Mapping cell-specific functional connections in the mouse brain using ChR2-evoked hemodynamics (Conference Presentation)

NASA Astrophysics Data System (ADS)

Bauer, Adam Q.; Kraft, Andrew; Baxter, Grant A.; Bruchas, Michael; Lee, Jin-Moo; Culver, Joseph P.

2017-02-01

Functional magnetic resonance imaging (fMRI) has transformed our understanding of the brain's functional organization. However, mapping subunits of a functional network using hemoglobin alone presents several disadvantages. Evoked and spontaneous hemodynamic fluctuations reflect ensemble activity from several populations of neurons making it difficult to discern excitatory vs inhibitory network activity. Still, blood-based methods of brain mapping remain powerful because hemoglobin provides endogenous contrast in all mammalian brains. To add greater specificity to hemoglobin assays, we integrated optical intrinsic signal(OIS) imaging with optogenetic stimulation to create an Opto-OIS mapping tool that combines the cell-specificity of optogenetics with label-free, hemoglobin imaging. Before mapping, titrated photostimuli determined which stimulus parameters elicited linear hemodynamic responses in the cortex. Optimized stimuli were then scanned over the left hemisphere to create a set of optogenetically-defined effective connectivity (Opto-EC) maps. For many sites investigated, Opto-EC maps exhibited higher spatial specificity than those determined using spontaneous hemodynamic fluctuations. For example, resting-state functional connectivity (RS-FC) patterns exhibited widespread ipsilateral connectivity while Opto-EC maps contained distinct short- and long-range constellations of ipsilateral connectivity. Further, RS-FC maps were usually symmetric about midline while Opto-EC maps displayed more heterogeneous contralateral homotopic connectivity. Both Opto-EC and RS-FC patterns were compared to mouse connectivity data from the Allen Institute. Unlike RS-FC maps, Thy1-based maps collected in awake, behaving mice closely recapitulated the connectivity structure derived using ex vivo anatomical tracer methods. Opto-OIS mapping could be a powerful tool for understanding cellular and molecular contributions to network dynamics and processing in the mouse brain.

An Intracranial Electroencephalography (iEEG) Brain Function Mapping Tool with an Application to Epilepsy Surgery Evaluation

PubMed Central

Wang, Yinghua; Yan, Jiaqing; Wen, Jianbin; Yu, Tao; Li, Xiaoli

2016-01-01

Objects: Before epilepsy surgeries, intracranial electroencephalography (iEEG) is often employed in function mapping and epileptogenic foci localization. Although the implanted electrodes provide crucial information for epileptogenic zone resection, a convenient clinical tool for electrode position registration and Brain Function Mapping (BFM) visualization is still lacking. In this study, we developed a BFM Tool, which facilitates electrode position registration and BFM visualization, with an application to epilepsy surgeries. Methods: The BFM Tool mainly utilizes electrode location registration and function mapping based on pre-defined brain models from other software. In addition, the electrode node and mapping properties, such as the node size/color, edge color/thickness, mapping method, can be adjusted easily using the setting panel. Moreover, users may manually import/export location and connectivity data to generate figures for further application. The role of this software is demonstrated by a clinical study of language area localization. Results: The BFM Tool helps clinical doctors and researchers visualize implanted electrodes and brain functions in an easy, quick and flexible manner. Conclusions: Our tool provides convenient electrode registration, easy brain function visualization, and has good performance. It is clinical-oriented and is easy to deploy and use. The BFM tool is suitable for epilepsy and other clinical iEEG applications. PMID:27199729

aMAP is a validated pipeline for registration and segmentation of high-resolution mouse brain data

PubMed Central

Niedworok, Christian J.; Brown, Alexander P. Y.; Jorge Cardoso, M.; Osten, Pavel; Ourselin, Sebastien; Modat, Marc; Margrie, Troy W.

2016-01-01

The validation of automated image registration and segmentation is crucial for accurate and reliable mapping of brain connectivity and function in three-dimensional (3D) data sets. While validation standards are necessarily high and routinely met in the clinical arena, they have to date been lacking for high-resolution microscopy data sets obtained from the rodent brain. Here we present a tool for optimized automated mouse atlas propagation (aMAP) based on clinical registration software (NiftyReg) for anatomical segmentation of high-resolution 3D fluorescence images of the adult mouse brain. We empirically evaluate aMAP as a method for registration and subsequent segmentation by validating it against the performance of expert human raters. This study therefore establishes a benchmark standard for mapping the molecular function and cellular connectivity of the rodent brain. PMID:27384127

Hemispherical map for the human brain cortex

NASA Astrophysics Data System (ADS)

Tosun, Duygu; Prince, Jerry L.

2001-07-01

Understanding the function of the human brain cortex is a primary goal in human brain mapping. Methods to unfold and flatten the cortical surface for visualization and measurement have been described in previous literature; but comparison across multiple subjects is still difficult because of the lack of a standard mapping technique. We describe a new approach that maps each hemisphere of the cortex to a portion of a sphere in a standard way, making comparison of anatomy and function across different subjects possible. Starting with a three-dimensional magnetic resonance image of the brain, the cortex is segmented and represented as a triangle mesh. Defining a cut around the corpus collosum identifies the left and right hemispheres. Together, the two hemispheres are mapped to the complex plane using a conformal mapping technique. A Mobius transformation, which is conformal, is used to transform the points on the complex plane so that a projective transformation maps each brain hemisphere onto a spherical segment comprising a sphere with a cap removed. We determined the best size of the spherical cap by minimizing the relative area distortion between hemispherical maps and original cortical surfaces. The relative area distortion between the hemispherical maps and the original cortical surfaces for fifteen human brains is analyzed.

Minocycline Effects on Intracerebral Hemorrhage-Induced Iron Overload in Aged Rats: Brain Iron Quantification With Magnetic Resonance Imaging.

PubMed

Cao, Shenglong; Hua, Ya; Keep, Richard F; Chaudhary, Neeraj; Xi, Guohua

2018-04-01

Brain iron overload is a key factor causing brain injury after intracerebral hemorrhage (ICH). This study quantified brain iron levels after ICH with magnetic resonance imaging R2* mapping. The effect of minocycline on iron overload and ICH-induced brain injury in aged rats was also determined. Aged (18 months old) male Fischer 344 rats had an intracerebral injection of autologous blood or saline, and brain iron levels were measured by magnetic resonance imaging R2* mapping. Some ICH rats were treated with minocycline or vehicle. The rats were euthanized at days 7 and 28 after ICH, and brains were used for immunohistochemistry and Western blot analyses. Magnetic resonance imaging (T2-weighted, T2* gradient-echo, and R2* mapping) sequences were performed at different time points. ICH-induced brain iron overload in the perihematomal area could be quantified by R2* mapping. Minocycline treatment reduced brain iron accumulation, T2* lesion volume, iron-handling protein upregulation, neuronal cell death, and neurological deficits ( P <0.05). Magnetic resonance imaging R2* mapping is a reliable and noninvasive method, which can quantitatively measure brain iron levels after ICH. Minocycline reduced ICH-related perihematomal iron accumulation and brain injury in aged rats. © 2018 American Heart Association, Inc.

Quantitative Assessment of Normal Fetal Brain Myelination Using Fast Macromolecular Proton Fraction Mapping.

PubMed

Yarnykh, V L; Prihod'ko, I Y; Savelov, A A; Korostyshevskaya, A M

2018-05-10

Fast macromolecular proton fraction mapping is a recently emerged MRI method for quantitative myelin imaging. Our aim was to develop a clinically targeted technique for macromolecular proton fraction mapping of the fetal brain and test its capability to characterize normal prenatal myelination. This prospective study included 41 pregnant women (gestational age range, 18-38 weeks) without abnormal findings on fetal brain MR imaging performed for clinical indications. A fast fetal brain macromolecular proton fraction mapping protocol was implemented on a clinical 1.5T MR imaging scanner without software modifications and was performed after a clinical examination with an additional scan time of <5 minutes. 3D macromolecular proton fraction maps were reconstructed from magnetization transfer-weighted, T1-weighted, and proton density-weighted images by the single-point method. Mean macromolecular proton fraction in the brain stem, cerebellum, and thalamus and frontal, temporal, and occipital WM was compared between structures and pregnancy trimesters using analysis of variance. Gestational age dependence of the macromolecular proton fraction was assessed using the Pearson correlation coefficient ( r ). The mean macromolecular proton fraction in the fetal brain structures varied between 2.3% and 4.3%, being 5-fold lower than macromolecular proton fraction in adult WM. The macromolecular proton fraction in the third trimester was higher compared with the second trimester in the brain stem, cerebellum, and thalamus. The highest macromolecular proton fraction was observed in the brain stem, followed by the thalamus, cerebellum, and cerebral WM. The macromolecular proton fraction in the brain stem, cerebellum, and thalamus strongly correlated with gestational age ( r = 0.88, 0.80, and 0.73; P < .001). No significant correlations were found for cerebral WM regions. Myelin is the main factor determining macromolecular proton fraction in brain tissues. Macromolecular proton fraction mapping is sensitive to the earliest stages of the fetal brain myelination and can be implemented in a clinical setting. © 2018 by American Journal of Neuroradiology.

Event-related functional MRI: Past, present, and future

PubMed Central

Rosen, Bruce R.; Buckner, Randy L.; Dale, Anders M.

1998-01-01

The past two decades have seen an enormous growth in the field of human brain mapping. Investigators have extensively exploited techniques such as positron emission tomography and MRI to map patterns of brain activity based on changes in cerebral hemodynamics. However, until recently, most studies have investigated equilibrium changes in blood flow measured over time periods upward of 1 min. The advent of high-speed MRI methods, capable of imaging the entire brain with a temporal resolution of a few seconds, allows for brain mapping based on more transient aspects of the hemodynamic response. Today it is now possible to map changes in cerebrovascular parameters essentially in real time, conferring the ability to observe changes in brain state that occur over time periods of seconds. Furthermore, because robust hemodynamic alterations are detectable after neuronal stimuli lasting only a few tens of milliseconds, a new class of task paradigms designed to measure regional responses to single sensory or cognitive events can now be studied. Such “event related” functional MRI should provide for fundamentally new ways to interrogate brain function, and allow for the direct comparison and ultimately integration of data acquired by using more traditional behavioral and electrophysiological methods. PMID:9448240

A high-resolution computational localization method for transcranial magnetic stimulation mapping.

PubMed

Aonuma, Shinta; Gomez-Tames, Jose; Laakso, Ilkka; Hirata, Akimasa; Takakura, Tomokazu; Tamura, Manabu; Muragaki, Yoshihiro

2018-05-15

Transcranial magnetic stimulation (TMS) is used for the mapping of brain motor functions. The complexity of the brain deters determining the exact localization of the stimulation site using simplified methods (e.g., the region below the center of the TMS coil) or conventional computational approaches. This study aimed to present a high-precision localization method for a specific motor area by synthesizing computed non-uniform current distributions in the brain for multiple sessions of TMS. Peritumoral mapping by TMS was conducted on patients who had intra-axial brain neoplasms located within or close to the motor speech area. The electric field induced by TMS was computed using realistic head models constructed from magnetic resonance images of patients. A post-processing method was implemented to determine a TMS hotspot by combining the computed electric fields for the coil orientations and positions that delivered high motor-evoked potentials during peritumoral mapping. The method was compared to the stimulation site localized via intraoperative direct brain stimulation and navigated TMS. Four main results were obtained: 1) the dependence of the computed hotspot area on the number of peritumoral measurements was evaluated; 2) the estimated localization of the hand motor area in eight non-affected hemispheres was in good agreement with the position of a so-called "hand-knob"; 3) the estimated hotspot areas were not sensitive to variations in tissue conductivity; and 4) the hand motor areas estimated by this proposal and direct electric stimulation (DES) were in good agreement in the ipsilateral hemisphere of four glioma patients. The TMS localization method was validated by well-known positions of the "hand-knob" in brains for the non-affected hemisphere, and by a hotspot localized via DES during awake craniotomy for the tumor-containing hemisphere. Copyright © 2018 Elsevier Inc. All rights reserved.

Altered Whole-Brain and Network-Based Functional Connectivity in Parkinson's Disease.

PubMed

de Schipper, Laura J; Hafkemeijer, Anne; van der Grond, Jeroen; Marinus, Johan; Henselmans, Johanna M L; van Hilten, Jacobus J

2018-01-01

Background: Functional imaging methods, such as resting-state functional magnetic resonance imaging, reflect changes in neural connectivity and may help to assess the widespread consequences of disease-specific network changes in Parkinson's disease. In this study we used a relatively new graph analysis approach in functional imaging: eigenvector centrality mapping. This model-free method, applied to all voxels in the brain, identifies prominent regions in the brain network hierarchy and detects localized differences between patient populations. In other neurological disorders, eigenvector centrality mapping has been linked to changes in functional connectivity in certain nodes of brain networks. Objectives: Examining changes in functional brain connectivity architecture on a whole brain and network level in patients with Parkinson's disease. Methods: Whole brain resting-state functional architecture was studied with a recently introduced graph analysis approach (eigenvector centrality mapping). Functional connectivity was further investigated in relation to eight known resting-state networks. Cross-sectional analyses included group comparison of functional connectivity measures of Parkinson's disease patients ( n = 107) with control subjects ( n = 58) and correlations with clinical data, including motor and cognitive impairment and a composite measure of predominantly non-dopaminergic symptoms. Results: Eigenvector centrality mapping revealed that frontoparietal regions were more prominent in the whole-brain network function in patients compared to control subjects, while frontal and occipital brain areas were less prominent in patients. Using standard resting-state networks, we found predominantly increased functional connectivity, namely within sensorimotor system and visual networks in patients. Regional group differences in functional connectivity of both techniques between patients and control subjects partly overlapped for highly connected posterior brain regions, in particular in the posterior cingulate cortex and precuneus. Clinico-functional imaging relations were not found. Conclusions: Changes on the level of functional brain connectivity architecture might provide a different perspective of pathological consequences of Parkinson's disease. The involvement of specific, highly connected (hub) brain regions may influence whole brain functional network architecture in Parkinson's disease.

Interpretability of Multivariate Brain Maps in Linear Brain Decoding: Definition, and Heuristic Quantification in Multivariate Analysis of MEG Time-Locked Effects.

PubMed

Kia, Seyed Mostafa; Vega Pons, Sandro; Weisz, Nathan; Passerini, Andrea

2016-01-01

Brain decoding is a popular multivariate approach for hypothesis testing in neuroimaging. Linear classifiers are widely employed in the brain decoding paradigm to discriminate among experimental conditions. Then, the derived linear weights are visualized in the form of multivariate brain maps to further study spatio-temporal patterns of underlying neural activities. It is well known that the brain maps derived from weights of linear classifiers are hard to interpret because of high correlations between predictors, low signal to noise ratios, and the high dimensionality of neuroimaging data. Therefore, improving the interpretability of brain decoding approaches is of primary interest in many neuroimaging studies. Despite extensive studies of this type, at present, there is no formal definition for interpretability of multivariate brain maps. As a consequence, there is no quantitative measure for evaluating the interpretability of different brain decoding methods. In this paper, first, we present a theoretical definition of interpretability in brain decoding; we show that the interpretability of multivariate brain maps can be decomposed into their reproducibility and representativeness. Second, as an application of the proposed definition, we exemplify a heuristic for approximating the interpretability in multivariate analysis of evoked magnetoencephalography (MEG) responses. Third, we propose to combine the approximated interpretability and the generalization performance of the brain decoding into a new multi-objective criterion for model selection. Our results, for the simulated and real MEG data, show that optimizing the hyper-parameters of the regularized linear classifier based on the proposed criterion results in more informative multivariate brain maps. More importantly, the presented definition provides the theoretical background for quantitative evaluation of interpretability, and hence, facilitates the development of more effective brain decoding algorithms in the future.

Interpretability of Multivariate Brain Maps in Linear Brain Decoding: Definition, and Heuristic Quantification in Multivariate Analysis of MEG Time-Locked Effects

PubMed Central

Kia, Seyed Mostafa; Vega Pons, Sandro; Weisz, Nathan; Passerini, Andrea

2017-01-01

Brain decoding is a popular multivariate approach for hypothesis testing in neuroimaging. Linear classifiers are widely employed in the brain decoding paradigm to discriminate among experimental conditions. Then, the derived linear weights are visualized in the form of multivariate brain maps to further study spatio-temporal patterns of underlying neural activities. It is well known that the brain maps derived from weights of linear classifiers are hard to interpret because of high correlations between predictors, low signal to noise ratios, and the high dimensionality of neuroimaging data. Therefore, improving the interpretability of brain decoding approaches is of primary interest in many neuroimaging studies. Despite extensive studies of this type, at present, there is no formal definition for interpretability of multivariate brain maps. As a consequence, there is no quantitative measure for evaluating the interpretability of different brain decoding methods. In this paper, first, we present a theoretical definition of interpretability in brain decoding; we show that the interpretability of multivariate brain maps can be decomposed into their reproducibility and representativeness. Second, as an application of the proposed definition, we exemplify a heuristic for approximating the interpretability in multivariate analysis of evoked magnetoencephalography (MEG) responses. Third, we propose to combine the approximated interpretability and the generalization performance of the brain decoding into a new multi-objective criterion for model selection. Our results, for the simulated and real MEG data, show that optimizing the hyper-parameters of the regularized linear classifier based on the proposed criterion results in more informative multivariate brain maps. More importantly, the presented definition provides the theoretical background for quantitative evaluation of interpretability, and hence, facilitates the development of more effective brain decoding algorithms in the future. PMID:28167896

Development of quantitative analysis method for stereotactic brain image: assessment of reduced accumulation in extent and severity using anatomical segmentation.

PubMed

Mizumura, Sunao; Kumita, Shin-ichiro; Cho, Keiichi; Ishihara, Makiko; Nakajo, Hidenobu; Toba, Masahiro; Kumazaki, Tatsuo

2003-06-01

Through visual assessment by three-dimensional (3D) brain image analysis methods using stereotactic brain coordinates system, such as three-dimensional stereotactic surface projections and statistical parametric mapping, it is difficult to quantitatively assess anatomical information and the range of extent of an abnormal region. In this study, we devised a method to quantitatively assess local abnormal findings by segmenting a brain map according to anatomical structure. Through quantitative local abnormality assessment using this method, we studied the characteristics of distribution of reduced blood flow in cases with dementia of the Alzheimer type (DAT). Using twenty-five cases with DAT (mean age, 68.9 years old), all of whom were diagnosed as probable Alzheimer's disease based on NINCDS-ADRDA, we collected I-123 iodoamphetamine SPECT data. A 3D brain map using the 3D-SSP program was compared with the data of 20 cases in the control group, who age-matched the subject cases. To study local abnormalities on the 3D images, we divided the whole brain into 24 segments based on anatomical classification. We assessed the extent of an abnormal region in each segment (rate of the coordinates with a Z-value that exceeds the threshold value, in all coordinates within a segment), and severity (average Z-value of the coordinates with a Z-value that exceeds the threshold value). This method clarified orientation and expansion of reduced accumulation, through classifying stereotactic brain coordinates according to the anatomical structure. This method was considered useful for quantitatively grasping distribution abnormalities in the brain and changes in abnormality distribution.

Mapping social behavior-induced brain activation at cellular resolution in the mouse

PubMed Central

Kim, Yongsoo; Venkataraju, Kannan Umadevi; Pradhan, Kith; Mende, Carolin; Taranda, Julian; Turaga, Srinivas C.; Arganda-Carreras, Ignacio; Ng, Lydia; Hawrylycz, Michael J.; Rockland, Kathleen; Seung, H. Sebastian; Osten, Pavel

2014-01-01

Understanding how brain activation mediates behaviors is a central goal of systems neuroscience. Here we apply an automated method for mapping brain activation in the mouse in order to probe how sex-specific social behaviors are represented in the male brain. Our method uses the immediate early gene c-fos, a marker of neuronal activation, visualized by serial two-photon tomography: the c-fos-GFP-positive neurons are computationally detected, their distribution is registered to a reference brain and a brain atlas, and their numbers are analyzed by statistical tests. Our results reveal distinct and shared female and male interaction-evoked patterns of male brain activation representing sex discrimination and social recognition. We also identify brain regions whose degree of activity correlates to specific features of social behaviors and estimate the total numbers and the densities of activated neurons per brain areas. Our study opens the door to automated screening of behavior-evoked brain activation in the mouse. PMID:25558063

Direct estimation of tracer-kinetic parameter maps from highly undersampled brain dynamic contrast enhanced MRI.

PubMed

Guo, Yi; Lingala, Sajan Goud; Zhu, Yinghua; Lebel, R Marc; Nayak, Krishna S

2017-10-01

The purpose of this work was to develop and evaluate a T 1 -weighted dynamic contrast enhanced (DCE) MRI methodology where tracer-kinetic (TK) parameter maps are directly estimated from undersampled (k,t)-space data. The proposed reconstruction involves solving a nonlinear least squares optimization problem that includes explicit use of a full forward model to convert parameter maps to (k,t)-space, utilizing the Patlak TK model. The proposed scheme is compared against an indirect method that creates intermediate images by parallel imaging and compressed sensing before to TK modeling. Thirteen fully sampled brain tumor DCE-MRI scans with 5-second temporal resolution are retrospectively undersampled at rates R = 20, 40, 60, 80, and 100 for each dynamic frame. TK maps are quantitatively compared based on root mean-squared-error (rMSE) and Bland-Altman analysis. The approach is also applied to four prospectively R = 30 undersampled whole-brain DCE-MRI data sets. In the retrospective study, the proposed method performed statistically better than indirect method at R ≥ 80 for all 13 cases. This approach provided restoration of TK parameter values with less errors in tumor regions of interest, an improvement compared to a state-of-the-art indirect method. Applied prospectively, the proposed method provided whole-brain, high-resolution TK maps with good image quality. Model-based direct estimation of TK maps from k,t-space DCE-MRI data is feasible and is compatible up to 100-fold undersampling. Magn Reson Med 78:1566-1578, 2017. © 2016 International Society for Magnetic Resonance in Medicine. © 2016 International Society for Magnetic Resonance in Medicine.

ICA model order selection of task co-activation networks.

PubMed

Ray, Kimberly L; McKay, D Reese; Fox, Peter M; Riedel, Michael C; Uecker, Angela M; Beckmann, Christian F; Smith, Stephen M; Fox, Peter T; Laird, Angela R

2013-01-01

Independent component analysis (ICA) has become a widely used method for extracting functional networks in the brain during rest and task. Historically, preferred ICA dimensionality has widely varied within the neuroimaging community, but typically varies between 20 and 100 components. This can be problematic when comparing results across multiple studies because of the impact ICA dimensionality has on the topology of its resultant components. Recent studies have demonstrated that ICA can be applied to peak activation coordinates archived in a large neuroimaging database (i.e., BrainMap Database) to yield whole-brain task-based co-activation networks. A strength of applying ICA to BrainMap data is that the vast amount of metadata in BrainMap can be used to quantitatively assess tasks and cognitive processes contributing to each component. In this study, we investigated the effect of model order on the distribution of functional properties across networks as a method for identifying the most informative decompositions of BrainMap-based ICA components. Our findings suggest dimensionality of 20 for low model order ICA to examine large-scale brain networks, and dimensionality of 70 to provide insight into how large-scale networks fractionate into sub-networks. We also provide a functional and organizational assessment of visual, motor, emotion, and interoceptive task co-activation networks as they fractionate from low to high model-orders.

ICA model order selection of task co-activation networks

PubMed Central

Ray, Kimberly L.; McKay, D. Reese; Fox, Peter M.; Riedel, Michael C.; Uecker, Angela M.; Beckmann, Christian F.; Smith, Stephen M.; Fox, Peter T.; Laird, Angela R.

2013-01-01

Independent component analysis (ICA) has become a widely used method for extracting functional networks in the brain during rest and task. Historically, preferred ICA dimensionality has widely varied within the neuroimaging community, but typically varies between 20 and 100 components. This can be problematic when comparing results across multiple studies because of the impact ICA dimensionality has on the topology of its resultant components. Recent studies have demonstrated that ICA can be applied to peak activation coordinates archived in a large neuroimaging database (i.e., BrainMap Database) to yield whole-brain task-based co-activation networks. A strength of applying ICA to BrainMap data is that the vast amount of metadata in BrainMap can be used to quantitatively assess tasks and cognitive processes contributing to each component. In this study, we investigated the effect of model order on the distribution of functional properties across networks as a method for identifying the most informative decompositions of BrainMap-based ICA components. Our findings suggest dimensionality of 20 for low model order ICA to examine large-scale brain networks, and dimensionality of 70 to provide insight into how large-scale networks fractionate into sub-networks. We also provide a functional and organizational assessment of visual, motor, emotion, and interoceptive task co-activation networks as they fractionate from low to high model-orders. PMID:24339802

Surface-Constrained Volumetric Brain Registration Using Harmonic Mappings

PubMed Central

Joshi, Anand A.; Shattuck, David W.; Thompson, Paul M.; Leahy, Richard M.

2015-01-01

In order to compare anatomical and functional brain imaging data across subjects, the images must first be registered to a common coordinate system in which anatomical features are aligned. Intensity-based volume registration methods can align subcortical structures well, but the variability in sulcal folding patterns typically results in misalignment of the cortical surface. Conversely, surface-based registration using sulcal features can produce excellent cortical alignment but the mapping between brains is restricted to the cortical surface. Here we describe a method for volumetric registration that also produces an accurate one-to-one point correspondence between cortical surfaces. This is achieved by first parameterizing and aligning the cortical surfaces using sulcal landmarks. We then use a constrained harmonic mapping to extend this surface correspondence to the entire cortical volume. Finally, this mapping is refined using an intensity-based warp. We demonstrate the utility of the method by applying it to T1-weighted magnetic resonance images (MRI). We evaluate the performance of our proposed method relative to existing methods that use only intensity information; for this comparison we compute the inter-subject alignment of expert-labeled sub-cortical structures after registration. PMID:18092736

Using a concept map as a tool for strategic planning: The Healthy Brain Initiative.

PubMed

Anderson, Lynda A; Day, Kristine L; Vandenberg, Anna E

2011-09-01

Concept mapping is a tool to assist in strategic planning that allows planners to work through a sequence of phases to produce a conceptual framework. Although several studies describe how concept mapping is applied to various public health problems, the flexibility of the methods used in each phase of the process is often overlooked. If practitioners were more aware of the flexibility, more public health endeavors could benefit from using concept mapping as a tool for strategic planning. The objective of this article is to describe how the 6 concept-mapping phases originally outlined by William Trochim guided our strategic planning process and how we adjusted the specific methods in the first 2 phases to meet the specialized needs and requirements to create The Healthy Brain Initiative: A National Public Health Road Map to Maintaining Cognitive Health. In the first stage (phases 1 and 2 of concept mapping), we formed a steering committee, convened 4 work groups over a period of 3 months, and generated an initial set of 42 action items grounded in science. In the second stage (phases 3 and 4), we engaged stakeholders in sorting and rating the action items and constructed a series of concept maps. In the third and final stage (phases 5 and 6), we examined and refined the action items and generated a final concept map consisting of 44 action items. We then selected the top 10 action items, and in 2007, we published The Healthy Brain Initiative: A National Public Health Road Map to Maintaining Cognitive Health, which represents the strategic plan for The Healthy Brain Initiative.

Subject-specific bone attenuation correction for brain PET/MR: can ZTE-MRI substitute CT scan accurately?

PubMed

Khalifé, Maya; Fernandez, Brice; Jaubert, Olivier; Soussan, Michael; Brulon, Vincent; Buvat, Irène; Comtat, Claude

2017-09-21

In brain PET/MR applications, accurate attenuation maps are required for accurate PET image quantification. An implemented attenuation correction (AC) method for brain imaging is the single-atlas approach that estimates an AC map from an averaged CT template. As an alternative, we propose to use a zero echo time (ZTE) pulse sequence to segment bone, air and soft tissue. A linear relationship between histogram normalized ZTE intensity and measured CT density in Hounsfield units ([Formula: see text]) in bone has been established thanks to a CT-MR database of 16 patients. Continuous AC maps were computed based on the segmented ZTE by setting a fixed linear attenuation coefficient (LAC) to air and soft tissue and by using the linear relationship to generate continuous μ values for the bone. Additionally, for the purpose of comparison, four other AC maps were generated: a ZTE derived AC map with a fixed LAC for the bone, an AC map based on the single-atlas approach as provided by the PET/MR manufacturer, a soft-tissue only AC map and, finally, the CT derived attenuation map used as the gold standard (CTAC). All these AC maps were used with different levels of smoothing for PET image reconstruction with and without time-of-flight (TOF). The subject-specific AC map generated by combining ZTE-based segmentation and linear scaling of the normalized ZTE signal into [Formula: see text] was found to be a good substitute for the measured CTAC map in brain PET/MR when used with a Gaussian smoothing kernel of [Formula: see text] corresponding to the PET scanner intrinsic resolution. As expected TOF reduces AC error regardless of the AC method. The continuous ZTE-AC performed better than the other alternative MR derived AC methods, reducing the quantification error between the MRAC corrected PET image and the reference CTAC corrected PET image.

Subject-specific bone attenuation correction for brain PET/MR: can ZTE-MRI substitute CT scan accurately?

NASA Astrophysics Data System (ADS)

Khalifé, Maya; Fernandez, Brice; Jaubert, Olivier; Soussan, Michael; Brulon, Vincent; Buvat, Irène; Comtat, Claude

2017-10-01

In brain PET/MR applications, accurate attenuation maps are required for accurate PET image quantification. An implemented attenuation correction (AC) method for brain imaging is the single-atlas approach that estimates an AC map from an averaged CT template. As an alternative, we propose to use a zero echo time (ZTE) pulse sequence to segment bone, air and soft tissue. A linear relationship between histogram normalized ZTE intensity and measured CT density in Hounsfield units (HU ) in bone has been established thanks to a CT-MR database of 16 patients. Continuous AC maps were computed based on the segmented ZTE by setting a fixed linear attenuation coefficient (LAC) to air and soft tissue and by using the linear relationship to generate continuous μ values for the bone. Additionally, for the purpose of comparison, four other AC maps were generated: a ZTE derived AC map with a fixed LAC for the bone, an AC map based on the single-atlas approach as provided by the PET/MR manufacturer, a soft-tissue only AC map and, finally, the CT derived attenuation map used as the gold standard (CTAC). All these AC maps were used with different levels of smoothing for PET image reconstruction with and without time-of-flight (TOF). The subject-specific AC map generated by combining ZTE-based segmentation and linear scaling of the normalized ZTE signal into HU was found to be a good substitute for the measured CTAC map in brain PET/MR when used with a Gaussian smoothing kernel of 4~mm corresponding to the PET scanner intrinsic resolution. As expected TOF reduces AC error regardless of the AC method. The continuous ZTE-AC performed better than the other alternative MR derived AC methods, reducing the quantification error between the MRAC corrected PET image and the reference CTAC corrected PET image.

Whole head quantitative susceptibility mapping using a least-norm direct dipole inversion method.

PubMed

Sun, Hongfu; Ma, Yuhan; MacDonald, M Ethan; Pike, G Bruce

2018-06-15

A new dipole field inversion method for whole head quantitative susceptibility mapping (QSM) is proposed. Instead of performing background field removal and local field inversion sequentially, the proposed method performs dipole field inversion directly on the total field map in a single step. To aid this under-determined and ill-posed inversion process and obtain robust QSM images, Tikhonov regularization is implemented to seek the local susceptibility solution with the least-norm (LN) using the L-curve criterion. The proposed LN-QSM does not require brain edge erosion, thereby preserving the cerebral cortex in the final images. This should improve its applicability for QSM-based cortical grey matter measurement, functional imaging and venography of full brain. Furthermore, LN-QSM also enables susceptibility mapping of the entire head without the need for brain extraction, which makes QSM reconstruction more automated and less dependent on intermediate pre-processing methods and their associated parameters. It is shown that the proposed LN-QSM method reduced errors in a numerical phantom simulation, improved accuracy in a gadolinium phantom experiment, and suppressed artefacts in nine subjects, as compared to two-step and other single-step QSM methods. Measurements of deep grey matter and skull susceptibilities from LN-QSM are consistent with established reconstruction methods. Copyright © 2018 Elsevier Inc. All rights reserved.

Change detection and classification in brain MR images using change vector analysis.

PubMed

Simões, Rita; Slump, Cornelis

2011-01-01

The automatic detection of longitudinal changes in brain images is valuable in the assessment of disease evolution and treatment efficacy. Most existing change detection methods that are currently used in clinical research to monitor patients suffering from neurodegenerative diseases--such as Alzheimer's--focus on large-scale brain deformations. However, such patients often have other brain impairments, such as infarcts, white matter lesions and hemorrhages, which are typically overlooked by the deformation-based methods. Other unsupervised change detection algorithms have been proposed to detect tissue intensity changes. The outcome of these methods is typically a binary change map, which identifies changed brain regions. However, understanding what types of changes these regions underwent is likely to provide equally important information about lesion evolution. In this paper, we present an unsupervised 3D change detection method based on Change Vector Analysis. We compute and automatically threshold the Generalized Likelihood Ratio map to obtain a binary change map. Subsequently, we perform histogram-based clustering to classify the change vectors. We obtain a Kappa Index of 0.82 using various types of simulated lesions. The classification error is 2%. Finally, we are able to detect and discriminate both small changes and ventricle expansions in datasets from Mild Cognitive Impairment patients.

Single-shot T2 mapping using overlapping-echo detachment planar imaging and a deep convolutional neural network.

PubMed

Cai, Congbo; Wang, Chao; Zeng, Yiqing; Cai, Shuhui; Liang, Dong; Wu, Yawen; Chen, Zhong; Ding, Xinghao; Zhong, Jianhui

2018-04-24

An end-to-end deep convolutional neural network (CNN) based on deep residual network (ResNet) was proposed to efficiently reconstruct reliable T 2 mapping from single-shot overlapping-echo detachment (OLED) planar imaging. The training dataset was obtained from simulations that were carried out on SPROM (Simulation with PRoduct Operator Matrix) software developed by our group. The relationship between the original OLED image containing two echo signals and the corresponding T 2 mapping was learned by ResNet training. After the ResNet was trained, it was applied to reconstruct the T 2 mapping from simulation and in vivo human brain data. Although the ResNet was trained entirely on simulated data, the trained network was generalized well to real human brain data. The results from simulation and in vivo human brain experiments show that the proposed method significantly outperforms the echo-detachment-based method. Reliable T 2 mapping with higher accuracy is achieved within 30 ms after the network has been trained, while the echo-detachment-based OLED reconstruction method took approximately 2 min. The proposed method will facilitate real-time dynamic and quantitative MR imaging via OLED sequence, and deep convolutional neural network has the potential to reconstruct maps from complex MRI sequences efficiently. © 2018 International Society for Magnetic Resonance in Medicine.

Brain Entropy Mapping Using fMRI

PubMed Central

Wang, Ze; Li, Yin; Childress, Anna Rose; Detre, John A.

2014-01-01

Entropy is an important trait for life as well as the human brain. Characterizing brain entropy (BEN) may provide an informative tool to assess brain states and brain functions. Yet little is known about the distribution and regional organization of BEN in normal brain. The purpose of this study was to examine the whole brain entropy patterns using a large cohort of normal subjects. A series of experiments were first performed to validate an approximate entropy measure regarding its sensitivity, specificity, and reliability using synthetic data and fMRI data. Resting state fMRI data from a large cohort of normal subjects (n = 1049) from multi-sites were then used to derive a 3-dimensional BEN map, showing a sharp low-high entropy contrast between the neocortex and the rest of brain. The spatial heterogeneity of resting BEN was further studied using a data-driven clustering method, and the entire brain was found to be organized into 7 hierarchical regional BEN networks that are consistent with known structural and functional brain parcellations. These findings suggest BEN mapping as a physiologically and functionally meaningful measure for studying brain functions. PMID:24657999

Mapping fetal brain development in utero using magnetic resonance imaging: the Big Bang of brain mapping.

PubMed

Studholme, Colin

2011-08-15

The development of tools to construct and investigate probabilistic maps of the adult human brain from magnetic resonance imaging (MRI) has led to advances in both basic neuroscience and clinical diagnosis. These tools are increasingly being applied to brain development in adolescence and childhood, and even to neonatal and premature neonatal imaging. Even earlier in development, parallel advances in clinical fetal MRI have led to its growing use as a tool in challenging medical conditions. This has motivated new engineering developments encompassing optimal fast MRI scans and techniques derived from computer vision, the combination of which allows full 3D imaging of the moving fetal brain in utero without sedation. These promise to provide a new and unprecedented window into early human brain growth. This article reviews the developments that have led us to this point, examines the current state of the art in the fields of fast fetal imaging and motion correction, and describes the tools to analyze dynamically changing fetal brain structure. New methods to deal with developmental tissue segmentation and the construction of spatiotemporal atlases are examined, together with techniques to map fetal brain growth patterns.

Cerebral cartography and connectomics

PubMed Central

Sporns, Olaf

2015-01-01

Cerebral cartography and connectomics pursue similar goals in attempting to create maps that can inform our understanding of the structural and functional organization of the cortex. Connectome maps explicitly aim at representing the brain as a complex network, a collection of nodes and their interconnecting edges. This article reflects on some of the challenges that currently arise in the intersection of cerebral cartography and connectomics. Principal challenges concern the temporal dynamics of functional brain connectivity, the definition of areal parcellations and their hierarchical organization into large-scale networks, the extension of whole-brain connectivity to cellular-scale networks, and the mapping of structure/function relations in empirical recordings and computational models. Successfully addressing these challenges will require extensions of methods and tools from network science to the mapping and analysis of human brain connectivity data. The emerging view that the brain is more than a collection of areas, but is fundamentally operating as a complex networked system, will continue to drive the creation of ever more detailed and multi-modal network maps as tools for on-going exploration and discovery in human connectomics. PMID:25823870

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.

Brain Friendly Techniques: Mind Mapping

ERIC Educational Resources Information Center

Goldberg, Cristine

2004-01-01

Mind Mapping can be called the Swiss Army Knife for the brain, a total visual thinking tool or a multi-handed thought catcher. Invented by Tony Buzan in the early 1970s and used by millions around the world, it is a method that can be a part of a techniques repertoire when teaching information literacy, planning, presenting, thinking, and so…

Template‐based field map prediction for rapid whole brain B0 shimming

PubMed Central

Shi, Yuhang; Vannesjo, S. Johanna; Miller, Karla L.

2017-01-01

Purpose In typical MRI protocols, time is spent acquiring a field map to calculate the shim settings for best image quality. We propose a fast template‐based field map prediction method that yields near‐optimal shims without measuring the field. Methods The template‐based prediction method uses prior knowledge of the B0 distribution in the human brain, based on a large database of field maps acquired from different subjects, together with subject‐specific structural information from a quick localizer scan. The shimming performance of using the template‐based prediction is evaluated in comparison to a range of potential fast shimming methods. Results Static B0 shimming based on predicted field maps performed almost as well as shimming based on individually measured field maps. In experimental evaluations at 7 T, the proposed approach yielded a residual field standard deviation in the brain of on average 59 Hz, compared with 50 Hz using measured field maps and 176 Hz using no subject‐specific shim. Conclusions This work demonstrates that shimming based on predicted field maps is feasible. The field map prediction accuracy could potentially be further improved by generating the template from a subset of subjects, based on parameters such as head rotation and body mass index. Magn Reson Med 80:171–180, 2018. © 2017 The Authors Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. PMID:29193340

Passive language mapping combining real-time oscillation analysis with cortico-cortical evoked potentials for awake craniotomy.

