the voxel to the center of the CST (Rc), and the number of neighboring voxels (Nadj from 0 to 26) with ≥25% decrease in FA. Logistic regression and the area under the receiver-operating characteristics curve (AUC) analysis were performed for each patient. Results: Median age of the cohort was 59 years (range: 40–85). The average number of voxels in the CST amongst all patients was 1181 (±172, SD). In logistic regression, the probability of FA change was highly associated with Nadj in all 15 patients with corresponding AUCs between 0.81 and 0.97. With all three metrics included in the logistic regression models, Nadj was highly significant (p<0.001) in all patients, voxel dose significant (p<0.05) in 3/15 patients, and Rc significant in 12/15 patients (p<0.05). Conclusion: The number of neighboring voxels with change in FA was the dominant predictor of FA change at any given voxel. This suggests that the microenvironment of surrounding white matter disruption after radiation therapy may drive local effects along a white matter tract. Pettersson and Cervino are funded by a Varian Medical Systems grant.« less

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.

A parallel implementation of 3D Zernike moment analysis

NASA Astrophysics Data System (ADS)

Berjón, Daniel; Arnaldo, Sergio; Morán, Francisco

2011-01-01

Zernike polynomials are a well known set of functions that find many applications in image or pattern characterization because they allow to construct shape descriptors that are invariant against translations, rotations or scale changes. The concepts behind them can be extended to higher dimension spaces, making them also fit to describe volumetric data. They have been less used than their properties might suggest due to their high computational cost. We present a parallel implementation of 3D Zernike moments analysis, written in C with CUDA extensions, which makes it practical to employ Zernike descriptors in interactive applications, yielding a performance of several frames per second in voxel datasets about 2003 in size. In our contribution, we describe the challenges of implementing 3D Zernike analysis in a general-purpose GPU. These include how to deal with numerical inaccuracies, due to the high precision demands of the algorithm, or how to deal with the high volume of input data so that it does not become a bottleneck for the system.

Characterising the grey matter correlates of leukoaraiosis in cerebral small vessel disease.

PubMed

Lambert, Christian; Sam Narean, Janakan; Benjamin, Philip; Zeestraten, Eva; Barrick, Thomas R; Markus, Hugh S

2015-01-01

Cerebral small vessel disease (SVD) is a heterogeneous group of pathological disorders that affect the small vessels of the brain and are an important cause of cognitive impairment. The ischaemic consequences of this disease can be detected using MRI, and include white matter hyperintensities (WMH), lacunar infarcts and microhaemorrhages. The relationship between SVD disease severity, as defined by WMH volume, in sporadic age-related SVD and cortical thickness has not been well defined. However, regional cortical thickness change would be expected due to associated phenomena such as underlying ischaemic white matter damage, and the observation that widespread cortical thinning is observed in the related genetic condition CADASIL (Righart et al., 2013). Using MRI data, we have developed a semi-automated processing pipeline for the anatomical analysis of individuals with cerebral small vessel disease and applied it cross-sectionally to 121 subjects diagnosed with this condition. Using a novel combined automated white matter lesion segmentation algorithm and lesion repair step, highly accurate warping to a group average template was achieved. The volume of white matter affected by WMH was calculated, and used as a covariate of interest in a voxel-based morphometry and voxel-based cortical thickness analysis. Additionally, Gaussian Process Regression (GPR) was used to assess if the severity of SVD, measured by WMH volume, could be predicted from the morphometry and cortical thickness measures. We found significant (Family Wise Error corrected p < 0.05) volumetric decline with increasing lesion load predominately in the parietal lobes, anterior insula and caudate nuclei bilaterally. Widespread significant cortical thinning was found bilaterally in the dorsolateral prefrontal, parietal and posterio-superior temporal cortices. These represent distinctive patterns of cortical thinning and volumetric reduction compared to ageing effects in the same cohort, which exhibited greater changes in the occipital and sensorimotor cortices. Using GPR, the absolute WMH volume could be significantly estimated from the grey matter density and cortical thickness maps (Pearson's coefficients 0.80 and 0.75 respectively). We demonstrate that SVD severity is associated with regional cortical thinning. Furthermore a quantitative measure of SVD severity (WMH volume) can be predicted from grey matter measures, supporting an association between white and grey matter damage. The pattern of cortical thinning and volumetric decline is distinctive for SVD severity compared to ageing. These results, taken together, suggest that there is a phenotypic pattern of atrophy associated with SVD severity.

A global quality assurance system for personalized radiation therapy treatment planning for the prostate (or other sites)

NASA Astrophysics Data System (ADS)

Nwankwo, Obioma; Sihono, Dwi Seno K.; Schneider, Frank; Wenz, Frederik

2014-09-01

Introduction: the quality of radiotherapy treatment plans varies across institutions and depends on the experience of the planner. For the purpose of intra- and inter-institutional homogenization of treatment plan quality, we present an algorithm that learns the organs-at-risk (OARs) sparing patterns from a database of high quality plans. Thereafter, the algorithm predicts the dose that similar organs will receive in future radiotherapy plans prior to treatment planning on the basis of the anatomies of the organs. The predicted dose provides the basis for the individualized specification of planning objectives, and for the objective assessment of the quality of radiotherapy plans. Materials and method: one hundred and twenty eight (128) Volumetric Modulated Arc Therapy (VMAT) plans were selected from a database of prostate cancer plans. The plans were divided into two groups, namely a training set that is made up of 95 plans and a validation set that consists of 33 plans. A multivariate analysis technique was used to determine the relationships between the positions of voxels and their dose. This information was used to predict the likely sparing of the OARs of the plans of the validation set. The predicted doses were visually and quantitatively compared to the reference data using dose volume histograms, the 3D dose distribution, and a novel evaluation metric that is based on the dose different test. Results: a voxel of the bladder on the average receives a higher dose than a voxel of the rectum in optimized radiotherapy plans for the treatment of prostate cancer in our institution if both voxels are at the same distance to the PTV. Based on our evaluation metric, the predicted and reference dose to the bladder agree to within 5% of the prescribed dose to the PTV in 18 out of 33 cases, while the predicted and reference doses to the rectum agree to within 5% in 28 out of the 33 plans of the validation set. Conclusion: We have described a method to predict the likely dose that OARs will receive before treatment planning. This prospective knowledge could be used to implement a global quality assurance system for personalized radiation therapy treatment planning.

From blood oxygenation level dependent (BOLD) signals to brain temperature maps.