PubMed

Tamura, Yukie; Ogawa, Hiroshi; Kapeller, Christoph; Prueckl, Robert; Takeuchi, Fumiya; Anei, Ryogo; Ritaccio, Anthony; Guger, Christoph; Kamada, Kyousuke

2016-12-01

OBJECTIVE Electrocortical stimulation (ECS) is the gold standard for functional brain mapping; however, precise functional mapping is still difficult in patients with language deficits. High gamma activity (HGA) between 80 and 140 Hz on electrocorticography is assumed to reflect localized cortical processing, whereas the cortico-cortical evoked potential (CCEP) can reflect bidirectional responses evoked by monophasic pulse stimuli to the language cortices when there is no patient cooperation. The authors propose the use of "passive" mapping by combining HGA mapping and CCEP recording without active tasks during conscious resections of brain tumors. METHODS Five patients, each with an intraaxial tumor in their dominant hemisphere, underwent conscious resection of their lesion with passive mapping. The authors performed functional localization for the receptive language area, using real-time HGA mapping, by listening passively to linguistic sounds. Furthermore, single electrical pulses were delivered to the identified receptive temporal language area to detect CCEPs in the frontal lobe. All mapping results were validated by ECS, and the sensitivity and specificity were evaluated. RESULTS Linguistic HGA mapping quickly identified the language area in the temporal lobe. Electrical stimulation by linguistic HGA mapping to the identified temporal receptive language area evoked CCEPs on the frontal lobe. The combination of linguistic HGA and frontal CCEPs needed no patient cooperation or effort. In this small case series, the sensitivity and specificity were 93.8% and 89%, respectively. CONCLUSIONS The described technique allows for simple and quick functional brain mapping with higher sensitivity and specificity than ECS mapping. The authors believe that this could improve the reliability of functional brain mapping and facilitate rational and objective operations. Passive mapping also sheds light on the underlying physiological mechanisms of language in the human brain.

Specificities of Awake Craniotomy and Brain Mapping in Children for Resection of Supratentorial Tumors in the Language Area.

PubMed

Delion, Matthieu; Terminassian, Aram; Lehousse, Thierry; Aubin, Ghislaine; Malka, Jean; N'Guyen, Sylvie; Mercier, Philippe; Menei, Philippe

2015-12-01

In the pediatric population, awake craniotomy began to be used for the resection of brain tumor located close to eloquent areas. Some specificities must be taken into account to adapt this method to children. The aim of this clinical study is to not only confirm the feasibility of awake craniotomy and language brain mapping in the pediatric population but also identify the specificities and necessary adaptations of the procedure. Six children aged 11 to 16 were operated on while awake under local anesthesia with language brain mapping for supratentorial brain lesions (tumor and cavernoma). The preoperative planning comprised functional magnetic resonance imaging (MRI) and neuropsychologic and psychologic assessment. The specific preoperative preparation is clearly explained including hypnosis conditioning and psychiatric evaluation. The success of the procedure was based on the ability to perform the language brain mapping and the tumor removal without putting the patient to sleep. We investigated the pediatric specificities, psychological experience, and neuropsychologic follow-up. The children experienced little anxiety, probably in large part due to the use of hypnosis. We succeeded in doing the cortical-subcortical mapping and removing the tumor without putting the patient to sleep in all cases. The psychological experience was good, and the neuropsychologic follow-up showed a favorable evolution. Preoperative preparation and hypnosis in children seemed important for performing awake craniotomy and contributing language brain mapping with the best possible psychological experience. The pediatrics specificities are discussed. Copyright © 2015 Elsevier Inc. All rights reserved.

Theoretical evaluation of accuracy in position and size of brain activity obtained by near-infrared topography

NASA Astrophysics Data System (ADS)

Kawaguchi, Hiroshi; Hayashi, Toshiyuki; Kato, Toshinori; Okada, Eiji

2004-06-01

Near-infrared (NIR) topography can obtain a topographical distribution of the activated region in the brain cortex. Near-infrared light is strongly scattered in the head, and the volume of tissue sampled by a source-detector pair on the head surface is broadly distributed in the brain. This scattering effect results in poor resolution and contrast in the topographic image of the brain activity. In this study, a one-dimensional distribution of absorption change in a head model is calculated by mapping and reconstruction methods to evaluate the effect of the image reconstruction algorithm and the interval of measurement points for topographic imaging on the accuracy of the topographic image. The light propagation in the head model is predicted by Monte Carlo simulation to obtain the spatial sensitivity profile for a source-detector pair. The measurement points are one-dimensionally arranged on the surface of the model, and the distance between adjacent measurement points is varied from 4 mm to 28 mm. Small intervals of the measurement points improve the topographic image calculated by both the mapping and reconstruction methods. In the conventional mapping method, the limit of the spatial resolution depends upon the interval of the measurement points and spatial sensitivity profile for source-detector pairs. The reconstruction method has advantages over the mapping method which improve the results of one-dimensional analysis when the interval of measurement points is less than 12 mm. The effect of overlapping of spatial sensitivity profiles indicates that the reconstruction method may be effective to improve the spatial resolution of a two-dimensional reconstruction of topographic image obtained with larger interval of measurement points. Near-infrared topography with the reconstruction method potentially obtains an accurate distribution of absorption change in the brain even if the size of absorption change is less than 10 mm.

An Intracranial Electroencephalography (iEEG) Brain Function Mapping Tool with an Application to Epilepsy Surgery Evaluation.

PubMed

Wang, Yinghua; Yan, Jiaqing; Wen, Jianbin; Yu, Tao; Li, Xiaoli

2016-01-01

Before epilepsy surgeries, intracranial electroencephalography (iEEG) is often employed in function mapping and epileptogenic foci localization. Although the implanted electrodes provide crucial information for epileptogenic zone resection, a convenient clinical tool for electrode position registration and Brain Function Mapping (BFM) visualization is still lacking. In this study, we developed a BFM Tool, which facilitates electrode position registration and BFM visualization, with an application to epilepsy surgeries. The BFM Tool mainly utilizes electrode location registration and function mapping based on pre-defined brain models from other software. In addition, the electrode node and mapping properties, such as the node size/color, edge color/thickness, mapping method, can be adjusted easily using the setting panel. Moreover, users may manually import/export location and connectivity data to generate figures for further application. The role of this software is demonstrated by a clinical study of language area localization. The BFM Tool helps clinical doctors and researchers visualize implanted electrodes and brain functions in an easy, quick and flexible manner. Our tool provides convenient electrode registration, easy brain function visualization, and has good performance. It is clinical-oriented and is easy to deploy and use. The BFM tool is suitable for epilepsy and other clinical iEEG applications.

Cerebral cartography and connectomics.

PubMed

Sporns, Olaf

2015-05-19

Cerebral cartography and connectomics pursue similar goals in attempting to create maps that can inform our understanding of the structural and functional organization of the cortex. Connectome maps explicitly aim at representing the brain as a complex network, a collection of nodes and their interconnecting edges. This article reflects on some of the challenges that currently arise in the intersection of cerebral cartography and connectomics. Principal challenges concern the temporal dynamics of functional brain connectivity, the definition of areal parcellations and their hierarchical organization into large-scale networks, the extension of whole-brain connectivity to cellular-scale networks, and the mapping of structure/function relations in empirical recordings and computational models. Successfully addressing these challenges will require extensions of methods and tools from network science to the mapping and analysis of human brain connectivity data. The emerging view that the brain is more than a collection of areas, but is fundamentally operating as a complex networked system, will continue to drive the creation of ever more detailed and multi-modal network maps as tools for on-going exploration and discovery in human connectomics. © 2015 The Author(s) Published by the Royal Society. All rights reserved.

Language Mapping in Awake Surgery: Report of Two Cases with Review of Language Networks.

PubMed

Lim, Liang Hooi; Idris, Zamzuri; Reza, Faruque; Wan Hassan, Wan Mohd Nazaruddin; Mukmin, Laila Abd; Abdullah, Jafri Malin

2018-01-01

The role of language in communication plays a crucial role in human development and function. In patients who have a surgical lesion at the functional language areas, surgery should be intricately planned to avoid incurring further morbidity. This normally requires extensive functional and anatomical mappings of the brain to identify regions that are involved in language processing and production. In our case report, regions of the brain that are important for language functions were studied before surgery by employing (a) extraoperative methods such as functional magnetic resonance imaging, transmagnetic stimulation, and magnetoencephalography; (b) during the surgery by utilizing intraoperative awake surgical methods such as an intraoperative electrical stimulation; and (c) a two-stage surgery, in which electrical stimulation and first mapping are made thoroughly in the ward before second remapping during surgery. The extraoperative methods before surgery can guide the neurosurgeon to localize the functional language regions and tracts preoperatively. This will be confirmed using single-stage intraoperative electrical brain stimulation during surgery or a two-stage electrical brain stimulation before and during surgery. Here, we describe two cases in whom one has a superficial lesion and another a deep-seated lesion at language-related regions, in which language mapping was done to preserve its function. Additional review on the neuroanatomy of language regions, language network, and its impairment was also described.

Neonatal brain resting-state functional connectivity imaging modalities.

PubMed

Mohammadi-Nejad, Ali-Reza; Mahmoudzadeh, Mahdi; Hassanpour, Mahlegha S; Wallois, Fabrice; Muzik, Otto; Papadelis, Christos; Hansen, Anne; Soltanian-Zadeh, Hamid; Gelovani, Juri; Nasiriavanaki, Mohammadreza

2018-06-01

Infancy is the most critical period in human brain development. Studies demonstrate that subtle brain abnormalities during this state of life may greatly affect the developmental processes of the newborn infants. One of the rapidly developing methods for early characterization of abnormal brain development is functional connectivity of the brain at rest. While the majority of resting-state studies have been conducted using magnetic resonance imaging (MRI), there is clear evidence that resting-state functional connectivity (rs-FC) can also be evaluated using other imaging modalities. The aim of this review is to compare the advantages and limitations of different modalities used for the mapping of infants' brain functional connectivity at rest. In addition, we introduce photoacoustic tomography, a novel functional neuroimaging modality, as a complementary modality for functional mapping of infants' brain.

Decoding fMRI events in sensorimotor motor network using sparse paradigm free mapping and activation likelihood estimates.

PubMed

Tan, Francisca M; Caballero-Gaudes, César; Mullinger, Karen J; Cho, Siu-Yeung; Zhang, Yaping; Dryden, Ian L; Francis, Susan T; Gowland, Penny A

2017-11-01

Most functional MRI (fMRI) studies map task-driven brain activity using a block or event-related paradigm. Sparse paradigm free mapping (SPFM) can detect the onset and spatial distribution of BOLD events in the brain without prior timing information, but relating the detected events to brain function remains a challenge. In this study, we developed a decoding method for SPFM using a coordinate-based meta-analysis method of activation likelihood estimation (ALE). We defined meta-maps of statistically significant ALE values that correspond to types of events and calculated a summation overlap between the normalized meta-maps and SPFM maps. As a proof of concept, this framework was applied to relate SPFM-detected events in the sensorimotor network (SMN) to six motor functions (left/right fingers, left/right toes, swallowing, and eye blinks). We validated the framework using simultaneous electromyography (EMG)-fMRI experiments and motor tasks with short and long duration, and random interstimulus interval. The decoding scores were considerably lower for eye movements relative to other movement types tested. The average successful rate for short and long motor events were 77 ± 13% and 74 ± 16%, respectively, excluding eye movements. We found good agreement between the decoding results and EMG for most events and subjects, with a range in sensitivity between 55% and 100%, excluding eye movements. The proposed method was then used to classify the movement types of spontaneous single-trial events in the SMN during resting state, which produced an average successful rate of 22 ± 12%. Finally, this article discusses methodological implications and improvements to increase the decoding performance. Hum Brain Mapp 38:5778-5794, 2017. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

Brain/MINDS: brain-mapping project in Japan

PubMed Central

Okano, Hideyuki; Miyawaki, Atsushi; Kasai, Kiyoto

2015-01-01

There is an emerging interest in brain-mapping projects in countries across the world, including the USA, Europe, Australia and China. In 2014, Japan started a brain-mapping project called Brain Mapping by Integrated Neurotechnologies for Disease Studies (Brain/MINDS). Brain/MINDS aims to map the structure and function of neuronal circuits to ultimately understand the vast complexity of the human brain, and takes advantage of a unique non-human primate animal model, the common marmoset (Callithrix jacchus). In Brain/MINDS, the RIKEN Brain Science Institute acts as a central institute. The objectives of Brain/MINDS can be categorized into the following three major subject areas: (i) structure and functional mapping of a non-human primate brain (the marmoset brain); (ii) development of innovative neurotechnologies for brain mapping; and (iii) human brain mapping; and clinical research. Brain/MINDS researchers are highly motivated to identify the neuronal circuits responsible for the phenotype of neurological and psychiatric disorders, and to understand the development of these devastating disorders through the integration of these three subject areas. PMID:25823872

Important considerations in lesion-symptom mapping: Illustrations from studies of word comprehension.

PubMed

Shahid, Hinna; Sebastian, Rajani; Schnur, Tatiana T; Hanayik, Taylor; Wright, Amy; Tippett, Donna C; Fridriksson, Julius; Rorden, Chris; Hillis, Argye E

2017-06-01

Lesion-symptom mapping is an important method of identifying networks of brain regions critical for functions. However, results might be influenced substantially by the imaging modality and timing of assessment. We tested the hypothesis that brain regions found to be associated with acute language deficits depend on (1) timing of behavioral measurement, (2) imaging sequences utilized to define the "lesion" (structural abnormality only or structural plus perfusion abnormality), and (3) power of the study. We studied 191 individuals with acute left hemisphere stroke with MRI and language testing to identify areas critical for spoken word comprehension. We use the data from this study to examine the potential impact of these three variables on lesion-symptom mapping. We found that only the combination of structural and perfusion imaging within 48 h of onset identified areas where more abnormal voxels was associated with more severe acute deficits, after controlling for lesion volume and multiple comparisons. The critical area identified with this methodology was the left posterior superior temporal gyrus, consistent with other methods that have identified an important role of this area in spoken word comprehension. Results have implications for interpretation of other lesion-symptom mapping studies, as well as for understanding areas critical for auditory word comprehension in the healthy brain. We propose that lesion-symptom mapping at the acute stage of stroke addresses a different sort of question about brain-behavior relationships than lesion-symptom mapping at the chronic stage, but that timing of behavioral measurement and imaging modalities should be considered in either case. Hum Brain Mapp 38:2990-3000, 2017. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

Brain functional BOLD perturbation modelling for forward fMRI and inverse mapping

PubMed Central

Robinson, Jennifer; Calhoun, Vince

2018-01-01

Purpose To computationally separate dynamic brain functional BOLD responses from static background in a brain functional activity for forward fMRI signal analysis and inverse mapping. Methods A brain functional activity is represented in terms of magnetic source by a perturbation model: χ = χ0 +δχ, with δχ for BOLD magnetic perturbations and χ0 for background. A brain fMRI experiment produces a timeseries of complex-valued images (T2* images), whereby we extract the BOLD phase signals (denoted by δP) by a complex division. By solving an inverse problem, we reconstruct the BOLD δχ dataset from the δP dataset, and the brain χ distribution from a (unwrapped) T2* phase image. Given a 4D dataset of task BOLD fMRI, we implement brain functional mapping by temporal correlation analysis. Results Through a high-field (7T) and high-resolution (0.5mm in plane) task fMRI experiment, we demonstrated in detail the BOLD perturbation model for fMRI phase signal separation (P + δP) and reconstructing intrinsic brain magnetic source (χ and δχ). We also provided to a low-field (3T) and low-resolution (2mm) task fMRI experiment in support of single-subject fMRI study. Our experiments show that the δχ-depicted functional map reveals bidirectional BOLD χ perturbations during the task performance. Conclusions The BOLD perturbation model allows us to separate fMRI phase signal (by complex division) and to perform inverse mapping for pure BOLD δχ reconstruction for intrinsic functional χ mapping. The full brain χ reconstruction (from unwrapped fMRI phase) provides a new brain tissue image that allows to scrutinize the brain tissue idiosyncrasy for the pure BOLD δχ response through an automatic function/structure co-localization. PMID:29351339

Pig brain stereotaxic standard space: mapping of cerebral blood flow normative values and effect of MPTP-lesioning.

PubMed

Andersen, Flemming; Watanabe, Hideaki; Bjarkam, Carsten; Danielsen, Erik H; Cumming, Paul

2005-07-15

The analysis of physiological processes in brain by position emission tomography (PET) is facilitated when images are spatially normalized to a standard coordinate system. Thus, PET activation studies of human brain frequently employ the common stereotaxic coordinates of Talairach. We have developed an analogous stereotaxic coordinate system for the brain of the Gottingen miniature pig, based on automatic co-registration of magnetic resonance (MR) images obtained in 22 male pigs. The origin of the pig brain stereotaxic space (0, 0, 0) was arbitrarily placed in the centroid of the pineal gland as identified on the average MRI template. The orthogonal planes were imposed using the line between stereotaxic zero and the optic chiasm. A series of mean MR images in the coronal, sagittal and horizontal planes were generated. To test the utility of the common coordinate system for functional imaging studies of minipig brain, we calculated cerebral blood flow (CBF) maps from normal minipigs and from minipigs with a syndrome of parkisonism induced by 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP)-poisoning. These maps were transformed from the native space into the common stereotaxic space. After global normalization of these maps, an undirected search for differences between the groups was then performed using statistical parametric mapping. Using this method, we detected a statistically significant focal increase in CBF in the left cerebellum of the MPTP-lesioned group. We expect the present approach to be of general use in the statistical parametric mapping of CBF and other physiological parameters in living pig brain.

Anatomical accuracy of brain connections derived from diffusion MRI tractography is inherently limited.

PubMed

Thomas, Cibu; Ye, Frank Q; Irfanoglu, M Okan; Modi, Pooja; Saleem, Kadharbatcha S; Leopold, David A; Pierpaoli, Carlo

2014-11-18

Tractography based on diffusion-weighted MRI (DWI) is widely used for mapping the structural connections of the human brain. Its accuracy is known to be limited by technical factors affecting in vivo data acquisition, such as noise, artifacts, and data undersampling resulting from scan time constraints. It generally is assumed that improvements in data quality and implementation of sophisticated tractography methods will lead to increasingly accurate maps of human anatomical connections. However, assessing the anatomical accuracy of DWI tractography is difficult because of the lack of independent knowledge of the true anatomical connections in humans. Here we investigate the future prospects of DWI-based connectional imaging by applying advanced tractography methods to an ex vivo DWI dataset of the macaque brain. The results of different tractography methods were compared with maps of known axonal projections from previous tracer studies in the macaque. Despite the exceptional quality of the DWI data, none of the methods demonstrated high anatomical accuracy. The methods that showed the highest sensitivity showed the lowest specificity, and vice versa. Additionally, anatomical accuracy was highly dependent upon parameters of the tractography algorithm, with different optimal values for mapping different pathways. These results suggest that there is an inherent limitation in determining long-range anatomical projections based on voxel-averaged estimates of local fiber orientation obtained from DWI data that is unlikely to be overcome by improvements in data acquisition and analysis alone.

Mapping whole-brain activity with cellular resolution by light-sheet microscopy and high-throughput image analysis (Conference Presentation)

NASA Astrophysics Data System (ADS)

Silvestri, Ludovico; Rudinskiy, Nikita; Paciscopi, Marco; Müllenbroich, Marie Caroline; Costantini, Irene; Sacconi, Leonardo; Frasconi, Paolo; Hyman, Bradley T.; Pavone, Francesco S.

2016-03-01

Mapping neuronal activity patterns across the whole brain with cellular resolution is a challenging task for state-of-the-art imaging methods. Indeed, despite a number of technological efforts, quantitative cellular-resolution activation maps of the whole brain have not yet been obtained. Many techniques are limited by coarse resolution or by a narrow field of view. High-throughput imaging methods, such as light sheet microscopy, can be used to image large specimens with high resolution and in reasonable times. However, the bottleneck is then moved from image acquisition to image analysis, since many TeraBytes of data have to be processed to extract meaningful information. Here, we present a full experimental pipeline to quantify neuronal activity in the entire mouse brain with cellular resolution, based on a combination of genetics, optics and computer science. We used a transgenic mouse strain (Arc-dVenus mouse) in which neurons which have been active in the last hours before brain fixation are fluorescently labelled. Samples were cleared with CLARITY and imaged with a custom-made confocal light sheet microscope. To perform an automatic localization of fluorescent cells on the large images produced, we used a novel computational approach called semantic deconvolution. The combined approach presented here allows quantifying the amount of Arc-expressing neurons throughout the whole mouse brain. When applied to cohorts of mice subject to different stimuli and/or environmental conditions, this method helps finding correlations in activity between different neuronal populations, opening the possibility to infer a sort of brain-wide 'functional connectivity' with cellular resolution.

Mapping the Regional Influence of Genetics on Brain Structure Variability - A Tensor-Based Morphometry Study

PubMed Central

Brun, Caroline; Leporé, Natasha; Pennec, Xavier; Lee, Agatha D.; Barysheva, Marina; Madsen, Sarah K.; Avedissian, Christina; Chou, Yi-Yu; de Zubicaray, Greig I.; McMahon, Katie; Wright, Margaret; Toga, Arthur W.; Thompson, Paul M.

2010-01-01

Genetic and environmental factors influence brain structure and function profoundly The search for heritable anatomical features and their influencing genes would be accelerated with detailed 3D maps showing the degree to which brain morphometry is genetically determined. As part of an MRI study that will scan 1150 twins, we applied Tensor-Based Morphometry to compute morphometric differences in 23 pairs of identical twins and 23 pairs of same-sex fraternal twins (mean age: 23.8 ± 1.8 SD years). All 92 twins’ 3D brain MRI scans were nonlinearly registered to a common space using a Riemannian fluid-based warping approach to compute volumetric differences across subjects. A multi-template method was used to improve volume quantification. Vector fields driving each subject’s anatomy onto the common template were analyzed to create maps of local volumetric excesses and deficits relative to the standard template. Using a new structural equation modeling method, we computed the voxelwise proportion of variance in volumes attributable to additive (A) or dominant (D) genetic factors versus shared environmental (C) or unique environmental factors (E). The method was also applied to various anatomical regions of interest (ROIs). As hypothesized, the overall volumes of the brain, basal ganglia, thalamus, and each lobe were under strong genetic control; local white matter volumes were mostly controlled by common environment. After adjusting for individual differences in overall brain scale, genetic influences were still relatively high in the corpus callosum and in early-maturing brain regions such as the occipital lobes, while environmental influences were greater in frontal brain regions which have a more protracted maturational time-course. PMID:19446645

Brain-mapping projects using the common marmoset.

PubMed

Okano, Hideyuki; Mitra, Partha

2015-04-01

Globally, there is an increasing interest in brain-mapping projects, including the Brain Research through Advancing Innovative Neurotechnologies (BRAIN) Initiative project in the USA, the Human Brain Project (HBP) in Europe, and the Brain Mapping by Integrated Neurotechnologies for Disease Studies (Brain/MINDS) project in Japan. These projects aim to map the structure and function of neuronal circuits to ultimately understand the vast complexity of the human brain. Brain/MINDS is focused on structural and functional mapping of the common marmoset (Callithrix jacchus) brain. This non-human primate has numerous advantages for brain mapping, including a well-developed frontal cortex and a compact brain size, as well as the availability of transgenic technologies. In the present review article, we discuss strategies for structural and functional mapping of the marmoset brain and the relation of the common marmoset to other animals models. Copyright © 2014 The Authors. Published by Elsevier Ireland Ltd.. All rights reserved.

Non-imaged based method for matching brains in a common anatomical space for cellular imagery.

PubMed

Midroit, Maëllie; Thevenet, Marc; Fournel, Arnaud; Sacquet, Joelle; Bensafi, Moustafa; Breton, Marine; Chalençon, Laura; Cavelius, Matthias; Didier, Anne; Mandairon, Nathalie

2018-04-22

Cellular imagery using histology sections is one of the most common techniques used in Neuroscience. However, this inescapable technique has severe limitations due to the need to delineate regions of interest on each brain, which is time consuming and variable across experimenters. We developed algorithms based on a vectors field elastic registration allowing fast, automatic realignment of experimental brain sections and associated labeling in a brain atlas with high accuracy and in a streamlined way. Thereby, brain areas of interest can be finely identified without outlining them and different experimental groups can be easily analyzed using conventional tools. This method directly readjusts labeling in the brain atlas without any intermediate manipulation of images. We mapped the expression of cFos, in the mouse brain (C57Bl/6J) after olfactory stimulation or a non-stimulated control condition and found an increased density of cFos-positive cells in the primary olfactory cortex but not in non-olfactory areas of the odor-stimulated animals compared to the controls. Existing methods of matching are based on image registration which often requires expensive material (two-photon tomography mapping or imaging with iDISCO) or are less accurate since they are based on mutual information contained in the images. Our new method is non-imaged based and relies only on the positions of detected labeling and the external contours of sections. We thus provide a new method that permits automated matching of histology sections of experimental brains with a brain reference atlas. Copyright © 2018 Elsevier B.V. All rights reserved.

On testing for spatial correspondence between maps of human brain structure and function.

PubMed

Alexander-Bloch, Aaron F; Shou, Haochang; Liu, Siyuan; Satterthwaite, Theodore D; Glahn, David C; Shinohara, Russell T; Vandekar, Simon N; Raznahan, Armin

2018-06-01

A critical issue in many neuroimaging studies is the comparison between brain maps. Nonetheless, it remains unclear how one should test hypotheses focused on the overlap or spatial correspondence between two or more brain maps. This "correspondence problem" affects, for example, the interpretation of comparisons between task-based patterns of functional activation, resting-state networks or modules, and neuroanatomical landmarks. To date, this problem has been addressed with remarkable variability in terms of methodological approaches and statistical rigor. In this paper, we address the correspondence problem using a spatial permutation framework to generate null models of overlap by applying random rotations to spherical representations of the cortical surface, an approach for which we also provide a theoretical statistical foundation. We use this method to derive clusters of cognitive functions that are correlated in terms of their functional neuroatomical substrates. In addition, using publicly available data, we formally demonstrate the correspondence between maps of task-based functional activity, resting-state fMRI networks and gyral-based anatomical landmarks. We provide open-access code to implement the methods presented for two commonly-used tools for surface based cortical analysis (https://www.github.com/spin-test). This spatial permutation approach constitutes a useful advance over widely-used methods for the comparison of cortical maps, thereby opening new possibilities for the integration of diverse neuroimaging data. Copyright © 2018 Elsevier Inc. All rights reserved.

Selection of independent components based on cortical mapping of electromagnetic activity

NASA Astrophysics Data System (ADS)

Chan, Hui-Ling; Chen, Yong-Sheng; Chen, Li-Fen

2012-10-01

Independent component analysis (ICA) has been widely used to attenuate interference caused by noise components from the electromagnetic recordings of brain activity. However, the scalp topographies and associated temporal waveforms provided by ICA may be insufficient to distinguish functional components from artifactual ones. In this work, we proposed two component selection methods, both of which first estimate the cortical distribution of the brain activity for each component, and then determine the functional components based on the parcellation of brain activity mapped onto the cortical surface. Among all independent components, the first method can identify the dominant components, which have strong activity in the selected dominant brain regions, whereas the second method can identify those inter-regional associating components, which have similar component spectra between a pair of regions. For a targeted region, its component spectrum enumerates the amplitudes of its parceled brain activity across all components. The selected functional components can be remixed to reconstruct the focused electromagnetic signals for further analysis, such as source estimation. Moreover, the inter-regional associating components can be used to estimate the functional brain network. The accuracy of the cortical activation estimation was evaluated on the data from simulation studies, whereas the usefulness and feasibility of the component selection methods were demonstrated on the magnetoencephalography data recorded from a gender discrimination study.

Metric Optimization for Surface Analysis in the Laplace-Beltrami Embedding Space

PubMed Central

Lai, Rongjie; Wang, Danny J.J.; Pelletier, Daniel; Mohr, David; Sicotte, Nancy; Toga, Arthur W.

2014-01-01

In this paper we present a novel approach for the intrinsic mapping of anatomical surfaces and its application in brain mapping research. Using the Laplace-Beltrami eigen-system, we represent each surface with an isometry invariant embedding in a high dimensional space. The key idea in our system is that we realize surface deformation in the embedding space via the iterative optimization of a conformal metric without explicitly perturbing the surface or its embedding. By minimizing a distance measure in the embedding space with metric optimization, our method generates a conformal map directly between surfaces with highly uniform metric distortion and the ability of aligning salient geometric features. Besides pairwise surface maps, we also extend the metric optimization approach for group-wise atlas construction and multi-atlas cortical label fusion. In experimental results, we demonstrate the robustness and generality of our method by applying it to map both cortical and hippocampal surfaces in population studies. For cortical labeling, our method achieves excellent performance in a cross-validation experiment with 40 manually labeled surfaces, and successfully models localized brain development in a pediatric study of 80 subjects. For hippocampal mapping, our method produces much more significant results than two popular tools on a multiple sclerosis study of 109 subjects. PMID:24686245

[AWAKE CRANIOTOMY: IN SEARCH FOR OPTIMAL SEDATION].

PubMed

Kulikova, A S; Sel'kov, D A; Kobyakov, G L; Shmigel'skiy, A V; Lubnin, A Yu

2015-01-01

Awake craniotomy is a "gold standard"for intraoperative brain language mapping. One of the main anesthetic challenge of awake craniotomy is providing of optimal sedation for initial stages of intervention. The goal of this study was comparison of different technics of anesthesia for awake craniotomy. Materials and methods: 162 operations were divided in 4 groups: 76 cases with propofol sedation (2-4mg/kg/h) without airway protection; 11 cases with propofol sedation (4-5 mg/kg/h) with MV via LMA; 36 cases of xenon anesthesia; and 39 cases with dexmedetomidine sedation without airway protection. Results and discussion: brain language mapping was successful in 90% of cases. There was no difference between groups in successfulness of brain mapping. However in the first group respiratory complications were more frequent. Three other technics were more safer Xenon anesthesia was associated with ultrafast awakening for mapping (5±1 min). Dexmedetomidine sedation provided high hemodynamic and respiratory stability during the procedure.

Parcellating an Individual Subject's Cortical and Subcortical Brain Structures Using Snowball Sampling of Resting-State Correlations

PubMed Central

Wig, Gagan S.; Laumann, Timothy O.; Cohen, Alexander L.; Power, Jonathan D.; Nelson, Steven M.; Glasser, Matthew F.; Miezin, Francis M.; Snyder, Abraham Z.; Schlaggar, Bradley L.; Petersen, Steven E.

2014-01-01

We describe methods for parcellating an individual subject's cortical and subcortical brain structures using resting-state functional correlations (RSFCs). Inspired by approaches from social network analysis, we first describe the application of snowball sampling on RSFC data (RSFC-Snowballing) to identify the centers of cortical areas, subdivisions of subcortical nuclei, and the cerebellum. RSFC-Snowballing parcellation is then compared with parcellation derived from identifying locations where RSFC maps exhibit abrupt transitions (RSFC-Boundary Mapping). RSFC-Snowballing and RSFC-Boundary Mapping largely complement one another, but also provide unique parcellation information; together, the methods identify independent entities with distinct functional correlations across many cortical and subcortical locations in the brain. RSFC parcellation is relatively reliable within a subject scanned across multiple days, and while the locations of many area centers and boundaries appear to exhibit considerable overlap across subjects, there is also cross-subject variability—reinforcing the motivation to parcellate brains at the level of individuals. Finally, examination of a large meta-analysis of task-evoked functional magnetic resonance imaging data reveals that area centers defined by task-evoked activity exhibit correspondence with area centers defined by RSFC-Snowballing. This observation provides important evidence for the ability of RSFC to parcellate broad expanses of an individual's brain into functionally meaningful units. PMID:23476025

Errors in MR-based attenuation correction for brain imaging with PET/MR scanners

NASA Astrophysics Data System (ADS)

Rota Kops, Elena; Herzog, Hans

2013-02-01

AimAttenuation correction of PET data acquired by hybrid MR/PET scanners remains a challenge, even if several methods for brain and whole-body measurements have been developed recently. A template-based attenuation correction for brain imaging proposed by our group is easy to handle and delivers reliable attenuation maps in a short time. However, some potential error sources are analyzed in this study. We investigated the choice of template reference head among all the available data (error A), and possible skull anomalies of the specific patient, such as discontinuities due to surgery (error B). Materials and methodsAn anatomical MR measurement and a 2-bed-position transmission scan covering the whole head and neck region were performed in eight normal subjects (4 females, 4 males). Error A: Taking alternatively one of the eight heads as reference, eight different templates were created by nonlinearly registering the images to the reference and calculating the average. Eight patients (4 females, 4 males; 4 with brain lesions, 4 w/o brain lesions) were measured in the Siemens BrainPET/MR scanner. The eight templates were used to generate the patients' attenuation maps required for reconstruction. ROI and VOI atlas-based comparisons were performed employing all the reconstructed images. Error B: CT-based attenuation maps of two volunteers were manipulated by manually inserting several skull lesions and filling a nasal cavity. The corresponding attenuation coefficients were substituted with the water's coefficient (0.096/cm). ResultsError A: The mean SUVs over the eight templates pairs for all eight patients and all VOIs did not differ significantly one from each other. Standard deviations up to 1.24% were found. Error B: After reconstruction of the volunteers' BrainPET data with the CT-based attenuation maps without and with skull anomalies, a VOI-atlas analysis was performed revealing very little influence of the skull lesions (less than 3%), while the filled nasal cavity yielded an overestimation in cerebellum up to 5%. ConclusionsThe present error analysis confirms that our template-based attenuation method provides reliable attenuation corrections of PET brain imaging measured in PET/MR scanners.

Enhanced cortical thickness measurements for rodent brains via Lagrangian-based RK4 streamline computation

NASA Astrophysics Data System (ADS)

Lee, Joohwi; Kim, Sun Hyung; Oguz, Ipek; Styner, Martin

2016-03-01

The cortical thickness of the mammalian brain is an important morphological characteristic that can be used to investigate and observe the brain's developmental changes that might be caused by biologically toxic substances such as ethanol or cocaine. Although various cortical thickness analysis methods have been proposed that are applicable for human brain and have developed into well-validated open-source software packages, cortical thickness analysis methods for rodent brains have not yet become as robust and accurate as those designed for human brains. Based on a previously proposed cortical thickness measurement pipeline for rodent brain analysis,1 we present an enhanced cortical thickness pipeline in terms of accuracy and anatomical consistency. First, we propose a Lagrangian-based computational approach in the thickness measurement step in order to minimize local truncation error using the fourth-order Runge-Kutta method. Second, by constructing a line object for each streamline of the thickness measurement, we can visualize the way the thickness is measured and achieve sub-voxel accuracy by performing geometric post-processing. Last, with emphasis on the importance of an anatomically consistent partial differential equation (PDE) boundary map, we propose an automatic PDE boundary map generation algorithm that is specific to rodent brain anatomy, which does not require manual labeling. The results show that the proposed cortical thickness pipeline can produce statistically significant regions that are not observed in the previous cortical thickness analysis pipeline.

Evaluation of MRI sequences for quantitative T1 brain mapping

NASA Astrophysics Data System (ADS)

Tsialios, P.; Thrippleton, M.; Glatz, A.; Pernet, C.

2017-11-01

T1 mapping constitutes a quantitative MRI technique finding significant application in brain imaging. It allows evaluation of contrast uptake, blood perfusion, volume, providing a more specific biomarker of disease progression compared to conventional T1-weighted images. While there are many techniques for T1-mapping there is a wide range of reported T1-values in tissues, raising the issue of protocols reproducibility and standardization. The gold standard for obtaining T1-maps is based on acquiring IR-SE sequence. Widely used alternative sequences are IR-SE-EPI, VFA (DESPOT), DESPOT-HIFI and MP2RAGE that speed up scanning and fitting procedures. A custom MRI phantom was used to assess the reproducibility and accuracy of the different methods. All scans were performed using a 3T Siemens Prisma scanner. The acquired data processed using two different codes. The main difference was observed for VFA (DESPOT) which grossly overestimated T1 relaxation time by 214 ms [126 270] compared to the IR-SE sequence. MP2RAGE and DESPOT-HIFI sequences gave slightly shorter time than IR-SE (~20 to 30ms) and can be considered as alternative and time-efficient methods for acquiring accurate T1 maps of the human brain, while IR-SE-EPI gave identical result, at a cost of a lower image quality.

On initial Brain Activity Mapping of episodic and semantic memory code in the hippocampus.

PubMed

Tsien, Joe Z; Li, Meng; Osan, Remus; Chen, Guifen; Lin, Longian; Wang, Phillip Lei; Frey, Sabine; Frey, Julietta; Zhu, Dajiang; Liu, Tianming; Zhao, Fang; Kuang, Hui

2013-10-01

It has been widely recognized that the understanding of the brain code would require large-scale recording and decoding of brain activity patterns. In 2007 with support from Georgia Research Alliance, we have launched the Brain Decoding Project Initiative with the basic idea which is now similarly advocated by BRAIN project or Brain Activity Map proposal. As the planning of the BRAIN project is currently underway, we share our insights and lessons from our efforts in mapping real-time episodic memory traces in the hippocampus of freely behaving mice. We show that appropriate large-scale statistical methods are essential to decipher and measure real-time memory traces and neural dynamics. We also provide an example of how the carefully designed, sometime thinking-outside-the-box, behavioral paradigms can be highly instrumental to the unraveling of memory-coding cell assembly organizing principle in the hippocampus. Our observations to date have led us to conclude that the specific-to-general categorical and combinatorial feature-coding cell assembly mechanism represents an emergent property for enabling the neural networks to generate and organize not only episodic memory, but also semantic knowledge and imagination. Copyright © 2013 The Authors. Published by Elsevier Inc. All rights reserved.