PubMed

Sotero, Roberto C; Iturria-Medina, Yasser

2011-11-01

A theoretical framework is presented for converting Blood Oxygenation Level Dependent (BOLD) images to brain temperature maps, based on the idea that disproportional local changes in cerebral blood flow (CBF) as compared with cerebral metabolic rate of oxygen consumption (CMRO₂) during functional brain activity, lead to both brain temperature changes and the BOLD effect. Using an oxygen limitation model and a BOLD signal model, we obtain a transcendental equation relating CBF and CMRO₂ changes with the corresponding BOLD signal, which is solved in terms of the Lambert W function. Inserting this result in the dynamic bioheat equation describing the rate of temperature changes in the brain, we obtain a nonautonomous ordinary differential equation that depends on the BOLD response, which is solved numerically for each brain voxel. Temperature maps obtained from a real BOLD dataset registered in an attention to visual motion experiment were calculated, obtaining temperature variations in the range: (-0.15, 0.1) which is consistent with experimental results. The statistical analysis revealed that significant temperature activations have a similar distribution pattern than BOLD activations. An interesting difference was the activation of the precuneus in temperature maps, a region involved in visuospatial processing, an effect that was not observed on BOLD maps. Furthermore, temperature maps were more localized to gray matter regions than the original BOLD maps, showing less activated voxels in white matter and cerebrospinal fluid.

Movement coordination patterns between the foot joints during walking.

PubMed

Arnold, John B; Caravaggi, Paolo; Fraysse, François; Thewlis, Dominic; Leardini, Alberto

2017-01-01

In 3D gait analysis, kinematics of the foot joints are usually reported via isolated time histories of joint rotations and no information is provided on the relationship between rotations at different joints. The aim of this study was to identify movement coordination patterns in the foot during walking by expanding an existing vector coding technique according to an established multi-segment foot and ankle model. A graphical representation is also described to summarise the coordination patterns of joint rotations across multiple patients. Three-dimensional multi-segment foot kinematics were recorded in 13 adults during walking. A modified vector coding technique was used to identify coordination patterns between foot joints involving calcaneus, midfoot, metatarsus and hallux segments. According to the type and direction of joints rotations, these were classified as in-phase (same direction), anti-phase (opposite directions), proximal or distal joint dominant. In early stance, 51 to 75% of walking trials showed proximal-phase coordination between foot joints comprising the calcaneus, midfoot and metatarsus. In-phase coordination was more prominent in late stance, reflecting synergy in the simultaneous inversion occurring at multiple foot joints. Conversely, a distal-phase coordination pattern was identified for sagittal plane motion of the ankle relative to the midtarsal joint, highlighting the critical role of arch shortening to locomotor function in push-off. This study has identified coordination patterns between movement of the calcaneus, midfoot, metatarsus and hallux by expanding an existing vector cording technique for assessing and classifying coordination patterns of foot joints rotations during walking. This approach provides a different perspective in the analysis of multi-segment foot kinematics, and may be used for the objective quantification of the alterations in foot joint coordination patterns due to lower limb pathologies or following injuries.

Human connectome module pattern detection using a new multi-graph MinMax cut model.

PubMed

De, Wang; Wang, Yang; Nie, Feiping; Yan, Jingwen; Cai, Weidong; Saykin, Andrew J; Shen, Li; Huang, Heng

2014-01-01

Many recent scientific efforts have been devoted to constructing the human connectome using Diffusion Tensor Imaging (DTI) data for understanding the large-scale brain networks that underlie higher-level cognition in human. However, suitable computational network analysis tools are still lacking in human connectome research. To address this problem, we propose a novel multi-graph min-max cut model to detect the consistent network modules from the brain connectivity networks of all studied subjects. A new multi-graph MinMax cut model is introduced to solve this challenging computational neuroscience problem and the efficient optimization algorithm is derived. In the identified connectome module patterns, each network module shows similar connectivity patterns in all subjects, which potentially associate to specific brain functions shared by all subjects. We validate our method by analyzing the weighted fiber connectivity networks. The promising empirical results demonstrate the effectiveness of our method.

A Voxel-Based Approach for Imaging Voids in Three-Dimensional Point Clouds

NASA Astrophysics Data System (ADS)

Salvaggio, Katie N.

Geographically accurate scene models have enormous potential beyond that of just simple visualizations in regard to automated scene generation. In recent years, thanks to ever increasing computational efficiencies, there has been significant growth in both the computer vision and photogrammetry communities pertaining to automatic scene reconstruction from multiple-view imagery. The result of these algorithms is a three-dimensional (3D) point cloud which can be used to derive a final model using surface reconstruction techniques. However, the fidelity of these point clouds has not been well studied, and voids often exist within the point cloud. Voids exist in texturally difficult areas, as well as areas where multiple views were not obtained during collection, constant occlusion existed due to collection angles or overlapping scene geometry, or in regions that failed to triangulate accurately. It may be possible to fill in small voids in the scene using surface reconstruction or hole-filling techniques, but this is not the case with larger more complex voids, and attempting to reconstruct them using only the knowledge of the incomplete point cloud is neither accurate nor aesthetically pleasing. A method is presented for identifying voids in point clouds by using a voxel-based approach to partition the 3D space. By using collection geometry and information derived from the point cloud, it is possible to detect unsampled voxels such that voids can be identified. This analysis takes into account the location of the camera and the 3D points themselves to capitalize on the idea of free space, such that voxels that lie on the ray between the camera and point are devoid of obstruction, as a clear line of sight is a necessary requirement for reconstruction. Using this approach, voxels are classified into three categories: occupied (contains points from the point cloud), free (rays from the camera to the point passed through the voxel), and unsampled (does not contain points and no rays passed through the area). Voids in the voxel space are manifested as unsampled voxels. A similar line-of-sight analysis can then be used to pinpoint locations at aircraft altitude at which the voids in the point clouds could theoretically be imaged. This work is based on the assumption that inclusion of more images of the void areas in the 3D reconstruction process will reduce the number of voids in the point cloud that were a result of lack of coverage. Voids resulting from texturally difficult areas will not benefit from more imagery in the reconstruction process, and thus are identified and removed prior to the determination of future potential imaging locations.

Multi-enzyme logic network architectures for assessing injuries: digital processing of biomarkers.

PubMed

Halámek, Jan; Bocharova, Vera; Chinnapareddy, Soujanya; Windmiller, Joshua Ray; Strack, Guinevere; Chuang, Min-Chieh; Zhou, Jian; Santhosh, Padmanabhan; Ramirez, Gabriela V; Arugula, Mary A; Wang, Joseph; Katz, Evgeny

2010-12-01

A multi-enzyme biocatalytic cascade processing simultaneously five biomarkers characteristic of traumatic brain injury (TBI) and soft tissue injury (STI) was developed. The system operates as a digital biosensor based on concerted function of 8 Boolean AND logic gates, resulting in the decision about the physiological conditions based on the logic analysis of complex patterns of the biomarkers. The system represents the first example of a multi-step/multi-enzyme biosensor with the built-in logic for the analysis of complex combinations of biochemical inputs. The approach is based on recent advances in enzyme-based biocomputing systems and the present paper demonstrates the potential applicability of biocomputing for developing novel digital biosensor networks.