On Initial Brain Activity Mapping of Associative Memory Code in the Hippocampus

PubMed Central

Tsien, Joe Z.; Li, Meng; Osan, Remus; Chen, Guifen; Lin, Longian; Lei Wang, Phillip; Frey, Sabine; Frey, Julietta; Zhu, Dajiang; Liu, Tianming; Zhao, Fang; Kuang, Hui

2013-01-01

It has been widely recognized that the understanding of the brain code would require large-scale recording and decoding of brain activity patterns. In 2007 with support from Georgia Research Alliance, we have launched the Brain Decoding Project Initiative with the basic idea which is now similarly advocated by BRAIN project or Brain Activity Map proposal. As the planning of the BRAIN project is currently underway, we share our insights and lessons from our efforts in mapping real-time episodic memory traces in the hippocampus of freely behaving mice. We show that appropriate large-scale statistical methods are essential to decipher and measure real-time memory traces and neural dynamics. We also provide an example of how the carefully designed, sometime thinking-outside-the-box, behavioral paradigms can be highly instrumental to the unraveling of memory-coding cell assembly organizing principle in the hippocampus. Our observations to date have led us to conclude that the specific-to-general categorical and combinatorial feature-coding cell assembly mechanism represents an emergent property for enabling the neural networks to generate and organize not only episodic memory, but also semantic knowledge and imagination. PMID:23838072

Spatiotemporal patterns of ERP based on combined ICA-LORETA analysis

NASA Astrophysics Data System (ADS)

Zhang, Jiacai; Guo, Taomei; Xu, Yaqin; Zhao, Xiaojie; Yao, Li

2007-03-01

In contrast to the FMRI methods widely used up to now, this method try to understand more profoundly how the brain systems work under sentence processing task map accurately the spatiotemporal patterns of activity of the large neuronal populations in the human brain from the analysis of ERP data recorded on the brain scalp. In this study, an event-related brain potential (ERP) paradigm to record the on-line responses to the processing of sentences is chosen as an example. In order to give attention to both utilizing the ERPs' temporal resolution of milliseconds and overcoming the insensibility of cerebral location ERP sources, we separate these sources in space and time based on a combined method of independent component analysis (ICA) and low-resolution tomography (LORETA) algorithms. ICA blindly separate the input ERP data into a sum of temporally independent and spatially fixed components arising from distinct or overlapping brain or extra-brain sources. And then the spatial maps associated with each ICA component are analyzed, with use of LORETA to uniquely locate its cerebral sources throughout the full brain according to the assumption that neighboring neurons are simultaneously and synchronously activated. Our results show that the cerebral computation mechanism underlies content words reading is mediated by the orchestrated activity of several spatially distributed brain sources located in the temporal, frontal, and parietal areas, and activate at distinct time intervals and are grouped into different statistically independent components. Thus ICA-LORETA analysis provides an encouraging and effective method to study brain dynamics from ERP.

Baby Brain Map

MedlinePlus

... a Member Home Resources & Services Professional Resource Baby Brain Map Mar 17, 2016 The Brain Map was adapted in 2006 by ZERO TO ... supports Adobe Flash Player. To view the Baby Brain Map, please visit this page on a browser ...

Riemannian Metric Optimization on Surfaces (RMOS) for Intrinsic Brain Mapping in the Laplace-Beltrami Embedding Space

PubMed Central

Gahm, Jin Kyu; Shi, Yonggang

2018-01-01

Surface mapping methods play an important role in various brain imaging studies from tracking the maturation of adolescent brains to mapping gray matter atrophy patterns in Alzheimer’s disease. Popular surface mapping approaches based on spherical registration, however, have inherent numerical limitations when severe metric distortions are present during the spherical parameterization step. In this paper, we propose a novel computational framework for intrinsic surface mapping in the Laplace-Beltrami (LB) embedding space based on Riemannian metric optimization on surfaces (RMOS). Given a diffeomorphism between two surfaces, an isometry can be defined using the pullback metric, which in turn results in identical LB embeddings from the two surfaces. The proposed RMOS approach builds upon this mathematical foundation and achieves general feature-driven surface mapping in the LB embedding space by iteratively optimizing the Riemannian metric defined on the edges of triangular meshes. At the core of our framework is an optimization engine that converts an energy function for surface mapping into a distance measure in the LB embedding space, which can be effectively optimized using gradients of the LB eigen-system with respect to the Riemannian metrics. In the experimental results, we compare the RMOS algorithm with spherical registration using large-scale brain imaging data, and show that RMOS achieves superior performance in the prediction of hippocampal subfields and cortical gyral labels, and the holistic mapping of striatal surfaces for the construction of a striatal connectivity atlas from substantia nigra. PMID:29574399

Susceptibility-based functional brain mapping by 3D deconvolution of an MR-phase activation map.

PubMed

Chen, Zikuan; Liu, Jingyu; Calhoun, Vince D

2013-05-30

The underlying source of T2*-weighted magnetic resonance imaging (T2*MRI) for brain imaging is magnetic susceptibility (denoted by χ). T2*MRI outputs a complex-valued MR image consisting of magnitude and phase information. Recent research has shown that both the magnitude and the phase images are morphologically different from the source χ, primarily due to 3D convolution, and that the source χ can be reconstructed from complex MR images by computed inverse MRI (CIMRI). Thus, we can obtain a 4D χ dataset from a complex 4D MR dataset acquired from a brain functional MRI study by repeating CIMRI to reconstruct 3D χ volumes at each timepoint. Because the reconstructed χ is a more direct representation of neuronal activity than the MR image, we propose a method for χ-based functional brain mapping, which is numerically characterised by a temporal correlation map of χ responses to a stimulant task. Under the linear imaging conditions used for T2*MRI, we show that the χ activation map can be calculated from the MR phase map by CIMRI. We validate our approach using numerical simulations and Gd-phantom experiments. We also analyse real data from a finger-tapping visuomotor experiment and show that the χ-based functional mapping provides additional activation details (in the form of positive and negative correlation patterns) beyond those generated by conventional MR-magnitude-based mapping. Copyright © 2013 Elsevier B.V. All rights reserved.

Modeling the role of microglia during neurovascular development (WC10)

EPA Science Inventory

Microglia, resident brain macrophages, have important roles in blood-brain barrier (BBB) development and during focal BBB disruption. We reconstructed these complex dynamics utilizing computational and molecular methods: 1) constructing a systems map of BBB development from known...

Combined Ultrasound and MR Imaging to Guide Focused Ultrasound Therapies in the Brain

PubMed Central

Arvanitis, Costas D.; Livingstone, Margaret S.; McDannold, Nathan

2013-01-01

Purpose Several emerging therapies with potential for use in the brain harness effects produced by acoustic cavitation – the interaction between ultrasound and microbubbles either generated during sonication or introduced into the vasculature. Systems developed for transcranial MRI-guided focused ultrasound (MRgFUS) thermal ablation can enable their clinical translation, but methods for real-time monitoring and control are currently lacking. Acoustic emissions produced during sonication can provide information about the location, strength, and type of the microbubble oscillations within the ultrasound field, and they can be mapped in real-time using passive imaging approaches. Here, we tested whether such mapping can be achieved transcranially within a clinical brain MRgFUS system. Materials and Methods We integrated an ultrasound imaging array into the hemisphere transducer of the MRgFUS device. Passive cavitation maps were obtained during sonications combined with a circulating microbubble agent at 20 targets in the cingulate cortex in three macaques. The maps were compared with MRI-evident tissue effects. Results The system successfully mapped microbubble activity during both stable and inertial cavitation, which was correlated with MRI-evident transient blood-brain barrier disruption and vascular damage, respectively. The location of this activity was coincident with the resulting tissue changes within the expected resolution limits of the system. Conclusion While preliminary, these data clearly demonstrate, for the first time, that is possible to construct maps of stable and inertial cavitation transcranially, in a large animal model, and under clinically relevant conditions. Further, these results suggest that this hybrid ultrasound/MRI approach can provide comprehensive guidance for targeted drug delivery via blood-brain barrier disruption and other emerging ultrasound treatments, facilitating their clinical translation. We anticipate it will also prove to be an important research tool that will further the development of a broad range of microbubble-enhanced therapies. PMID:23788054

Robust and fast characterization of OCT-based optical attenuation using a novel frequency-domain algorithm for brain cancer detection

NASA Astrophysics Data System (ADS)

Yuan, Wu; Kut, Carmen; Liang, Wenxuan; Li, Xingde

2017-03-01

Cancer is known to alter the local optical properties of tissues. The detection of OCT-based optical attenuation provides a quantitative method to efficiently differentiate cancer from non-cancer tissues. In particular, the intraoperative use of quantitative OCT is able to provide a direct visual guidance in real time for accurate identification of cancer tissues, especially these without any obvious structural layers, such as brain cancer. However, current methods are suboptimal in providing high-speed and accurate OCT attenuation mapping for intraoperative brain cancer detection. In this paper, we report a novel frequency-domain (FD) algorithm to enable robust and fast characterization of optical attenuation as derived from OCT intensity images. The performance of this FD algorithm was compared with traditional fitting methods by analyzing datasets containing images from freshly resected human brain cancer and from a silica phantom acquired by a 1310 nm swept-source OCT (SS-OCT) system. With graphics processing unit (GPU)-based CUDA C/C++ implementation, this new attenuation mapping algorithm can offer robust and accurate quantitative interpretation of OCT images in real time during brain surgery.

A super-resolution ultrasound method for brain vascular mapping

PubMed Central

O'Reilly, Meaghan A.; Hynynen, Kullervo

2013-01-01

Purpose: High-resolution vascular imaging has not been achieved in the brain due to limitations of current clinical imaging modalities. The authors present a method for transcranial ultrasound imaging of single micrometer-size bubbles within a tube phantom. Methods: Emissions from single bubbles within a tube phantom were mapped through an ex vivo human skull using a sparse hemispherical receiver array and a passive beamforming algorithm. Noninvasive phase and amplitude correction techniques were applied to compensate for the aberrating effects of the skull bone. The positions of the individual bubbles were estimated beyond the diffraction limit of ultrasound to produce a super-resolution image of the tube phantom, which was compared with microcomputed tomography (micro-CT). Results: The resulting super-resolution ultrasound image is comparable to results obtained via the micro-CT for small tissue specimen imaging. Conclusions: This method provides superior resolution to deep-tissue contrast ultrasound and has the potential to be extended to provide complete vascular network imaging in the brain. PMID:24320408

Awake craniotomy for brain tumor: indications, technique and benefits.

PubMed

Dziedzic, Tomasz; Bernstein, Mark

2014-12-01

Increasing interest in the quality of life of patients after treatment of brain tumors has led to the exploration of methods that can improve intraoperative assessment of neurological status to avoid neurological deficits. The only method that can provide assessment of all eloquent areas of cerebral cortex and white matter is brain mapping during awake craniotomy. This method helps ensure that the quality of life and the neuro-oncological result of treatment are not compromised. Apart from the medical aspects of awake surgery, its economic issues are also favorable. Here, we review the main aspects of awake brain tumor surgery. Neurosurgical, neuropsychological, neurophysiological and anesthetic issues are briefly discussed.

Rapid Parametric Mapping of the Longitudinal Relaxation Time T1 Using Two-Dimensional Variable Flip Angle Magnetic Resonance Imaging at 1.5 Tesla, 3 Tesla, and 7 Tesla

PubMed Central

Dieringer, Matthias A.; Deimling, Michael; Santoro, Davide; Wuerfel, Jens; Madai, Vince I.; Sobesky, Jan; von Knobelsdorff-Brenkenhoff, Florian; Schulz-Menger, Jeanette; Niendorf, Thoralf

2014-01-01

Introduction Visual but subjective reading of longitudinal relaxation time (T1) weighted magnetic resonance images is commonly used for the detection of brain pathologies. For this non-quantitative measure, diagnostic quality depends on hardware configuration, imaging parameters, radio frequency transmission field (B1+) uniformity, as well as observer experience. Parametric quantification of the tissue T1 relaxation parameter offsets the propensity for these effects, but is typically time consuming. For this reason, this study examines the feasibility of rapid 2D T1 quantification using a variable flip angles (VFA) approach at magnetic field strengths of 1.5 Tesla, 3 Tesla, and 7 Tesla. These efforts include validation in phantom experiments and application for brain T1 mapping. Methods T1 quantification included simulations of the Bloch equations to correct for slice profile imperfections, and a correction for B1+. Fast gradient echo acquisitions were conducted using three adjusted flip angles for the proposed T1 quantification approach that was benchmarked against slice profile uncorrected 2D VFA and an inversion-recovery spin-echo based reference method. Brain T1 mapping was performed in six healthy subjects, one multiple sclerosis patient, and one stroke patient. Results Phantom experiments showed a mean T1 estimation error of (-63±1.5)% for slice profile uncorrected 2D VFA and (0.2±1.4)% for the proposed approach compared to the reference method. Scan time for single slice T1 mapping including B1+ mapping could be reduced to 5 seconds using an in-plane resolution of (2×2) mm2, which equals a scan time reduction of more than 99% compared to the reference method. Conclusion Our results demonstrate that rapid 2D T1 quantification using a variable flip angle approach is feasible at 1.5T/3T/7T. It represents a valuable alternative for rapid T1 mapping due to the gain in speed versus conventional approaches. This progress may serve to enhance the capabilities of parametric MR based lesion detection and brain tissue characterization. PMID:24621588

Shape Classification Using Wasserstein Distance for Brain Morphometry Analysis.

PubMed

Su, Zhengyu; Zeng, Wei; Wang, Yalin; Lu, Zhong-Lin; Gu, Xianfeng

2015-01-01

Brain morphometry study plays a fundamental role in medical imaging analysis and diagnosis. This work proposes a novel framework for brain cortical surface classification using Wasserstein distance, based on uniformization theory and Riemannian optimal mass transport theory. By Poincare uniformization theorem, all shapes can be conformally deformed to one of the three canonical spaces: the unit sphere, the Euclidean plane or the hyperbolic plane. The uniformization map will distort the surface area elements. The area-distortion factor gives a probability measure on the canonical uniformization space. All the probability measures on a Riemannian manifold form the Wasserstein space. Given any 2 probability measures, there is a unique optimal mass transport map between them, the transportation cost defines the Wasserstein distance between them. Wasserstein distance gives a Riemannian metric for the Wasserstein space. It intrinsically measures the dissimilarities between shapes and thus has the potential for shape classification. To the best of our knowledge, this is the first. work to introduce the optimal mass transport map to general Riemannian manifolds. The method is based on geodesic power Voronoi diagram. Comparing to the conventional methods, our approach solely depends on Riemannian metrics and is invariant under rigid motions and scalings, thus it intrinsically measures shape distance. Experimental results on classifying brain cortical surfaces with different intelligence quotients demonstrated the efficiency and efficacy of our method.

Shape Classification Using Wasserstein Distance for Brain Morphometry Analysis

PubMed Central

Su, Zhengyu; Zeng, Wei; Wang, Yalin; Lu, Zhong-Lin; Gu, Xianfeng

2015-01-01

Brain morphometry study plays a fundamental role in medical imaging analysis and diagnosis. This work proposes a novel framework for brain cortical surface classification using Wasserstein distance, based on uniformization theory and Riemannian optimal mass transport theory. By Poincare uniformization theorem, all shapes can be conformally deformed to one of the three canonical spaces: the unit sphere, the Euclidean plane or the hyperbolic plane. The uniformization map will distort the surface area elements. The area-distortion factor gives a probability measure on the canonical uniformization space. All the probability measures on a Riemannian manifold form the Wasserstein space. Given any 2 probability measures, there is a unique optimal mass transport map between them, the transportation cost defines the Wasserstein distance between them. Wasserstein distance gives a Riemannian metric for the Wasserstein space. It intrinsically measures the dissimilarities between shapes and thus has the potential for shape classification. To the best of our knowledge, this is the first work to introduce the optimal mass transport map to general Riemannian manifolds. The method is based on geodesic power Voronoi diagram. Comparing to the conventional methods, our approach solely depends on Riemannian metrics and is invariant under rigid motions and scalings, thus it intrinsically measures shape distance. Experimental results on classifying brain cortical surfaces with different intelligence quotients demonstrated the efficiency and efficacy of our method. PMID:26221691

Effects of tissue susceptibility on brain temperature mapping.

PubMed

Maudsley, Andrew A; Goryawala, Mohammed Z; Sheriff, Sulaiman

2017-02-01

A method for mapping of temperature over a large volume of the brain using volumetric proton MR spectroscopic imaging has been implemented and applied to 150 normal subjects. Magnetic susceptibility-induced frequency shifts in gray- and white-matter regions were measured and included as a correction in the temperature mapping calculation. Additional sources of magnetic susceptibility variations of the individual metabolite resonance frequencies were also observed that reflect the cellular-level organization of the brain metabolites, with the most notable differences being attributed to changes of the N-Acetylaspartate resonance frequency that reflect the intra-axonal distribution and orientation of the white-matter tracts with respect to the applied magnetic field. These metabolite-specific susceptibility effects are also shown to change with age. Results indicate no change of apparent brain temperature with age from 18 to 84 years old, with a trend for increased brain temperature throughout the cerebrum in females relative for males on the order of 0.1°C; slightly increased temperatures in the left hemisphere relative to the right; and a lower temperature of 0.3°C in the cerebellum relative to that of cerebral white-matter. This study presents a novel acquisition method for noninvasive measurement of brain temperature that is of potential value for diagnostic purposes and treatment monitoring, while also demonstrating limitations of the measurement due to the confounding effects of tissue susceptibility variations. Copyright © 2016 Elsevier Inc. All rights reserved.

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

Parcellating an individual subject's cortical and subcortical brain structures using snowball sampling of resting-state correlations.

PubMed

Wig, Gagan S; Laumann, Timothy O; Cohen, Alexander L; Power, Jonathan D; Nelson, Steven M; Glasser, Matthew F; Miezin, Francis M; Snyder, Abraham Z; Schlaggar, Bradley L; Petersen, Steven E

2014-08-01

We describe methods for parcellating an individual subject's cortical and subcortical brain structures using resting-state functional correlations (RSFCs). Inspired by approaches from social network analysis, we first describe the application of snowball sampling on RSFC data (RSFC-Snowballing) to identify the centers of cortical areas, subdivisions of subcortical nuclei, and the cerebellum. RSFC-Snowballing parcellation is then compared with parcellation derived from identifying locations where RSFC maps exhibit abrupt transitions (RSFC-Boundary Mapping). RSFC-Snowballing and RSFC-Boundary Mapping largely complement one another, but also provide unique parcellation information; together, the methods identify independent entities with distinct functional correlations across many cortical and subcortical locations in the brain. RSFC parcellation is relatively reliable within a subject scanned across multiple days, and while the locations of many area centers and boundaries appear to exhibit considerable overlap across subjects, there is also cross-subject variability-reinforcing the motivation to parcellate brains at the level of individuals. Finally, examination of a large meta-analysis of task-evoked functional magnetic resonance imaging data reveals that area centers defined by task-evoked activity exhibit correspondence with area centers defined by RSFC-Snowballing. This observation provides important evidence for the ability of RSFC to parcellate broad expanses of an individual's brain into functionally meaningful units. © The Author 2013. Published by Oxford University Press.

Quantification of blood-to-brain transfer rate in multiple sclerosis

PubMed Central

Taheri, Saeid; Rosenberg, Gary A.; Ford, Corey

2016-01-01

Blood–brain barrier (BBB) disruption visualized in lesions by MRI is a major biomarker of disease activity in multiple sclerosis (MS). However, in MS, destruction occurs to a variable extent in lesions as well as in gray matter (GM) and in the normal appearing white matter (NAWM). A method to quantify the BBB disruption in lesions as well as in non-lesion areas would be useful for assessment of MS progression and treatments. The objective of this study was to quantify the BBB transfer rate (Ki) in WM lesions, in the NAWM, and in the full-brain of MS patients. Thirteen MS patients with active lesions and 10 healthy controls with age and gender matching were recruited for full-brain and WM Ki studies. Dynamic contrast-enhanced MRI (DCEMRI) scans were conducted using T1 mapping with partial inversion recovery (TAPIR), a fast T1 mapping technique, following administration of a quarter-dose of the contrast agent Gadolinium-DTPA (Gd-DTPA). The Patlak modeling technique was used to derive a voxel-based map of Ki. In all patients contrast-enhanced lesions, quantified by Ki maps, were observed. Compared with controls, patients with MS exhibited an increase in mean Ki of the full-brain (P-value<0.05) but no significant difference in mean Ki of NAWM. The identified increase in full-brain Ki of MS patients suggests a global vascular involvement associated with MS disease. The lack of observed significant decrease in Ki in NAWM suggests lower involvement of WM vasculature than full-brain vasculature in MS. Ki maps constructed from time series data acquired by DCEMRI provide additional information about BBB that could be used for evaluation of vascular involvement in MS and monitoring treatment effectiveness. PMID:25877634

Decoding natural images from evoked brain activities using encoding models with invertible mapping.

PubMed

Li, Chao; Xu, Junhai; Liu, Baolin

2018-05-21

Recent studies have built encoding models in the early visual cortex, and reliable mappings have been made between the low-level visual features of stimuli and brain activities. However, these mappings are irreversible, so that the features cannot be directly decoded. To solve this problem, we designed a sparse framework-based encoding model that predicted brain activities from a complete feature representation. Moreover, according to the distribution and activation rules of neurons in the primary visual cortex (V1), three key transformations were introduced into the basic feature to improve the model performance. In this setting, the mapping was simple enough that it could be inverted using a closed-form formula. Using this mapping, we designed a hybrid identification method based on the support vector machine (SVM), and tested it on a published functional magnetic resonance imaging (fMRI) dataset. The experiments confirmed the rationality of our encoding model, and the identification accuracies for 2 subjects increased from 92% and 72% to 98% and 92% with the chance level only 0.8%. Copyright © 2018 Elsevier Ltd. All rights reserved.

Combining task-evoked and spontaneous activity to improve pre-operative brain mapping with fMRI

PubMed Central

Fox, Michael D.; Qian, Tianyi; Madsen, Joseph R.; Wang, Danhong; Li, Meiling; Ge, Manling; Zuo, Huan-cong; Groppe, David M.; Mehta, Ashesh D.; Hong, Bo; Liu, Hesheng

2016-01-01

Noninvasive localization of brain function is used to understand and treat neurological disease, exemplified by pre-operative fMRI mapping prior to neurosurgical intervention. The principal approach for generating these maps relies on brain responses evoked by a task and, despite known limitations, has dominated clinical practice for over 20 years. Recently, pre-operative fMRI mapping based on correlations in spontaneous brain activity has been demonstrated, however this approach has its own limitations and has not seen widespread clinical use. Here we show that spontaneous and task-based mapping can be performed together using the same pre-operative fMRI data, provide complimentary information relevant for functional localization, and can be combined to improve identification of eloquent motor cortex. Accuracy, sensitivity, and specificity of our approach are quantified through comparison with electrical cortical stimulation mapping in eight patients with intractable epilepsy. Broad applicability and reproducibility of our approach is demonstrated through prospective replication in an independent dataset of six patients from a different center. In both cohorts and every individual patient, we see a significant improvement in signal to noise and mapping accuracy independent of threshold, quantified using receiver operating characteristic curves. Collectively, our results suggest that modifying the processing of fMRI data to incorporate both task-based and spontaneous activity significantly improves functional localization in pre-operative patients. Because this method requires no additional scan time or modification to conventional pre-operative data acquisition protocols it could have widespread utility. PMID:26408860

Simultaneous mapping of water shift and B1 (WASABI)-Application to field-Inhomogeneity correction of CEST MRI data.

PubMed

Schuenke, Patrick; Windschuh, Johannes; Roeloffs, Volkert; Ladd, Mark E; Bachert, Peter; Zaiss, Moritz

2017-02-01

Together with the development of MRI contrasts that are inherently small in their magnitude, increased magnetic field accuracy is also required. Hence, mapping of the static magnetic field (B 0 ) and the excitation field (B 1 ) is not only important to feedback shim algorithms, but also for postprocess contrast-correction procedures. A novel field-inhomogeneity mapping method is presented that allows simultaneous mapping of the water shift and B 1 (WASABI) using an off-resonant rectangular preparation pulse. The induced Rabi oscillations lead to a sinc-like spectrum in the frequency-offset dimension and allow for determination of B 0 by its symmetry axis and of B 1 by its oscillation frequency. Stability of the WASABI method with regard to the influences of T 1 , T 2 , magnetization transfer, and repetition time was investigated and its convergence interval was verified. B 0 and B 1 maps obtained simultaneously by means of WASABI in the human brain at 3 T and 7 T can compete well with maps obtained by standard methods. Finally, the method was applied successfully for B 0 and B 1 correction of chemical exchange saturation transfer MRI (CEST-MRI) data of the human brain. The proposed WASABI method yields a novel simultaneous B 0 and B 1 mapping within 1 min that is robust and easy to implement. Magn Reson Med 77:571-580, 2017. © 2016 International Society for Magnetic Resonance in Medicine. © 2016 International Society for Magnetic Resonance in Medicine.

Background field removal using a region adaptive kernel for quantitative susceptibility mapping of human brain

NASA Astrophysics Data System (ADS)

Fang, Jinsheng; Bao, Lijun; Li, Xu; van Zijl, Peter C. M.; Chen, Zhong

2017-08-01

Background field removal is an important MR phase preprocessing step for quantitative susceptibility mapping (QSM). It separates the local field induced by tissue magnetic susceptibility sources from the background field generated by sources outside a region of interest, e.g. brain, such as air-tissue interface. In the vicinity of air-tissue boundary, e.g. skull and paranasal sinuses, where large susceptibility variations exist, present background field removal methods are usually insufficient and these regions often need to be excluded by brain mask erosion at the expense of losing information of local field and thus susceptibility measures in these regions. In this paper, we propose an extension to the variable-kernel sophisticated harmonic artifact reduction for phase data (V-SHARP) background field removal method using a region adaptive kernel (R-SHARP), in which a scalable spherical Gaussian kernel (SGK) is employed with its kernel radius and weights adjustable according to an energy "functional" reflecting the magnitude of field variation. Such an energy functional is defined in terms of a contour and two fitting functions incorporating regularization terms, from which a curve evolution model in level set formation is derived for energy minimization. We utilize it to detect regions of with a large field gradient caused by strong susceptibility variation. In such regions, the SGK will have a small radius and high weight at the sphere center in a manner adaptive to the voxel energy of the field perturbation. Using the proposed method, the background field generated from external sources can be effectively removed to get a more accurate estimation of the local field and thus of the QSM dipole inversion to map local tissue susceptibility sources. Numerical simulation, phantom and in vivo human brain data demonstrate improved performance of R-SHARP compared to V-SHARP and RESHARP (regularization enabled SHARP) methods, even when the whole paranasal sinus regions are preserved in the brain mask. Shadow artifacts due to strong susceptibility variations in the derived QSM maps could also be largely eliminated using the R-SHARP method, leading to more accurate QSM reconstruction.

Background field removal using a region adaptive kernel for quantitative susceptibility mapping of human brain.

PubMed

Fang, Jinsheng; Bao, Lijun; Li, Xu; van Zijl, Peter C M; Chen, Zhong

2017-08-01

Background field removal is an important MR phase preprocessing step for quantitative susceptibility mapping (QSM). It separates the local field induced by tissue magnetic susceptibility sources from the background field generated by sources outside a region of interest, e.g. brain, such as air-tissue interface. In the vicinity of air-tissue boundary, e.g. skull and paranasal sinuses, where large susceptibility variations exist, present background field removal methods are usually insufficient and these regions often need to be excluded by brain mask erosion at the expense of losing information of local field and thus susceptibility measures in these regions. In this paper, we propose an extension to the variable-kernel sophisticated harmonic artifact reduction for phase data (V-SHARP) background field removal method using a region adaptive kernel (R-SHARP), in which a scalable spherical Gaussian kernel (SGK) is employed with its kernel radius and weights adjustable according to an energy "functional" reflecting the magnitude of field variation. Such an energy functional is defined in terms of a contour and two fitting functions incorporating regularization terms, from which a curve evolution model in level set formation is derived for energy minimization. We utilize it to detect regions of with a large field gradient caused by strong susceptibility variation. In such regions, the SGK will have a small radius and high weight at the sphere center in a manner adaptive to the voxel energy of the field perturbation. Using the proposed method, the background field generated from external sources can be effectively removed to get a more accurate estimation of the local field and thus of the QSM dipole inversion to map local tissue susceptibility sources. Numerical simulation, phantom and in vivo human brain data demonstrate improved performance of R-SHARP compared to V-SHARP and RESHARP (regularization enabled SHARP) methods, even when the whole paranasal sinus regions are preserved in the brain mask. Shadow artifacts due to strong susceptibility variations in the derived QSM maps could also be largely eliminated using the R-SHARP method, leading to more accurate QSM reconstruction. Copyright © 2017. Published by Elsevier Inc.

Decoding fMRI events in Sensorimotor Motor Network using Sparse Paradigm Free Mapping and Activation Likelihood Estimates

PubMed Central

Tan, Francisca M.; Caballero-Gaudes, César; Mullinger, Karen J.; Cho, Siu-Yeung; Zhang, Yaping; Dryden, Ian L.; Francis, Susan T.; Gowland, Penny A.

2017-01-01

Most fMRI studies map task-driven brain activity using a block or event-related paradigm. Sparse Paradigm Free Mapping (SPFM) can detect the onset and spatial distribution of BOLD events in the brain without prior timing information; but relating the detected events to brain function remains a challenge. In this study, we developed a decoding method for SPFM using a coordinate-based meta-analysis method of Activation Likelihood Estimation (ALE). We defined meta-maps of statistically significant ALE values that correspond to types of events and calculated a summation overlap between the normalized meta-maps and SPFM maps. As a proof of concept, this framework was applied to relate SPFM-detected events in the Sensorimotor Network (SMN) to six motor function (left/right fingers, left/right toes, swallowing and eye blinks). We validated the framework using simultaneous Electromyography-fMRI experiments and motor tasks with short and long duration, and random inter-stimulus interval. The decoding scores were considerably lower for eye movements relative to other movement types tested. The average successful rate for short and long motor events was 77 ± 13% and 74 ± 16% respectively, excluding eye movements. We found good agreement between the decoding results and EMG for most events and subjects, with a range in sensitivity between 55 and 100%, excluding eye movements. The proposed method was then used to classify the movement types of spontaneous single-trial events in the SMN during resting state, which produced an average successful rate of 22 ± 12%. Finally, this paper discusses methodological implications and improvements to increase the decoding performance. PMID:28815863

Implementation of fast macromolecular proton fraction mapping on 1.5 and 3 Tesla clinical MRI scanners: preliminary experience

NASA Astrophysics Data System (ADS)

Yarnykh, V.; Korostyshevskaya, A.

2017-08-01

Macromolecular proton fraction (MPF) is a biophysical parameter describing the amount of macromolecular protons involved into magnetization exchange with water protons in tissues. MPF represents a significant interest as a magnetic resonance imaging (MRI) biomarker of myelin for clinical applications. A recent fast MPF mapping method enabled clinical translation of MPF measurements due to time-efficient acquisition based on the single-point constrained fit algorithm. However, previous MPF mapping applications utilized only 3 Tesla MRI scanners and modified pulse sequences, which are not commonly available. This study aimed to test the feasibility of MPF mapping implementation on a 1.5 Tesla clinical scanner using standard manufacturer’s sequences and compare the performance of this method between 1.5 and 3 Tesla scanners. MPF mapping was implemented on 1.5 and 3 Tesla MRI units of one manufacturer with either optimized custom-written or standard product pulse sequences. Whole-brain three-dimensional MPF maps obtained from a single volunteer were compared between field strengths and implementation options. MPF maps demonstrated similar quality at both field strengths. MPF values in segmented brain tissues and specific anatomic regions appeared in close agreement. This experiment demonstrates the feasibility of fast MPF mapping using standard sequences on 1.5 T and 3 T clinical scanners.

Mapping Neurodegenerative Disease Onset and Progression.

PubMed

Seeley, William W

2017-08-01

Brain networks have been of long-standing interest to neurodegeneration researchers, including but not limited to investigators focusing on conventional prion diseases, which are known to propagate along neural pathways. Tools for human network mapping, however, remained inadequate, limiting our understanding of human brain network architecture and preventing clinical research applications. Until recently, neuropathological studies were the only viable approach to mapping disease onset and progression in humans but required large autopsy cohorts and laborious methods for whole-brain sectioning and staining. Despite important advantages, postmortem studies cannot address in vivo, physiological, or longitudinal questions and have limited potential to explore early-stage disease except for the most common disorders. Emerging in vivo network-based neuroimaging strategies have begun to address these issues, providing data that complement the neuropathological tradition. Overall, findings to date highlight several fundamental principles of neurodegenerative disease anatomy and pathogenesis, as well as some enduring mysteries. These principles and mysteries provide a road map for future research. Copyright © 2017 Cold Spring Harbor Laboratory Press; all rights reserved.

Riemannian metric optimization on surfaces (RMOS) for intrinsic brain mapping in the Laplace-Beltrami embedding space.

PubMed

Gahm, Jin Kyu; Shi, Yonggang

2018-05-01

Surface mapping methods play an important role in various brain imaging studies from tracking the maturation of adolescent brains to mapping gray matter atrophy patterns in Alzheimer's disease. Popular surface mapping approaches based on spherical registration, however, have inherent numerical limitations when severe metric distortions are present during the spherical parameterization step. In this paper, we propose a novel computational framework for intrinsic surface mapping in the Laplace-Beltrami (LB) embedding space based on Riemannian metric optimization on surfaces (RMOS). Given a diffeomorphism between two surfaces, an isometry can be defined using the pullback metric, which in turn results in identical LB embeddings from the two surfaces. The proposed RMOS approach builds upon this mathematical foundation and achieves general feature-driven surface mapping in the LB embedding space by iteratively optimizing the Riemannian metric defined on the edges of triangular meshes. At the core of our framework is an optimization engine that converts an energy function for surface mapping into a distance measure in the LB embedding space, which can be effectively optimized using gradients of the LB eigen-system with respect to the Riemannian metrics. In the experimental results, we compare the RMOS algorithm with spherical registration using large-scale brain imaging data, and show that RMOS achieves superior performance in the prediction of hippocampal subfields and cortical gyral labels, and the holistic mapping of striatal surfaces for the construction of a striatal connectivity atlas from substantia nigra. Copyright © 2018 Elsevier B.V. All rights reserved.

Macromolecule mapping of the brain using ultrashort-TE acquisition and reference-based metabolite removal.

PubMed

Lam, Fan; Li, Yudu; Clifford, Bryan; Liang, Zhi-Pei

2018-05-01

To develop a practical method for mapping macromolecule distribution in the brain using ultrashort-TE MRSI data. An FID-based chemical shift imaging acquisition without metabolite-nulling pulses was used to acquire ultrashort-TE MRSI data that capture the macromolecule signals with high signal-to-noise-ratio (SNR) efficiency. To remove the metabolite signals from the ultrashort-TE data, single voxel spectroscopy data were obtained to determine a set of high-quality metabolite reference spectra. These spectra were then incorporated into a generalized series (GS) model to represent general metabolite spatiospectral distributions. A time-segmented algorithm was developed to back-extrapolate the GS model-based metabolite distribution from truncated FIDs and remove it from the MRSI data. Numerical simulations and in vivo experiments have been performed to evaluate the proposed method. Simulation results demonstrate accurate metabolite signal extrapolation by the proposed method given a high-quality reference. For in vivo experiments, the proposed method is able to produce spatiospectral distributions of macromolecules in the brain with high SNR from data acquired in about 10 minutes. We further demonstrate that the high-dimensional macromolecule spatiospectral distribution resides in a low-dimensional subspace. This finding provides a new opportunity to use subspace models for quantification and accelerated macromolecule mapping. Robustness of the proposed method is also demonstrated using multiple data sets from the same and different subjects. The proposed method is able to obtain macromolecule distributions in the brain from ultrashort-TE acquisitions. It can also be used for acquiring training data to determine a low-dimensional subspace to represent the macromolecule signals for subspace-based MRSI. Magn Reson Med 79:2460-2469, 2018. © 2017 International Society for Magnetic Resonance in Medicine. © 2017 International Society for Magnetic Resonance in Medicine.

Areas activated during naturalistic reading comprehension overlap topological visual, auditory, and somatotomotor maps.

PubMed

Sood, Mariam R; Sereno, Martin I

2016-08-01

Cortical mapping techniques using fMRI have been instrumental in identifying the boundaries of topological (neighbor-preserving) maps in early sensory areas. The presence of topological maps beyond early sensory areas raises the possibility that they might play a significant role in other cognitive systems, and that topological mapping might help to delineate areas involved in higher cognitive processes. In this study, we combine surface-based visual, auditory, and somatomotor mapping methods with a naturalistic reading comprehension task in the same group of subjects to provide a qualitative and quantitative assessment of the cortical overlap between sensory-motor maps in all major sensory modalities, and reading processing regions. Our results suggest that cortical activation during naturalistic reading comprehension overlaps more extensively with topological sensory-motor maps than has been heretofore appreciated. Reading activation in regions adjacent to occipital lobe and inferior parietal lobe almost completely overlaps visual maps, whereas a significant portion of frontal activation for reading in dorsolateral and ventral prefrontal cortex overlaps both visual and auditory maps. Even classical language regions in superior temporal cortex are partially overlapped by topological visual and auditory maps. By contrast, the main overlap with somatomotor maps is restricted to a small region on the anterior bank of the central sulcus near the border between the face and hand representations of M-I. Hum Brain Mapp 37:2784-2810, 2016. © 2016 The Authors Human Brain Mapping Published by Wiley Periodicals, Inc. © 2016 The Authors Human Brain Mapping Published by Wiley Periodicals, Inc.

Allen Brain Atlas: an integrated spatio-temporal portal for exploring the central nervous system

PubMed Central

Sunkin, Susan M.; Ng, Lydia; Lau, Chris; Dolbeare, Tim; Gilbert, Terri L.; Thompson, Carol L.; Hawrylycz, Michael; Dang, Chinh

2013-01-01

The Allen Brain Atlas (http://www.brain-map.org) provides a unique online public resource integrating extensive gene expression data, connectivity data and neuroanatomical information with powerful search and viewing tools for the adult and developing brain in mouse, human and non-human primate. Here, we review the resources available at the Allen Brain Atlas, describing each product and data type [such as in situ hybridization (ISH) and supporting histology, microarray, RNA sequencing, reference atlases, projection mapping and magnetic resonance imaging]. In addition, standardized and unique features in the web applications are described that enable users to search and mine the various data sets. Features include both simple and sophisticated methods for gene searches, colorimetric and fluorescent ISH image viewers, graphical displays of ISH, microarray and RNA sequencing data, Brain Explorer software for 3D navigation of anatomy and gene expression, and an interactive reference atlas viewer. In addition, cross data set searches enable users to query multiple Allen Brain Atlas data sets simultaneously. All of the Allen Brain Atlas resources can be accessed through the Allen Brain Atlas data portal. PMID:23193282

Iron in Multiple Sclerosis and Its Noninvasive Imaging with Quantitative Susceptibility Mapping

PubMed Central

Stüber, Carsten; Pitt, David; Wang, Yi

2016-01-01

Iron is considered to play a key role in the development and progression of Multiple Sclerosis (MS). In particular, iron that accumulates in myeloid cells after the blood-brain barrier (BBB) seals may contribute to chronic inflammation, oxidative stress and eventually neurodegeneration. Magnetic resonance imaging (MRI) is a well-established tool for the non-invasive study of MS. In recent years, an advanced MRI method, quantitative susceptibility mapping (QSM), has made it possible to study brain iron through in vivo imaging. Moreover, immunohistochemical investigations have helped defining the lesional and cellular distribution of iron in MS brain tissue. Imaging studies in MS patients and of brain tissue combined with histological studies have provided important insights into the role of iron in inflammation and neurodegeneration in MS. PMID:26784172

Connectopic mapping with resting-state fMRI.