Segmentation and Estimation of the Histological Composition of the Tumor Mass in Computed Tomographic Images of Neuroblastoma

DTIC Science & Technology

2001-10-25

a CT image, each voxel contains an integer number which is the CT value, in Hounsfield units (HU), of the voxel. Therefore, the standard method of...Task Number Work Unit Number Performing Organization Name(s) and Address(es) Department of Electrical and Computer Engineering, University of...34, Journal of Pediatric Surgery, vol 24(7), pp. 708-711, 1989. [4] I. N. Bankman, editor, Handbook of Medical Image Analysis, Academic Press, London, UK

Three-Dimensional Medical Image Registration Using a Patient Space Correlation Technique

DTIC Science & Technology

1991-12-01

dates (e.g. 10 Seenon Technial Jun 87 - 30 Jun 88). Statements on TechnicalDocuments." Block 4. Title and Subtitle. A title is taken from DOE - See...requirements ( 30 :6). The context analysis for this development was conducted primarily to bound the image regis- tration problem and to isolate the required...a series of 30 transverse slices. Each slice is composed of 240 voxels in the x-dimension and 164 voxels in the y-dimension. The dataset was provided

Pilot study for supervised target detection applied to spatially registered multiparametric MRI in order to non-invasively score prostate cancer.

PubMed

Mayer, Rulon; Simone, Charles B; Skinner, William; Turkbey, Baris; Choykey, Peter

2018-03-01

Gleason Score (GS) is a validated predictor of prostate cancer (PCa) disease progression and outcomes. GS from invasive needle biopsies suffers from significant inter-observer variability and possible sampling error, leading to underestimating disease severity ("underscoring") and can result in possible complications. A robust non-invasive image-based approach is, therefore, needed. Use spatially registered multi-parametric MRI (MP-MRI), signatures, and supervised target detection algorithms (STDA) to non-invasively GS PCa at the voxel level. This study retrospectively analyzed 26 MP-MRI from The Cancer Imaging Archive. The MP-MRI (T2, Diffusion Weighted, Dynamic Contrast Enhanced) were spatially registered to each other, combined into stacks, and stitched together to form hypercubes. Multi-parametric (or multi-spectral) signatures derived from a training set of registered MP-MRI were transformed using statistics-based Whitening-Dewhitening (WD). Transformed signatures were inserted into STDA (having conical decision surfaces) applied to registered MP-MRI determined the tumor GS. The MRI-derived GS was quantitatively compared to the pathologist's assessment of the histology of sectioned whole mount prostates from patients who underwent radical prostatectomy. In addition, a meta-analysis of 17 studies of needle biopsy determined GS with confusion matrices and was compared to the MRI-determined GS. STDA and histology determined GS are highly correlated (R = 0.86, p < 0.02). STDA more accurately determined GS and reduced GS underscoring of PCa relative to needle biopsy as summarized by meta-analysis (p < 0.05). This pilot study found registered MP-MRI, STDA, and WD transforms of signatures shows promise in non-invasively GS PCa and reducing underscoring with high spatial resolution. Copyright © 2018 Elsevier Ltd. All rights reserved.

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.

C-State: an interactive web app for simultaneous multi-gene visualization and comparative epigenetic pattern search.

PubMed

Sowpati, Divya Tej; Srivastava, Surabhi; Dhawan, Jyotsna; Mishra, Rakesh K

2017-09-13

Comparative epigenomic analysis across multiple genes presents a bottleneck for bench biologists working with NGS data. Despite the development of standardized peak analysis algorithms, the identification of novel epigenetic patterns and their visualization across gene subsets remains a challenge. We developed a fast and interactive web app, C-State (Chromatin-State), to query and plot chromatin landscapes across multiple loci and cell types. C-State has an interactive, JavaScript-based graphical user interface and runs locally in modern web browsers that are pre-installed on all computers, thus eliminating the need for cumbersome data transfer, pre-processing and prior programming knowledge. C-State is unique in its ability to extract and analyze multi-gene epigenetic information. It allows for powerful GUI-based pattern searching and visualization. We include a case study to demonstrate its potential for identifying user-defined epigenetic trends in context of gene expression profiles.

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.

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.

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.

Widespread age-related differences in the human brain microstructure revealed by quantitative magnetic resonance imaging.

PubMed

Callaghan, Martina F; Freund, Patrick; Draganski, Bogdan; Anderson, Elaine; Cappelletti, Marinella; Chowdhury, Rumana; Diedrichsen, Joern; Fitzgerald, Thomas H B; Smittenaar, Peter; Helms, Gunther; Lutti, Antoine; Weiskopf, Nikolaus

2014-08-01

A pressing need exists to disentangle age-related changes from pathologic neurodegeneration. This study aims to characterize the spatial pattern and age-related differences of biologically relevant measures in vivo over the course of normal aging. Quantitative multiparameter maps that provide neuroimaging biomarkers for myelination and iron levels, parameters sensitive to aging, were acquired from 138 healthy volunteers (age range: 19-75 years). Whole-brain voxel-wise analysis revealed a global pattern of age-related degeneration. Significant demyelination occurred principally in the white matter. The observed age-related differences in myelination were anatomically specific. In line with invasive histologic reports, higher age-related differences were seen in the genu of the corpus callosum than the splenium. Iron levels were significantly increased in the basal ganglia, red nucleus, and extensive cortical regions but decreased along the superior occipitofrontal fascicle and optic radiation. This whole-brain pattern of age-associated microstructural differences in the asymptomatic population provides insight into the neurobiology of aging. The results help build a quantitative baseline from which to examine and draw a dividing line between healthy aging and pathologic neurodegeneration. Copyright © 2014 The Authors. Published by Elsevier Inc. All rights reserved.

Widespread age-related differences in the human brain microstructure revealed by quantitative magnetic resonance imaging☆

PubMed Central

Callaghan, Martina F.; Freund, Patrick; Draganski, Bogdan; Anderson, Elaine; Cappelletti, Marinella; Chowdhury, Rumana; Diedrichsen, Joern; FitzGerald, Thomas H.B.; Smittenaar, Peter; Helms, Gunther; Lutti, Antoine; Weiskopf, Nikolaus

2014-01-01

A pressing need exists to disentangle age-related changes from pathologic neurodegeneration. This study aims to characterize the spatial pattern and age-related differences of biologically relevant measures in vivo over the course of normal aging. Quantitative multiparameter maps that provide neuroimaging biomarkers for myelination and iron levels, parameters sensitive to aging, were acquired from 138 healthy volunteers (age range: 19–75 years). Whole-brain voxel-wise analysis revealed a global pattern of age-related degeneration. Significant demyelination occurred principally in the white matter. The observed age-related differences in myelination were anatomically specific. In line with invasive histologic reports, higher age-related differences were seen in the genu of the corpus callosum than the splenium. Iron levels were significantly increased in the basal ganglia, red nucleus, and extensive cortical regions but decreased along the superior occipitofrontal fascicle and optic radiation. This whole-brain pattern of age-associated microstructural differences in the asymptomatic population provides insight into the neurobiology of aging. The results help build a quantitative baseline from which to examine and draw a dividing line between healthy aging and pathologic neurodegeneration. PMID:24656835

Bayesian spatial transformation models with applications in neuroimaging data

PubMed Central

Miranda, Michelle F.; Zhu, Hongtu; Ibrahim, Joseph G.

2013-01-01

Summary The aim of this paper is to develop a class of spatial transformation models (STM) to spatially model the varying association between imaging measures in a three-dimensional (3D) volume (or 2D surface) and a set of covariates. Our STMs include a varying Box-Cox transformation model for dealing with the issue of non-Gaussian distributed imaging data and a Gaussian Markov Random Field model for incorporating spatial smoothness of the imaging data. Posterior computation proceeds via an efficient Markov chain Monte Carlo algorithm. Simulations and real data analysis demonstrate that the STM significantly outperforms the voxel-wise linear model with Gaussian noise in recovering meaningful geometric patterns. Our STM is able to reveal important brain regions with morphological changes in children with attention deficit hyperactivity disorder. PMID:24128143

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.

A peak position comparison method for high-speed quantitative Laue microdiffraction data processing

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

Kou, Jiawei; Chen, Kai; Tamura, Nobumichi

Indexing Laue patterns of a synchrotron microdiffraction scan can take as much as ten times longer than collecting the data, impeding efficient structural analysis using this technique. Here in this paper, a novel strategy is developed. By comparing the peak positions of adjacent Laue patterns and checking the intensity sequence, grain and phase boundaries are identified, requiring only a limited number of indexing steps for each individual grain. Using this protocol, the Laue patterns can be indexed on the fly as they are taken. The validation of this method is demonstrated by analyzing the microstructure of a laser 3D printedmore » multi-phase/multi-grain Ni-based superalloy.« less

A peak position comparison method for high-speed quantitative Laue microdiffraction data processing

DOE PAGES

Kou, Jiawei; Chen, Kai; Tamura, Nobumichi

2018-09-12

Indexing Laue patterns of a synchrotron microdiffraction scan can take as much as ten times longer than collecting the data, impeding efficient structural analysis using this technique. Here in this paper, a novel strategy is developed. By comparing the peak positions of adjacent Laue patterns and checking the intensity sequence, grain and phase boundaries are identified, requiring only a limited number of indexing steps for each individual grain. Using this protocol, the Laue patterns can be indexed on the fly as they are taken. The validation of this method is demonstrated by analyzing the microstructure of a laser 3D printedmore » multi-phase/multi-grain Ni-based superalloy.« less

Simultaneously extracting multiple parameters via multi-distance and multi-exposure diffuse speckle contrast analysis

PubMed Central

Liu, Jialin; Zhang, Hongchao; Lu, Jian; Ni, Xiaowu; Shen, Zhonghua

2017-01-01

Recent advancements in diffuse speckle contrast analysis (DSCA) have opened the path for noninvasive acquisition of deep tissue microvasculature blood flow. In fact, in addition to blood flow index αDB, the variations of tissue optical absorption μa, reduced scattering coefficients μs′, as well as coherence factor β can modulate temporal fluctuations of speckle patterns. In this study, we use multi-distance and multi-exposure DSCA (MDME-DSCA) to simultaneously extract multiple parameters such as μa, μs′, αDB, and β. The validity of MDME-DSCA has been validated by the simulated data and phantoms experiments. Moreover, as a comparison, the results also show that it is impractical to simultaneously obtain multiple parameters by multi-exposure DSCA (ME-DSCA). PMID:29082083

Automatic coronary artery calcium scoring in cardiac CT angiography using paired convolutional neural networks.

PubMed

Wolterink, Jelmer M; Leiner, Tim; de Vos, Bob D; van Hamersvelt, Robbert W; Viergever, Max A; Išgum, Ivana

2016-12-01

The amount of coronary artery calcification (CAC) is a strong and independent predictor of cardiovascular events. CAC is clinically quantified in cardiac calcium scoring CT (CSCT), but it has been shown that cardiac CT angiography (CCTA) may also be used for this purpose. We present a method for automatic CAC quantification in CCTA. This method uses supervised learning to directly identify and quantify CAC without a need for coronary artery extraction commonly used in existing methods. The study included cardiac CT exams of 250 patients for whom both a CCTA and a CSCT scan were available. To restrict the volume-of-interest for analysis, a bounding box around the heart is automatically determined. The bounding box detection algorithm employs a combination of three ConvNets, where each detects the heart in a different orthogonal plane (axial, sagittal, coronal). These ConvNets were trained using 50 cardiac CT exams. In the remaining 200 exams, a reference standard for CAC was defined in CSCT and CCTA. Out of these, 100 CCTA scans were used for training, and the remaining 100 for evaluation of a voxel classification method for CAC identification. The method uses ConvPairs, pairs of convolutional neural networks (ConvNets). The first ConvNet in a pair identifies voxels likely to be CAC, thereby discarding the majority of non-CAC-like voxels such as lung and fatty tissue. The identified CAC-like voxels are further classified by the second ConvNet in the pair, which distinguishes between CAC and CAC-like negatives. Given the different task of each ConvNet, they share their architecture, but not their weights. Input patches are either 2.5D or 3D. The ConvNets are purely convolutional, i.e. no pooling layers are present and fully connected layers are implemented as convolutions, thereby allowing efficient voxel classification. The performance of individual 2.5D and 3D ConvPairs with input sizes of 15 and 25 voxels, as well as the performance of ensembles of these ConvPairs, were evaluated by a comparison with reference annotations in CCTA and CSCT. In all cases, ensembles of ConvPairs outperformed their individual members. The best performing individual ConvPair detected 72% of lesions in the test set, with on average 0.85 false positive (FP) errors per scan. The best performing ensemble combined all ConvPairs and obtained a sensitivity of 71% at 0.48 FP errors per scan. For this ensemble, agreement with the reference mass score in CSCT was excellent (ICC 0.944 [0.918-0.962]). Aditionally, based on the Agatston score in CCTA, this ensemble assigned 83% of patients to the same cardiovascular risk category as reference CSCT. In conclusion, CAC can be accurately automatically identified and quantified in CCTA using the proposed pattern recognition method. This might obviate the need to acquire a dedicated CSCT scan for CAC scoring, which is regularly acquired prior to a CCTA, and thus reduce the CT radiation dose received by patients. Copyright © 2016 Elsevier B.V. All rights reserved.

Predicting stroke outcome using DCE-CT measured blood velocity

NASA Astrophysics Data System (ADS)

Oosterbroek, Jaap; Bennink, Edwin; Dankbaar, Jan Willem; Horsch, Alexander D.; Viergever, Max A.; Velthuis, Birgitta K.; de Jong, Hugo W. A. M.