PubMed

Haak, Koen V; Marquand, Andre F; Beckmann, Christian F

2018-04-15

Brain regions are often topographically connected: nearby locations within one brain area connect with nearby locations in another area. Mapping these connection topographies, or 'connectopies' in short, is crucial for understanding how information is processed in the brain. Here, we propose principled, fully data-driven methods for mapping connectopies using functional magnetic resonance imaging (fMRI) data acquired at rest by combining spectral embedding of voxel-wise connectivity 'fingerprints' with a novel approach to spatial statistical inference. We apply the approach in human primary motor and visual cortex, and show that it can trace biologically plausible, overlapping connectopies in individual subjects that follow these regions' somatotopic and retinotopic maps. As a generic mechanism to perform inference over connectopies, the new spatial statistics approach enables rigorous statistical testing of hypotheses regarding the fine-grained spatial profile of functional connectivity and whether that profile is different between subjects or between experimental conditions. The combined framework offers a fundamental alternative to existing approaches to investigating functional connectivity in the brain, from voxel- or seed-pair wise characterizations of functional association, towards a full, multivariate characterization of spatial topography. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

Fast periodic stimulation (FPS): a highly effective approach in fMRI brain mapping.

PubMed

Gao, Xiaoqing; Gentile, Francesco; Rossion, Bruno

2018-06-01

Defining the neural basis of perceptual categorization in a rapidly changing natural environment with low-temporal resolution methods such as functional magnetic resonance imaging (fMRI) is challenging. Here, we present a novel fast periodic stimulation (FPS)-fMRI approach to define face-selective brain regions with natural images. Human observers are presented with a dynamic stream of widely variable natural object images alternating at a fast rate (6 images/s). Every 9 s, a short burst of variable face images contrasting with object images in pairs induces an objective face-selective neural response at 0.111 Hz. A model-free Fourier analysis achieves a twofold increase in signal-to-noise ratio compared to a conventional block-design approach with identical stimuli and scanning duration, allowing to derive a comprehensive map of face-selective areas in the ventral occipito-temporal cortex, including the anterior temporal lobe (ATL), in all individual brains. Critically, periodicity of the desired category contrast and random variability among widely diverse images effectively eliminates the contribution of low-level visual cues, and lead to the highest values (80-90%) of test-retest reliability in the spatial activation map yet reported in imaging higher level visual functions. FPS-fMRI opens a new avenue for understanding brain function with low-temporal resolution methods.

Localization of MEG human brain responses to retinotopic visual stimuli with contrasting source reconstruction approaches

PubMed Central

Cicmil, Nela; Bridge, Holly; Parker, Andrew J.; Woolrich, Mark W.; Krug, Kristine

2014-01-01

Magnetoencephalography (MEG) allows the physiological recording of human brain activity at high temporal resolution. However, spatial localization of the source of the MEG signal is an ill-posed problem as the signal alone cannot constrain a unique solution and additional prior assumptions must be enforced. An adequate source reconstruction method for investigating the human visual system should place the sources of early visual activity in known locations in the occipital cortex. We localized sources of retinotopic MEG signals from the human brain with contrasting reconstruction approaches (minimum norm, multiple sparse priors, and beamformer) and compared these to the visual retinotopic map obtained with fMRI in the same individuals. When reconstructing brain responses to visual stimuli that differed by angular position, we found reliable localization to the appropriate retinotopic visual field quadrant by a minimum norm approach and by beamforming. Retinotopic map eccentricity in accordance with the fMRI map could not consistently be localized using an annular stimulus with any reconstruction method, but confining eccentricity stimuli to one visual field quadrant resulted in significant improvement with the minimum norm. These results inform the application of source analysis approaches for future MEG studies of the visual system, and indicate some current limits on localization accuracy of MEG signals. PMID:24904268

Brain Mapping in a Patient with Congenital Blindness – A Case for Multimodal Approaches

PubMed Central

Roland, Jarod L.; Hacker, Carl D.; Breshears, Jonathan D.; Gaona, Charles M.; Hogan, R. Edward; Burton, Harold; Corbetta, Maurizio; Leuthardt, Eric C.

2013-01-01

Recent advances in basic neuroscience research across a wide range of methodologies have contributed significantly to our understanding of human cortical electrophysiology and functional brain imaging. Translation of this research into clinical neurosurgery has opened doors for advanced mapping of functionality that previously was prohibitively difficult, if not impossible. Here we present the case of a unique individual with congenital blindness and medically refractory epilepsy who underwent neurosurgical treatment of her seizures. Pre-operative evaluation presented the challenge of accurately and robustly mapping the cerebral cortex for an individual with a high probability of significant cortical re-organization. Additionally, a blind individual has unique priorities in one’s ability to read Braille by touch and sense the environment primarily by sound than the non-vision impaired person. For these reasons we employed additional measures to map sensory, motor, speech, language, and auditory perception by employing a number of cortical electrophysiologic mapping and functional magnetic resonance imaging methods. Our data show promising results in the application of these adjunctive methods in the pre-operative mapping of otherwise difficult to localize, and highly variable, functional cortical areas. PMID:23914170

Constructing fine-granularity functional brain network atlases via deep convolutional autoencoder.

PubMed

Zhao, Yu; Dong, Qinglin; Chen, Hanbo; Iraji, Armin; Li, Yujie; Makkie, Milad; Kou, Zhifeng; Liu, Tianming

2017-12-01

State-of-the-art functional brain network reconstruction methods such as independent component analysis (ICA) or sparse coding of whole-brain fMRI data can effectively infer many thousands of volumetric brain network maps from a large number of human brains. However, due to the variability of individual brain networks and the large scale of such networks needed for statistically meaningful group-level analysis, it is still a challenging and open problem to derive group-wise common networks as network atlases. Inspired by the superior spatial pattern description ability of the deep convolutional neural networks (CNNs), a novel deep 3D convolutional autoencoder (CAE) network is designed here to extract spatial brain network features effectively, based on which an Apache Spark enabled computational framework is developed for fast clustering of larger number of network maps into fine-granularity atlases. To evaluate this framework, 10 resting state networks (RSNs) were manually labeled from the sparsely decomposed networks of Human Connectome Project (HCP) fMRI data and 5275 network training samples were obtained, in total. Then the deep CAE models are trained by these functional networks' spatial maps, and the learned features are used to refine the original 10 RSNs into 17 network atlases that possess fine-granularity functional network patterns. Interestingly, it turned out that some manually mislabeled outliers in training networks can be corrected by the deep CAE derived features. More importantly, fine granularities of networks can be identified and they reveal unique network patterns specific to different brain task states. By further applying this method to a dataset of mild traumatic brain injury study, it shows that the technique can effectively identify abnormal small networks in brain injury patients in comparison with controls. In general, our work presents a promising deep learning and big data analysis solution for modeling functional connectomes, with fine granularities, based on fMRI data. Copyright © 2017 Elsevier B.V. All rights reserved.

Hubs of Anticorrelation in High-Resolution Resting-State Functional Connectivity Network Architecture.

PubMed

Gopinath, Kaundinya; Krishnamurthy, Venkatagiri; Cabanban, Romeo; Crosson, Bruce A

2015-06-01

A major focus of brain research recently has been to map the resting-state functional connectivity (rsFC) network architecture of the normal brain and pathology through functional magnetic resonance imaging. However, the phenomenon of anticorrelations in resting-state signals between different brain regions has not been adequately examined. The preponderance of studies on resting-state fMRI (rsFMRI) have either ignored anticorrelations in rsFC networks or adopted methods in data analysis, which have rendered anticorrelations in rsFC networks uninterpretable. The few studies that have examined anticorrelations in rsFC networks using conventional methods have found anticorrelations to be weak in strength and not very reproducible across subjects. Anticorrelations in rsFC network architecture could reflect mechanisms that subserve a number of important brain processes. In this preliminary study, we examined the properties of anticorrelated rsFC networks by systematically focusing on negative cross-correlation coefficients (CCs) among rsFMRI voxel time series across the brain with graph theory-based network analysis. A number of methods were implemented to enhance the neuronal specificity of resting-state functional connections that yield negative CCs, although at the cost of decreased sensitivity. Hubs of anticorrelation were seen in a number of cortical and subcortical brain regions. Examination of the anticorrelation maps of these hubs indicated that negative CCs in rsFC network architecture highlight a number of regulatory interactions between brain networks and regions, including reciprocal modulations, suppression, inhibition, and neurofeedback.

HITS-CLIP yields genome-wide insights into brain alternative RNA processing

NASA Astrophysics Data System (ADS)

Licatalosi, Donny D.; Mele, Aldo; Fak, John J.; Ule, Jernej; Kayikci, Melis; Chi, Sung Wook; Clark, Tyson A.; Schweitzer, Anthony C.; Blume, John E.; Wang, Xuning; Darnell, Jennifer C.; Darnell, Robert B.

2008-11-01

Protein-RNA interactions have critical roles in all aspects of gene expression. However, applying biochemical methods to understand such interactions in living tissues has been challenging. Here we develop a genome-wide means of mapping protein-RNA binding sites in vivo, by high-throughput sequencing of RNA isolated by crosslinking immunoprecipitation (HITS-CLIP). HITS-CLIP analysis of the neuron-specific splicing factor Nova revealed extremely reproducible RNA-binding maps in multiple mouse brains. These maps provide genome-wide in vivo biochemical footprints confirming the previous prediction that the position of Nova binding determines the outcome of alternative splicing; moreover, they are sufficiently powerful to predict Nova action de novo. HITS-CLIP revealed a large number of Nova-RNA interactions in 3' untranslated regions, leading to the discovery that Nova regulates alternative polyadenylation in the brain. HITS-CLIP, therefore, provides a robust, unbiased means to identify functional protein-RNA interactions in vivo.

Brain temperature in volunteers subjected to intranasal cooling.

PubMed

Covaciu, L; Weis, J; Bengtsson, C; Allers, M; Lunderquist, A; Ahlström, H; Rubertsson, S

2011-08-01

Intranasal cooling can be used to initiate therapeutic hypothermia. However, direct measurement of brain temperature is difficult and the intra-cerebral distribution of temperature changes with cooling is unknown. The purpose of this study was to measure the brain temperature of human volunteers subjected to intranasal cooling using non-invasive magnetic resonance (MR) methods. Intranasal balloons catheters circulated with saline at 20°C were applied for 60 min in ten awake volunteers. No sedation was used. Brain temperature changes were measured and mapped using MR spectroscopic imaging (MRSI) and phase-mapping techniques. Heart rate and blood pressure were monitored throughout the experiment. Rectal temperature was measured before and after the cooling. Mini Mental State Examination (MMSE) test and nasal inspection were done before and after the cooling. Questionnaires about the subjects' personal experience were completed after the experiment. Brain temperature decrease measured by MRSI was -1.7 ± 0.8°C and by phase-mapping -1.8 ± 0.9°C (n = 9) at the end of cooling. Spatial distribution of temperature changes was relatively uniform. Rectal temperature decreased by -0.5 ± 0.3°C (n = 5). The physiological parameters were stable and no shivering was reported. The volunteers remained alert during cooling and no cognitive dysfunctions were apparent in the MMSE test. Postcooling nasal examination detected increased nasal secretion in nine of the ten volunteers. Volunteers' acceptance of the method was good. Both MR techniques revealed brain temperature reductions after 60 min of intranasal cooling with balloons circulated with saline at 20°C in awake, unsedated volunteers.

Changes in Gray Matter Density, Regional Homogeneity, and Functional Connectivity in Methamphetamine-Associated Psychosis: A Resting-State Functional Magnetic Resonance Imaging (fMRI) Study.

PubMed

Zhang, Shengyu; Hu, Qiang; Tang, Tao; Liu, Chao; Li, Chengchong; Zang, Yin-Yin; Cai, Wei-Xiong

2018-06-13

BACKGROUND Using regional homogeneity (ReHo) blood oxygen level-dependent functional MR (BOLD-fMRI), we investigated the structural and functional alterations of brain regions among patients with methamphetamine-associated psychosis (MAP). MATERIAL AND METHODS This retrospective study included 17 MAP patients, 16 schizophrenia (SCZ) patients, and 18 healthy controls. Informed consent was obtained from all patients before the clinical assessment, the severity of clinical symptoms was evaluated prior to the fMRI scanning, and then images were acquired and preprocessed after each participant received 6-min fRMI scanning. The participants all underwent BOLD-fMRI scanning. Voxel-based morphometry was used to measure gray matter density (GMD). Resting-state fMRI (rs-fMRI) was conducted to analyze functional MR, ReHo, and functional connectivity (FC). RESULTS GMD analysis results suggest that MAP patients, SCZ patients, and healthy volunteers show different GMDs within different brain regions. Similarly, the ReHo analysis results suggest that MAP patients, SCZ patients, and healthy volunteers have different GMDs within different brain regions. Negative correlations were found between ReHo- and the PANSS-positive scores within the left orbital interior frontal gyrus (L-orb-IFG) of MAP patients. ReHo- and PANSS-negative scores of R-SFG were negatively correlated among SCZ patients. The abnormal FC of R-MFG showed a negative correlation with the PANSS score among MAP patients. CONCLUSIONS The abnormalities in brain structure and FC were associated with the development of MAP.

Human brain mapping: A systematic comparison of parcellation methods for the human cerebral cortex.

PubMed

Arslan, Salim; Ktena, Sofia Ira; Makropoulos, Antonios; Robinson, Emma C; Rueckert, Daniel; Parisot, Sarah

2018-04-15

The macro-connectome elucidates the pathways through which brain regions are structurally connected or functionally coupled to perform a specific cognitive task. It embodies the notion of representing and understanding all connections within the brain as a network, while the subdivision of the brain into interacting functional units is inherent in its architecture. As a result, the definition of network nodes is one of the most critical steps in connectivity network analysis. Although brain atlases obtained from cytoarchitecture or anatomy have long been used for this task, connectivity-driven methods have arisen only recently, aiming to delineate more homogeneous and functionally coherent regions. This study provides a systematic comparison between anatomical, connectivity-driven and random parcellation methods proposed in the thriving field of brain parcellation. Using resting-state functional MRI data from the Human Connectome Project and a plethora of quantitative evaluation techniques investigated in the literature, we evaluate 10 subject-level and 24 groupwise parcellation methods at different resolutions. We assess the accuracy of parcellations from four different aspects: (1) reproducibility across different acquisitions and groups, (2) fidelity to the underlying connectivity data, (3) agreement with fMRI task activation, myelin maps, and cytoarchitectural areas, and (4) network analysis. This extensive evaluation of different parcellations generated at the subject and group level highlights the strengths and shortcomings of the various methods and aims to provide a guideline for the choice of parcellation technique and resolution according to the task at hand. The results obtained in this study suggest that there is no optimal method able to address all the challenges faced in this endeavour simultaneously. Copyright © 2017 Elsevier Inc. All rights reserved.

Structure-Function Network Mapping and Its Assessment via Persistent Homology

PubMed Central

2017-01-01

Understanding the relationship between brain structure and function is a fundamental problem in network neuroscience. This work deals with the general method of structure-function mapping at the whole-brain level. We formulate the problem as a topological mapping of structure-function connectivity via matrix function, and find a stable solution by exploiting a regularization procedure to cope with large matrices. We introduce a novel measure of network similarity based on persistent homology for assessing the quality of the network mapping, which enables a detailed comparison of network topological changes across all possible thresholds, rather than just at a single, arbitrary threshold that may not be optimal. We demonstrate that our approach can uncover the direct and indirect structural paths for predicting functional connectivity, and our network similarity measure outperforms other currently available methods. We systematically validate our approach with (1) a comparison of regularized vs. non-regularized procedures, (2) a null model of the degree-preserving random rewired structural matrix, (3) different network types (binary vs. weighted matrices), and (4) different brain parcellation schemes (low vs. high resolutions). Finally, we evaluate the scalability of our method with relatively large matrices (2514x2514) of structural and functional connectivity obtained from 12 healthy human subjects measured non-invasively while at rest. Our results reveal a nonlinear structure-function relationship, suggesting that the resting-state functional connectivity depends on direct structural connections, as well as relatively parsimonious indirect connections via polysynaptic pathways. PMID:28046127

Methyllycaconitine: a non-radiolabeled ligand for mapping α7 neuronal nicotinic acetylcholine receptors - in vivo target localization and biodistribution in rat brain.

PubMed

Nirogi, Ramakrishna; Kandikere, Vishwottam; Bhyrapuneni, Gopinadh; Saralaya, Ramanatha; Muddana, Nageswararao; Komarneni, Prashanth

2012-07-01

Reduction of cerebral cortical and hippocampal α7 neuronal nicotinic acetylcholine receptor (nAChR) density was observed in the Alzheimer's disease (AD) and other neurodegenerative diseases. Mapping the subtypes of nAChRs with selective ligand by viable, quick and consistent method in preclinical drug discovery may lead to rapid development of more effective therapeutic agents. The objective of this study was to evaluate the use of methyllycaconitine (MLA) in non-radiolabeled form for mapping α7 nAChRs in rat brain. MLA pharmacokinetic and brain penetration properties were assessed in male Wistar rats. The tracer properties of MLA were evaluated in rat brain by dose and time dependent differential regional distribution studies. Target specificity was validated after blocking with potent α7 nAChR agonists ABBF, PNU282987 and nicotine. High performance liquid chromatography combined with triple quad mass spectral detector (LC-MS/MS) was used to measure the plasma and brain tissue concentrations of MLA. MLA has shown rapid brain uptake followed by a 3-5 fold higher specific binding in regions containing the α7 nAChRs (hypothalamus - 1.60 ng/g), when compared to non-specific regions (striatum - 0.53 ng/g, hippocampus - 0.46 ng/g, midbrain - 0.37 ng/g, frontal cortex - 0.35 ng/g and cerebellum - 0.30 ng/g). Pretreatment with potent α7 nAChR agonists significantly blocked the MLA uptake in hypothalamus. The non-radiolabeled MLA binding to brain region was comparable with the α7 mRNA localization and receptor distribution reported for [(3)H] MLA in rat brain. The rat pharmacokinetic, brain penetration and differential brain regional distribution features favor that MLA is suitable to use in preclinical stage for mapping α7 nAChRs. Hence, this approach can be employed as an essential tool for quicker development of novel selective ligand to map variation in the α7 receptor densities, as well as to evaluate potential new chemical entities targeting neurodegenerative diseases. Copyright © 2012 Elsevier Inc. All rights reserved.

Rapid parametric mapping of the longitudinal relaxation time T1 using two-dimensional variable flip angle magnetic resonance imaging at 1.5 Tesla, 3 Tesla, and 7 Tesla.

PubMed

Dieringer, Matthias A; Deimling, Michael; Santoro, Davide; Wuerfel, Jens; Madai, Vince I; Sobesky, Jan; von Knobelsdorff-Brenkenhoff, Florian; Schulz-Menger, Jeanette; Niendorf, Thoralf

2014-01-01

Visual but subjective reading of longitudinal relaxation time (T1) weighted magnetic resonance images is commonly used for the detection of brain pathologies. For this non-quantitative measure, diagnostic quality depends on hardware configuration, imaging parameters, radio frequency transmission field (B1+) uniformity, as well as observer experience. Parametric quantification of the tissue T1 relaxation parameter offsets the propensity for these effects, but is typically time consuming. For this reason, this study examines the feasibility of rapid 2D T1 quantification using a variable flip angles (VFA) approach at magnetic field strengths of 1.5 Tesla, 3 Tesla, and 7 Tesla. These efforts include validation in phantom experiments and application for brain T1 mapping. T1 quantification included simulations of the Bloch equations to correct for slice profile imperfections, and a correction for B1+. Fast gradient echo acquisitions were conducted using three adjusted flip angles for the proposed T1 quantification approach that was benchmarked against slice profile uncorrected 2D VFA and an inversion-recovery spin-echo based reference method. Brain T1 mapping was performed in six healthy subjects, one multiple sclerosis patient, and one stroke patient. Phantom experiments showed a mean T1 estimation error of (-63±1.5)% for slice profile uncorrected 2D VFA and (0.2±1.4)% for the proposed approach compared to the reference method. Scan time for single slice T1 mapping including B1+ mapping could be reduced to 5 seconds using an in-plane resolution of (2×2) mm2, which equals a scan time reduction of more than 99% compared to the reference method. Our results demonstrate that rapid 2D T1 quantification using a variable flip angle approach is feasible at 1.5T/3T/7T. It represents a valuable alternative for rapid T1 mapping due to the gain in speed versus conventional approaches. This progress may serve to enhance the capabilities of parametric MR based lesion detection and brain tissue characterization.

Individualized localization and cortical surface-based registration of intracranial electrodes

PubMed Central

Dykstra, Andrew R.; Chan, Alexander M.; Quinn, Brian T.; Zepeda, Rodrigo; Keller, Corey J.; Cormier, Justine; Madsen, Joseph R.; Eskandar, Emad N.; Cash, Sydney S.

2011-01-01

In addition to its widespread clinical use, the intracranial electroencephalogram (iEEG) is increasingly being employed as a tool to map the neural correlates of normal cognitive function as well as for developing neuroprosthetics. Despite recent advances, and unlike other established brain mapping modalities (e.g. functional MRI, magneto- and electroencephalography), registering the iEEG with respect to neuroanatomy in individuals – and coregistering functional results across subjects – remains a significant challenge. Here we describe a method which coregisters high-resolution preoperative MRI with postoperative computerized tomography (CT) for the purpose of individualized functional mapping of both normal and pathological (e.g., interictal discharges and seizures) brain activity. Our method accurately (within 3mm, on average) localizes electrodes with respect to an individual’s neuroanatomy. Furthermore, we outline a principled procedure for either volumetric or surface-based group analyses. We demonstrate our method in five patients with medically-intractable epilepsy undergoing invasive monitoring of the seizure focus prior to its surgical removal. The straight-forward application of this procedure to all types of intracranial electrodes, robustness to deformations in both skull and brain, and the ability to compare electrode locations across groups of patients makes this procedure an important tool for basic scientists as well as clinicians. PMID:22155045

Individualized localization and cortical surface-based registration of intracranial electrodes.

PubMed

Dykstra, Andrew R; Chan, Alexander M; Quinn, Brian T; Zepeda, Rodrigo; Keller, Corey J; Cormier, Justine; Madsen, Joseph R; Eskandar, Emad N; Cash, Sydney S

2012-02-15

In addition to its widespread clinical use, the intracranial electroencephalogram (iEEG) is increasingly being employed as a tool to map the neural correlates of normal cognitive function as well as for developing neuroprosthetics. Despite recent advances, and unlike other established brain-mapping modalities (e.g. functional MRI, magneto- and electroencephalography), registering the iEEG with respect to neuroanatomy in individuals-and coregistering functional results across subjects-remains a significant challenge. Here we describe a method which coregisters high-resolution preoperative MRI with postoperative computerized tomography (CT) for the purpose of individualized functional mapping of both normal and pathological (e.g., interictal discharges and seizures) brain activity. Our method accurately (within 3mm, on average) localizes electrodes with respect to an individual's neuroanatomy. Furthermore, we outline a principled procedure for either volumetric or surface-based group analyses. We demonstrate our method in five patients with medically-intractable epilepsy undergoing invasive monitoring of the seizure focus prior to its surgical removal. The straight-forward application of this procedure to all types of intracranial electrodes, robustness to deformations in both skull and brain, and the ability to compare electrode locations across groups of patients makes this procedure an important tool for basic scientists as well as clinicians. Copyright © 2011 Elsevier Inc. All rights reserved.

Model for Improvement of Learning Using Topographic Mapping.

ERIC Educational Resources Information Center

Andrews, David B.

The paper develops a method for learning improvement which incorporates the learner in the development of the learning/instructional strategy. To this end, a rate limiting model using topographical brain mapping as an educational intervention is presented. It is suggested that such intervention programs focus on those factors which are…

Combining self-organizing mapping and supervised affinity propagation clustering approach to investigate functional brain networks involved in motor imagery and execution with fMRI measurements.

PubMed

Zhang, Jiang; Liu, Qi; Chen, Huafu; Yuan, Zhen; Huang, Jin; Deng, Lihua; Lu, Fengmei; Zhang, Junpeng; Wang, Yuqing; Wang, Mingwen; Chen, Liangyin

2015-01-01

Clustering analysis methods have been widely applied to identifying the functional brain networks of a multitask paradigm. However, the previously used clustering analysis techniques are computationally expensive and thus impractical for clinical applications. In this study a novel method, called SOM-SAPC that combines self-organizing mapping (SOM) and supervised affinity propagation clustering (SAPC), is proposed and implemented to identify the motor execution (ME) and motor imagery (MI) networks. In SOM-SAPC, SOM was first performed to process fMRI data and SAPC is further utilized for clustering the patterns of functional networks. As a result, SOM-SAPC is able to significantly reduce the computational cost for brain network analysis. Simulation and clinical tests involving ME and MI were conducted based on SOM-SAPC, and the analysis results indicated that functional brain networks were clearly identified with different response patterns and reduced computational cost. In particular, three activation clusters were clearly revealed, which include parts of the visual, ME and MI functional networks. These findings validated that SOM-SAPC is an effective and robust method to analyze the fMRI data with multitasks.

Function-specific and Enhanced Brain Structural Connectivity Mapping via Joint Modeling of Diffusion and Functional MRI.

PubMed

Chu, Shu-Hsien; Parhi, Keshab K; Lenglet, Christophe

2018-03-16

A joint structural-functional brain network model is presented, which enables the discovery of function-specific brain circuits, and recovers structural connections that are under-estimated by diffusion MRI (dMRI). Incorporating information from functional MRI (fMRI) into diffusion MRI to estimate brain circuits is a challenging task. Usually, seed regions for tractography are selected from fMRI activation maps to extract the white matter pathways of interest. The proposed method jointly analyzes whole brain dMRI and fMRI data, allowing the estimation of complete function-specific structural networks instead of interactively investigating the connectivity of individual cortical/sub-cortical areas. Additionally, tractography techniques are prone to limitations, which can result in erroneous pathways. The proposed framework explicitly models the interactions between structural and functional connectivity measures thereby improving anatomical circuit estimation. Results on Human Connectome Project (HCP) data demonstrate the benefits of the approach by successfully identifying function-specific anatomical circuits, such as the language and resting-state networks. In contrast to correlation-based or independent component analysis (ICA) functional connectivity mapping, detailed anatomical connectivity patterns are revealed for each functional module. Results on a phantom (Fibercup) also indicate improvements in structural connectivity mapping by rejecting false-positive connections with insufficient support from fMRI, and enhancing under-estimated connectivity with strong functional correlation.

Combined ultrasound and MR imaging to guide focused ultrasound therapies in the brain

NASA Astrophysics Data System (ADS)

Arvanitis, Costas D.; Livingstone, Margaret S.; McDannold, Nathan

2013-07-01

Several emerging therapies with potential for use in the brain, harness effects produced by acoustic cavitation—the interaction between ultrasound and microbubbles either generated during sonication or introduced into the vasculature. Systems developed for transcranial MRI-guided focused ultrasound (MRgFUS) thermal ablation can enable their clinical translation, but methods for real-time monitoring and control are currently lacking. Acoustic emissions produced during sonication can provide information about the location, strength and type of the microbubble oscillations within the ultrasound field, and they can be mapped in real-time using passive imaging approaches. Here, we tested whether such mapping can be achieved transcranially within a clinical brain MRgFUS system. We integrated an ultrasound imaging array into the hemisphere transducer of the MRgFUS device. Passive cavitation maps were obtained during sonications combined with a circulating microbubble agent at 20 targets in the cingulate cortex in three macaques. The maps were compared with MRI-evident tissue effects. The system successfully mapped microbubble activity during both stable and inertial cavitation, which was correlated with MRI-evident transient blood-brain barrier disruption and vascular damage, respectively. The location of this activity was coincident with the resulting tissue changes within the expected resolution limits of the system. While preliminary, these data clearly demonstrate, for the first time, that it is possible to construct maps of stable and inertial cavitation transcranially, in a large animal model, and under clinically relevant conditions. Further, these results suggest that this hybrid ultrasound/MRI approach can provide comprehensive guidance for targeted drug delivery via blood-brain barrier disruption and other emerging ultrasound treatments, facilitating their clinical translation. We anticipate that it will also prove to be an important research tool that will further the development of a broad range of microbubble-enhanced therapies.

The Kantian brain: brain dynamics from a neurophenomenological perspective.

PubMed

Fazelpour, Sina; Thompson, Evan

2015-04-01

Current research on spontaneous, self-generated brain rhythms and dynamic neural network coordination cast new light on Immanuel Kant's idea of the 'spontaneity' of cognition, that is, the mind's capacity to organize and synthesize sensory stimuli in novel, unprecedented ways. Nevertheless, determining the precise nature of the brain-cognition mapping remains an outstanding challenge. Neurophenomenology, which uses phenomenological information about the variability of subjective experience in order to illuminate the variability of brain dynamics, offers a promising method for addressing this challenge. Copyright © 2014 Elsevier Ltd. All rights reserved.

Brain white matter fiber estimation and tractography using Q-ball imaging and Bayesian MODEL.

PubMed

Lu, Meng

2015-01-01

Diffusion tensor imaging allows for the non-invasive in vivo mapping of the brain tractography. However, fiber bundles have complex structures such as fiber crossings, fiber branchings and fibers with large curvatures that tensor imaging (DTI) cannot accurately handle. This study presents a novel brain white matter tractography method using Q-ball imaging as the data source instead of DTI, because QBI can provide accurate information about multiple fiber crossings and branchings in a single voxel using an orientation distribution function (ODF). The presented method also uses graph theory to construct the Bayesian model-based graph, so that the fiber tracking between two voxels can be represented as the shortest path in a graph. Our experiment showed that our new method can accurately handle brain white matter fiber crossings and branchings, and reconstruct brain tractograhpy both in phantom data and real brain data.

Long term reshaping of language, sensory, and motor maps after glioma resection: a new parameter to integrate in the surgical strategy

PubMed Central

Duffau, H; Denvil, D; Capelle, L

2002-01-01

Objectives: To describe cortical reorganisation and the effects of glioma infiltration on local brain function in three patients who underwent two operations 12–24 months apart. Methods: Three patients who had no neurological deficit underwent two operations for low grade glioma, located in functionally important brain regions. During each operation, local brain function was characterised by electrical mapping and awake craniotomy. Results: Language or sensorimotor areas had been invaded by the tumour at the time of the first operation, leading to incomplete glioma removal in all cases. Because of a tumour recurrence, the patients were reoperated on between 12 and 24 months later. Functional reorganisation of the language, sensory, and motor maps was detected by electrical stimulation of the brain, and this allowed total glioma removal without neurological sequelae. Conclusions: These findings show that surgical resection of a glioma can lead to functional reorganisation in the peritumorous and infiltrated brain. It may be that this reorganisation is directly or indirectly caused by the surgical procedure. If this hypothesis is confirmed by other studies, the use of such brain plasticity potential could be used when planning surgical options in some patients with low grade glioma. Such a strategy could extend the limits of tumour resection in gliomas involving eloquent brain areas without causing permanent morbidity. PMID:11909913

Correlation mapping method of OCT for visualization blood vessels in brain

NASA Astrophysics Data System (ADS)

Izotova, O. A.; Kalyanov, A. L.; Lychagov, V. V.; Semyachkina-Glushkovskaya, O. V.

2013-11-01

The burning issue in modern medicine is the diagnosis and treatment of various life-threatening diseases, in particular the diseases of brain. One of them is intracranial hemorrhage (ICH). It occurs especially among newborn babies and is hard-diagnosed. In order to understand the nature of the ICH, the microcirculation of blood, which serves key functions within the body, is analyzed. On this basis a series of experiments was done, in the results of which it was showed, that latent stage of ICH is characterized by decrease of venous blood outflow and the loss of sensitivity of sagittal vein to vasoconstrictor effect of adrenaline. So, stress-related changes of the cerebral venous blood flow (CVBF) can be the source of this disease. In this paper registration CVBF was made with the help of commercially available Thorlabs Swept Source OCT System, using the correlation mapping method. In this method values of correlation coefficient of several images are analyzed. In the result of the algorithm the correlation map was obtained. By the resulting map the diameter of vessels was calculated, which is necessary for examination of effects of adrenalin to the vessels and identification symptoms of ICH.

Learning-based subject-specific estimation of dynamic maps of cortical morphology at missing time points in longitudinal infant studies.

PubMed

Meng, Yu; Li, Gang; Gao, Yaozong; Lin, Weili; Shen, Dinggang

2016-11-01

Longitudinal neuroimaging analysis of the dynamic brain development in infants has received increasing attention recently. Many studies expect a complete longitudinal dataset in order to accurately chart the brain developmental trajectories. However, in practice, a large portion of subjects in longitudinal studies often have missing data at certain time points, due to various reasons such as the absence of scan or poor image quality. To make better use of these incomplete longitudinal data, in this paper, we propose a novel machine learning-based method to estimate the subject-specific, vertex-wise cortical morphological attributes at the missing time points in longitudinal infant studies. Specifically, we develop a customized regression forest, named dynamically assembled regression forest (DARF), as the core regression tool. DARF ensures the spatial smoothness of the estimated maps for vertex-wise cortical morphological attributes and also greatly reduces the computational cost. By employing a pairwise estimation followed by a joint refinement, our method is able to fully exploit the available information from both subjects with complete scans and subjects with missing scans for estimation of the missing cortical attribute maps. The proposed method has been applied to estimating the dynamic cortical thickness maps at missing time points in an incomplete longitudinal infant dataset, which includes 31 healthy infant subjects, each having up to five time points in the first postnatal year. The experimental results indicate that our proposed framework can accurately estimate the subject-specific vertex-wise cortical thickness maps at missing time points, with the average error less than 0.23 mm. Hum Brain Mapp 37:4129-4147, 2016. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

Group search algorithm recovers effective connectivity maps for individuals in homogeneous and heterogeneous samples.

PubMed

Gates, Kathleen M; Molenaar, Peter C M

2012-10-15

At its best, connectivity mapping can offer researchers great insight into how spatially disparate regions of the human brain coordinate activity during brain processing. A recent investigation conducted by Smith and colleagues (2011) on methods for estimating connectivity maps suggested that those which attempt to ascertain the direction of influence among ROIs rarely provide reliable results. Another problem gaining increasing attention is heterogeneity in connectivity maps. Most group-level methods require that the data come from homogeneous samples, and misleading findings may arise from current methods if the connectivity maps for individuals vary across the sample (which is likely the case). The utility of maps resulting from effective connectivity on the individual or group levels is thus diminished because they do not accurately inform researchers. The present paper introduces a novel estimation technique for fMRI researchers, Group Iterative Multiple Model Estimation (GIMME), which demonstrates that using information across individuals assists in the recovery of the existence of connections among ROIs used by Smith and colleagues (2011) and the direction of the influence. Using heterogeneous in-house data, we demonstrate that GIMME offers a unique improvement over current approaches by arriving at reliable group and individual structures even when the data are highly heterogeneous across individuals comprising the group. An added benefit of GIMME is that it obtains reliable connectivity map estimates equally well using the data from resting state, block, or event-related designs. GIMME provides researchers with a powerful, flexible tool for identifying directed connectivity maps at the group and individual levels. Copyright © 2012 Elsevier Inc. All rights reserved.

Mapping of Courtship Behavior-Induced Neural Activity in the Thoracic Ganglia of Silkmoth Bombyx mori by an Immediate Early Gene, Hr38.

PubMed

Morishita, Koudai; Iwami, Masafumi; Kiya, Taketoshi

2018-06-01

In the central nervous system of insects, motor patterns are generated in the thoracic ganglia under the control of brain, where sensory information is integrated and behavioral decisions are made. Previously, we established neural activity-mapping methods using an immediate early gene, BmHr38, as a neural activity marker in the brain of male silkmoth Bombyx mori. In the present study, to gain insights into neural mechanisms of motor-pattern generation in the thoracic ganglia, we investigated expression of BmHr38 in response to sex pheromone-induced courtship behavior. Levels of BmHr38 expression were strongly correlated between the brain and thoracic ganglia, suggesting that neural activity in the thoracic ganglia is tightly controlled by the brain. In situ hybridization of BmHr38 revealed that 20-30% of thoracic neurons are activated by courtship behavior. Using serial sections, we constructed a comprehensive map of courtship behaviorinduced activity in the thoracic ganglia. These results provide important clues into how complex courtship behavior is generated in the neural circuits of thoracic ganglia.

Uses, misuses, new uses and fundamental limitations of magnetic resonance imaging in cognitive science

PubMed Central

2016-01-01

When blood oxygenation level-dependent (BOLD) contrast functional magnetic resonance imaging (fMRI) was discovered in the early 1990s, it provoked an explosion of interest in exploring human cognition, using brain mapping techniques based on MRI. Standards for data acquisition and analysis were rapidly put in place, in order to assist comparison of results across laboratories. Recently, MRI data acquisition capabilities have improved dramatically, inviting a rethink of strategies for relating functional brain activity at the systems level with its neuronal substrates and functional connections. This paper reviews the established capabilities of BOLD contrast fMRI, the perceived weaknesses of major methods of analysis, and current results that may provide insights into improved brain modelling. These results have inspired the use of in vivo myeloarchitecture for localizing brain activity, individual subject analysis without spatial smoothing and mapping of changes in cerebral blood volume instead of BOLD activation changes. The apparent fundamental limitations of all methods based on nuclear magnetic resonance are also discussed. This article is part of the themed issue ‘Interpreting BOLD: a dialogue between cognitive and cellular neuroscience’. PMID:27574303

Uses, misuses, new uses and fundamental limitations of magnetic resonance imaging in cognitive science.

PubMed

Turner, Robert

2016-10-05

When blood oxygenation level-dependent (BOLD) contrast functional magnetic resonance imaging (fMRI) was discovered in the early 1990s, it provoked an explosion of interest in exploring human cognition, using brain mapping techniques based on MRI. Standards for data acquisition and analysis were rapidly put in place, in order to assist comparison of results across laboratories. Recently, MRI data acquisition capabilities have improved dramatically, inviting a rethink of strategies for relating functional brain activity at the systems level with its neuronal substrates and functional connections. This paper reviews the established capabilities of BOLD contrast fMRI, the perceived weaknesses of major methods of analysis, and current results that may provide insights into improved brain modelling. These results have inspired the use of in vivo myeloarchitecture for localizing brain activity, individual subject analysis without spatial smoothing and mapping of changes in cerebral blood volume instead of BOLD activation changes. The apparent fundamental limitations of all methods based on nuclear magnetic resonance are also discussed.This article is part of the themed issue 'Interpreting BOLD: a dialogue between cognitive and cellular neuroscience'. © 2016 The Authors.

larvalign: Aligning Gene Expression Patterns from the Larval Brain of Drosophila melanogaster.

PubMed

Muenzing, Sascha E A; Strauch, Martin; Truman, James W; Bühler, Katja; Thum, Andreas S; Merhof, Dorit

2018-01-01

The larval brain of the fruit fly Drosophila melanogaster is a small, tractable model system for neuroscience. Genes for fluorescent marker proteins can be expressed in defined, spatially restricted neuron populations. Here, we introduce the methods for 1) generating a standard template of the larval central nervous system (CNS), 2) spatial mapping of expression patterns from different larvae into a reference space defined by the standard template. We provide a manually annotated gold standard that serves for evaluation of the registration framework involved in template generation and mapping. A method for registration quality assessment enables the automatic detection of registration errors, and a semi-automatic registration method allows one to correct registrations, which is a prerequisite for a high-quality, curated database of expression patterns. All computational methods are available within the larvalign software package: https://github.com/larvalign/larvalign/releases/tag/v1.0.