2015-03-01

CT plays an important role in the diagnosis of acute stroke patients. Dynamic contrast enhanced CT (DCE-CT) can estimate local tissue perfusion and extent of ischemia. However, hemodynamic information of the large intracranial vessels may also be obtained from DCE-CT data and may contain valuable diagnostic information. We describe a novel method to estimate intravascular blood velocity (IBV) in large cerebral vessels using DCE-CT data, which may be useful to help predict stroke outcome. DCE-CT scans from 34 patients with isolated M1 occlusions were included from a large prospective multi-center cohort study of patients with acute ischemic stroke. Gaussians fitted to the intravascular data yielded the time-to-peak (TTP) and cerebral-blood-volume (CBV). IBV was computed by taking the inverse of the TTP gradient magnitude. Voxels with a CBV of at least 10% of the CBV found in the arterial input function were considered part of a vessel. Mid-sagittal planes were drawn manually and averages of the IBV over all vessel-voxels (arterial and venous) were computed for each hemisphere. Mean-hemisphere IBV differences, mean-hemisphere TTP differences, and hemisphere vessel volume differences were used to differentiate between patients with good and bad outcome (modified Rankin Scale score <3 versus ≥3 at 90 days) using ROC analysis. AUCs from the ROC for IBV, TTP, and vessel volume were 0.80, 0.67 and 0.62 respectively. In conclusion, IBV was found to be a better predictor of patient outcome than the parameters used to compute it and may be a promising new parameter for stroke outcome prediction.

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

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

Apparent Fibre Density: a novel measure for the analysis of diffusion-weighted magnetic resonance images.

PubMed

Raffelt, David; Tournier, J-Donald; Rose, Stephen; Ridgway, Gerard R; Henderson, Robert; Crozier, Stuart; Salvado, Olivier; Connelly, Alan

2012-02-15

This article proposes a new measure called Apparent Fibre Density (AFD) for the analysis of high angular resolution diffusion-weighted images using higher-order information provided by fibre orientation distributions (FODs) computed using spherical deconvolution. AFD has the potential to provide specific information regarding differences between populations by identifying not only the location, but also the orientations along which differences exist. In this work, analytical and numerical Monte-Carlo simulations are used to support the use of the FOD amplitude as a quantitative measure (i.e. AFD) for population and longitudinal analysis. To perform robust voxel-based analysis of AFD, we present and evaluate a novel method to modulate the FOD to account for changes in fibre bundle cross-sectional area that occur during spatial normalisation. We then describe a novel approach for statistical analysis of AFD that uses cluster-based inference of differences extended throughout space and orientation. Finally, we demonstrate the capability of the proposed method by performing voxel-based AFD comparisons between a group of Motor Neurone Disease patients and healthy control subjects. A significant decrease in AFD was detected along voxels and orientations corresponding to both the corticospinal tract and corpus callosal fibres that connect the primary motor cortices. In addition to corroborating previous findings in MND, this study demonstrates the clear advantage of using this type of analysis by identifying differences along single fibre bundles in regions containing multiple fibre populations. Copyright © 2011 Elsevier Inc. All rights reserved.

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.

VOXEL-LEVEL MAPPING OF TRACER KINETICS IN PET STUDIES: A STATISTICAL APPROACH EMPHASIZING TISSUE LIFE TABLES.

PubMed

O'Sullivan, Finbarr; Muzi, Mark; Mankoff, David A; Eary, Janet F; Spence, Alexander M; Krohn, Kenneth A

2014-06-01

Most radiotracers used in dynamic positron emission tomography (PET) scanning act in a linear time-invariant fashion so that the measured time-course data are a convolution between the time course of the tracer in the arterial supply and the local tissue impulse response, known as the tissue residue function. In statistical terms the residue is a life table for the transit time of injected radiotracer atoms. The residue provides a description of the tracer kinetic information measurable by a dynamic PET scan. Decomposition of the residue function allows separation of rapid vascular kinetics from slower blood-tissue exchanges and tissue retention. For voxel-level analysis, we propose that residues be modeled by mixtures of nonparametrically derived basis residues obtained by segmentation of the full data volume. Spatial and temporal aspects of diagnostics associated with voxel-level model fitting are emphasized. Illustrative examples, some involving cancer imaging studies, are presented. Data from cerebral PET scanning with 18 F fluoro-deoxyglucose (FDG) and 15 O water (H2O) in normal subjects is used to evaluate the approach. Cross-validation is used to make regional comparisons between residues estimated using adaptive mixture models with more conventional compartmental modeling techniques. Simulations studies are used to theoretically examine mean square error performance and to explore the benefit of voxel-level analysis when the primary interest is a statistical summary of regional kinetics. The work highlights the contribution that multivariate analysis tools and life-table concepts can make in the recovery of local metabolic information from dynamic PET studies, particularly ones in which the assumptions of compartmental-like models, with residues that are sums of exponentials, might not be certain.

A voxel based comparative analysis using magnetization transfer imaging and T1-weighted magnetic resonance imaging in progressive supranuclear palsy

PubMed Central

Sandhya, Mangalore; Saini, Jitender; Pasha, Shaik Afsar; Yadav, Ravi; Pal, Pramod Kumar

2014-01-01

Aims: In progressive supranuclear palsy (PSP) tissue damage occurs in specific cortical and subcortical regions. Voxel based analysis using T1-weighted images depict quantitative gray matter (GM) atrophy changes. Magnetization transfer (MT) imaging depicts qualitative changes in the brain parenchyma. The purpose of our study was to investigate whether MT imaging could indicate abnormalities in PSP. Settings and Design: A total of 10 patients with PSP (9 men and 1 woman) and 8 controls (5 men and 3 women) were studied with T1-weighted magnetic resonance imaging (MRI) and 3DMT imaging. Voxel based analysis of T1-weighted MRI was performed to investigate brain atrophy while MT was used to study qualitative abnormalities in the brain tissue. We used SPM8 to investigate group differences (with two sample t-test) using the GM and white matter (WM) segmented data. Results: T1-weighted imaging and MT are equally sensitive to detect changes in GM and WM in PSP. Magnetization transfer ratio images and magnetization-prepared rapid acquisition of gradient echo revealed extensive bilateral volume and qualitative changes in the orbitofrontal, prefrontal cortex and limbic lobe and sub cortical GM. The prefrontal structures involved were the rectal gyrus, medial, inferior frontal gyrus (IFG) and middle frontal gyrus (MFG). The anterior cingulate, cingulate gyrus and lingual gyrus of limbic lobe and subcortical structures such as caudate, thalamus, insula and claustrum were also involved. Cerebellar involvement mainly of anterior lobe was also noted. Conclusions: The findings suggest that voxel based MT imaging permits a whole brain unbiased investigation of central nervous system structural integrity in PSP. PMID:25024571

Analysis of a multi-wavelength multi-camera phase-shifting profilometric system for real-time operation

NASA Astrophysics Data System (ADS)

Stoykova, Elena; Gotchev, Atanas; Sainov, Ventseslav

2011-01-01

Real-time accomplishment of a phase-shifting profilometry through simultaneous projection and recording of fringe patterns requires a reliable phase retrieval procedure. In the present work we consider a four-wavelength multi-camera system with four sinusoidal phase gratings for pattern projection that implements a four-step algorithm. Successful operation of the system depends on overcoming two challenges which stem out from the inherent limitations of the phase-shifting algorithm, namely the demand for a sinusoidal fringe profile and the necessity to ensure equal background and contrast of fringes in the recorded fringe patterns. As a first task, we analyze the systematic errors due to the combined influence of the higher harmonics and multi-wavelength illumination in the Fresnel diffraction zone considering the case when the modulation parameters of the four gratings are different. As a second task we simulate the system performance to evaluate the degrading effect of the speckle noise and the spatially varying fringe modulation at non-uniform illumination on the overall accuracy of the profilometric measurement. We consider the case of non-correlated speckle realizations in the recorded fringe patterns due to four-wavelength illumination. Finally, we apply a phase retrieval procedure which includes normalization, background removal and denoising of the recorded fringe patterns to both simulated and measured data obtained for a dome surface.

Identification and visualization of dominant patterns and anomalies in remotely sensed vegetation phenology using a parallel tool for principal components analysis

Treesearch

Richard Tran Mills; Jitendra Kumar; Forrest M. Hoffman; William W. Hargrove; Joseph P. Spruce; Steven P. Norman

2013-01-01

We investigated the use of principal components analysis (PCA) to visualize dominant patterns and identify anomalies in a multi-year land surface phenology data set (231 m × 231 m normalized difference vegetation index (NDVI) values derived from the Moderate Resolution Imaging Spectroradiometer (MODIS)) used for detecting threats to forest health in the conterminous...

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

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.

Multi-Target/Multi-Sensor Tracking using Only Range and Doppler Measurements

DTIC Science & Technology

2009-04-01

I+1) k ) represents a global maximum, given the Gaussian components used in our model, and given that P(I) has been fixed using parameter values...on Pattern Analysis and Machine Intelligence, 24 (2002), 381—396. [37] Perlovsky, L. I., Plum, C. P., Franchi , P. R., Tichovolsky, E. J., Choi, D. S

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

Long-Term Functional Outcomes and Correlation with Regional Brain Connectivity by MRI Diffusion Tractography Metrics in a Near-Term Rabbit Model of Intrauterine Growth Restriction

PubMed Central

Illa, Miriam; Eixarch, Elisenda; Batalle, Dafnis; Arbat-Plana, Ariadna; Muñoz-Moreno, Emma; Figueras, Francesc; Gratacos, Eduard

2013-01-01

Background Intrauterine growth restriction (IUGR) affects 5–10% of all newborns and is associated with increased risk of memory, attention and anxiety problems in late childhood and adolescence. The neurostructural correlates of long-term abnormal neurodevelopment associated with IUGR are unknown. Thus, the aim of this study was to provide a comprehensive description of the long-term functional and neurostructural correlates of abnormal neurodevelopment associated with IUGR in a near-term rabbit model (delivered at 30 days of gestation) and evaluate the development of quantitative imaging biomarkers of abnormal neurodevelopment based on diffusion magnetic resonance imaging (MRI) parameters and connectivity. Methodology At +70 postnatal days, 10 cases and 11 controls were functionally evaluated with the Open Field Behavioral Test which evaluates anxiety and attention and the Object Recognition Task that evaluates short-term memory and attention. Subsequently, brains were collected, fixed and a high resolution MRI was performed. Differences in diffusion parameters were analyzed by means of voxel-based and connectivity analysis measuring the number of fibers reconstructed within anxiety, attention and short-term memory networks over the total fibers. Principal Findings The results of the neurobehavioral and cognitive assessment showed a significant higher degree of anxiety, attention and memory problems in cases compared to controls in most of the variables explored. Voxel-based analysis (VBA) revealed significant differences between groups in multiple brain regions mainly in grey matter structures, whereas connectivity analysis demonstrated lower ratios of fibers within the networks in cases, reaching the statistical significance only in the left hemisphere for both networks. Finally, VBA and connectivity results were also correlated with functional outcome. Conclusions The rabbit model used reproduced long-term functional impairments and their neurostructural correlates of abnormal neurodevelopment associated with IUGR. The description of the pattern of microstructural changes underlying functional defects may help to develop biomarkers based in diffusion MRI and connectivity analysis. PMID:24143189

Brain metabolism in patients with vegetative state after post-resuscitated hypoxic-ischemic brain injury: statistical parametric mapping analysis of F-18 fluorodeoxyglucose positron emission tomography.

PubMed

Kim, Yong Wook; Kim, Hyoung Seop; An, Young-sil

2013-03-01

Hypoxic-ischemic brain injury (HIBI) after cardiopulmonary resuscitation is one of the most devastating neurological conditions that causing the impaired consciousness. However, there were few studies investigated the changes of brain metabolism in patients with vegetative state (VS) after post-resuscitated HIBI. This study aimed to analyze the change of overall brain metabolism and elucidated the brain area correlated with the level of consciousness (LOC) in patients with VS after post-resuscitated HIBI. We consecutively enrolled 17 patients with VS after HIBI, who experienced cardiopulmonary resuscitation. Overall brain metabolism was measured by F-18 fluorodeoxyglucose positron emission tomography (F-18 FDG PET) and we compared regional brain metabolic patterns from 17 patients with those from 15 normal controls using voxel-by-voxel based statistical parametric mapping analysis. Additionally, we correlated the LOC measured by the JFK-coma recovery scale-revised of each patient with brain metabolism by covariance analysis. Compared with normal controls, the patients with VS after post-resuscitated HIBI revealed significantly decreased brain metabolism in bilateral precuneus, bilateral posterior cingulate gyrus, bilateral middle frontal gyri, bilateral superior parietal gyri, bilateral middle occipital gyri, bilateral precentral gyri (PFEW correctecd < 0.0001), and increased brain metabolism in bilateral insula, bilateral cerebella, and the brainstem (PFEW correctecd < 0.0001). In covariance analysis, the LOC was significantly correlated with brain metabolism in bilateral fusiform and superior temporal gyri (Puncorrected < 0.005). Our study demonstrated that the precuneus, the posterior cingulate area and the frontoparietal cortex, which is a component of neural correlate for consciousness, may be relevant structure for impaired consciousness in patient with VS after post-resuscitated HIBI. In post-resuscitated HIBI, measurement of brain metabolism using PET images may be helpful for investigating the brain function that cannot be obtained by morphological imaging and can be used to assess the brain area responsible for consciousness.

Long-term functional outcomes and correlation with regional brain connectivity by MRI diffusion tractography metrics in a near-term rabbit model of intrauterine growth restriction.