Nanotools for Neuroscience and Brain Activity Mapping

PubMed Central

Alivisatos, A. Paul; Andrews, Anne M.; Boyden, Edward S.; Chun, Miyoung; Church, George M.; Deisseroth, Karl; Donoghue, John P.; Fraser, Scott E.; Lippincott-Schwartz, Jennifer; Looger, Loren L.; Masmanidis, Sotiris; McEuen, Paul L.; Nurmikko, Arto V.; Park, Hongkun; Peterka, Darcy S.; Reid, Clay; Roukes, Michael L.; Scherer, Axel; Schnitzer, Mark; Sejnowski, Terrence J.; Shepard, Kenneth L.; Tsao, Doris; Turrigiano, Gina; Weiss, Paul S.; Xu, Chris; Yuste, Rafael; Zhuang, Xiaowei

2013-01-01

Neuroscience is at a crossroads. Great effort is being invested into deciphering specific neural interactions and circuits. At the same time, there exist few general theories or principles that explain brain function. We attribute this disparity, in part, to limitations in current methodologies. Traditional neurophysiological approaches record the activities of one neuron or a few neurons at a time. Neurochemical approaches focus on single neurotransmitters. Yet, there is an increasing realization that neural circuits operate at emergent levels, where the interactions between hundreds or thousands of neurons, utilizing multiple chemical transmitters, generate functional states. Brains function at the nanoscale, so tools to study brains must ultimately operate at this scale, as well. Nanoscience and nanotechnology are poised to provide a rich toolkit of novel methods to explore brain function by enabling simultaneous measurement and manipulation of activity of thousands or even millions of neurons. We and others refer to this goal as the Brain Activity Mapping Project. In this Nano Focus, we discuss how recent developments in nanoscale analysis tools and in the design and synthesis of nanomaterials have generated optical, electrical, and chemical methods that can readily be adapted for use in neuroscience. These approaches represent exciting areas of technical development and research. Moreover, unique opportunities exist for nanoscientists, nanotechnologists, and other physical scientists and engineers to contribute to tackling the challenging problems involved in understanding the fundamentals of brain function. PMID:23514423

Whole-brain activity mapping onto a zebrafish brain atlas.

PubMed

Randlett, Owen; Wee, Caroline L; Naumann, Eva A; Nnaemeka, Onyeka; Schoppik, David; Fitzgerald, James E; Portugues, Ruben; Lacoste, Alix M B; Riegler, Clemens; Engert, Florian; Schier, Alexander F

2015-11-01

In order to localize the neural circuits involved in generating behaviors, it is necessary to assign activity onto anatomical maps of the nervous system. Using brain registration across hundreds of larval zebrafish, we have built an expandable open-source atlas containing molecular labels and definitions of anatomical regions, the Z-Brain. Using this platform and immunohistochemical detection of phosphorylated extracellular signal–regulated kinase (ERK) as a readout of neural activity, we have developed a system to create and contextualize whole-brain maps of stimulus- and behavior-dependent neural activity. This mitogen-activated protein kinase (MAP)-mapping assay is technically simple, and data analysis is completely automated. Because MAP-mapping is performed on freely swimming fish, it is applicable to studies of nearly any stimulus or behavior. Here we demonstrate our high-throughput approach using pharmacological, visual and noxious stimuli, as well as hunting and feeding. The resultant maps outline hundreds of areas associated with behaviors.

Quantitative analysis of MRI-guided attenuation correction techniques in time-of-flight brain PET/MRI.

PubMed

Mehranian, Abolfazl; Arabi, Hossein; Zaidi, Habib

2016-04-15

In quantitative PET/MR imaging, attenuation correction (AC) of PET data is markedly challenged by the need of deriving accurate attenuation maps from MR images. A number of strategies have been developed for MRI-guided attenuation correction with different degrees of success. In this work, we compare the quantitative performance of three generic AC methods, including standard 3-class MR segmentation-based, advanced atlas-registration-based and emission-based approaches in the context of brain time-of-flight (TOF) PET/MRI. Fourteen patients referred for diagnostic MRI and (18)F-FDG PET/CT brain scans were included in this comparative study. For each study, PET images were reconstructed using four different attenuation maps derived from CT-based AC (CTAC) serving as reference, standard 3-class MR-segmentation, atlas-registration and emission-based AC methods. To generate 3-class attenuation maps, T1-weighted MRI images were segmented into background air, fat and soft-tissue classes followed by assignment of constant linear attenuation coefficients of 0, 0.0864 and 0.0975 cm(-1) to each class, respectively. A robust atlas-registration based AC method was developed for pseudo-CT generation using local weighted fusion of atlases based on their morphological similarity to target MR images. Our recently proposed MRI-guided maximum likelihood reconstruction of activity and attenuation (MLAA) algorithm was employed to estimate the attenuation map from TOF emission data. The performance of the different AC algorithms in terms of prediction of bones and quantification of PET tracer uptake was objectively evaluated with respect to reference CTAC maps and CTAC-PET images. Qualitative evaluation showed that the MLAA-AC method could sparsely estimate bones and accurately differentiate them from air cavities. It was found that the atlas-AC method can accurately predict bones with variable errors in defining air cavities. Quantitative assessment of bone extraction accuracy based on Dice similarity coefficient (DSC) showed that MLAA-AC and atlas-AC resulted in DSC mean values of 0.79 and 0.92, respectively, in all patients. The MLAA-AC and atlas-AC methods predicted mean linear attenuation coefficients of 0.107 and 0.134 cm(-1), respectively, for the skull compared to reference CTAC mean value of 0.138cm(-1). The evaluation of the relative change in tracer uptake within 32 distinct regions of the brain with respect to CTAC PET images showed that the 3-class MRAC, MLAA-AC and atlas-AC methods resulted in quantification errors of -16.2 ± 3.6%, -13.3 ± 3.3% and 1.0 ± 3.4%, respectively. Linear regression and Bland-Altman concordance plots showed that both 3-class MRAC and MLAA-AC methods result in a significant systematic bias in PET tracer uptake, while the atlas-AC method results in a negligible bias. The standard 3-class MRAC method significantly underestimated cerebral PET tracer uptake. While current state-of-the-art MLAA-AC methods look promising, they were unable to noticeably reduce quantification errors in the context of brain imaging. Conversely, the proposed atlas-AC method provided the most accurate attenuation maps, and thus the lowest quantification bias. Copyright © 2016 Elsevier Inc. All rights reserved.

A new method for automated high-dimensional lesion segmentation evaluated in vascular injury and applied to the human occipital lobe.

PubMed

Mah, Yee-Haur; Jager, Rolf; Kennard, Christopher; Husain, Masud; Nachev, Parashkev

2014-07-01

Making robust inferences about the functional neuroanatomy of the brain is critically dependent on experimental techniques that examine the consequences of focal loss of brain function. Unfortunately, the use of the most comprehensive such technique-lesion-function mapping-is complicated by the need for time-consuming and subjective manual delineation of the lesions, greatly limiting the practicability of the approach. Here we exploit a recently-described general measure of statistical anomaly, zeta, to devise a fully-automated, high-dimensional algorithm for identifying the parameters of lesions within a brain image given a reference set of normal brain images. We proceed to evaluate such an algorithm in the context of diffusion-weighted imaging of the commonest type of lesion used in neuroanatomical research: ischaemic damage. Summary performance metrics exceed those previously published for diffusion-weighted imaging and approach the current gold standard-manual segmentation-sufficiently closely for fully-automated lesion-mapping studies to become a possibility. We apply the new method to 435 unselected images of patients with ischaemic stroke to derive a probabilistic map of the pattern of damage in lesions involving the occipital lobe, demonstrating the variation of anatomical resolvability of occipital areas so as to guide future lesion-function studies of the region. Copyright © 2012 Elsevier Ltd. All rights reserved.

Whole brain myelin mapping using T1- and T2-weighted MR imaging data

PubMed Central

Ganzetti, Marco; Wenderoth, Nicole; Mantini, Dante

2014-01-01

Despite recent advancements in MR imaging, non-invasive mapping of myelin in the brain still remains an open issue. Here we attempted to provide a potential solution. Specifically, we developed a processing workflow based on T1-w and T2-w MR data to generate an optimized myelin enhanced contrast image. The workflow allows whole brain mapping using the T1-w/T2-w technique, which was originally introduced as a non-invasive method for assessing cortical myelin content. The hallmark of our approach is a retrospective calibration algorithm, applied to bias-corrected T1-w and T2-w images, that relies on image intensities outside the brain. This permits standardizing the intensity histogram of the ratio image, thereby allowing for across-subject statistical analyses. Quantitative comparisons of image histograms within and across different datasets confirmed the effectiveness of our normalization procedure. Not only did the calibrated T1-w/T2-w images exhibit a comparable intensity range, but also the shape of the intensity histograms was largely corresponding. We also assessed the reliability and specificity of the ratio image compared to other MR-based techniques, such as magnetization transfer ratio (MTR), fractional anisotropy (FA), and fluid-attenuated inversion recovery (FLAIR). With respect to these other techniques, T1-w/T2-w had consistently high values, as well as low inter-subject variability, in brain structures where myelin is most abundant. Overall, our results suggested that the T1-w/T2-w technique may be a valid tool supporting the non-invasive mapping of myelin in the brain. Therefore, it might find important applications in the study of brain development, aging and disease. PMID:25228871

Amide proton transfer imaging of brain tumors using a self-corrected 3D fast spin-echo dixon method: Comparison With separate B0 correction.

PubMed

Togao, Osamu; Keupp, Jochen; Hiwatashi, Akio; Yamashita, Koji; Kikuchi, Kazufumi; Yoneyama, Masami; Honda, Hiroshi

2017-06-01

To assess the quantitative performance of three-dimensional (3D) fast spin-echo (FSE) Dixon amide proton transfer (APT) imaging of brain tumors compared with B 0 correction with separate mapping methods. Twenty-two patients with brain tumors (54.2 ± 18.7 years old, 12 males and 10 females) were scanned at 3 Tesla (T). Z-spectra were obtained at seven different frequency offsets at ±3.1 ppm, ± 3.5 ppm, ± 3.9 ppm, and -1560 ppm. The scan was repeated three times at +3.5 ppm with echo shifts for Dixon B 0 mapping. The APT image corrected by a three-point Dixon-type B 0 map from the same scan (3D-Dixon) or a separate B 0 map (2D-separate and 3D-separate), and an uncorrected APT image (3D-uncorrected) were generated. We compared the APT-weighted signals within a tumor obtained with each 3D method with those obtained with 2D-separate as a reference standard. Excellent agreements and correlations with the 2D-separate were obtained by the 3D-Dixon method for both mean (ICC = 0.964, r = 0.93, P < 0.0001) and 90th-percentile (ICC = 0.972, r = 0.95, P < 0.0001) APT-weighted signals. These agreements and correlations for 3D-Dixon were better than those obtained by the 3D-uncorrected and 3D-separate methods. The 3D FSE Dixon APT method with intrinsic B 0 correction offers a quantitative performance that is similar to that of established two-dimensional (2D) methods. Magn Reson Med 77:2272-2279, 2017. © 2016 International Society for Magnetic Resonance in Medicine. © 2016 International Society for Magnetic Resonance in Medicine.

A Mathematical Model for Storage and Recall of Images using Targeted Synchronization of Coupled Maps.

PubMed

Palaniyandi, P; Rangarajan, Govindan

2017-08-21

We propose a mathematical model for storage and recall of images using coupled maps. We start by theoretically investigating targeted synchronization in coupled map systems wherein only a desired (partial) subset of the maps is made to synchronize. A simple method is introduced to specify coupling coefficients such that targeted synchronization is ensured. The principle of this method is extended to storage/recall of images using coupled Rulkov maps. The process of adjusting coupling coefficients between Rulkov maps (often used to model neurons) for the purpose of storing a desired image mimics the process of adjusting synaptic strengths between neurons to store memories. Our method uses both synchronisation and synaptic weight modification, as the human brain is thought to do. The stored image can be recalled by providing an initial random pattern to the dynamical system. The storage and recall of the standard image of Lena is explicitly demonstrated.

Whole-brain activity mapping onto a zebrafish brain atlas

PubMed Central

Randlett, Owen; Wee, Caroline L.; Naumann, Eva A.; Nnaemeka, Onyeka; Schoppik, David; Fitzgerald, James E.; Portugues, Ruben; Lacoste, Alix M.B.; Riegler, Clemens; Engert, Florian; Schier, Alexander F.

2015-01-01

In order to localize the neural circuits involved in generating behaviors, it is necessary to assign activity onto anatomical maps of the nervous system. Using brain registration across hundreds of larval zebrafish, we have built an expandable open source atlas containing molecular labels and anatomical region definitions, the Z-Brain. Using this platform and immunohistochemical detection of phosphorylated-Extracellular signal-regulated kinase (ERK/MAPK) as a readout of neural activity, we have developed a system to create and contextualize whole brain maps of stimulus- and behavior-dependent neural activity. This MAP-Mapping (Mitogen Activated Protein kinase – Mapping) assay is technically simple, fast, inexpensive, and data analysis is completely automated. Since MAP-Mapping is performed on fish that are freely swimming, it is applicable to nearly any stimulus or behavior. We demonstrate the utility of our high-throughput approach using hunting/feeding, pharmacological, visual and noxious stimuli. The resultant maps outline hundreds of areas associated with behaviors. PMID:26778924

Generating Text from Functional Brain Images

PubMed Central

Pereira, Francisco; Detre, Greg; Botvinick, Matthew

2011-01-01

Recent work has shown that it is possible to take brain images acquired during viewing of a scene and reconstruct an approximation of the scene from those images. Here we show that it is also possible to generate text about the mental content reflected in brain images. We began with images collected as participants read names of concrete items (e.g., “Apartment’’) while also seeing line drawings of the item named. We built a model of the mental semantic representation of concrete concepts from text data and learned to map aspects of such representation to patterns of activation in the corresponding brain image. In order to validate this mapping, without accessing information about the items viewed for left-out individual brain images, we were able to generate from each one a collection of semantically pertinent words (e.g., “door,” “window” for “Apartment’’). Furthermore, we show that the ability to generate such words allows us to perform a classification task and thus validate our method quantitatively. PMID:21927602

From Brain Maps to Cognitive Ontologies: Informatics and the Search for Mental Structure.

PubMed

Poldrack, Russell A; Yarkoni, Tal

2016-01-01

A major goal of cognitive neuroscience is to delineate how brain systems give rise to mental function. Here we review the increasingly large role informatics-driven approaches are playing in such efforts. We begin by reviewing a number of challenges conventional neuroimaging approaches face in trying to delineate brain-cognition mappings--for example, the difficulty in establishing the specificity of postulated associations. Next, we demonstrate how these limitations can potentially be overcome using complementary approaches that emphasize large-scale analysis--including meta-analytic methods that synthesize hundreds or thousands of studies at a time; latent-variable approaches that seek to extract structure from data in a bottom-up manner; and predictive modeling approaches capable of quantitatively inferring mental states from patterns of brain activity. We highlight the underappreciated but critical role for formal cognitive ontologies in helping to clarify, refine, and test theories of brain and cognitive function. Finally, we conclude with a speculative discussion of what future informatics developments may hold for cognitive neuroscience.

Brain tissue segmentation based on DTI data

PubMed Central

Liu, Tianming; Li, Hai; Wong, Kelvin; Tarokh, Ashley; Guo, Lei; Wong, Stephen T.C.

2008-01-01

We present a method for automated brain tissue segmentation based on the multi-channel fusion of diffusion tensor imaging (DTI) data. The method is motivated by the evidence that independent tissue segmentation based on DTI parametric images provides complementary information of tissue contrast to the tissue segmentation based on structural MRI data. This has important applications in defining accurate tissue maps when fusing structural data with diffusion data. In the absence of structural data, tissue segmentation based on DTI data provides an alternative means to obtain brain tissue segmentation. Our approach to the tissue segmentation based on DTI data is to classify the brain into two compartments by utilizing the tissue contrast existing in a single channel. Specifically, because the apparent diffusion coefficient (ADC) values in the cerebrospinal fluid (CSF) are more than twice that of gray matter (GM) and white matter (WM), we use ADC images to distinguish CSF and non-CSF tissues. Additionally, fractional anisotropy (FA) images are used to separate WM from non-WM tissues, as highly directional white matter structures have much larger fractional anisotropy values. Moreover, other channels to separate tissue are explored, such as eigenvalues of the tensor, relative anisotropy (RA), and volume ratio (VR). We developed an approach based on the Simultaneous Truth and Performance Level Estimation (STAPLE) algorithm that combines these two-class maps to obtain a complete tissue segmentation map of CSF, GM, and WM. Evaluations are provided to demonstrate the performance of our approach. Experimental results of applying this approach to brain tissue segmentation and deformable registration of DTI data and spoiled gradient-echo (SPGR) data are also provided. PMID:17804258

Flat-detector computed tomography PBV map in the evaluation of presurgical embolization for hypervascular brain tumors.

PubMed

Wen, Li-Li; Zhang, Xin; Zhang, Qing-Rong; Wu, Qi; Chen, Shu-Juan; Deng, Jin-Long; Huang, Kaiyi; Wang, Han-Dong

2017-11-01

Preoperative embolization of hypervascular brain tumors is frequently used to minimize intraoperative bleeding. To explore the efficacy of embolization using flat-detector CT (FDCT) parenchymal blood volume (PBV) maps before and after the intervention. Twenty-five patients with hypervascular brain tumors prospectively received pre- and postprocedural FDCT PBV scans using a biplane system under a protocol approved by the institutional research ethics committee. Semiquantitative analysis, based on region of interest measurements of the pre- and post-embolization PBV maps, operating time, and blood loss, was performed to assess the feasibility of PBV maps in detecting the perfusion deficit and to evaluate the efficacy of embolization. Preoperative embolization was successful in 18 patients. The relative PBV decreased significantly from 3.98±1.41 before embolization to 2.10±2.00 after embolization. Seventeen patients underwent surgical removal of tumors 24 hours after embolization. The post-embolic tumor perfusion index correlated significantly with blood loss (ρ=0.55) and operating time (ρ=0.60). FDCT PBV mapping is a useful method for evaluating the perfusion of hypervascular brain tumors and the efficacy of embolization. It can be used as a supplement to CT perfusion, MRI, and DSA in the evaluation of tumor embolization. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/.

Spatio-Temporal Brain Mapping of Motion-Onset VEPs Combined with fMRI and Retinotopic Maps

PubMed Central

Pitzalis, Sabrina; Strappini, Francesca; De Gasperis, Marco; Bultrini, Alessandro; Di Russo, Francesco

2012-01-01

Neuroimaging studies have identified several motion-sensitive visual areas in the human brain, but the time course of their activation cannot be measured with these techniques. In the present study, we combined electrophysiological and neuroimaging methods (including retinotopic brain mapping) to determine the spatio-temporal profile of motion-onset visual evoked potentials for slow and fast motion stimuli and to localize its neural generators. We found that cortical activity initiates in the primary visual area (V1) for slow stimuli, peaking 100 ms after the onset of motion. Subsequently, activity in the mid-temporal motion-sensitive areas, MT+, peaked at 120 ms, followed by peaks in activity in the more dorsal area, V3A, at 160 ms and the lateral occipital complex at 180 ms. Approximately 250 ms after stimulus onset, activity fast motion stimuli was predominant in area V6 along the parieto-occipital sulcus. Finally, at 350 ms (100 ms after the motion offset) brain activity was visible again in area V1. For fast motion stimuli, the spatio-temporal brain pattern was similar, except that the first activity was detected at 70 ms in area MT+. Comparing functional magnetic resonance data for slow vs. fast motion, we found signs of slow-fast motion stimulus topography along the posterior brain in at least three cortical regions (MT+, V3A and LOR). PMID:22558222

Estimation of effective brain connectivity with dual Kalman filter and EEG source localization methods.

PubMed

Rajabioun, Mehdi; Nasrabadi, Ali Motie; Shamsollahi, Mohammad Bagher

2017-09-01

Effective connectivity is one of the most important considerations in brain functional mapping via EEG. It demonstrates the effects of a particular active brain region on others. In this paper, a new method is proposed which is based on dual Kalman filter. In this method, firstly by using a brain active localization method (standardized low resolution brain electromagnetic tomography) and applying it to EEG signal, active regions are extracted, and appropriate time model (multivariate autoregressive model) is fitted to extracted brain active sources for evaluating the activity and time dependence between sources. Then, dual Kalman filter is used to estimate model parameters or effective connectivity between active regions. The advantage of this method is the estimation of different brain parts activity simultaneously with the calculation of effective connectivity between active regions. By combining dual Kalman filter with brain source localization methods, in addition to the connectivity estimation between parts, source activity is updated during the time. The proposed method performance has been evaluated firstly by applying it to simulated EEG signals with interacting connectivity simulation between active parts. Noisy simulated signals with different signal to noise ratios are used for evaluating method sensitivity to noise and comparing proposed method performance with other methods. Then the method is applied to real signals and the estimation error during a sweeping window is calculated. By comparing proposed method results in different simulation (simulated and real signals), proposed method gives acceptable results with least mean square error in noisy or real conditions.

Functional Geometry Alignment and Localization of Brain Areas.

PubMed

Langs, Georg; Golland, Polina; Tie, Yanmei; Rigolo, Laura; Golby, Alexandra J

2010-01-01

Matching functional brain regions across individuals is a challenging task, largely due to the variability in their location and extent. It is particularly difficult, but highly relevant, for patients with pathologies such as brain tumors, which can cause substantial reorganization of functional systems. In such cases spatial registration based on anatomical data is only of limited value if the goal is to establish correspondences of functional areas among different individuals, or to localize potentially displaced active regions. Rather than rely on spatial alignment, we propose to perform registration in an alternative space whose geometry is governed by the functional interaction patterns in the brain. We first embed each brain into a functional map that reflects connectivity patterns during a fMRI experiment. The resulting functional maps are then registered, and the obtained correspondences are propagated back to the two brains. In application to a language fMRI experiment, our preliminary results suggest that the proposed method yields improved functional correspondences across subjects. This advantage is pronounced for subjects with tumors that affect the language areas and thus cause spatial reorganization of the functional regions.

Effects of spatial variation of skull and cerebrospinal fluid layers on optical mapping of brain activities

NASA Astrophysics Data System (ADS)

Wang, Shuping; Shibahara, Nanae; Kuramashi, Daishi; Okawa, Shinpei; Kakuta, Naoto; Okada, Eiji; Maki, Atsushi; Yamada, Yukio

2010-07-01

In order to investigate the effects of anatomical variation in human heads on the optical mapping of brain activity, we perform simulations of optical mapping by solving the photon diffusion equation for layered-models simulating human heads using the finite element method (FEM). Particularly, the effects of the spatial variations in the thicknesses of the skull and cerebrospinal fluid (CSF) layers on mapping images are investigated. Mapping images of single active regions in the gray matter layer are affected by the spatial variations in the skull and CSF layer thicknesses, although the effects are smaller than those of the positions of the active region relative to the data points. The increase in the skull thickness decreases the sensitivity of the images to active regions, while the increase in the CSF layer thickness increases the sensitivity in general. The images of multiple active regions are also influenced by their positions relative to the data points and by their depths from the skin surface.

Three-dimensional mapping of the lateral ventricles in autism

PubMed Central

Vidal, Christine N.; Nicolsonln, Rob; Boire, Jean-Yves; Barra, Vincent; DeVito, Timothy J.; Hayashi, Kiralee M.; Geaga, Jennifer A.; Drost, Dick J.; Williamson, Peter C.; Rajakumar, Nagalingam; Toga, Arthur W.; Thompson, Paul M.

2009-01-01

In this study, a computational mapping technique was used to examine the three-dimensional profile of the lateral ventricles in autism. T1-weighted three-dimensional magnetic resonance images of the brain were acquired from 20 males with autism (age: 10.1 ± 3.5 years) and 22 male control subjects (age: 10.7 ± 2.5 years). The lateral ventricles were delineated manually and ventricular volumes were compared between the two groups. Ventricular traces were also converted into statistical three-dimensional maps, based on anatomical surface meshes. These maps were used to visualize regional morphological differences in the thickness of the lateral ventricles between patients and controls. Although ventricular volumes measured using traditional methods did not differ significantly between groups, statistical surface maps revealed subtle, highly localized reductions in ventricular size in patients with autism in the left frontal and occipital horns. These localized reductions in the lateral ventricles may result from exaggerated brain growth early in life. PMID:18502618

Laser speckle imaging identification of increases in cortical microcirculatory blood flow induced by motor activity during awake craniotomy.

PubMed

Klijn, Eva; Hulscher, Hester C; Balvers, Rutger K; Holland, Wim P J; Bakker, Jan; Vincent, Arnaud J P E; Dirven, Clemens M F; Ince, Can

2013-02-01

The goal of awake neurosurgery is to maximize resection of brain lesions with minimal injury to functional brain areas. Laser speckle imaging (LSI) is a noninvasive macroscopic technique with high spatial and temporal resolution used to monitor changes in capillary perfusion. In this study, the authors hypothesized that LSI can be useful as a noncontact method of functional brain mapping during awake craniotomy for tumor removal. Such a modality would be an advance in this type of neurosurgery since current practice involves the application of invasive intraoperative single-point electrocortical (electrode) stimulation and measurements. After opening the dura mater, patients were woken up, and LSI was set up to image the exposed brain area. Patients were instructed to follow a rest-activation-rest protocol in which activation consisted of the hand-clenching motor task. Subsequently, exposed brain areas were mapped for functional motor areas by using standard electrocortical stimulation (ECS). Changes in the LSI signal were analyzed offline and compared with the results of ECS. In functional motor areas of the hand mapped with ECS, cortical blood flow measured using LSI significantly increased from 2052 ± 818 AU to 2471 ± 675 AU during hand clenching, whereas capillary blood flow did not change in the control regions (areas mapped using ECS with no functional activity). The main finding of this study was that changes in laser speckle perfusion as a measure of cortical microvascular blood flow when performing a motor task with the hand relate well to the ECS map. The authors have shown the feasibility of using LSI for direct visualization of cortical microcirculatory blood flow changes during neurosurgery.

3D resolved mapping of optical aberrations in thick tissues

PubMed Central

Zeng, Jun; Mahou, Pierre; Schanne-Klein, Marie-Claire; Beaurepaire, Emmanuel; Débarre, Delphine

2012-01-01

We demonstrate a simple method for mapping optical aberrations with 3D resolution within thick samples. The method relies on the local measurement of the variation in image quality with externally applied aberrations. We discuss the accuracy of the method as a function of the signal strength and of the aberration amplitude and we derive the achievable resolution for the resulting measurements. We then report on measured 3D aberration maps in human skin biopsies and mouse brain slices. From these data, we analyse the consequences of tissue structure and refractive index distribution on aberrations and imaging depth in normal and cleared tissue samples. The aberration maps allow the estimation of the typical aplanetism region size over which aberrations can be uniformly corrected. This method and data pave the way towards efficient correction strategies for tissue imaging applications. PMID:22876353

Brain-Mind Operational Architectonics Imaging: Technical and Methodological Aspects

PubMed Central

Fingelkurts, Andrew A; Fingelkurts, Alexander A

2008-01-01

This review paper deals with methodological and technical foundations of the Operational Architectonics framework of brain and mind functioning. This theory provides a framework for mapping and understanding important aspects of the brain mechanisms that constitute perception, cognition, and eventually consciousness. The methods utilized within Operational Architectonics framework allow analyzing with an incredible detail the operational behavior of local neuronal assemblies and their joint activity in the form of unified and metastable operational modules, which constitute the whole hierarchy of brain operations, operations of cognition and phenomenal consciousness. PMID:19526071

New Clinically Feasible 3T MRI Protocol to Discriminate Internal Brain Stem Anatomy.

PubMed

Hoch, M J; Chung, S; Ben-Eliezer, N; Bruno, M T; Fatterpekar, G M; Shepherd, T M

2016-06-01

Two new 3T MR imaging contrast methods, track density imaging and echo modulation curve T2 mapping, were combined with simultaneous multisection acquisition to reveal exquisite anatomic detail at 7 canonical levels of the brain stem. Compared with conventional MR imaging contrasts, many individual brain stem tracts and nuclear groups were directly visualized for the first time at 3T. This new approach is clinically practical and feasible (total scan time = 20 minutes), allowing better brain stem anatomic localization and characterization. © 2016 by American Journal of Neuroradiology.

[Impact to Z-score Mapping of Hyperacute Stroke Images by Computed Tomography in Adaptive Statistical Iterative Reconstruction].

PubMed

Watanabe, Shota; Sakaguchi, Kenta; Hosono, Makoto; Ishii, Kazunari; Murakami, Takamichi; Ichikawa, Katsuhiro

The purpose of this study was to evaluate the effect of a hybrid-type iterative reconstruction method on Z-score mapping of hyperacute stroke in unenhanced computed tomography (CT) images. We used a hybrid-type iterative reconstruction [adaptive statistical iterative reconstruction (ASiR)] implemented in a CT system (Optima CT660 Pro advance, GE Healthcare). With 15 normal brain cases, we reconstructed CT images with a filtered back projection (FBP) and ASiR with a blending factor of 100% (ASiR100%). Two standardized normal brain data were created from normal databases of FBP images (FBP-NDB) and ASiR100% images (ASiR-NDB), and standard deviation (SD) values in basal ganglia were measured. The Z-score mapping was performed for 12 hyperacute stroke cases by using FBP-NDB and ASiR-NDB, and compared Z-score value on hyperacute stroke area and normal area between FBP-NDB and ASiR-NDB. By using ASiR-NDB, the SD value of standardized brain was decreased by 16%. The Z-score value of ASiR-NDB on hyperacute stroke area was significantly higher than FBP-NDB (p<0.05). Therefore, the use of images reconstructed with ASiR100% for Z-score mapping had potential to improve the accuracy of Z-score mapping.

CAD system for automatic analysis of CT perfusion maps

NASA Astrophysics Data System (ADS)

Hachaj, T.; Ogiela, M. R.

2011-03-01

In this article, authors present novel algorithms developed for the computer-assisted diagnosis (CAD) system for analysis of dynamic brain perfusion, computer tomography (CT) maps, cerebral blood flow (CBF), and cerebral blood volume (CBV). Those methods perform both quantitative analysis [detection and measurement and description with brain anatomy atlas (AA) of potential asymmetries/lesions] and qualitative analysis (semantic interpretation of visualized symptoms). The semantic interpretation (decision about type of lesion: ischemic/hemorrhagic, is the brain tissue at risk of infraction or not) of visualized symptoms is done by, so-called, cognitive inference processes allowing for reasoning on character of pathological regions based on specialist image knowledge. The whole system is implemented in.NET platform (C# programming language) and can be used on any standard PC computer with.NET framework installed.

iELVis: An open source MATLAB toolbox for localizing and visualizing human intracranial electrode data.

PubMed

Groppe, David M; Bickel, Stephan; Dykstra, Andrew R; Wang, Xiuyuan; Mégevand, Pierre; Mercier, Manuel R; Lado, Fred A; Mehta, Ashesh D; Honey, Christopher J

2017-04-01

Intracranial electrical recordings (iEEG) and brain stimulation (iEBS) are invaluable human neuroscience methodologies. However, the value of such data is often unrealized as many laboratories lack tools for localizing electrodes relative to anatomy. To remedy this, we have developed a MATLAB toolbox for intracranial electrode localization and visualization, iELVis. NEW METHOD: iELVis uses existing tools (BioImage Suite, FSL, and FreeSurfer) for preimplant magnetic resonance imaging (MRI) segmentation, neuroimaging coregistration, and manual identification of electrodes in postimplant neuroimaging. Subsequently, iELVis implements methods for correcting electrode locations for postimplant brain shift with millimeter-scale accuracy and provides interactive visualization on 3D surfaces or in 2D slices with optional functional neuroimaging overlays. iELVis also localizes electrodes relative to FreeSurfer-based atlases and can combine data across subjects via the FreeSurfer average brain. It takes 30-60min of user time and 12-24h of computer time to localize and visualize electrodes from one brain. We demonstrate iELVis's functionality by showing that three methods for mapping primary hand somatosensory cortex (iEEG, iEBS, and functional MRI) provide highly concordant results. COMPARISON WITH EXISTING METHODS: iELVis is the first public software for electrode localization that corrects for brain shift, maps electrodes to an average brain, and supports neuroimaging overlays. Moreover, its interactive visualizations are powerful and its tutorial material is extensive. iELVis promises to speed the progress and enhance the robustness of intracranial electrode research. The software and extensive tutorial materials are freely available as part of the EpiSurg software project: https://github.com/episurg/episurg. Copyright © 2017 Elsevier B.V. All rights reserved.

Multi-atlas attenuation correction supports full quantification of static and dynamic brain PET data in PET-MR

NASA Astrophysics Data System (ADS)

Mérida, Inés; Reilhac, Anthonin; Redouté, Jérôme; Heckemann, Rolf A.; Costes, Nicolas; Hammers, Alexander

2017-04-01

In simultaneous PET-MR, attenuation maps are not directly available. Essential for absolute radioactivity quantification, they need to be derived from MR or PET data to correct for gamma photon attenuation by the imaged object. We evaluate a multi-atlas attenuation correction method for brain imaging (MaxProb) on static [18F]FDG PET and, for the first time, on dynamic PET, using the serotoninergic tracer [18F]MPPF. A database of 40 MR/CT image pairs (atlases) was used. The MaxProb method synthesises subject-specific pseudo-CTs by registering each atlas to the target subject space. Atlas CT intensities are then fused via label propagation and majority voting. Here, we compared these pseudo-CTs with the real CTs in a leave-one-out design, contrasting the MaxProb approach with a simplified single-atlas method (SingleAtlas). We evaluated the impact of pseudo-CT accuracy on reconstructed PET images, compared to PET data reconstructed with real CT, at the regional and voxel levels for the following: radioactivity images; time-activity curves; and kinetic parameters (non-displaceable binding potential, BPND). On static [18F]FDG, the mean bias for MaxProb ranged between 0 and 1% for 73 out of 84 regions assessed, and exceptionally peaked at 2.5% for only one region. Statistical parametric map analysis of MaxProb-corrected PET data showed significant differences in less than 0.02% of the brain volume, whereas SingleAtlas-corrected data showed significant differences in 20% of the brain volume. On dynamic [18F]MPPF, most regional errors on BPND ranged from -1 to  +3% (maximum bias 5%) for the MaxProb method. With SingleAtlas, errors were larger and had higher variability in most regions. PET quantification bias increased over the duration of the dynamic scan for SingleAtlas, but not for MaxProb. We show that this effect is due to the interaction of the spatial tracer-distribution heterogeneity variation over time with the degree of accuracy of the attenuation maps. This work demonstrates that inaccuracies in attenuation maps can induce bias in dynamic brain PET studies. Multi-atlas attenuation correction with MaxProb enables quantification on hybrid PET-MR scanners, eschewing the need for CT.

Multi-atlas attenuation correction supports full quantification of static and dynamic brain PET data in PET-MR.

PubMed

Mérida, Inés; Reilhac, Anthonin; Redouté, Jérôme; Heckemann, Rolf A; Costes, Nicolas; Hammers, Alexander

2017-04-07

In simultaneous PET-MR, attenuation maps are not directly available. Essential for absolute radioactivity quantification, they need to be derived from MR or PET data to correct for gamma photon attenuation by the imaged object. We evaluate a multi-atlas attenuation correction method for brain imaging (MaxProb) on static [ 18 F]FDG PET and, for the first time, on dynamic PET, using the serotoninergic tracer [ 18 F]MPPF. A database of 40 MR/CT image pairs (atlases) was used. The MaxProb method synthesises subject-specific pseudo-CTs by registering each atlas to the target subject space. Atlas CT intensities are then fused via label propagation and majority voting. Here, we compared these pseudo-CTs with the real CTs in a leave-one-out design, contrasting the MaxProb approach with a simplified single-atlas method (SingleAtlas). We evaluated the impact of pseudo-CT accuracy on reconstructed PET images, compared to PET data reconstructed with real CT, at the regional and voxel levels for the following: radioactivity images; time-activity curves; and kinetic parameters (non-displaceable binding potential, BP ND ). On static [ 18 F]FDG, the mean bias for MaxProb ranged between 0 and 1% for 73 out of 84 regions assessed, and exceptionally peaked at 2.5% for only one region. Statistical parametric map analysis of MaxProb-corrected PET data showed significant differences in less than 0.02% of the brain volume, whereas SingleAtlas-corrected data showed significant differences in 20% of the brain volume. On dynamic [ 18 F]MPPF, most regional errors on BP ND ranged from -1 to  +3% (maximum bias 5%) for the MaxProb method. With SingleAtlas, errors were larger and had higher variability in most regions. PET quantification bias increased over the duration of the dynamic scan for SingleAtlas, but not for MaxProb. We show that this effect is due to the interaction of the spatial tracer-distribution heterogeneity variation over time with the degree of accuracy of the attenuation maps. This work demonstrates that inaccuracies in attenuation maps can induce bias in dynamic brain PET studies. Multi-atlas attenuation correction with MaxProb enables quantification on hybrid PET-MR scanners, eschewing the need for CT.

The utilization of mind map painting on 3D shapes with curved faces

NASA Astrophysics Data System (ADS)

Nur Sholikhah, Ayuk; Usodo, Budi; Pramudya, Ikrar

2017-12-01

This paper aims to study on the use of mind map painting media on material with 3D shapes with curved faces and its effect on student’s interest. Observation and literature studies were applied as the research method with the sake design of utilization of mind map painting. The result of this research is the design of mind map painting media can improve students' ability to solve problems, improve the ability to think, and maximize brain power. In relation, mind map painting in learning activities is considered to improve student interest.

Mapping and reconstruction of domoic acid-induced neurodegeneration in the mouse brain.

PubMed

Colman, J R; Nowocin, K J; Switzer, R C; Trusk, T C; Ramsdell, J S

2005-01-01

Domoic acid, a potent neurotoxin and glutamate analog produced by certain species of the marine diatom Pseudonitzschia, is responsible for several human and wildlife intoxication events. The toxin characteristically damages the hippocampus in exposed humans, rodents, and marine mammals. Histochemical studies have identified this, and other regions of neurodegeneration, though none have sought to map all brain regions affected by domoic acid. In this study, mice exposed (i.p.) to 4 mg/kg domoic acid for 72 h exhibited behavioral and pathological signs of neurotoxicity. Brains were fixed by intracardial perfusion and processed for histochemical analysis. Serial coronal sections (50 microm) were stained using the degeneration-sensitive cupric silver staining method of DeOlmos. Degenerated axons, terminals, and cell bodies, which stained black, were identified and the areas of degeneration were mapped onto Paxinos mouse atlas brain plates using Adobe Illustrator CS. The plates were then combined to reconstruct a 3-dimensional image of domoic acid-induced neurodegeneration using Amira 3.1 software. Affected regions included the olfactory bulb, septal area, and limbic system. These findings are consistent with behavioral and pathological studies demonstrating the effects of domoic acid on cognitive function and neurodegeneration in rodents.