PubMed

Illa, Miriam; Eixarch, Elisenda; Batalle, Dafnis; Arbat-Plana, Ariadna; Muñoz-Moreno, Emma; Figueras, Francesc; Gratacos, Eduard

2013-01-01

Intrauterine growth restriction (IUGR) affects 5-10% of all newborns and is associated with increased risk of memory, attention and anxiety problems in late childhood and adolescence. The neurostructural correlates of long-term abnormal neurodevelopment associated with IUGR are unknown. Thus, the aim of this study was to provide a comprehensive description of the long-term functional and neurostructural correlates of abnormal neurodevelopment associated with IUGR in a near-term rabbit model (delivered at 30 days of gestation) and evaluate the development of quantitative imaging biomarkers of abnormal neurodevelopment based on diffusion magnetic resonance imaging (MRI) parameters and connectivity. At +70 postnatal days, 10 cases and 11 controls were functionally evaluated with the Open Field Behavioral Test which evaluates anxiety and attention and the Object Recognition Task that evaluates short-term memory and attention. Subsequently, brains were collected, fixed and a high resolution MRI was performed. Differences in diffusion parameters were analyzed by means of voxel-based and connectivity analysis measuring the number of fibers reconstructed within anxiety, attention and short-term memory networks over the total fibers. The results of the neurobehavioral and cognitive assessment showed a significant higher degree of anxiety, attention and memory problems in cases compared to controls in most of the variables explored. Voxel-based analysis (VBA) revealed significant differences between groups in multiple brain regions mainly in grey matter structures, whereas connectivity analysis demonstrated lower ratios of fibers within the networks in cases, reaching the statistical significance only in the left hemisphere for both networks. Finally, VBA and connectivity results were also correlated with functional outcome. The rabbit model used reproduced long-term functional impairments and their neurostructural correlates of abnormal neurodevelopment associated with IUGR. The description of the pattern of microstructural changes underlying functional defects may help to develop biomarkers based in diffusion MRI and connectivity analysis.

Improvement of Speckle Contrast Image Processing by an Efficient Algorithm.

PubMed

Steimers, A; Farnung, W; Kohl-Bareis, M

2016-01-01

We demonstrate an efficient algorithm for the temporal and spatial based calculation of speckle contrast for the imaging of blood flow by laser speckle contrast analysis (LASCA). It reduces the numerical complexity of necessary calculations, facilitates a multi-core and many-core implementation of the speckle analysis and enables an independence of temporal or spatial resolution and SNR. The new algorithm was evaluated for both spatial and temporal based analysis of speckle patterns with different image sizes and amounts of recruited pixels as sequential, multi-core and many-core code.

Analyzing gene expression time-courses based on multi-resolution shape mixture model.

PubMed

Li, Ying; He, Ye; Zhang, Yu

2016-11-01

Biological processes actually are a dynamic molecular process over time. Time course gene expression experiments provide opportunities to explore patterns of gene expression change over a time and understand the dynamic behavior of gene expression, which is crucial for study on development and progression of biology and disease. Analysis of the gene expression time-course profiles has not been fully exploited so far. It is still a challenge problem. We propose a novel shape-based mixture model clustering method for gene expression time-course profiles to explore the significant gene groups. Based on multi-resolution fractal features and mixture clustering model, we proposed a multi-resolution shape mixture model algorithm. Multi-resolution fractal features is computed by wavelet decomposition, which explore patterns of change over time of gene expression at different resolution. Our proposed multi-resolution shape mixture model algorithm is a probabilistic framework which offers a more natural and robust way of clustering time-course gene expression. We assessed the performance of our proposed algorithm using yeast time-course gene expression profiles compared with several popular clustering methods for gene expression profiles. The grouped genes identified by different methods are evaluated by enrichment analysis of biological pathways and known protein-protein interactions from experiment evidence. The grouped genes identified by our proposed algorithm have more strong biological significance. A novel multi-resolution shape mixture model algorithm based on multi-resolution fractal features is proposed. Our proposed model provides a novel horizons and an alternative tool for visualization and analysis of time-course gene expression profiles. The R and Matlab program is available upon the request. Copyright © 2016 Elsevier Inc. All rights reserved.

Metabolic injury in a variable rat model of post-status epilepticus.

PubMed

Pearce, Patrice S; Wu, Yijen; Rapuano, Amedeo; Kelly, Kevin M; de Lanerolle, Nihal; Pan, Jullie W

2016-12-01

In vivo studies of epilepsy typically use prolonged status epilepticus to generate recurrent seizures. However, reports on variable status duration have found discrete differences in injury after 40-50 min of seizures, suggesting a pathophysiologic sensitivity to seizure duration. In this report we take a multivariate cluster analysis to study a short duration status epilepticus model using in vivo 7T magnetic resonance spectroscopy (MRS) and histologic evaluation. The Hellier Dudek model was applied with 45 min of status epilepticus after which the animals were imaged twice, at 3 days and 3 weeks post-status epilepticus. Single voxel point resolved spectroscopy (PRESS) MRS was used to acquire data from the dentate gyrus and CA3 region of the hippocampus, assessing metabolite ratios to total creatine (tCr). In a subset of animals after the second imaging study, brains were analyzed histologically by Nissl staining. A hierarchical cluster analysis performed on the 3-day data from 21 kainate-treated animals (dentate gyrus voxel) segregated into two clusters, denoted by KM (more injured, n = 6) and KL (less injured, n = 15). Although there was no difference in kainate dosing or seizure count between them, the metabolic pattern of injury was different. The KM group displayed the largest significant changes in neuronal and glial parameters; the KL group displayed milder but significant changes. At 3 weeks, the KL group returned to normal compared to controls, whereas the KM group persisted with depressed N-acetyl aspartate (NAA)/tCr, glutamate/tCr, and increased inositol/tCr and glutamine/tCr. The classification was also consistent with subsequent histologic patterns at 3 weeks. Although a short status period might be expected to generate a continuous distribution of metabolic injury, these data show that the short Hellier Dudek model appears to generate two levels of injury. The changes seen in segregated groups persisted into 3 weeks, and can be interpreted according to neuronal and glial biomarkers consistent with histology results. © 2016 The Authors. Epilepsia published by Wiley Periodicals, Inc. on behalf of International League Against Epilepsy.

Methodological improvements in voxel-based analysis of diffusion tensor images: applications to study the impact of apolipoprotein E on white matter integrity.