Spatial Mapping of Structural and Connectional Imaging Data for the Developing Human Brain with Diffusion Tensor Imaging

PubMed Central

Ouyang, Austin; Jeon, Tina; Sunkin, Susan M.; Pletikos, Mihovil; Sedmak, Goran; Sestan, Nenad; Lein, Ed S.; Huang, Hao

2014-01-01

During human brain development from fetal stage to adulthood, the white matter (WM) tracts undergo dramatic changes. Diffusion tensor imaging (DTI), a widely used magnetic resonance imaging (MRI) modality, offers insight into the dynamic changes of WM fibers as these fibers can be noninvasively traced and three-dimensionally (3D) reconstructed with DTI tractography. The DTI and conventional T1 weighted MRI images also provide sufficient cortical anatomical details for mapping the cortical regions of interests (ROIs). In this paper, we described basic concepts and methods of DTI techniques that can be used to trace major WM tracts noninvasively from fetal brain of 14 postconceptional weeks (pcw) to adult brain. We applied these techniques to acquire DTI data and trace, reconstruct and visualize major WM tracts during development. After categorizing major WM fiber bundles into five unique functional tract groups, namely limbic, brain stem, projection, commissural and association tracts, we revealed formation and maturation of these 3D reconstructed WM tracts of the developing human brain. The structural and connectional imaging data offered by DTI provides the anatomical backbone of transcriptional atlas of the developing human brain. PMID:25448302

Mapping heritability and molecular genetic associations with cortical features using probabilistic brain atlases: methods and applications to schizophrenia.

PubMed

Cannon, Tyrone D; Thompson, Paul M; van Erp, Theo G M; Huttunen, Matti; Lonnqvist, Jouko; Kaprio, Jaakko; Toga, Arthur W

2006-01-01

There is an urgent need to decipher the complex nature of genotype-phenotype relationships within the multiple dimensions of brain structure and function that are compromised in neuropsychiatric syndromes such as schizophrenia. Doing so requires sophisticated methodologies to represent population variability in neural traits and to probe their heritable and molecular genetic bases. We have recently developed and applied computational algorithms to map the heritability of, as well as genetic linkage and association to, neural features encoded using brain imaging in the context of three-dimensional (3D), populationbased, statistical brain atlases. One set of algorithms builds on our prior work using classical twin study methods to estimate heritability by fitting biometrical models for additive genetic, unique, and common environmental influences. Another set of algorithms performs regression-based (Haseman-Elston) identical-bydescent linkage analysis and genetic association analysis of DNA polymorphisms in relation to neural traits of interest in the same 3D population-based brain atlas format. We demonstrate these approaches using samples of healthy monozygotic (MZ) and dizygotic (DZ) twin pairs, as well as MZ and DZ twin pairs discordant for schizophrenia, but the methods can be generalized to other classes of relatives and to other diseases. The results confirm prior evidence of genetic influences on gray matter density in frontal brain regions. They also provide converging evidence that the chromosome 1q42 region is relevant to schizophrenia by demonstrating linkage and association of markers of the Transelin-Associated-Factor-X and Disrupted-In- Schizophrenia-1 genes with prefrontal cortical gray matter deficits in twins discordant for schizophrenia.

Deep Convolutional Neural Networks for Multi-Modality Isointense Infant Brain Image Segmentation

PubMed Central

Zhang, Wenlu; Li, Rongjian; Deng, Houtao; Wang, Li; Lin, Weili; Ji, Shuiwang; Shen, Dinggang

2015-01-01

The segmentation of infant brain tissue images into white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF) plays an important role in studying early brain development in health and disease. In the isointense stage (approximately 6–8 months of age), WM and GM exhibit similar levels of intensity in both T1 and T2 MR images, making the tissue segmentation very challenging. Only a small number of existing methods have been designed for tissue segmentation in this isointense stage; however, they only used a single T1 or T2 images, or the combination of T1 and T2 images. In this paper, we propose to use deep convolutional neural networks (CNNs) for segmenting isointense stage brain tissues using multi-modality MR images. CNNs are a type of deep models in which trainable filters and local neighborhood pooling operations are applied alternatingly on the raw input images, resulting in a hierarchy of increasingly complex features. Specifically, we used multimodality information from T1, T2, and fractional anisotropy (FA) images as inputs and then generated the segmentation maps as outputs. The multiple intermediate layers applied convolution, pooling, normalization, and other operations to capture the highly nonlinear mappings between inputs and outputs. We compared the performance of our approach with that of the commonly used segmentation methods on a set of manually segmented isointense stage brain images. Results showed that our proposed model significantly outperformed prior methods on infant brain tissue segmentation. In addition, our results indicated that integration of multi-modality images led to significant performance improvement. PMID:25562829

Analysis of neoplastic lesions in magnetic resonance imaging using self-organizing maps.

PubMed

Mei, Paulo Afonso; de Carvalho Carneiro, Cleyton; Fraser, Stephen J; Min, Li Li; Reis, Fabiano

2015-12-15

To provide an improved method for the identification and analysis of brain tumors in MRI scans using a semi-automated computational approach, that has the potential to provide a more objective, precise and quantitatively rigorous analysis, compared to human visual analysis. Self-Organizing Maps (SOM) is an unsupervised, exploratory data analysis tool, which can automatically domain an image into selfsimilar regions or clusters, based on measures of similarity. It can be used to perform image-domain of brain tissue on MR images, without prior knowledge. We used SOM to analyze T1, T2 and FLAIR acquisitions from two MRI machines in our service from 14 patients with brain tumors confirmed by biopsies--three lymphomas, six glioblastomas, one meningioma, one ganglioglioma, two oligoastrocytomas and one astrocytoma. The SOM software was used to analyze the data from the three image acquisitions from each patient and generated a self-organized map for each containing 25 clusters. Damaged tissue was separated from the normal tissue using the SOM technique. Furthermore, in some cases it allowed to separate different areas from within the tumor--like edema/peritumoral infiltration and necrosis. In lesions with less precise boundaries in FLAIR, the estimated damaged tissue area in the resulting map appears bigger. Our results showed that SOM has the potential to be a powerful MR imaging analysis technique for the assessment of brain tumors. Copyright © 2015. Published by Elsevier B.V.

Brain tumor segmentation using holistically nested neural networks in MRI images.

PubMed

Zhuge, Ying; Krauze, Andra V; Ning, Holly; Cheng, Jason Y; Arora, Barbara C; Camphausen, Kevin; Miller, Robert W

2017-10-01

Gliomas are rapidly progressive, neurologically devastating, largely fatal brain tumors. Magnetic resonance imaging (MRI) is a widely used technique employed in the diagnosis and management of gliomas in clinical practice. MRI is also the standard imaging modality used to delineate the brain tumor target as part of treatment planning for the administration of radiation therapy. Despite more than 20 yr of research and development, computational brain tumor segmentation in MRI images remains a challenging task. We are presenting a novel method of automatic image segmentation based on holistically nested neural networks that could be employed for brain tumor segmentation of MRI images. Two preprocessing techniques were applied to MRI images. The N4ITK method was employed for correction of bias field distortion. A novel landmark-based intensity normalization method was developed so that tissue types have a similar intensity scale in images of different subjects for the same MRI protocol. The holistically nested neural networks (HNN), which extend from the convolutional neural networks (CNN) with a deep supervision through an additional weighted-fusion output layer, was trained to learn the multiscale and multilevel hierarchical appearance representation of the brain tumor in MRI images and was subsequently applied to produce a prediction map of the brain tumor on test images. Finally, the brain tumor was obtained through an optimum thresholding on the prediction map. The proposed method was evaluated on both the Multimodal Brain Tumor Image Segmentation (BRATS) Benchmark 2013 training datasets, and clinical data from our institute. A dice similarity coefficient (DSC) and sensitivity of 0.78 and 0.81 were achieved on 20 BRATS 2013 training datasets with high-grade gliomas (HGG), based on a two-fold cross-validation. The HNN model built on the BRATS 2013 training data was applied to ten clinical datasets with HGG from a locally developed database. DSC and sensitivity of 0.83 and 0.85 were achieved. A quantitative comparison indicated that the proposed method outperforms the popular fully convolutional network (FCN) method. In terms of efficiency, the proposed method took around 10 h for training with 50,000 iterations, and approximately 30 s for testing of a typical MRI image in the BRATS 2013 dataset with a size of 160 × 216 × 176, using a DELL PRECISION workstation T7400, with an NVIDIA Tesla K20c GPU. An effective brain tumor segmentation method for MRI images based on a HNN has been developed. The high level of accuracy and efficiency make this method practical in brain tumor segmentation. It may play a crucial role in both brain tumor diagnostic analysis and in the treatment planning of radiation therapy. Published 2017. This article is a U.S. Government work and is in the public domain in the USA.

sfDM: Open-Source Software for Temporal Analysis and Visualization of Brain Tumor Diffusion MR Using Serial Functional Diffusion Mapping.

PubMed

Ceschin, Rafael; Panigrahy, Ashok; Gopalakrishnan, Vanathi

2015-01-01

A major challenge in the diagnosis and treatment of brain tumors is tissue heterogeneity leading to mixed treatment response. Additionally, they are often difficult or at very high risk for biopsy, further hindering the clinical management process. To overcome this, novel advanced imaging methods are increasingly being adapted clinically to identify useful noninvasive biomarkers capable of disease stage characterization and treatment response prediction. One promising technique is called functional diffusion mapping (fDM), which uses diffusion-weighted imaging (DWI) to generate parametric maps between two imaging time points in order to identify significant voxel-wise changes in water diffusion within the tumor tissue. Here we introduce serial functional diffusion mapping (sfDM), an extension of existing fDM methods, to analyze the entire tumor diffusion profile along the temporal course of the disease. sfDM provides the tools necessary to analyze a tumor data set in the context of spatiotemporal parametric mapping: the image registration pipeline, biomarker extraction, and visualization tools. We present the general workflow of the pipeline, along with a typical use case for the software. sfDM is written in Python and is freely available as an open-source package under the Berkley Software Distribution (BSD) license to promote transparency and reproducibility.

Morphology enabled dipole inversion (MEDI) from a single-angle acquisition: comparison with COSMOS in human brain imaging.

PubMed

Liu, Tian; Liu, Jing; de Rochefort, Ludovic; Spincemaille, Pascal; Khalidov, Ildar; Ledoux, James Robert; Wang, Yi

2011-09-01

Magnetic susceptibility varies among brain structures and provides insights into the chemical and molecular composition of brain tissues. However, the determination of an arbitrary susceptibility distribution from the measured MR signal phase is a challenging, ill-conditioned inverse problem. Although a previous method named calculation of susceptibility through multiple orientation sampling (COSMOS) has solved this inverse problem both theoretically and experimentally using multiple angle acquisitions, it is often impractical to carry out on human subjects. Recently, the feasibility of calculating the brain susceptibility distribution from a single-angle acquisition was demonstrated using morphology enabled dipole inversion (MEDI). In this study, we further improved the original MEDI method by sparsifying the edges in the quantitative susceptibility map that do not have a corresponding edge in the magnitude image. Quantitative susceptibility maps generated by the improved MEDI were compared qualitatively and quantitatively with those generated by calculation of susceptibility through multiple orientation sampling. The results show a high degree of agreement between MEDI and calculation of susceptibility through multiple orientation sampling, and the practicality of MEDI allows many potential clinical applications. Copyright © 2011 Wiley-Liss, Inc.

Passive Acoustic Mapping with the Angular Spectrum Method

PubMed Central

Crake, Calum; McDannold, Nathan; Clement, Gregory T.

2017-01-01

In the present proof of principle study, we evaluated the homogenous angular spectrum method for passive acoustic mapping (AS-PAM) of microbubble oscillations using simulated and experimental data. In the simulated data we assessed the ability of AS-PAM to form 3D maps of a single and multiple point sources. Then, in the two dimensional limit, we compared the 2D maps from AS-PAM with alternative frequency and time domain passive acoustic mapping (FD- and TD-PAM) approaches. Finally, we assessed the ability of AS-PAM to visualize microbubble activity in vivo with data obtained during 8 different experiments of FUS-induced blood-brain barrier disruption in 3 nonhuman primates, using a clinical MR-guided FUS system. Our in silico results demonstrate AS-PAM can be used to perform 3D passive acoustic mapping. 2D AS-PAM as compared to FD- PAM and TD-PAM is 10 and 200 times faster respectively and has similar sensitivity, resolution, and localization accuracy, even when the noise was 10-fold higher than the signal. In-vivo, the AS-PAM reconstructions of emissions at frequency bands pertinent to the different types of microbubble oscillations were also found to be more sensitive than TD-PAM. AS-PAM of harmonic-only components predicted safe blood-brain barrier disruption, whereas AS-PAM of broadband emissions correctly identified MR-evident tissue damage. The disparity (3.2mm) in the location of the cavitation activity between the three methods was within their resolution limits. These data clearly demonstrate that AS-PAM is a sensitive and fast approach for PAM, thus providing a clinically relevant method to guide therapeutic ultrasound procedures. PMID:28026755

Initial constructs for patient-centered outcome measures to evaluate brain-computer interfaces.

PubMed

Andresen, Elena M; Fried-Oken, Melanie; Peters, Betts; Patrick, Donald L

2016-10-01

The authors describe preliminary work toward the creation of patient-centered outcome (PCO) measures to evaluate brain-computer interface (BCI) as an assistive technology (AT) for individuals with severe speech and physical impairments (SSPI). In Phase 1, 591 items from 15 existing measures were mapped to the International Classification of Functioning, Disability and Health (ICF). In Phase 2, qualitative interviews were conducted with eight people with SSPI and seven caregivers. Resulting text data were coded in an iterative analysis. Most items (79%) were mapped to the ICF environmental domain; over half (53%) were mapped to more than one domain. The ICF framework was well suited for mapping items related to body functions and structures, but less so for items in other areas, including personal factors. Two constructs emerged from qualitative data: quality of life (QOL) and AT. Component domains and themes were identified for each. Preliminary constructs, domains and themes were generated for future PCO measures relevant to BCI. Existing instruments are sufficient for initial items but do not adequately match the values of people with SSPI and their caregivers. Field methods for interviewing people with SSPI were successful, and support the inclusion of these individuals in PCO research. Implications for Rehabilitation Adapted interview methods allow people with severe speech and physical impairments to participate in patient-centered outcomes research. Patient-centered outcome measures are needed to evaluate the clinical implementation of brain-computer interface as an assistive technology.

A brain-region-based meta-analysis method utilizing the Apriori algorithm.

PubMed

Niu, Zhendong; Nie, Yaoxin; Zhou, Qian; Zhu, Linlin; Wei, Jieyao

2016-05-18

Brain network connectivity modeling is a crucial method for studying the brain's cognitive functions. Meta-analyses can unearth reliable results from individual studies. Meta-analytic connectivity modeling is a connectivity analysis method based on regions of interest (ROIs) which showed that meta-analyses could be used to discover brain network connectivity. In this paper, we propose a new meta-analysis method that can be used to find network connectivity models based on the Apriori algorithm, which has the potential to derive brain network connectivity models from activation information in the literature, without requiring ROIs. This method first extracts activation information from experimental studies that use cognitive tasks of the same category, and then maps the activation information to corresponding brain areas by using the automatic anatomical label atlas, after which the activation rate of these brain areas is calculated. Finally, using these brain areas, a potential brain network connectivity model is calculated based on the Apriori algorithm. The present study used this method to conduct a mining analysis on the citations in a language review article by Price (Neuroimage 62(2):816-847, 2012). The results showed that the obtained network connectivity model was consistent with that reported by Price. The proposed method is helpful to find brain network connectivity by mining the co-activation relationships among brain regions. Furthermore, results of the co-activation relationship analysis can be used as a priori knowledge for the corresponding dynamic causal modeling analysis, possibly achieving a significant dimension-reducing effect, thus increasing the efficiency of the dynamic causal modeling analysis.

Initial constructs for patient-centered outcome measures to evaluate brain-computer interfaces

PubMed Central

Andresen, Elena M.; Fried-Oken, Melanie; Peters, Betts; Patrick, Donald L.

2016-01-01

Purpose The authors describe preliminary work toward the creation of patient-centered outcome (PCO) measures to evaluate brain-computer interface (BCI) as an assistive technology for individuals with severe speech and physical impairments (SSPI). Method In Phase 1, 591 items from 15 existing measures were mapped to the International Classification of Functioning, Disability and Health (ICF). In Phase 2, qualitative interviews were conducted with eight people with SSPI and seven caregivers. Resulting text data were coded in an iterative analysis. Results Most items (79%) mapped to the ICF environmental domain; over half (53%) mapped to more than one domain. The ICF framework was well suited for mapping items related to body functions and structures, but less so for items in other areas, including personal factors. Two constructs emerged from qualitative data: Quality of Life (QOL) and Assistive Technology. Component domains and themes were identified for each. Conclusions Preliminary constructs, domains, and themes were generated for future PCO measures relevant to BCI. Existing instruments are sufficient for initial items but do not adequately match the values of people with SSPI and their caregivers. Field methods for interviewing people with SSPI were successful, and support the inclusion of these individuals in PCO research. PMID:25806719

Spatio-spectral classification of hyperspectral images for brain cancer detection during surgical operations.

PubMed

Fabelo, Himar; Ortega, Samuel; Ravi, Daniele; Kiran, B Ravi; Sosa, Coralia; Bulters, Diederik; Callicó, Gustavo M; Bulstrode, Harry; Szolna, Adam; Piñeiro, Juan F; Kabwama, Silvester; Madroñal, Daniel; Lazcano, Raquel; J-O'Shanahan, Aruma; Bisshopp, Sara; Hernández, María; Báez, Abelardo; Yang, Guang-Zhong; Stanciulescu, Bogdan; Salvador, Rubén; Juárez, Eduardo; Sarmiento, Roberto

2018-01-01

Surgery for brain cancer is a major problem in neurosurgery. The diffuse infiltration into the surrounding normal brain by these tumors makes their accurate identification by the naked eye difficult. Since surgery is the common treatment for brain cancer, an accurate radical resection of the tumor leads to improved survival rates for patients. However, the identification of the tumor boundaries during surgery is challenging. Hyperspectral imaging is a non-contact, non-ionizing and non-invasive technique suitable for medical diagnosis. This study presents the development of a novel classification method taking into account the spatial and spectral characteristics of the hyperspectral images to help neurosurgeons to accurately determine the tumor boundaries in surgical-time during the resection, avoiding excessive excision of normal tissue or unintentionally leaving residual tumor. The algorithm proposed in this study to approach an efficient solution consists of a hybrid framework that combines both supervised and unsupervised machine learning methods. Firstly, a supervised pixel-wise classification using a Support Vector Machine classifier is performed. The generated classification map is spatially homogenized using a one-band representation of the HS cube, employing the Fixed Reference t-Stochastic Neighbors Embedding dimensional reduction algorithm, and performing a K-Nearest Neighbors filtering. The information generated by the supervised stage is combined with a segmentation map obtained via unsupervised clustering employing a Hierarchical K-Means algorithm. The fusion is performed using a majority voting approach that associates each cluster with a certain class. To evaluate the proposed approach, five hyperspectral images of surface of the brain affected by glioblastoma tumor in vivo from five different patients have been used. The final classification maps obtained have been analyzed and validated by specialists. These preliminary results are promising, obtaining an accurate delineation of the tumor area.

Spatio-spectral classification of hyperspectral images for brain cancer detection during surgical operations

PubMed Central

Kabwama, Silvester; Madroñal, Daniel; Lazcano, Raquel; J-O’Shanahan, Aruma; Bisshopp, Sara; Hernández, María; Báez, Abelardo; Yang, Guang-Zhong; Stanciulescu, Bogdan; Salvador, Rubén; Juárez, Eduardo; Sarmiento, Roberto

2018-01-01

Surgery for brain cancer is a major problem in neurosurgery. The diffuse infiltration into the surrounding normal brain by these tumors makes their accurate identification by the naked eye difficult. Since surgery is the common treatment for brain cancer, an accurate radical resection of the tumor leads to improved survival rates for patients. However, the identification of the tumor boundaries during surgery is challenging. Hyperspectral imaging is a non-contact, non-ionizing and non-invasive technique suitable for medical diagnosis. This study presents the development of a novel classification method taking into account the spatial and spectral characteristics of the hyperspectral images to help neurosurgeons to accurately determine the tumor boundaries in surgical-time during the resection, avoiding excessive excision of normal tissue or unintentionally leaving residual tumor. The algorithm proposed in this study to approach an efficient solution consists of a hybrid framework that combines both supervised and unsupervised machine learning methods. Firstly, a supervised pixel-wise classification using a Support Vector Machine classifier is performed. The generated classification map is spatially homogenized using a one-band representation of the HS cube, employing the Fixed Reference t-Stochastic Neighbors Embedding dimensional reduction algorithm, and performing a K-Nearest Neighbors filtering. The information generated by the supervised stage is combined with a segmentation map obtained via unsupervised clustering employing a Hierarchical K-Means algorithm. The fusion is performed using a majority voting approach that associates each cluster with a certain class. To evaluate the proposed approach, five hyperspectral images of surface of the brain affected by glioblastoma tumor in vivo from five different patients have been used. The final classification maps obtained have been analyzed and validated by specialists. These preliminary results are promising, obtaining an accurate delineation of the tumor area. PMID:29554126

Direct and accelerated parameter mapping using the unscented Kalman filter.

PubMed

Zhao, Li; Feng, Xue; Meyer, Craig H

2016-05-01

To accelerate parameter mapping using a new paradigm that combines image reconstruction and model regression as a parameter state-tracking problem. In T2 mapping, the T2 map is first encoded in parameter space by multi-TE measurements and then encoded by Fourier transformation with readout/phase encoding gradients. Using a state transition function and a measurement function, the unscented Kalman filter can describe T2 mapping as a dynamic system and directly estimate the T2 map from the k-space data. The proposed method was validated with a numerical brain phantom and volunteer experiments with a multiple-contrast spin echo sequence. Its performance was compared with a conjugate-gradient nonlinear inversion method at undersampling factors of 2 to 8. An accelerated pulse sequence was developed based on this method to achieve prospective undersampling. Compared with the nonlinear inversion reconstruction, the proposed method had higher precision, improved structural similarity and reduced normalized root mean squared error, with acceleration factors up to 8 in numerical phantom and volunteer studies. This work describes a new perspective on parameter mapping by state tracking. The unscented Kalman filter provides a highly accelerated and efficient paradigm for T2 mapping. © 2015 Wiley Periodicals, Inc.

Technical Aspects of Awake Craniotomy with Mapping for Brain Tumors in a Limited Resource Setting.

PubMed

Leal, Rafael Teixeira Magalhaes; Barcellos, Bruno Mendonça; Landeiro, Jose Alberto

2018-05-01

Brain tumor surgery near or within eloquent regions is increasingly common and is associated with a high risk of neurologic injury. Awake craniotomy with mapping has been shown to be a valid method to preserve neurologic function and increase the extent of resection. However, the technique used varies greatly among centers. Most count on professionals such as neuropsychologists, speech therapists, neurophysiologists, or neurologists to help in intraoperative patient evaluation. We describe our technique with the sole participation of neurosurgeons and anesthesiologists. A retrospective review of 19 patients who underwent awake craniotomies for brain tumors between January 2013 and February 2017 at a tertiary university hospital was performed. We sought to identify and describe the most critical stages involved in this surgery as well as show the complications associated with our technique. Preoperative preparation, positioning, anesthesia, brain mapping, resection, and management of seizures and pain were stages deemed relevant to the accomplishment of an awake craniotomy. Sixteen percent of the patients developed new postoperative deficit. Seizures occurred in 24%. None led to awake craniotomy failure. We provide a thorough description of the technique used in awake craniotomies with mapping used in our institution, where the intraoperative patient evaluation is carried out solely by neurosurgeons and anesthesiologists. The absence of other specialized personnel and equipment does not necessarily preclude successful mapping during awake craniotomy. We hope to provide helpful information for those who wish to offer function-guided tumor resection in their own centers. Copyright © 2018 Elsevier Inc. All rights reserved.

Linking brain, mind and behavior.

PubMed

Makeig, Scott; Gramann, Klaus; Jung, Tzyy-Ping; Sejnowski, Terrence J; Poizner, Howard

2009-08-01

Cortical brain areas and dynamics evolved to organize motor behavior in our three-dimensional environment also support more general human cognitive processes. Yet traditional brain imaging paradigms typically allow and record only minimal participant behavior, then reduce the recorded data to single map features of averaged responses. To more fully investigate the complex links between distributed brain dynamics and motivated natural behavior, we propose the development of wearable mobile brain/body imaging (MoBI) systems that continuously capture the wearer's high-density electrical brain and muscle signals, three-dimensional body movements, audiovisual scene and point of regard, plus new data-driven analysis methods to model their interrelationships. The new imaging modality should allow new insights into how spatially distributed brain dynamics support natural human cognition and agency.

Connectome analysis for pre-operative brain mapping in neurosurgery

PubMed Central

Hart, Michael G.; Price, Stephen J.; Suckling, John

2016-01-01

Abstract Object: Brain mapping has entered a new era focusing on complex network connectivity. Central to this is the search for the connectome or the brains ‘wiring diagram’. Graph theory analysis of the connectome allows understanding of the importance of regions to network function, and the consequences of their impairment or excision. Our goal was to apply connectome analysis in patients with brain tumours to characterise overall network topology and individual patterns of connectivity alterations. Methods: Resting-state functional MRI data were acquired using multi-echo, echo planar imaging pre-operatively from five participants each with a right temporal–parietal–occipital glioblastoma. Complex networks analysis was initiated by parcellating the brain into anatomically regions amongst which connections were identified by retaining the most significant correlations between the respective wavelet decomposed time-series. Results: Key characteristics of complex networks described in healthy controls were preserved in these patients, including ubiquitous small world organization. An exponentially truncated power law fit to the degree distribution predicted findings of general network robustness to injury but with a core of hubs exhibiting disproportionate vulnerability. Tumours produced a consistent reduction in local and long-range connectivity with distinct patterns of connection loss depending on lesion location. Conclusions: Connectome analysis is a feasible and novel approach to brain mapping in individual patients with brain tumours. Applications to pre-surgical planning include identifying regions critical to network function that should be preserved and visualising connections at risk from tumour resection. In the future one could use such data to model functional plasticity and recovery of cognitive deficits. PMID:27447756

Post traumatic brain perfusion SPECT analysis using reconstructed ROI maps of radioactive microsphere derived cerebral blood flow and statistical parametric mapping

PubMed Central

McGoron, Anthony J; Capille, Michael; Georgiou, Michael F; Sanchez, Pablo; Solano, Juan; Gonzalez-Brito, Manuel; Kuluz, John W

2008-01-01

Background Assessment of cerebral blood flow (CBF) by SPECT could be important in the management of patients with severe traumatic brain injury (TBI) because changes in regional CBF can affect outcome by promoting edema formation and intracranial pressure elevation (with cerebral hyperemia), or by causing secondary ischemic injury including post-traumatic stroke. The purpose of this study was to establish an improved method for evaluating regional CBF changes after TBI in piglets. Methods The focal effects of moderate traumatic brain injury (TBI) on cerebral blood flow (CBF) by SPECT cerebral blood perfusion (CBP) imaging in an animal model were investigated by parallelized statistical techniques. Regional CBF was measured by radioactive microspheres and by SPECT 2 hours after injury in sham-operated piglets versus those receiving severe TBI by fluid-percussion injury to the left parietal lobe. Qualitative SPECT CBP accuracy was assessed against reference radioactive microsphere regional CBF measurements by map reconstruction, registration and smoothing. Cerebral hypoperfusion in the test group was identified at the voxel level using statistical parametric mapping (SPM). Results A significant area of hypoperfusion (P < 0.01) was found as a response to the TBI. Statistical mapping of the reference microsphere CBF data confirms a focal decrease found with SPECT and SPM. Conclusion The suitability of SPM for application to the experimental model and ability to provide insight into CBF changes in response to traumatic injury was validated by the SPECT SPM result of a decrease in CBP at the left parietal region injury area of the test group. Further study and correlation of this characteristic lesion with long-term outcomes and auxiliary diagnostic modalities is critical to developing more effective critical care treatment guidelines and automated medical imaging processing techniques. PMID:18312639

Clinical feasibility of using mean apparent propagator (MAP) MRI to characterize brain tissue microstructure.

PubMed

Avram, Alexandru V; Sarlls, Joelle E; Barnett, Alan S; Özarslan, Evren; Thomas, Cibu; Irfanoglu, M Okan; Hutchinson, Elizabeth; Pierpaoli, Carlo; Basser, Peter J

2016-02-15

Diffusion tensor imaging (DTI) is the most widely used method for characterizing noninvasively structural and architectural features of brain tissues. However, the assumption of a Gaussian spin displacement distribution intrinsic to DTI weakens its ability to describe intricate tissue microanatomy. Consequently, the biological interpretation of microstructural parameters, such as fractional anisotropy or mean diffusivity, is often equivocal. We evaluate the clinical feasibility of assessing brain tissue microstructure with mean apparent propagator (MAP) MRI, a powerful analytical framework that efficiently measures the probability density function (PDF) of spin displacements and quantifies useful metrics of this PDF indicative of diffusion in complex microstructure (e.g., restrictions, multiple compartments). Rotation invariant and scalar parameters computed from the MAP show consistent variation across neuroanatomical brain regions and increased ability to differentiate tissues with distinct structural and architectural features compared with DTI-derived parameters. The return-to-origin probability (RTOP) appears to reflect cellularity and restrictions better than MD, while the non-Gaussianity (NG) measures diffusion heterogeneity by comprehensively quantifying the deviation between the spin displacement PDF and its Gaussian approximation. Both RTOP and NG can be decomposed in the local anatomical frame for reference determined by the orientation of the diffusion tensor and reveal additional information complementary to DTI. The propagator anisotropy (PA) shows high tissue contrast even in deep brain nuclei and cortical gray matter and is more uniform in white matter than the FA, which drops significantly in regions containing crossing fibers. Orientational profiles of the propagator computed analytically from the MAP MRI series coefficients allow separation of different fiber populations in regions of crossing white matter pathways, which in turn improves our ability to perform whole-brain fiber tractography. Reconstructions from subsampled data sets suggest that MAP MRI parameters can be computed from a relatively small number of DWIs acquired with high b-value and good signal-to-noise ratio in clinically achievable scan durations of less than 10min. The neuroanatomical consistency across healthy subjects and reproducibility in test-retest experiments of MAP MRI microstructural parameters further substantiate the robustness and clinical feasibility of this technique. The MAP MRI metrics could potentially provide more sensitive clinical biomarkers with increased pathophysiological specificity compared to microstructural measures derived using conventional diffusion MRI techniques. Published by Elsevier Inc.

Exploring sex differences in the adult zebra finch brain: In vivo diffusion tensor imaging and ex vivo super-resolution track density imaging.

PubMed

Hamaide, Julie; De Groof, Geert; Van Steenkiste, Gwendolyn; Jeurissen, Ben; Van Audekerke, Johan; Naeyaert, Maarten; Van Ruijssevelt, Lisbeth; Cornil, Charlotte; Sijbers, Jan; Verhoye, Marleen; Van der Linden, Annemie

2017-02-01

Zebra finches are an excellent model to study the process of vocal learning, a complex socially-learned tool of communication that forms the basis of spoken human language. So far, structural investigation of the zebra finch brain has been performed ex vivo using invasive methods such as histology. These methods are highly specific, however, they strongly interfere with performing whole-brain analyses and exclude longitudinal studies aimed at establishing causal correlations between neuroplastic events and specific behavioral performances. Therefore, the aim of the current study was to implement an in vivo Diffusion Tensor Imaging (DTI) protocol sensitive enough to detect structural sex differences in the adult zebra finch brain. Voxel-wise comparison of male and female DTI parameter maps shows clear differences in several components of the song control system (i.e. Area X surroundings, the high vocal center (HVC) and the lateral magnocellular nucleus of the anterior nidopallium (LMAN)), which corroborate previous findings and are in line with the clear behavioral difference as only males sing. Furthermore, to obtain additional insights into the 3-dimensional organization of the zebra finch brain and clarify findings obtained by the in vivo study, ex vivo DTI data of the male and female brain were acquired as well, using a recently established super-resolution reconstruction (SRR) imaging strategy. Interestingly, the SRR-DTI approach led to a marked reduction in acquisition time without interfering with the (spatial and angular) resolution and SNR which enabled to acquire a data set characterized by a 78μm isotropic resolution including 90 diffusion gradient directions within 44h of scanning time. Based on the reconstructed SRR-DTI maps, whole brain probabilistic Track Density Imaging (TDI) was performed for the purpose of super resolved track density imaging, further pushing the resolution up to 40μm isotropic. The DTI and TDI maps realized atlas-quality anatomical maps that enable a clear delineation of most components of the song control and auditory systems. In conclusion, this study paves the way for longitudinal in vivo and high-resolution ex vivo experiments aimed at disentangling neuroplastic events that characterize the critical period for vocal learning in zebra finch ontogeny. Copyright © 2016 Elsevier Inc. All rights reserved.

FreeSurfer-initiated fully-automated subcortical brain segmentation in MRI using Large Deformation Diffeomorphic Metric Mapping.

PubMed

Khan, Ali R; Wang, Lei; Beg, Mirza Faisal

2008-07-01

Fully-automated brain segmentation methods have not been widely adopted for clinical use because of issues related to reliability, accuracy, and limitations of delineation protocol. By combining the probabilistic-based FreeSurfer (FS) method with the Large Deformation Diffeomorphic Metric Mapping (LDDMM)-based label-propagation method, we are able to increase reliability and accuracy, and allow for flexibility in template choice. Our method uses the automated FreeSurfer subcortical labeling to provide a coarse-to-fine introduction of information in the LDDMM template-based segmentation resulting in a fully-automated subcortical brain segmentation method (FS+LDDMM). One major advantage of the FS+LDDMM-based approach is that the automatically generated segmentations generated are inherently smooth, thus subsequent steps in shape analysis can directly follow without manual post-processing or loss of detail. We have evaluated our new FS+LDDMM method on several databases containing a total of 50 subjects with different pathologies, scan sequences and manual delineation protocols for labeling the basal ganglia, thalamus, and hippocampus. In healthy controls we report Dice overlap measures of 0.81, 0.83, 0.74, 0.86 and 0.75 for the right caudate nucleus, putamen, pallidum, thalamus and hippocampus respectively. We also find statistically significant improvement of accuracy in FS+LDDMM over FreeSurfer for the caudate nucleus and putamen of Huntington's disease and Tourette's syndrome subjects, and the right hippocampus of Schizophrenia subjects.

Clustering Multiple Sclerosis Subgroups with Multifractal Methods and Self-Organizing Map Algorithm

NASA Astrophysics Data System (ADS)

Karaca, Yeliz; Cattani, Carlo

Magnetic resonance imaging (MRI) is the most sensitive method to detect chronic nervous system diseases such as multiple sclerosis (MS). In this paper, Brownian motion Hölder regularity functions (polynomial, periodic (sine), exponential) for 2D image, such as multifractal methods were applied to MR brain images, aiming to easily identify distressed regions, in MS patients. With these regions, we have proposed an MS classification based on the multifractal method by using the Self-Organizing Map (SOM) algorithm. Thus, we obtained a cluster analysis by identifying pixels from distressed regions in MR images through multifractal methods and by diagnosing subgroups of MS patients through artificial neural networks.

Connectome imaging for mapping human brain pathways

PubMed Central

Shi, Y; Toga, A W

2017-01-01

With the fast advance of connectome imaging techniques, we have the opportunity of mapping the human brain pathways in vivo at unprecedented resolution. In this article we review the current developments of diffusion magnetic resonance imaging (MRI) for the reconstruction of anatomical pathways in connectome studies. We first introduce the background of diffusion MRI with an emphasis on the technical advances and challenges in state-of-the-art multi-shell acquisition schemes used in the Human Connectome Project. Characterization of the microstructural environment in the human brain is discussed from the tensor model to the general fiber orientation distribution (FOD) models that can resolve crossing fibers in each voxel of the image. Using FOD-based tractography, we describe novel methods for fiber bundle reconstruction and graph-based connectivity analysis. Building upon these novel developments, there have already been successful applications of connectome imaging techniques in reconstructing challenging brain pathways. Examples including retinofugal and brainstem pathways will be reviewed. Finally, we discuss future directions in connectome imaging and its interaction with other aspects of brain imaging research. PMID:28461700

Three-Dimensional Computer Graphics Brain-Mapping Project.

DTIC Science & Technology

1987-03-15

NEUROQUANT . This package was directed towards quantitative microneuroanatomic data acquisition and analysis. Using this interface, image frames captured...populations of brains. This would have been aprohibitive task if done manually with a densitometer and film, due to user error and bias. NEUROQUANT functioned...of cells were of interest. NEUROQUANT is presently being implemented with a more fully automatic method of localizing the cell bodies directly

A Non-Parametric Approach for the Activation Detection of Block Design fMRI Simulated Data Using Self-Organizing Maps and Support Vector Machine.

PubMed

Bahrami, Sheyda; Shamsi, Mousa

2017-01-01

Functional magnetic resonance imaging (fMRI) is a popular method to probe the functional organization of the brain using hemodynamic responses. In this method, volume images of the entire brain are obtained with a very good spatial resolution and low temporal resolution. However, they always suffer from high dimensionality in the face of classification algorithms. In this work, we combine a support vector machine (SVM) with a self-organizing map (SOM) for having a feature-based classification by using SVM. Then, a linear kernel SVM is used for detecting the active areas. Here, we use SOM for feature extracting and labeling the datasets. SOM has two major advances: (i) it reduces dimension of data sets for having less computational complexity and (ii) it is useful for identifying brain regions with small onset differences in hemodynamic responses. Our non-parametric model is compared with parametric and non-parametric methods. We use simulated fMRI data sets and block design inputs in this paper and consider the contrast to noise ratio (CNR) value equal to 0.6 for simulated datasets. fMRI simulated dataset has contrast 1-4% in active areas. The accuracy of our proposed method is 93.63% and the error rate is 6.37%.

Mesoscale brain explorer, a flexible python-based image analysis and visualization tool.

PubMed

Haupt, Dirk; Vanni, Matthieu P; Bolanos, Federico; Mitelut, Catalin; LeDue, Jeffrey M; Murphy, Tim H

2017-07-01

Imaging of mesoscale brain activity is used to map interactions between brain regions. This work has benefited from the pioneering studies of Grinvald et al., who employed optical methods to image brain function by exploiting the properties of intrinsic optical signals and small molecule voltage-sensitive dyes. Mesoscale interareal brain imaging techniques have been advanced by cell targeted and selective recombinant indicators of neuronal activity. Spontaneous resting state activity is often collected during mesoscale imaging to provide the basis for mapping of connectivity relationships using correlation. However, the information content of mesoscale datasets is vast and is only superficially presented in manuscripts given the need to constrain measurements to a fixed set of frequencies, regions of interest, and other parameters. We describe a new open source tool written in python, termed mesoscale brain explorer (MBE), which provides an interface to process and explore these large datasets. The platform supports automated image processing pipelines with the ability to assess multiple trials and combine data from different animals. The tool provides functions for temporal filtering, averaging, and visualization of functional connectivity relations using time-dependent correlation. Here, we describe the tool and show applications, where previously published datasets were reanalyzed using MBE.

Areas activated during naturalistic reading comprehension overlap topological visual, auditory, and somatotomotor maps

PubMed Central

2016-01-01

Abstract Cortical mapping techniques using fMRI have been instrumental in identifying the boundaries of topological (neighbor‐preserving) maps in early sensory areas. The presence of topological maps beyond early sensory areas raises the possibility that they might play a significant role in other cognitive systems, and that topological mapping might help to delineate areas involved in higher cognitive processes. In this study, we combine surface‐based visual, auditory, and somatomotor mapping methods with a naturalistic reading comprehension task in the same group of subjects to provide a qualitative and quantitative assessment of the cortical overlap between sensory‐motor maps in all major sensory modalities, and reading processing regions. Our results suggest that cortical activation during naturalistic reading comprehension overlaps more extensively with topological sensory‐motor maps than has been heretofore appreciated. Reading activation in regions adjacent to occipital lobe and inferior parietal lobe almost completely overlaps visual maps, whereas a significant portion of frontal activation for reading in dorsolateral and ventral prefrontal cortex overlaps both visual and auditory maps. Even classical language regions in superior temporal cortex are partially overlapped by topological visual and auditory maps. By contrast, the main overlap with somatomotor maps is restricted to a small region on the anterior bank of the central sulcus near the border between the face and hand representations of M‐I. Hum Brain Mapp 37:2784–2810, 2016. © 2016 The Authors Human Brain Mapping Published by Wiley Periodicals, Inc. PMID:27061771

Directionality analysis on functional magnetic resonance imaging during motor task using Granger causality.

PubMed

Anwar, A R; Muthalib, M; Perrey, S; Galka, A; Granert, O; Wolff, S; Deuschl, G; Raethjen, J; Heute, U; Muthuraman, M

2012-01-01

Directionality analysis of signals originating from different parts of brain during motor tasks has gained a lot of interest. Since brain activity can be recorded over time, methods of time series analysis can be applied to medical time series as well. Granger Causality is a method to find a causal relationship between time series. Such causality can be referred to as a directional connection and is not necessarily bidirectional. The aim of this study is to differentiate between different motor tasks on the basis of activation maps and also to understand the nature of connections present between different parts of the brain. In this paper, three different motor tasks (finger tapping, simple finger sequencing, and complex finger sequencing) are analyzed. Time series for each task were extracted from functional magnetic resonance imaging (fMRI) data, which have a very good spatial resolution and can look into the sub-cortical regions of the brain. Activation maps based on fMRI images show that, in case of complex finger sequencing, most parts of the brain are active, unlike finger tapping during which only limited regions show activity. Directionality analysis on time series extracted from contralateral motor cortex (CMC), supplementary motor area (SMA), and cerebellum (CER) show bidirectional connections between these parts of the brain. In case of simple finger sequencing and complex finger sequencing, the strongest connections originate from SMA and CMC, while connections originating from CER in either direction are the weakest ones in magnitude during all paradigms.

Multichannel optical mapping: investigation of depth information

NASA Astrophysics Data System (ADS)

Sase, Ichiro; Eda, Hideo; Seiyama, Akitoshi; Tanabe, Hiroki C.; Takatsuki, Akira; Yanagida, Toshio

2001-06-01

Near infrared (NIR) light has become a powerful tool for non-invasive imaging of human brain activity. Many systems have been developed to capture the changes in regional brain blood flow and hemoglobin oxygenation, which occur in the human cortex in response to neural activity. We have developed a multi-channel reflectance imaging system, which can be used as a `mapping device' and also as a `multi-channel spectrophotometer'. In the present study, we visualized changes in the hemodynamics of the human occipital region in multiple ways. (1) Stimulating left and right primary visual cortex independently by showing sector shaped checkerboards sequentially over the contralateral visual field, resulted in corresponding changes in the hemodynamics observed by `mapping' measurement. (2) Simultaneous measurement of functional-MRI and NIR (changes in total hemoglobin) during visual stimulation showed good spatial and temporal correlation with each other. (3) Placing multiple channels densely over the occipital region demonstrated spatial patterns more precisely, and depth information was also acquired by placing each pair of illumination and detection fibers at various distances. These results indicate that optical method can provide data for 3D analysis of human brain functions.

Targeted, noninvasive blockade of cortical neuronal activity

NASA Astrophysics Data System (ADS)

McDannold, Nathan; Zhang, Yongzhi; Power, Chanikarn; Arvanitis, Costas D.; Vykhodtseva, Natalia; Livingstone, Margaret

2015-11-01

Here we describe a novel method to noninvasively modulate targeted brain areas through the temporary disruption of the blood-brain barrier (BBB) via focused ultrasound, enabling focal delivery of a neuroactive substance. Ultrasound was used to locally disrupt the BBB in rat somatosensory cortex, and intravenous administration of GABA then produced a dose-dependent suppression of somatosensory-evoked potentials in response to electrical stimulation of the sciatic nerve. No suppression was observed 1-5 days afterwards or in control animals where the BBB was not disrupted. This method has several advantages over existing techniques: it is noninvasive; it is repeatable via additional GABA injections; multiple brain regions can be affected simultaneously; suppression magnitude can be titrated by GABA dose; and the method can be used with freely behaving subjects. We anticipate that the application of neuroactive substances in this way will be a useful tool for noninvasively mapping brain function, and potentially for surgical planning or novel therapies.

Quantitative susceptibility mapping of human brain at 3T: a multisite reproducibility study.

PubMed

Lin, P-Y; Chao, T-C; Wu, M-L

2015-03-01

Quantitative susceptibility mapping of the human brain has demonstrated strong potential in examining iron deposition, which may help in investigating possible brain pathology. This study assesses the reproducibility of quantitative susceptibility mapping across different imaging sites. In this study, the susceptibility values of 5 regions of interest in the human brain were measured on 9 healthy subjects following calibration by using phantom experiments. Each of the subjects was imaged 5 times on 1 scanner with the same procedure repeated on 3 different 3T systems so that both within-site and cross-site quantitative susceptibility mapping precision levels could be assessed. Two quantitative susceptibility mapping algorithms, similar in principle, one by using iterative regularization (iterative quantitative susceptibility mapping) and the other with analytic optimal solutions (deterministic quantitative susceptibility mapping), were implemented, and their performances were compared. Results show that while deterministic quantitative susceptibility mapping had nearly 700 times faster computation speed, residual streaking artifacts seem to be more prominent compared with iterative quantitative susceptibility mapping. With quantitative susceptibility mapping, the putamen, globus pallidus, and caudate nucleus showed smaller imprecision on the order of 0.005 ppm, whereas the red nucleus and substantia nigra, closer to the skull base, had a somewhat larger imprecision of approximately 0.01 ppm. Cross-site errors were not significantly larger than within-site errors. Possible sources of estimation errors are discussed. The reproducibility of quantitative susceptibility mapping in the human brain in vivo is regionally dependent, and the precision levels achieved with quantitative susceptibility mapping should allow longitudinal and multisite studies such as aging-related changes in brain tissue magnetic susceptibility. © 2015 by American Journal of Neuroradiology.

Multiband tangent space mapping and feature selection for classification of EEG during motor imagery.

PubMed

Islam, Md Rabiul; Tanaka, Toshihisa; Molla, Md Khademul Islam

2018-05-08

When designing multiclass motor imagery-based brain-computer interface (MI-BCI), a so-called tangent space mapping (TSM) method utilizing the geometric structure of covariance matrices is an effective technique. This paper aims to introduce a method using TSM for finding accurate operational frequency bands related brain activities associated with MI tasks. A multichannel electroencephalogram (EEG) signal is decomposed into multiple subbands, and tangent features are then estimated on each subband. A mutual information analysis-based effective algorithm is implemented to select subbands containing features capable of improving motor imagery classification accuracy. Thus obtained features of selected subbands are combined to get feature space. A principal component analysis-based approach is employed to reduce the features dimension and then the classification is accomplished by a support vector machine (SVM). Offline analysis demonstrates the proposed multiband tangent space mapping with subband selection (MTSMS) approach outperforms state-of-the-art methods. It acheives the highest average classification accuracy for all datasets (BCI competition dataset 2a, IIIa, IIIb, and dataset JK-HH1). The increased classification accuracy of MI tasks with the proposed MTSMS approach can yield effective implementation of BCI. The mutual information-based subband selection method is implemented to tune operation frequency bands to represent actual motor imagery tasks.

Application and Evaluation of Independent Component Analysis Methods to Generalized Seizure Disorder Activities Exhibited in the Brain.

PubMed

George, S Thomas; Balakrishnan, R; Johnson, J Stanly; Jayakumar, J

2017-07-01

EEG records the spontaneous electrical activity of the brain using multiple electrodes placed on the scalp, and it provides a wealth of information related to the functions of brain. Nevertheless, the signals from the electrodes cannot be directly applied to a diagnostic tool like brain mapping as they undergo a "mixing" process because of the volume conduction effect in the scalp. A pervasive problem in neuroscience is determining which regions of the brain are active, given voltage measurements at the scalp. Because of which, there has been a surge of interest among the biosignal processing community to investigate the process of mixing and unmixing to identify the underlying active sources. According to the assumptions of independent component analysis (ICA) algorithms, the resultant mixture obtained from the scalp can be closely approximated by a linear combination of the "actual" EEG signals emanating from the underlying sources of electrical activity in the brain. As a consequence, using these well-known ICA techniques in preprocessing of the EEG signals prior to clinical applications could result in development of diagnostic tool like quantitative EEG which in turn can assist the neurologists to gain noninvasive access to patient-specific cortical activity, which helps in treating neuropathologies like seizure disorders. The popular and proven ICA schemes mentioned in various literature and applications were selected (which includes Infomax, JADE, and SOBI) and applied on generalized seizure disorder samples using EEGLAB toolbox in MATLAB environment to see their usefulness in source separations; and they were validated by the expert neurologist for clinical relevance in terms of pathologies on brain functionalities. The performance of Infomax method was found to be superior when compared with other ICA schemes applied on EEG and it has been established based on the validations carried by expert neurologist for generalized seizure and its clinical correlation. The results are encouraging for furthering the studies in the direction of developing useful brain mapping tools using ICA methods.

Physical principles for scalable neural recording

PubMed Central

Zamft, Bradley M.; Maguire, Yael G.; Shapiro, Mikhail G.; Cybulski, Thaddeus R.; Glaser, Joshua I.; Amodei, Dario; Stranges, P. Benjamin; Kalhor, Reza; Dalrymple, David A.; Seo, Dongjin; Alon, Elad; Maharbiz, Michel M.; Carmena, Jose M.; Rabaey, Jan M.; Boyden, Edward S.; Church, George M.; Kording, Konrad P.

2013-01-01

Simultaneously measuring the activities of all neurons in a mammalian brain at millisecond resolution is a challenge beyond the limits of existing techniques in neuroscience. Entirely new approaches may be required, motivating an analysis of the fundamental physical constraints on the problem. We outline the physical principles governing brain activity mapping using optical, electrical, magnetic resonance, and molecular modalities of neural recording. Focusing on the mouse brain, we analyze the scalability of each method, concentrating on the limitations imposed by spatiotemporal resolution, energy dissipation, and volume displacement. Based on this analysis, all existing approaches require orders of magnitude improvement in key parameters. Electrical recording is limited by the low multiplexing capacity of electrodes and their lack of intrinsic spatial resolution, optical methods are constrained by the scattering of visible light in brain tissue, magnetic resonance is hindered by the diffusion and relaxation timescales of water protons, and the implementation of molecular recording is complicated by the stochastic kinetics of enzymes. Understanding the physical limits of brain activity mapping may provide insight into opportunities for novel solutions. For example, unconventional methods for delivering electrodes may enable unprecedented numbers of recording sites, embedded optical devices could allow optical detectors to be placed within a few scattering lengths of the measured neurons, and new classes of molecularly engineered sensors might obviate cumbersome hardware architectures. We also study the physics of powering and communicating with microscale devices embedded in brain tissue and find that, while radio-frequency electromagnetic data transmission suffers from a severe power–bandwidth tradeoff, communication via infrared light or ultrasound may allow high data rates due to the possibility of spatial multiplexing. The use of embedded local recording and wireless data transmission would only be viable, however, given major improvements to the power efficiency of microelectronic devices. PMID:24187539

Joint Pairing and Structured Mapping of Convolutional Brain Morphological Multiplexes for Early Dementia Diagnosis.

PubMed

Lisowska, Anna; Rekik, Islem

2018-06-21

Diagnosis of brain dementia, particularly early mild cognitive impairment (eMCI), is critical for early intervention to prevent the onset of Alzheimer's Disease (AD), where cognitive decline is severe and irreversible. There is a large body of machine-learning based research investigating how dementia alters brain connectivity, mainly using structural (derived from diffusion MRI) and functional (derived from resting-state functional MRI) brain connectomic data. However, how early dementia affects cortical brain connections in morphology remains largely unexplored. To fill this gap, we propose a joint morphological brain multiplexes pairing and mapping strategy for early MCI detection, where a brain multiplex not only encodes the similarity in morphology between pairs of brain regions, but also a pair of brain morphological networks. Experimental results confirm that the proposed framework outperforms in classification accuracy several state-of-the-art methods. More importantly, we unprecedentedly identified most discriminative brain morphological networks between eMCI and NC, which included the paired views derived from maximum principal curvature and the sulcal depth for the left hemisphere and sulcal depth and the average curvature for the right hemisphere. We also identified the most highly correlated morphological brain connections in our cohort, which included the (pericalcarine cortex, insula cortex) on the maximum principal curvature view, (entorhinal cortex, insula cortex) on the mean sulcal depth view, and (entorhinal cortex, pericalcarine cortex) on the mean average curvature view, for both hemispheres. These highly correlated morphological connections might serve as biomarkers for early MCI diagnosis.

Non-invasive Imaging based Detection and Mapping of Brain Oxidative Stress and its Correlation with Cognitive Functions

DTIC Science & Technology

2017-05-14

AFRL-AFOSR-JP-TR-2017-0052 Non-invasive Imaging based Detection and Mapping of Brain Oxidative Stress and its Correlation with Cognative Functions...invasive Imaging based Detection and Mapping of Brain Oxidative Stress and its Correlation with Cognative Functions 5a.  CONTRACT NUMBER 5b.  GRANT...SUPPLEMENTARY NOTES 14. ABSTRACT Brain stress level measurement (non-invasively) in quantitative term is very helpful to correlate with various

Non invasive Imaging based Detection and Mapping of Brain Oxidative Stress and its Correlation with Cognative Functions

DTIC Science & Technology

2017-05-14

AFRL-AFOSR-JP-TR-2017-0052 Non-invasive Imaging based Detection and Mapping of Brain Oxidative Stress and its Correlation with Cognative Functions...invasive Imaging based Detection and Mapping of Brain Oxidative Stress and its Correlation with Cognative Functions 5a.  CONTRACT NUMBER 5b.  GRANT...SUPPLEMENTARY NOTES 14. ABSTRACT Brain stress level measurement (non-invasively) in quantitative term is very helpful to correlate with various

Towards Ultra-High Resolution Fibre Tract Mapping of the Human Brain – Registration of Polarised Light Images and Reorientation of Fibre Vectors

PubMed Central

Palm, Christoph; Axer, Markus; Gräßel, David; Dammers, Jürgen; Lindemeyer, Johannes; Zilles, Karl; Pietrzyk, Uwe; Amunts, Katrin

2009-01-01

Polarised light imaging (PLI) utilises the birefringence of the myelin sheaths in order to visualise the orientation of nerve fibres in microtome sections of adult human post-mortem brains at ultra-high spatial resolution. The preparation of post-mortem brains for PLI involves fixation, freezing and cutting into 100-μm-thick sections. Hence, geometrical distortions of histological sections are inevitable and have to be removed for 3D reconstruction and subsequent fibre tracking. We here present a processing pipeline for 3D reconstruction of these sections using PLI derived multimodal images of post-mortem brains. Blockface images of the brains were obtained during cutting; they serve as reference data for alignment and elimination of distortion artefacts. In addition to the spatial image transformation, fibre orientation vectors were reoriented using the transformation fields, which consider both affine and subsequent non-linear registration. The application of this registration and reorientation approach results in a smooth fibre vector field, which reflects brain morphology. PLI combined with 3D reconstruction and fibre tracking is a powerful tool for human brain mapping. It can also serve as an independent method for evaluating in vivo fibre tractography. PMID:20461231

Dynamic patterns of cortical expansion during folding of the preterm human brain.

PubMed

Garcia, Kara E; Robinson, Emma C; Alexopoulos, Dimitrios; Dierker, Donna L; Glasser, Matthew F; Coalson, Timothy S; Ortinau, Cynthia M; Rueckert, Daniel; Taber, Larry A; Van Essen, David C; Rogers, Cynthia E; Smyser, Christopher D; Bayly, Philip V

2018-03-20

During the third trimester of human brain development, the cerebral cortex undergoes dramatic surface expansion and folding. Physical models suggest that relatively rapid growth of the cortical gray matter helps drive this folding, and structural data suggest that growth may vary in both space (by region on the cortical surface) and time. In this study, we propose a unique method to estimate local growth from sequential cortical reconstructions. Using anatomically constrained multimodal surface matching (aMSM), we obtain accurate, physically guided point correspondence between younger and older cortical reconstructions of the same individual. From each pair of surfaces, we calculate continuous, smooth maps of cortical expansion with unprecedented precision. By considering 30 preterm infants scanned two to four times during the period of rapid cortical expansion (28-38 wk postmenstrual age), we observe significant regional differences in growth across the cortical surface that are consistent with the emergence of new folds. Furthermore, these growth patterns shift over the course of development, with noninjured subjects following a highly consistent trajectory. This information provides a detailed picture of dynamic changes in cortical growth, connecting what is known about patterns of development at the microscopic (cellular) and macroscopic (folding) scales. Since our method provides specific growth maps for individual brains, we are also able to detect alterations due to injury. This fully automated surface analysis, based on tools freely available to the brain-mapping community, may also serve as a useful approach for future studies of abnormal growth due to genetic disorders, injury, or other environmental variables.

MultiMap: A Tool to Automatically Extract and Analyse Spatial Microscopic Data From Large Stacks of Confocal Microscopy Images

PubMed Central

Varando, Gherardo; Benavides-Piccione, Ruth; Muñoz, Alberto; Kastanauskaite, Asta; Bielza, Concha; Larrañaga, Pedro; DeFelipe, Javier

2018-01-01

The development of 3D visualization and reconstruction methods to analyse microscopic structures at different levels of resolutions is of great importance to define brain microorganization and connectivity. MultiMap is a new tool that allows the visualization, 3D segmentation and quantification of fluorescent structures selectively in the neuropil from large stacks of confocal microscopy images. The major contribution of this tool is the posibility to easily navigate and create regions of interest of any shape and size within a large brain area that will be automatically 3D segmented and quantified to determine the density of puncta in the neuropil. As a proof of concept, we focused on the analysis of glutamatergic and GABAergic presynaptic axon terminals in the mouse hippocampal region to demonstrate its use as a tool to provide putative excitatory and inhibitory synaptic maps. The segmentation and quantification method has been validated over expert labeled images of the mouse hippocampus and over two benchmark datasets, obtaining comparable results to the expert detections. PMID:29875639

MultiMap: A Tool to Automatically Extract and Analyse Spatial Microscopic Data From Large Stacks of Confocal Microscopy Images.

PubMed

Varando, Gherardo; Benavides-Piccione, Ruth; Muñoz, Alberto; Kastanauskaite, Asta; Bielza, Concha; Larrañaga, Pedro; DeFelipe, Javier

2018-01-01

The development of 3D visualization and reconstruction methods to analyse microscopic structures at different levels of resolutions is of great importance to define brain microorganization and connectivity. MultiMap is a new tool that allows the visualization, 3D segmentation and quantification of fluorescent structures selectively in the neuropil from large stacks of confocal microscopy images. The major contribution of this tool is the posibility to easily navigate and create regions of interest of any shape and size within a large brain area that will be automatically 3D segmented and quantified to determine the density of puncta in the neuropil. As a proof of concept, we focused on the analysis of glutamatergic and GABAergic presynaptic axon terminals in the mouse hippocampal region to demonstrate its use as a tool to provide putative excitatory and inhibitory synaptic maps. The segmentation and quantification method has been validated over expert labeled images of the mouse hippocampus and over two benchmark datasets, obtaining comparable results to the expert detections.

Mapping Language Problems in the Brain

MedlinePlus

... issue Health Capsule Mapping Language Problems in the Brain En español Send us your comments We often ... more about how language is organized in the brain, an NIH-funded research team studied people with ...

Using Brain Electrical Activity Mapping to Diagnose Learning Disabilities.

ERIC Educational Resources Information Center

Torello, Michael, W.; Duffy, Frank H.

1985-01-01

Cognitive neuroscience assumes that measurement of brain electrical activity should relate to cognition. Brain Electrical Activity Mapping (BEAM), a non-invasive technique, is used to record changes in activity from one brain area to another and is 80 to 90 percent successful in classifying subjects as dyslexic or normal. (MT)

Construction of multi-scale consistent brain networks: methods and applications.

PubMed

Ge, Bao; Tian, Yin; Hu, Xintao; Chen, Hanbo; Zhu, Dajiang; Zhang, Tuo; Han, Junwei; Guo, Lei; Liu, Tianming

2015-01-01

Mapping human brain networks provides a basis for studying brain function and dysfunction, and thus has gained significant interest in recent years. However, modeling human brain networks still faces several challenges including constructing networks at multiple spatial scales and finding common corresponding networks across individuals. As a consequence, many previous methods were designed for a single resolution or scale of brain network, though the brain networks are multi-scale in nature. To address this problem, this paper presents a novel approach to constructing multi-scale common structural brain networks from DTI data via an improved multi-scale spectral clustering applied on our recently developed and validated DICCCOLs (Dense Individualized and Common Connectivity-based Cortical Landmarks). Since the DICCCOL landmarks possess intrinsic structural correspondences across individuals and populations, we employed the multi-scale spectral clustering algorithm to group the DICCCOL landmarks and their connections into sub-networks, meanwhile preserving the intrinsically-established correspondences across multiple scales. Experimental results demonstrated that the proposed method can generate multi-scale consistent and common structural brain networks across subjects, and its reproducibility has been verified by multiple independent datasets. As an application, these multi-scale networks were used to guide the clustering of multi-scale fiber bundles and to compare the fiber integrity in schizophrenia and healthy controls. In general, our methods offer a novel and effective framework for brain network modeling and tract-based analysis of DTI data.

Assessing Diffusion in the Extra-Cellular Space of Brain Tissue by Dynamic MRI Mapping of Contrast Agent Concentrations

NASA Astrophysics Data System (ADS)

Mériaux, Sébastien; Conti, Allegra; Larrat, Benoît

2018-05-01

The characterization of extracellular space (ECS) architecture represents valuable information for the understanding of transport mechanisms occurring in brain parenchyma. ECS tortuosity reflects the hindrance imposed by cell membranes to molecular diffusion. Numerous strategies have been proposed to measure the diffusion through ECS and to estimate its tortuosity. The first method implies the perfusion for several hours of a radiotracer which effective diffusion coefficient D* is determined after post mortem processing. The most well-established techniques are real-time iontophoresis that measures the concentration of a specific ion at known distance from its release point, and integrative optical imaging that relies on acquiring microscopy images of macromolecules labelled with fluorophore. After presenting these methods, we focus on a recent Magnetic Resonance Imaging (MRI)-based technique that consists in acquiring concentration maps of a contrast agent diffusing within ECS. Thanks to MRI properties, molecular diffusion and tortuosity can be estimated in 3D for deep brain regions. To further discuss the reliability of this technique, we point out the influence of the delivery method on the estimation of D*. We compare the value of D* for a contrast agent intracerebrally injected, with its value when the agent is delivered to the brain after an ultrasound-induced blood-brain barrier (BBB) permeabilization. Several studies have already shown that tortuosity may be modified in pathological conditions. Therefore, we believe that MRI-based techniques could be useful in a clinical context for characterizing the diffusion properties of pathological ECS and thus predicting the drug biodistribution into the targeted area.

Functional imaging with low-resolution brain electromagnetic tomography (LORETA): a review.

PubMed

Pascual-Marqui, R D; Esslen, M; Kochi, K; Lehmann, D

2002-01-01

This paper reviews several recent publications that have successfully used the functional brain imaging method known as LORETA. Emphasis is placed on the electrophysiological and neuroanatomical basis of the method, on the localization properties of the method, and on the validation of the method in real experimental human data. Papers that criticize LORETA are briefly discussed. LORETA publications in the 1994-1997 period based localization inference on images of raw electric neuronal activity. In 1998, a series of papers appeared that based localization inference on the statistical parametric mapping methodology applied to high-time resolution LORETA images. Starting in 1999, quantitative neuroanatomy was added to the methodology, based on the digitized Talairach atlas provided by the Brain Imaging Centre, Montreal Neurological Institute. The combination of these methodological developments has placed LORETA at a level that compares favorably to the more classical functional imaging methods, such as PET and fMRI.

Methodological dimensions of transcranial brain stimulation with the electrical current in human.

PubMed

Rostami, Maryam; Golesorkhi, Mehrshad; Ekhtiari, Hamed

2013-01-01

Transcranial current stimulation (TCS) is a neuromodulation method in which the patient is exposed to a mild electric current (direct or alternating) at 1-2 mA, resulting in an increase or a decrease in the brain excitability. This modification in neural activities can be used as a method for functional human brain mapping with causal inferences. This method might also facilitate the treatments of many neuropsychiatric disorders based on its inexpensive, simple, safe, noninvasive, painless, semi-focal excitatory and inhibitory effects. Given this, a comparison amongst different brain stimulation modalities has been made to determine the potential advantages of the TCS method. In addition, considerable methodological details on using TCS in basic and clinical neuroscience studies in human subjects have been introduced. Technical characteristics of TCS devices and their related accessories with regard to safety concerns have also been well articulated. Finally, some TCS application opportunities have been emphasized, including its potential use in the near future.

Comparison of NREM sleep and intravenous sedation through local information processing and whole brain network to explore the mechanism of general anesthesia

PubMed Central

Pan, Chuxiong; Xue, Fushan; Xian, Junfang; Huang, Yaqi; Wang, Xiaoyi; He, Huiguang

2018-01-01

Background The mechanism of general anesthesia (GA) has been explored for hundreds of years, but unclear. Previous studies indicated a possible correlation between NREM sleep and GA. The purpose of this study is to compare them by in vivo human brain function to probe the neuromechanism of consciousness, so as to find out a clue to GA mechanism. Methods 24 healthy participants were equally assigned to sleep or propofol sedation group by sleeping ability. EEG and Ramsay Sedation Scale were applied to determine sleep stage and sedation depth respectively. Resting-state functional magnetic resonance imaging (RS-fMRI) was acquired at each status. Regional homogeneity (ReHo) and seed-based whole brain functional connectivity maps (WB-FC maps) were compared. Results During sleep, ReHo primarily weakened on frontal lobe (especially preoptic area), but strengthened on brainstem. While during sedation, ReHo changed in various brain areas, including cingulate, precuneus, thalamus and cerebellum. Cingulate, fusiform and insula were concomitance of sleep and sedation. Comparing to sleep, FCs between the cortex and subcortical centers (centralized in cerebellum) were significantly attenuated under sedation. As sedation deepening, cerebellum-based FC maps were diminished, while thalamus- and brainstem-based FC maps were increased. Conclusion There’re huge distinctions in human brain function between sleep and GA. Sleep mainly rely on brainstem and frontal lobe function, while sedation is prone to affect widespread functional network. The most significant differences exist in the precuneus and cingulate, which may play important roles in mechanisms of inducing unconciousness by anesthetics. Trial registration Institutional Review Board (IRB) ChiCTR-IOC-15007454. PMID:29486001

Construction and comparative evaluation of different activity detection methods in brain FDG-PET.

PubMed

Buchholz, Hans-Georg; Wenzel, Fabian; Gartenschläger, Martin; Thiele, Frank; Young, Stewart; Reuss, Stefan; Schreckenberger, Mathias

2015-08-18

We constructed and evaluated reference brain FDG-PET databases for usage by three software programs (Computer-aided diagnosis for dementia (CAD4D), Statistical Parametric Mapping (SPM) and NEUROSTAT), which allow a user-independent detection of dementia-related hypometabolism in patients' brain FDG-PET. Thirty-seven healthy volunteers were scanned in order to construct brain FDG reference databases, which reflect the normal, age-dependent glucose consumption in human brain, using either software. Databases were compared to each other to assess the impact of different stereotactic normalization algorithms used by either software package. In addition, performance of the new reference databases in the detection of altered glucose consumption in the brains of patients was evaluated by calculating statistical maps of regional hypometabolism in FDG-PET of 20 patients with confirmed Alzheimer's dementia (AD) and of 10 non-AD patients. Extent (hypometabolic volume referred to as cluster size) and magnitude (peak z-score) of detected hypometabolism was statistically analyzed. Differences between the reference databases built by CAD4D, SPM or NEUROSTAT were observed. Due to the different normalization methods, altered spatial FDG patterns were found. When analyzing patient data with the reference databases created using CAD4D, SPM or NEUROSTAT, similar characteristic clusters of hypometabolism in the same brain regions were found in the AD group with either software. However, larger z-scores were observed with CAD4D and NEUROSTAT than those reported by SPM. Better concordance with CAD4D and NEUROSTAT was achieved using the spatially normalized images of SPM and an independent z-score calculation. The three software packages identified the peak z-scores in the same brain region in 11 of 20 AD cases, and there was concordance between CAD4D and SPM in 16 AD subjects. The clinical evaluation of brain FDG-PET of 20 AD patients with either CAD4D-, SPM- or NEUROSTAT-generated databases from an identical reference dataset showed similar patterns of hypometabolism in the brain regions known to be involved in AD. The extent of hypometabolism and peak z-score appeared to be influenced by the calculation method used in each software package rather than by different spatial normalization parameters.

Machine Learning Classification of Cirrhotic Patients with and without Minimal Hepatic Encephalopathy Based on Regional Homogeneity of Intrinsic Brain Activity.

PubMed

Chen, Qiu-Feng; Chen, Hua-Jun; Liu, Jun; Sun, Tao; Shen, Qun-Tai

2016-01-01

Machine learning-based approaches play an important role in examining functional magnetic resonance imaging (fMRI) data in a multivariate manner and extracting features predictive of group membership. This study was performed to assess the potential for measuring brain intrinsic activity to identify minimal hepatic encephalopathy (MHE) in cirrhotic patients, using the support vector machine (SVM) method. Resting-state fMRI data were acquired in 16 cirrhotic patients with MHE and 19 cirrhotic patients without MHE. The regional homogeneity (ReHo) method was used to investigate the local synchrony of intrinsic brain activity. Psychometric Hepatic Encephalopathy Score (PHES) was used to define MHE condition. SVM-classifier was then applied using leave-one-out cross-validation, to determine the discriminative ReHo-map for MHE. The discrimination map highlights a set of regions, including the prefrontal cortex, anterior cingulate cortex, anterior insular cortex, inferior parietal lobule, precentral and postcentral gyri, superior and medial temporal cortices, and middle and inferior occipital gyri. The optimized discriminative model showed total accuracy of 82.9% and sensitivity of 81.3%. Our results suggested that a combination of the SVM approach and brain intrinsic activity measurement could be helpful for detection of MHE in cirrhotic patients.

Examination of corticothalamic fiber projections in United States service members with mild traumatic brain injury

NASA Astrophysics Data System (ADS)

Rashid, Faisal M.; Dennis, Emily L.; Villalon-Reina, Julio E.; Jin, Yan; Lewis, Jeffrey D.; York, Gerald E.; Thompson, Paul M.; Tate, David F.

2017-11-01

Mild traumatic brain injury (mTBI) is characterized clinically by a closed head injury involving differential or rotational movement of the brain inside the skull. Over 3 million mTBIs occur annually in the United States alone. Many of the individuals who sustain an mTBI go on to recover fully, but around 20% experience persistent symptoms. These symptoms often last for many weeks to several months. The thalamus, a structure known to serve as a global networking or relay system for the rest of the brain, may play a critical role in neurorehabiliation and its integrity and connectivity after injury may also affect cognitive outcomes. To examine the thalamus, conventional tractography methods to map corticothalamic pathways with diffusion-weighted MRI (DWI) lead to sparse reconstructions that may contain false positive fibers that are anatomically inaccurate. Using a specialized method to zero in on corticothalamic pathways with greater robustness, we noninvasively examined corticothalamic fiber projections using DWI, in 68 service members. We found significantly lower fractional anisotropy (FA), a measure of white matter microstructural integrity, in pathways projecting to the left pre- and postcentral gyri - consistent with sensorimotor deficits often found post-mTBI. Mapping of neural circuitry in mTBI may help to further our understanding of mechanisms underlying recovery post-TBI.

Optogenetic mapping of brain circuitry

NASA Astrophysics Data System (ADS)

Augustine, George J.; Berglund, Ken; Gill, Harin; Hoffmann, Carolin; Katarya, Malvika; Kim, Jinsook; Kudolo, John; Lee, Li M.; Lee, Molly; Lo, Daniel; Nakajima, Ryuichi; Park, Min Yoon; Tan, Gregory; Tang, Yanxia; Teo, Peggy; Tsuda, Sachiko; Wen, Lei; Yoon, Su-In

2012-10-01

Studies of the brain promise to be revolutionized by new experimental strategies that harness the combined power of optical techniques and genetics. We have mapped the circuitry of the mouse brain by using both optogenetic actuators that control neuronal activity and optogenetic sensors that detect neuronal activity. Using the light-activated cation channel, channelrhodopsin-2, to locally photostimulate neurons allows high-speed mapping of local and long-range circuitry. For example, with this approach we have mapped local circuits in the cerebral cortex, cerebellum and many other brain regions. Using the fluorescent sensor for chloride ions, Clomeleon, allows imaging of the spatial and temporal dimensions of inhibitory circuits in the brain. This approach allows imaging of both conventional "phasic" synaptic inhibition as well as unconventional "tonic" inhibition. The combined use of light to both control and monitor neural activity creates unprecedented opportunities to explore brain function, screen pharmaceutical agents, and potentially to use light to ameliorate psychiatric and neurological disorders.

Fluorescent-Protein Stabilization and High-Resolution Imaging of Cleared, Intact Mouse Brains

PubMed Central

Schwarz, Martin K.; Scherbarth, Annemarie; Sprengel, Rolf; Engelhardt, Johann; Theer, Patrick; Giese, Guenter

2015-01-01

In order to observe and quantify long-range neuronal connections in intact mouse brain by light microscopy, it is first necessary to clear the brain, thus suppressing refractive-index variations. Here we describe a method that clears the brain and preserves the signal from proteinaceous fluorophores using a pH-adjusted non-aqueous index-matching medium. Successful clearing is enabled through the use of either 1-propanol or tert-butanol during dehydration whilst maintaining a basic pH. We show that high-resolution fluorescence imaging of entire, structurally intact juvenile and adult mouse brains is possible at subcellular resolution, even following many months in clearing solution. We also show that axonal long-range projections that are EGFP-labelled by modified Rabies virus can be imaged throughout the brain using a purpose-built light-sheet fluorescence microscope. To demonstrate the viability of the technique, we determined a detailed map of the monosynaptic projections onto a target cell population in the lateral entorhinal cortex. This example demonstrates that our method permits the quantification of whole-brain connectivity patterns at the subcellular level in the uncut brain. PMID:25993380

Neuroanatomical substrates of action perception and understanding: an anatomic likelihood estimation meta-analysis of lesion-symptom mapping studies in brain injured patients

PubMed Central

Urgesi, Cosimo; Candidi, Matteo; Avenanti, Alessio

2014-01-01

Several neurophysiologic and neuroimaging studies suggested that motor and perceptual systems are tightly linked along a continuum rather than providing segregated mechanisms supporting different functions. Using correlational approaches, these studies demonstrated that action observation activates not only visual but also motor brain regions. On the other hand, brain stimulation and brain lesion evidence allows tackling the critical question of whether our action representations are necessary to perceive and understand others’ actions. In particular, recent neuropsychological studies have shown that patients with temporal, parietal, and frontal lesions exhibit a number of possible deficits in the visual perception and the understanding of others’ actions. The specific anatomical substrates of such neuropsychological deficits however, are still a matter of debate. Here we review the existing literature on this issue and perform an anatomic likelihood estimation meta-analysis of studies using lesion-symptom mapping methods on the causal relation between brain lesions and non-linguistic action perception and understanding deficits. The meta-analysis encompassed data from 361 patients tested in 11 studies and identified regions in the inferior frontal cortex, the inferior parietal cortex and the middle/superior temporal cortex, whose damage is consistently associated with poor performance in action perception and understanding tasks across studies. Interestingly, these areas correspond to the three nodes of the action observation network that are strongly activated in response to visual action perception in neuroimaging research and that have been targeted in previous brain stimulation studies. Thus, brain lesion mapping research provides converging causal evidence that premotor, parietal and temporal regions play a crucial role in action recognition and understanding. PMID:24910603

An Improved Representation of Regional Boundaries on Parcellated Morphological Surfaces

PubMed Central

Hao, Xuejun; Xu, Dongrong; Bansal, Ravi; Liu, Jun; Peterson, Bradley S.

2010-01-01

Establishing the correspondences of brain anatomy with function is important for understanding neuroimaging data. Regional delineations on morphological surfaces define anatomical landmarks and help to visualize and interpret both functional data and morphological measures mapped onto the cortical surface. We present an efficient algorithm that accurately delineates the morphological surface of the cerebral cortex in real time during generation of the surface using information from parcellated 3D data. With this accurate region delineation, we then develop methods for boundary-preserved simplification and smoothing, as well as procedures for the automated correction of small, misclassified regions to improve the quality of the delineated surface. We demonstrate that our delineation algorithm, together with a new method for double-snapshot visualization of cortical regions, can be used to establish a clear correspondence between brain anatomy and mapped quantities, such as morphological measures, across groups of subjects. PMID:21144708

Whole-Brain Mapping of Neuronal Activity in the Learned Helplessness Model of Depression.

PubMed

Kim, Yongsoo; Perova, Zinaida; Mirrione, Martine M; Pradhan, Kith; Henn, Fritz A; Shea, Stephen; Osten, Pavel; Li, Bo

2016-01-01

Some individuals are resilient, whereas others succumb to despair in repeated stressful situations. The neurobiological mechanisms underlying such divergent behavioral responses remain unclear. Here, we employed an automated method for mapping neuronal activity in search of signatures of stress responses in the entire mouse brain. We used serial two-photon tomography to detect expression of c-FosGFP - a marker of neuronal activation - in c-fosGFP transgenic mice subjected to the learned helplessness (LH) procedure, a widely used model of stress-induced depression-like phenotype in laboratory animals. We found that mice showing "helpless" behavior had an overall brain-wide reduction in the level of neuronal activation compared with mice showing "resilient" behavior, with the exception of a few brain areas, including the locus coeruleus, that were more activated in the helpless mice. In addition, the helpless mice showed a strong trend of having higher similarity in whole-brain activity profile among individuals, suggesting that helplessness is represented by a more stereotypic brain-wide activation pattern. This latter effect was confirmed in rats subjected to the LH procedure, using 2-deoxy-2[18F]fluoro-D-glucose positron emission tomography to assess neural activity. Our findings reveal distinct brain activity markings that correlate with adaptive and maladaptive behavioral responses to stress, and provide a framework for further studies investigating the contribution of specific brain regions to maladaptive stress responses.