PubMed

Newlander, Shawn M; Chu, Alan; Sinha, Usha S; Lu, Po H; Bartzokis, George

2014-02-01

To identify regional differences in apparent diffusion coefficient (ADC) and fractional anisotropy (FA) using customized preprocessing before voxel-based analysis (VBA) in 14 normal subjects with the specific genes that decrease (apolipoprotein [APO] E ε2) and that increase (APOE ε4) the risk of Alzheimer's disease. Diffusion tensor images (DTI) acquired at 1.5 Tesla were denoised with a total variation tensor regularization algorithm before affine and nonlinear registration to generate a common reference frame for the image volumes of all subjects. Anisotropic and isotropic smoothing with varying kernel sizes was applied to the aligned data before VBA to determine regional differences between cohorts segregated by allele status. VBA on the denoised tensor data identified regions of reduced FA in APOE ε4 compared with the APOE ε2 healthy older carriers. The most consistent results were obtained using the denoised tensor and anisotropic smoothing before statistical testing. In contrast, isotropic smoothing identified regional differences for small filter sizes alone, emphasizing that this method introduces bias in FA values for higher kernel sizes. Voxel-based DTI analysis can be performed on low signal to noise ratio images to detect subtle regional differences in cohorts using the proposed preprocessing techniques. Copyright © 2013 Wiley Periodicals, Inc.

Voxel-based analysis of the immediate early gene, c-jun, in the honey bee brain after a sucrose stimulus.

PubMed

McNeill, M S; Robinson, G E

2015-06-01

Immediate early genes (IEGs) have served as useful markers of brain neuronal activity in mammals, and more recently in insects. The mammalian canonical IEG, c-jun, is part of regulatory pathways conserved in insects and has been shown to be responsive to alarm pheromone in honey bees. We tested whether c-jun was responsive in honey bees to another behaviourally relevant stimulus, sucrose, in order to further identify the brain regions involved in sucrose processing. To identify responsive regions, we developed a new method of voxel-based analysis of c-jun mRNA expression. We found that c-jun is expressed in somata throughout the brain. It was rapidly induced in response to sucrose stimuli, and it responded in somata near the antennal and mechanosensory motor centre, mushroom body calices and lateral protocerebrum, which are known to be involved in sucrose processing. c-jun also responded to sucrose in somata near the lateral suboesophageal ganglion, dorsal optic lobe, ventral optic lobe and dorsal posterior protocerebrum, which had not been previously identified by other methods. These results demonstrate the utility of voxel-based analysis of mRNA expression in the insect brain. © 2015 The Royal Entomological Society.

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.

An automated voxelized dosimetry tool for radionuclide therapy based on serial quantitative SPECT/CT imaging

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

Jackson, Price A.; Kron, Tomas; Beauregard, Jean-Mathieu

2013-11-15

Purpose: To create an accurate map of the distribution of radiation dose deposition in healthy and target tissues during radionuclide therapy.Methods: Serial quantitative SPECT/CT images were acquired at 4, 24, and 72 h for 28 {sup 177}Lu-octreotate peptide receptor radionuclide therapy (PRRT) administrations in 17 patients with advanced neuroendocrine tumors. Deformable image registration was combined with an in-house programming algorithm to interpolate pharmacokinetic uptake and clearance at a voxel level. The resultant cumulated activity image series are comprised of values representing the total number of decays within each voxel's volume. For PRRT, cumulated activity was translated to absorbed dose basedmore » on Monte Carlo-determined voxel S-values at a combination of long and short ranges. These dosimetric image sets were compared for mean radiation absorbed dose to at-risk organs using a conventional MIRD protocol (OLINDA 1.1).Results: Absorbed dose values to solid organs (liver, kidneys, and spleen) were within 10% using both techniques. Dose estimates to marrow were greater using the voxelized protocol, attributed to the software incorporating crossfire effect from nearby tumor volumes.Conclusions: The technique presented offers an efficient, automated tool for PRRT dosimetry based on serial post-therapy imaging. Following retrospective analysis, this method of high-resolution dosimetry may allow physicians to prescribe activity based on required dose to tumor volume or radiation limits to healthy tissue in individual patients.« less

Fuzzy C-mean clustering on kinetic parameter estimation with generalized linear least square algorithm in SPECT

NASA Astrophysics Data System (ADS)

Choi, Hon-Chit; Wen, Lingfeng; Eberl, Stefan; Feng, Dagan

2006-03-01

Dynamic Single Photon Emission Computed Tomography (SPECT) has the potential to quantitatively estimate physiological parameters by fitting compartment models to the tracer kinetics. The generalized linear least square method (GLLS) is an efficient method to estimate unbiased kinetic parameters and parametric images. However, due to the low sensitivity of SPECT, noisy data can cause voxel-wise parameter estimation by GLLS to fail. Fuzzy C-Mean (FCM) clustering and modified FCM, which also utilizes information from the immediate neighboring voxels, are proposed to improve the voxel-wise parameter estimation of GLLS. Monte Carlo simulations were performed to generate dynamic SPECT data with different noise levels and processed by general and modified FCM clustering. Parametric images were estimated by Logan and Yokoi graphical analysis and GLLS. The influx rate (K I), volume of distribution (V d) were estimated for the cerebellum, thalamus and frontal cortex. Our results show that (1) FCM reduces the bias and improves the reliability of parameter estimates for noisy data, (2) GLLS provides estimates of micro parameters (K I-k 4) as well as macro parameters, such as volume of distribution (Vd) and binding potential (BP I & BP II) and (3) FCM clustering incorporating neighboring voxel information does not improve the parameter estimates, but improves noise in the parametric images. These findings indicated that it is desirable for pre-segmentation with traditional FCM clustering to generate voxel-wise parametric images with GLLS from dynamic SPECT data.

Depressive symptoms and white matter dysfunction in retired NFL players with concussion history.

PubMed

Strain, Jeremy; Didehbani, Nyaz; Cullum, C Munro; Mansinghani, Sethesh; Conover, Heather; Kraut, Michael A; Hart, John; Womack, Kyle B

2013-07-02

To determine whether correlates of white matter integrity can provide general as well as specific insight into the chronic effects of head injury coupled with depression symptom expression in professional football players. We studied 26 retired National Football League (NFL) athletes who underwent diffusion tensor imaging (DTI) scanning. Depressive symptom severity was measured using the Beck Depression Inventory II (BDI-II) including affective, cognitive, and somatic subfactor scores (Buckley 3-factor model). Fractional anisotropy (FA) maps were processed using tract-based spatial statistics from FSL. Correlations between FA and BDI-II scores were assessed using both voxel-wise and region of interest (ROI) techniques, with ROIs that corresponded to white matter tracts. Tracts demonstrating significant correlations were further evaluated using a receiver operating characteristic curve that utilized the mean FA to distinguish depressed from nondepressed subjects. Voxel-wise analysis identified widely distributed voxels that negatively correlated with total BDI-II and cognitive and somatic subfactors, with voxels correlating with the affective component (p < 0.05 corrected) localized to frontal regions. Four tract ROIs negatively correlated (p < 0.01) with total BDI-II: forceps minor, right frontal aslant tract, right uncinate fasciculus, and left superior longitudinal fasciculus. FA of the forceps minor differentiated depressed from nondepressed athletes with 100% sensitivity and 95% specificity. Depressive symptoms in retired NFL athletes correlate negatively with FA using either an unbiased voxel-wise or an ROI-based, tract-wise approach. DTI is a promising biomarker for depression in this population.

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