Brain tumor segmentation from multimodal magnetic resonance images via sparse representation.

PubMed

Li, Yuhong; Jia, Fucang; Qin, Jing

2016-10-01

Accurately segmenting and quantifying brain gliomas from magnetic resonance (MR) images remains a challenging task because of the large spatial and structural variability among brain tumors. To develop a fully automatic and accurate brain tumor segmentation algorithm, we present a probabilistic model of multimodal MR brain tumor segmentation. This model combines sparse representation and the Markov random field (MRF) to solve the spatial and structural variability problem. We formulate the tumor segmentation problem as a multi-classification task by labeling each voxel as the maximum posterior probability. We estimate the maximum a posteriori (MAP) probability by introducing the sparse representation into a likelihood probability and a MRF into the prior probability. Considering the MAP as an NP-hard problem, we convert the maximum posterior probability estimation into a minimum energy optimization problem and employ graph cuts to find the solution to the MAP estimation. Our method is evaluated using the Brain Tumor Segmentation Challenge 2013 database (BRATS 2013) and obtained Dice coefficient metric values of 0.85, 0.75, and 0.69 on the high-grade Challenge data set, 0.73, 0.56, and 0.54 on the high-grade Challenge LeaderBoard data set, and 0.84, 0.54, and 0.57 on the low-grade Challenge data set for the complete, core, and enhancing regions. The experimental results show that the proposed algorithm is valid and ranks 2nd compared with the state-of-the-art tumor segmentation algorithms in the MICCAI BRATS 2013 challenge. Copyright © 2016 Elsevier B.V. All rights reserved.

Effect of spatial smoothing on t-maps: arguments for going back from t-maps to masked contrast images.

PubMed

Reimold, Matthias; Slifstein, Mark; Heinz, Andreas; Mueller-Schauenburg, Wolfgang; Bares, Roland

2006-06-01

Voxelwise statistical analysis has become popular in explorative functional brain mapping with fMRI or PET. Usually, results are presented as voxelwise levels of significance (t-maps), and for clusters that survive correction for multiple testing the coordinates of the maximum t-value are reported. Before calculating a voxelwise statistical test, spatial smoothing is required to achieve a reasonable statistical power. Little attention is being given to the fact that smoothing has a nonlinear effect on the voxel variances and thus the local characteristics of a t-map, which becomes most evident after smoothing over different types of tissue. We investigated the related artifacts, for example, white matter peaks whose position depend on the relative variance (variance over contrast) of the surrounding regions, and suggest improving spatial precision with 'masked contrast images': color-codes are attributed to the voxelwise contrast, and significant clusters (e.g., detected with statistical parametric mapping, SPM) are enlarged by including contiguous pixels with a contrast above the mean contrast in the original cluster, provided they satisfy P < 0.05. The potential benefit is demonstrated with simulations and data from a [11C]Carfentanil PET study. We conclude that spatial smoothing may lead to critical, sometimes-counterintuitive artifacts in t-maps, especially in subcortical brain regions. If significant clusters are detected, for example, with SPM, the suggested method is one way to improve spatial precision and may give the investigator a more direct sense of the underlying data. Its simplicity and the fact that no further assumptions are needed make it a useful complement for standard methods of statistical mapping.

White matter fiber-based analysis of T1w/T2w ratio map

NASA Astrophysics Data System (ADS)

Chen, Haiwei; Budin, Francois; Noel, Jean; Prieto, Juan Carlos; Gilmore, John; Rasmussen, Jerod; Wadhwa, Pathik D.; Entringer, Sonja; Buss, Claudia; Styner, Martin

2017-02-01

Purpose: To develop, test, evaluate and apply a novel tool for the white matter fiber-based analysis of T1w/T2w ratio maps quantifying myelin content. Background: The cerebral white matter in the human brain develops from a mostly non-myelinated state to a nearly fully mature white matter myelination within the first few years of life. High resolution T1w/T2w ratio maps are believed to be effective in quantitatively estimating myelin content on a voxel-wise basis. We propose the use of a fiber-tract-based analysis of such T1w/T2w ratio data, as it allows us to separate fiber bundles that a common regional analysis imprecisely groups together, and to associate effects to specific tracts rather than large, broad regions. Methods: We developed an intuitive, open source tool to facilitate such fiber-based studies of T1w/T2w ratio maps. Via its Graphical User Interface (GUI) the tool is accessible to non-technical users. The framework uses calibrated T1w/T2w ratio maps and a prior fiber atlas as an input to generate profiles of T1w/T2w values. The resulting fiber profiles are used in a statistical analysis that performs along-tract functional statistical analysis. We applied this approach to a preliminary study of early brain development in neonates. Results: We developed an open-source tool for the fiber based analysis of T1w/T2w ratio maps and tested it in a study of brain development.

[Three-dimensional reconstruction of functional brain images].

PubMed

Inoue, M; Shoji, K; Kojima, H; Hirano, S; Naito, Y; Honjo, I

1999-08-01

We consider PET (positron emission tomography) measurement with SPM (Statistical Parametric Mapping) analysis to be one of the most useful methods to identify activated areas of the brain involved in language processing. SPM is an effective analytical method that detects markedly activated areas over the whole brain. However, with the conventional presentations of these functional brain images, such as horizontal slices, three directional projection, or brain surface coloring, makes understanding and interpreting the positional relationships among various brain areas difficult. Therefore, we developed three-dimensionally reconstructed images from these functional brain images to improve the interpretation. The subjects were 12 normal volunteers. The following three types of images were constructed: 1) routine images by SPM, 2) three-dimensional static images, and 3) three-dimensional dynamic images, after PET images were analyzed by SPM during daily dialog listening. The creation of images of both the three-dimensional static and dynamic types employed the volume rendering method by VTK (The Visualization Toolkit). Since the functional brain images did not include original brain images, we synthesized SPM and MRI brain images by self-made C++ programs. The three-dimensional dynamic images were made by sequencing static images with available software. Images of both the three-dimensional static and dynamic types were processed by a personal computer system. Our newly created images showed clearer positional relationships among activated brain areas compared to the conventional method. To date, functional brain images have been employed in fields such as neurology or neurosurgery, however, these images may be useful even in the field of otorhinolaryngology, to assess hearing and speech. Exact three-dimensional images based on functional brain images are important for exact and intuitive interpretation, and may lead to new developments in brain science. Currently, the surface model is the most common method of three-dimensional display. However, the volume rendering method may be more effective for imaging regions such as the brain.

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.

Researching and Reducing the Health Burden of Stroke

MedlinePlus

... the result of continuing research to map the brain and interface it with a computer to enable stroke patients to regain function. How important is the new effort to map the human brain? The brain is more complex than any computer ...

BEaST: brain extraction based on nonlocal segmentation technique.

PubMed

Eskildsen, Simon F; Coupé, Pierrick; Fonov, Vladimir; Manjón, José V; Leung, Kelvin K; Guizard, Nicolas; Wassef, Shafik N; Østergaard, Lasse Riis; Collins, D Louis

2012-02-01

Brain extraction is an important step in the analysis of brain images. The variability in brain morphology and the difference in intensity characteristics due to imaging sequences make the development of a general purpose brain extraction algorithm challenging. To address this issue, we propose a new robust method (BEaST) dedicated to produce consistent and accurate brain extraction. This method is based on nonlocal segmentation embedded in a multi-resolution framework. A library of 80 priors is semi-automatically constructed from the NIH-sponsored MRI study of normal brain development, the International Consortium for Brain Mapping, and the Alzheimer's Disease Neuroimaging Initiative databases. In testing, a mean Dice similarity coefficient of 0.9834±0.0053 was obtained when performing leave-one-out cross validation selecting only 20 priors from the library. Validation using the online Segmentation Validation Engine resulted in a top ranking position with a mean Dice coefficient of 0.9781±0.0047. Robustness of BEaST is demonstrated on all baseline ADNI data, resulting in a very low failure rate. The segmentation accuracy of the method is better than two widely used publicly available methods and recent state-of-the-art hybrid approaches. BEaST provides results comparable to a recent label fusion approach, while being 40 times faster and requiring a much smaller library of priors. Copyright © 2011 Elsevier Inc. All rights reserved.

Mapping the information flow from one brain to another during gestural communication.

PubMed

Schippers, Marleen B; Roebroeck, Alard; Renken, Remco; Nanetti, Luca; Keysers, Christian

2010-05-18

Both the putative mirror neuron system (pMNS) and the ventral medial prefrontal cortex (vmPFC) are deemed important for social interaction: the pMNS because it supposedly "resonates" with the actions of others, the vmPFC because it is involved in mentalizing. Strictly speaking, the resonance property of the pMNS has never been investigated. Classical functional MRI experiments have only investigated whether pMNS regions augment their activity when an action is seen or executed. Resonance, however, entails more than only "going on and off together". Activity in the pMNS of an observer should continuously follow the more subtle changes over time in activity of the pMNS of the actor. Here we directly explore whether such resonance indeed occurs during continuous streams of actions. We let participants play the game of charades while we measured brain activity of both gesturer and guesser. We then applied a method to localize directed influences between the brains of the participants: between-brain Granger-causality mapping. Results show that a guesser's brain activity in regions involved in mentalizing and mirroring echoes the temporal structure of a gesturer's brain activity. This provides evidence for resonance theories and indicates a fine-grained temporal interplay between regions involved in motor planning and regions involved in thinking about the mental states of others. Furthermore, this method enables experiments to be more ecologically valid by providing the opportunity to leave social interaction unconstrained. This, in turn, would allow us to tap into the neural substrates of social deficits such as autism spectrum disorder.

Women's clitoris, vagina and cervix mapped on the sensory cortex: fMRI evidence

PubMed Central

Komisaruk, Barry R.; Wise, Nan; Frangos, Eleni; Liu, Wen-Ching; Allen, Kachina; Brody, Stuart

2011-01-01

Introduction The projection of vagina, uterine cervix, and nipple to the sensory cortex in humans has not been reported. Aims To map the sensory cortical fields of the clitoris, vagina, cervix and nipple, toward an elucidation of the neural systems underlying sexual response. Methods Using functional Magnetic Resonance Imaging (fMRI) we mapped sensory cortical responses to clitoral, vaginal, cervical, and nipple self-stimulation. For points of reference on the homunculus, we also mapped responses to the thumb and great toe (hallux) stimulation. Main Outcome Measures fMRI of brain regions activated by the various sensory stimuli. Results Clitoral, vaginal, and cervical self-stimulation activate differentiable sensory cortical regions, all clustered in the medial cortex (medial paracentral lobule). Nipple self-stimulation activated the genital sensory cortex (as well as the thoracic) region of the homuncular map. Conclusion The genital sensory cortex, identified in the classical Penfield homunculus based on electrical stimulation of the brain only in men, was confirmed for the first time in the literature by the present study in women, applying clitoral, vaginal, and cervical self-stimulation, and observing their regional brain responses using fMRI. Vaginal, clitoral, and cervical regions of activation were differentiable, consistent with innervation by different afferent nerves and different behavioral correlates. Activation of the genital sensory cortex by nipple self-stimulation was unexpected, but suggests a neurological basis for women’s reports of its erotogenic quality. PMID:21797981

Individual Brain Charting, a high-resolution fMRI dataset for cognitive mapping.

PubMed

Pinho, Ana Luísa; Amadon, Alexis; Ruest, Torsten; Fabre, Murielle; Dohmatob, Elvis; Denghien, Isabelle; Ginisty, Chantal; Becuwe-Desmidt, Séverine; Roger, Séverine; Laurier, Laurence; Joly-Testault, Véronique; Médiouni-Cloarec, Gaëlle; Doublé, Christine; Martins, Bernadette; Pinel, Philippe; Eger, Evelyn; Varoquaux, Gaël; Pallier, Christophe; Dehaene, Stanislas; Hertz-Pannier, Lucie; Thirion, Bertrand

2018-06-12

Functional Magnetic Resonance Imaging (fMRI) has furthered brain mapping on perceptual, motor, as well as higher-level cognitive functions. However, to date, no data collection has systematically addressed the functional mapping of cognitive mechanisms at a fine spatial scale. The Individual Brain Charting (IBC) project stands for a high-resolution multi-task fMRI dataset that intends to provide the objective basis toward a comprehensive functional atlas of the human brain. The data refer to a cohort of 12 participants performing many different tasks. The large amount of task-fMRI data on the same subjects yields a precise mapping of the underlying functions, free from both inter-subject and inter-site variability. The present article gives a detailed description of the first release of the IBC dataset. It comprises a dozen of tasks, addressing both low- and high- level cognitive functions. This openly available dataset is thus intended to become a reference for cognitive brain mapping.

Controlling feeding behavior by chemical or gene-directed targeting in the brain: what's so spatial about our methods?

PubMed Central

Khan, Arshad M.

2013-01-01

Intracranial chemical injection (ICI) methods have been used to identify the locations in the brain where feeding behavior can be controlled acutely. Scientists conducting ICI studies often document their injection site locations, thereby leaving kernels of valuable location data for others to use to further characterize feeding control circuits. Unfortunately, this rich dataset has not yet been formally contextualized with other published neuroanatomical data. In particular, axonal tracing studies have delineated several neural circuits originating in the same areas where ICI injection feeding-control sites have been documented, but it remains unclear whether these circuits participate in feeding control. Comparing injection sites with other types of location data would require careful anatomical registration between the datasets. Here, a conceptual framework is presented for how such anatomical registration efforts can be performed. For example, by using a simple atlas alignment tool, a hypothalamic locus sensitive to the orexigenic effects of neuropeptide Y (NPY) can be aligned accurately with the locations of neurons labeled by anterograde tracers or those known to express NPY receptors or feeding-related peptides. This approach can also be applied to those intracranial “gene-directed” injection (IGI) methods (e.g., site-specific recombinase methods, RNA expression or interference, optogenetics, and pharmacosynthetics) that involve viral injections to targeted neuronal populations. Spatial alignment efforts can be accelerated if location data from ICI/IGI methods are mapped to stereotaxic brain atlases to allow powerful neuroinformatics tools to overlay different types of data in the same reference space. Atlas-based mapping will be critical for community-based sharing of location data for feeding control circuits, and will accelerate our understanding of structure-function relationships in the brain for mammalian models of obesity and metabolic disorders. PMID:24385950

HDAC6 Brain Mapping with [ 18 F]Bavarostat Enabled by a Ru-Mediated Deoxyfluorination

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

Strebl, Martin G.; Campbell, Arthur J.; Zhao, Wen -Ning

Histone deacetylase 6 (HDAC6) function and dysregulation have been implicated in the etiology of certain cancers and more recently in central nervous system (CNS) disorders including Rett syndrome, Alzheimer’s and Parkinson’s diseases, and major depressive disorder. HDAC6-selective inhibitors have therapeutic potential, but in the CNS drug space the development of highly brain penetrant HDAC inhibitors has been a persistent challenge. Moreover, no tool exists to directly characterize HDAC6 and its related biology in the living human brain. Here, we report a highly brain penetrant HDAC6 inhibitor, Bavarostat, that exhibits excellent HDAC6 selectivity (>80-fold over all other Zn-containing HDAC paralogues), modulatesmore » tubulin acetylation selectively over histone acetylation, and has excellent brain penetrance. We further demonstrate that Bavarostat can be radiolabeled with 18F by deoxyfluorination through in situ formation of a ruthenium π-complex of the corresponding phenol precursor: the only method currently suitable for synthesis of [ 18F]Bavarostat. In conclusion, by using [ 18F]Bavarostat in a series of rodent and nonhuman primate imaging experiments, we demonstrate its utility for mapping HDAC6 in the living brain, which sets the stage for first-in-human neurochemical imaging of this important target.« less

HDAC6 Brain Mapping with [ 18 F]Bavarostat Enabled by a Ru-Mediated Deoxyfluorination

DOE PAGES

Strebl, Martin G.; Campbell, Arthur J.; Zhao, Wen -Ning; ...

2017-09-06

Histone deacetylase 6 (HDAC6) function and dysregulation have been implicated in the etiology of certain cancers and more recently in central nervous system (CNS) disorders including Rett syndrome, Alzheimer’s and Parkinson’s diseases, and major depressive disorder. HDAC6-selective inhibitors have therapeutic potential, but in the CNS drug space the development of highly brain penetrant HDAC inhibitors has been a persistent challenge. Moreover, no tool exists to directly characterize HDAC6 and its related biology in the living human brain. Here, we report a highly brain penetrant HDAC6 inhibitor, Bavarostat, that exhibits excellent HDAC6 selectivity (>80-fold over all other Zn-containing HDAC paralogues), modulatesmore » tubulin acetylation selectively over histone acetylation, and has excellent brain penetrance. We further demonstrate that Bavarostat can be radiolabeled with 18F by deoxyfluorination through in situ formation of a ruthenium π-complex of the corresponding phenol precursor: the only method currently suitable for synthesis of [ 18F]Bavarostat. In conclusion, by using [ 18F]Bavarostat in a series of rodent and nonhuman primate imaging experiments, we demonstrate its utility for mapping HDAC6 in the living brain, which sets the stage for first-in-human neurochemical imaging of this important target.« less

Assignment of functional activations to probabilistic cytoarchitectonic areas revisited.

PubMed

Eickhoff, Simon B; Paus, Tomas; Caspers, Svenja; Grosbras, Marie-Helene; Evans, Alan C; Zilles, Karl; Amunts, Katrin

2007-07-01

Probabilistic cytoarchitectonic maps in standard reference space provide a powerful tool for the analysis of structure-function relationships in the human brain. While these microstructurally defined maps have already been successfully used in the analysis of somatosensory, motor or language functions, several conceptual issues in the analysis of structure-function relationships still demand further clarification. In this paper, we demonstrate the principle approaches for anatomical localisation of functional activations based on probabilistic cytoarchitectonic maps by exemplary analysis of an anterior parietal activation evoked by visual presentation of hand gestures. After consideration of the conceptual basis and implementation of volume or local maxima labelling, we comment on some potential interpretational difficulties, limitations and caveats that could be encountered. Extending and supplementing these methods, we then propose a supplementary approach for quantification of structure-function correspondences based on distribution analysis. This approach relates the cytoarchitectonic probabilities observed at a particular functionally defined location to the areal specific null distribution of probabilities across the whole brain (i.e., the full probability map). Importantly, this method avoids the need for a unique classification of voxels to a single cortical area and may increase the comparability between results obtained for different areas. Moreover, as distribution-based labelling quantifies the "central tendency" of an activation with respect to anatomical areas, it will, in combination with the established methods, allow an advanced characterisation of the anatomical substrates of functional activations. Finally, the advantages and disadvantages of the various methods are discussed, focussing on the question of which approach is most appropriate for a particular situation.

A generative probabilistic model and discriminative extensions for brain lesion segmentation – with application to tumor and stroke

PubMed Central

Menze, Bjoern H.; Van Leemput, Koen; Lashkari, Danial; Riklin-Raviv, Tammy; Geremia, Ezequiel; Alberts, Esther; Gruber, Philipp; Wegener, Susanne; Weber, Marc-André; Székely, Gabor; Ayache, Nicholas; Golland, Polina

2016-01-01

We introduce a generative probabilistic model for segmentation of brain lesions in multi-dimensional images that generalizes the EM segmenter, a common approach for modelling brain images using Gaussian mixtures and a probabilistic tissue atlas that employs expectation-maximization (EM) to estimate the label map for a new image. Our model augments the probabilistic atlas of the healthy tissues with a latent atlas of the lesion. We derive an estimation algorithm with closed-form EM update equations. The method extracts a latent atlas prior distribution and the lesion posterior distributions jointly from the image data. It delineates lesion areas individually in each channel, allowing for differences in lesion appearance across modalities, an important feature of many brain tumor imaging sequences. We also propose discriminative model extensions to map the output of the generative model to arbitrary labels with semantic and biological meaning, such as “tumor core” or “fluid-filled structure”, but without a one-to-one correspondence to the hypo-or hyper-intense lesion areas identified by the generative model. We test the approach in two image sets: the publicly available BRATS set of glioma patient scans, and multimodal brain images of patients with acute and subacute ischemic stroke. We find the generative model that has been designed for tumor lesions to generalize well to stroke images, and the generative-discriminative model to be one of the top ranking methods in the BRATS evaluation. PMID:26599702

A Generative Probabilistic Model and Discriminative Extensions for Brain Lesion Segmentation--With Application to Tumor and Stroke.

PubMed

Menze, Bjoern H; Van Leemput, Koen; Lashkari, Danial; Riklin-Raviv, Tammy; Geremia, Ezequiel; Alberts, Esther; Gruber, Philipp; Wegener, Susanne; Weber, Marc-Andre; Szekely, Gabor; Ayache, Nicholas; Golland, Polina

2016-04-01

We introduce a generative probabilistic model for segmentation of brain lesions in multi-dimensional images that generalizes the EM segmenter, a common approach for modelling brain images using Gaussian mixtures and a probabilistic tissue atlas that employs expectation-maximization (EM), to estimate the label map for a new image. Our model augments the probabilistic atlas of the healthy tissues with a latent atlas of the lesion. We derive an estimation algorithm with closed-form EM update equations. The method extracts a latent atlas prior distribution and the lesion posterior distributions jointly from the image data. It delineates lesion areas individually in each channel, allowing for differences in lesion appearance across modalities, an important feature of many brain tumor imaging sequences. We also propose discriminative model extensions to map the output of the generative model to arbitrary labels with semantic and biological meaning, such as "tumor core" or "fluid-filled structure", but without a one-to-one correspondence to the hypo- or hyper-intense lesion areas identified by the generative model. We test the approach in two image sets: the publicly available BRATS set of glioma patient scans, and multimodal brain images of patients with acute and subacute ischemic stroke. We find the generative model that has been designed for tumor lesions to generalize well to stroke images, and the extended discriminative -discriminative model to be one of the top ranking methods in the BRATS evaluation.

The connectomics challenge

PubMed Central

Silvestri, Ludovico; Sacconi, Leonardo; Pavone, Francesco Saverio

Summary One of the most fascinating challenges in neuroscience is the reconstruction of the connectivity map of the brain. Recent years have seen a rapid expansion in the field of connectomics, whose aim is to trace this map and understand its relationship with neural computation. Many different approaches, ranging from electron and optical microscopy to magnetic resonance imaging, have been proposed to address the connectomics challenge on various spatial scales and in different species. Here, we review the main technological advances in the microscopy techniques applied to connectomics, highlighting the potential and limitations of the different methods. Finally, we briefly discuss the role of connectomics in the Human Brain Project, the Future and Emerging Technologies (FET) Flagship recently approved by the European Commission. PMID:24139653

In Vivo Fiber-Optic Raman Mapping Of Metastases In Mouse Brains

NASA Astrophysics Data System (ADS)

Stelling, A.; Kirsch, M.; Steiner, G.; Krafft, C.; Schackert, G.; Salzer, R.

2010-08-01

Vibrational spectroscopy, in particular Raman spectroscopy, has potential applications in the field of in vivo diagnostics. Raman and FT-IR spectroscopy analyze the complete biochemical information at any given pixel within the visual field. Here we demonstrate the feasibility of performing Raman spectroscopic measurements on living mice brains using a fiber-optic probe with a nominal spatial resolution of 60 μm. The objectives of this study were to 1) evaluate preclinical models, namely murine brain slices containing experimental tumors, 2) optimize the preparation of pristine brain tissue to obtain reference information, to 3) optimize the conditions for introducing a fiber-optic probe to acquire Raman maps in vivo, and 4) to transfer results obtained from human brain tumors to an animal model. Disseminated brain metastases of malignant melanomas were induced by injecting tumor cells into the carotid artery of mice. The procedure mimicked hematogenous tumor spread in one brain hemisphere while the other hemisphere remained tumor free. Three series of sections were prepared consecutively from whole mouse brains: pristine, 2-mm thick sections for Raman mapping and dried, thin sections for FT-IR imaging, hematoxylin and eosin-stained thin sections for histopathological assessment. Raman maps were collected serially using a spectrometer coupled to a fiber-optic probe. FT-IR images were recorded using a spectrometer with a multi-channel detector. The FT-IR images and the Raman maps were evaluated by multivariate data analysis. The results obtained from the thin section studies were employed to guide measurements of murine brains in vivo. Raman maps with an acquisition time of over an hour could be performed on the living animals. No damage to the tissue was observed.

Efficient Blood-Brain Barrier Opening in Primates with Neuronavigation-Guided Ultrasound and Real-Time Acoustic Mapping.

PubMed

Wu, Shih-Ying; Aurup, Christian; Sanchez, Carlos Sierra; Grondin, Julien; Zheng, Wenlan; Kamimura, Hermes; Ferrera, Vincent P; Konofagou, Elisa E

2018-05-22

Brain diseases including neurological disorders and tumors remain under treated due to the challenge to access the brain, and blood-brain barrier (BBB) restricting drug delivery which, also profoundly limits the development of pharmacological treatment. Focused ultrasound (FUS) with microbubbles is the sole method to open the BBB noninvasively, locally, and transiently and facilitate drug delivery, while translation to the clinic is challenging due to long procedure, targeting limitations, or invasiveness of current systems. In order to provide rapid, flexible yet precise applications, we have designed a noninvasive FUS and monitoring system with the protocol tested in monkeys (from in silico preplanning and simulation, real-time targeting and acoustic mapping, to post-treatment assessment). With a short procedure (30 min) similar to current clinical imaging duration or radiation therapy, the achieved targeting (both cerebral cortex and subcortical structures) and monitoring accuracy was close to the predicted 2-mm lower limit. This system would enable rapid clinical transcranial FUS applications outside of the MRI system without a stereotactic frame, thereby benefiting patients especially in the elderly population.

Molecular Neuroanatomy: A Generation of Progress

PubMed Central

Pollock, Jonathan D.; Wu, Da-Yu; Satterlee, John

2014-01-01

The neuroscience research landscape has changed dramatically over the past decade. An impressive array of neuroscience tools and technologies have been generated, including brain gene expression atlases, genetically encoded proteins to monitor and manipulate neuronal activity and function, cost effective genome sequencing, new technologies enabling genome manipulation, new imaging methods and new tools for mapping neuronal circuits. However, despite these technological advances, several significant scientific challenges must be overcome in the coming decade to enable a better understanding of brain function and to develop next generation cell type-targeted therapeutics to treat brain disorders. For example, we do not have an inventory of the different types of cells that exist in the brain, nor do we know how to molecularly phenotype them. We also lack robust technologies to map connections between cells. This review will provide an overview of some of the tools and technologies neuroscientists are currently using to move the field of molecular neuroanatomy forward and also discuss emerging technologies that may enable neuroscientists to address these critical scientific challenges over the coming decade. PMID:24388609

[Peroperative risks in cerebral aneurysm surgery].

PubMed

Mustaki, J P; Bissonnette, B; Archer, D; Boulard, G; Ravussin, P

1996-01-01

The perioperative complications associated with cerebral aneurysm surgery require a specific anaesthetic management. Four major perioperative accidents are discussed in this review. The anaesthetic and surgical management in case of rebleeding subsequent to the re-rupture of the aneurysm is mainly prophylactic. It includes haemodynamic stability assurance, maintenance of mean arterial pressure (MAP) between 80-90 mmHg during stimulation of the patient such as endotracheal intubation, application of the skull-pin head-holder, incision, and craniotomy. The aneurysmal transmural pressure should be adequately maintained by avoiding an aggressive decrease of intracranial pressure. Once the skull is open, the brain must be kept slack in order to decrease pressure under the retractors and avoid the risks of stretching and tearing of the adjacent vessels. If, despite these precautions, the aneurysm ruptures again. MAP should be decreased to 60 mmHg and the brain rendered more slack, in order to allow direct clipping of the aneurysm, or temporary clipping of the adjacent vessels. The optimal agents in this situation are isoflurane (which decreases CMRO2), intravenous anaesthetic agents (inspite their negative inotropic effect, they may potentially protect the brain) and sodium nitroprusside. Vasospasm occurs usually between the 3rd and the 7th day after subarachnoid haemorrhage. It may be seen peroperatively. The optimal treatment, as well as prophylaxis, is moderate controlled hypertension (MAP > 100 mmHg), associated with hypervolaemia and haemodilution, the so-called triple H therapy, with strict control of the filling pressures. Other beneficial therapies are calcium antagonists (nimodipine and nicardipine), the removal of the blood accumulated around the brain and in the cisternae, and possibly local administration of papaverine. Abrupt MAP increases are controlled in order to maintain adequate aneurysmal transmural pressure. Beta-blockers, local anaesthetics administered locally or intravenously, a carefully titrated level of anaesthesia, a maintained volaemia play a protective role. Cerebral oedema is sometimes already present at the opening of the skull or may arise later, due to a high pressure under the retractors, to the surgical manipulations of the brain or to brain ischaemia subsequent to temporary clipping. Its treatment is aggressive, with intravenous agents, mannitol, deep hypocapnia and/or lumbar drainage. Prophylaxis, according to the "brain homeostasis concept", is the preferred method to avoid these four peroperative accidents. It includes normal blood volume, normoglycaemia, moderate hypocapnia, normotension, soft manipulation of the brain and optimal brain relaxation.

Imaging of neural oscillations with embedded inferential and group prevalence statistics.

PubMed

Donhauser, Peter W; Florin, Esther; Baillet, Sylvain

2018-02-01

Magnetoencephalography and electroencephalography (MEG, EEG) are essential techniques for studying distributed signal dynamics in the human brain. In particular, the functional role of neural oscillations remains to be clarified. For that reason, imaging methods need to identify distinct brain regions that concurrently generate oscillatory activity, with adequate separation in space and time. Yet, spatial smearing and inhomogeneous signal-to-noise are challenging factors to source reconstruction from external sensor data. The detection of weak sources in the presence of stronger regional activity nearby is a typical complication of MEG/EEG source imaging. We propose a novel, hypothesis-driven source reconstruction approach to address these methodological challenges. The imaging with embedded statistics (iES) method is a subspace scanning technique that constrains the mapping problem to the actual experimental design. A major benefit is that, regardless of signal strength, the contributions from all oscillatory sources, which activity is consistent with the tested hypothesis, are equalized in the statistical maps produced. We present extensive evaluations of iES on group MEG data, for mapping 1) induced oscillations using experimental contrasts, 2) ongoing narrow-band oscillations in the resting-state, 3) co-modulation of brain-wide oscillatory power with a seed region, and 4) co-modulation of oscillatory power with peripheral signals (pupil dilation). Along the way, we demonstrate several advantages of iES over standard source imaging approaches. These include the detection of oscillatory coupling without rejection of zero-phase coupling, and detection of ongoing oscillations in deeper brain regions, where signal-to-noise conditions are unfavorable. We also show that iES provides a separate evaluation of oscillatory synchronization and desynchronization in experimental contrasts, which has important statistical advantages. The flexibility of iES allows it to be adjusted to many experimental questions in systems neuroscience.

Imaging of neural oscillations with embedded inferential and group prevalence statistics

PubMed Central

2018-01-01

Magnetoencephalography and electroencephalography (MEG, EEG) are essential techniques for studying distributed signal dynamics in the human brain. In particular, the functional role of neural oscillations remains to be clarified. For that reason, imaging methods need to identify distinct brain regions that concurrently generate oscillatory activity, with adequate separation in space and time. Yet, spatial smearing and inhomogeneous signal-to-noise are challenging factors to source reconstruction from external sensor data. The detection of weak sources in the presence of stronger regional activity nearby is a typical complication of MEG/EEG source imaging. We propose a novel, hypothesis-driven source reconstruction approach to address these methodological challenges. The imaging with embedded statistics (iES) method is a subspace scanning technique that constrains the mapping problem to the actual experimental design. A major benefit is that, regardless of signal strength, the contributions from all oscillatory sources, which activity is consistent with the tested hypothesis, are equalized in the statistical maps produced. We present extensive evaluations of iES on group MEG data, for mapping 1) induced oscillations using experimental contrasts, 2) ongoing narrow-band oscillations in the resting-state, 3) co-modulation of brain-wide oscillatory power with a seed region, and 4) co-modulation of oscillatory power with peripheral signals (pupil dilation). Along the way, we demonstrate several advantages of iES over standard source imaging approaches. These include the detection of oscillatory coupling without rejection of zero-phase coupling, and detection of ongoing oscillations in deeper brain regions, where signal-to-noise conditions are unfavorable. We also show that iES provides a separate evaluation of oscillatory synchronization and desynchronization in experimental contrasts, which has important statistical advantages. The flexibility of iES allows it to be adjusted to many experimental questions in systems neuroscience. PMID:29408902

Mapping of Brain Activity by Automated Volume Analysis of Immediate Early Genes.

PubMed

Renier, Nicolas; Adams, Eliza L; Kirst, Christoph; Wu, Zhuhao; Azevedo, Ricardo; Kohl, Johannes; Autry, Anita E; Kadiri, Lolahon; Umadevi Venkataraju, Kannan; Zhou, Yu; Wang, Victoria X; Tang, Cheuk Y; Olsen, Olav; Dulac, Catherine; Osten, Pavel; Tessier-Lavigne, Marc

2016-06-16

Understanding how neural information is processed in physiological and pathological states would benefit from precise detection, localization, and quantification of the activity of all neurons across the entire brain, which has not, to date, been achieved in the mammalian brain. We introduce a pipeline for high-speed acquisition of brain activity at cellular resolution through profiling immediate early gene expression using immunostaining and light-sheet fluorescence imaging, followed by automated mapping and analysis of activity by an open-source software program we term ClearMap. We validate the pipeline first by analysis of brain regions activated in response to haloperidol. Next, we report new cortical regions downstream of whisker-evoked sensory processing during active exploration. Last, we combine activity mapping with axon tracing to uncover new brain regions differentially activated during parenting behavior. This pipeline is widely applicable to different experimental paradigms, including animal species for which transgenic activity reporters are not readily available. Copyright © 2016 Elsevier Inc. All rights reserved.

Mapping of brain activity by automated volume analysis of immediate early genes

PubMed Central

Renier, Nicolas; Adams, Eliza L.; Kirst, Christoph; Wu, Zhuhao; Azevedo, Ricardo; Kohl, Johannes; Autry, Anita E.; Kadiri, Lolahon; Venkataraju, Kannan Umadevi; Zhou, Yu; Wang, Victoria X.; Tang, Cheuk Y.; Olsen, Olav; Dulac, Catherine; Osten, Pavel; Tessier-Lavigne, Marc

2016-01-01

Summary Understanding how neural information is processed in physiological and pathological states would benefit from precise detection, localization and quantification of the activity of all neurons across the entire brain, which has not to date been achieved in the mammalian brain. We introduce a pipeline for high speed acquisition of brain activity at cellular resolution through profiling immediate early gene expression using immunostaining and light-sheet fluorescence imaging, followed by automated mapping and analysis of activity by an open-source software program we term ClearMap. We validate the pipeline first by analysis of brain regions activated in response to Haloperidol. Next, we report new cortical regions downstream of whisker-evoked sensory processing during active exploration. Lastly, we combine activity mapping with axon tracing to uncover new brain regions differentially activated during parenting behavior. This pipeline is widely applicable to different experimental paradigms, including animal species for which transgenic activity reporters are not readily available. PMID:27238021

Nonlocal atlas-guided multi-channel forest learning for human brain labeling

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

Ma, Guangkai; Gao, Yaozong; Wu, Guorong

Purpose: It is important for many quantitative brain studies to label meaningful anatomical regions in MR brain images. However, due to high complexity of brain structures and ambiguous boundaries between different anatomical regions, the anatomical labeling of MR brain images is still quite a challenging task. In many existing label fusion methods, appearance information is widely used. However, since local anatomy in the human brain is often complex, the appearance information alone is limited in characterizing each image point, especially for identifying the same anatomical structure across different subjects. Recent progress in computer vision suggests that the context features canmore » be very useful in identifying an object from a complex scene. In light of this, the authors propose a novel learning-based label fusion method by using both low-level appearance features (computed from the target image) and high-level context features (computed from warped atlases or tentative labeling maps of the target image). Methods: In particular, the authors employ a multi-channel random forest to learn the nonlinear relationship between these hybrid features and target labels (i.e., corresponding to certain anatomical structures). Specifically, at each of the iterations, the random forest will output tentative labeling maps of the target image, from which the authors compute spatial label context features and then use in combination with original appearance features of the target image to refine the labeling. Moreover, to accommodate the high inter-subject variations, the authors further extend their learning-based label fusion to a multi-atlas scenario, i.e., they train a random forest for each atlas and then obtain the final labeling result according to the consensus of results from all atlases. Results: The authors have comprehensively evaluated their method on both public LONI-LBPA40 and IXI datasets. To quantitatively evaluate the labeling accuracy, the authors use the dice similarity coefficient to measure the overlap degree. Their method achieves average overlaps of 82.56% on 54 regions of interest (ROIs) and 79.78% on 80 ROIs, respectively, which significantly outperform the baseline method (random forests), with the average overlaps of 72.48% on 54 ROIs and 72.09% on 80 ROIs, respectively. Conclusions: The proposed methods have achieved the highest labeling accuracy, compared to several state-of-the-art methods in the literature.« less

Nonlocal atlas-guided multi-channel forest learning for human brain labeling

PubMed Central

Ma, Guangkai; Gao, Yaozong; Wu, Guorong; Wu, Ligang; Shen, Dinggang

2016-01-01

Purpose: It is important for many quantitative brain studies to label meaningful anatomical regions in MR brain images. However, due to high complexity of brain structures and ambiguous boundaries between different anatomical regions, the anatomical labeling of MR brain images is still quite a challenging task. In many existing label fusion methods, appearance information is widely used. However, since local anatomy in the human brain is often complex, the appearance information alone is limited in characterizing each image point, especially for identifying the same anatomical structure across different subjects. Recent progress in computer vision suggests that the context features can be very useful in identifying an object from a complex scene. In light of this, the authors propose a novel learning-based label fusion method by using both low-level appearance features (computed from the target image) and high-level context features (computed from warped atlases or tentative labeling maps of the target image). Methods: In particular, the authors employ a multi-channel random forest to learn the nonlinear relationship between these hybrid features and target labels (i.e., corresponding to certain anatomical structures). Specifically, at each of the iterations, the random forest will output tentative labeling maps of the target image, from which the authors compute spatial label context features and then use in combination with original appearance features of the target image to refine the labeling. Moreover, to accommodate the high inter-subject variations, the authors further extend their learning-based label fusion to a multi-atlas scenario, i.e., they train a random forest for each atlas and then obtain the final labeling result according to the consensus of results from all atlases. Results: The authors have comprehensively evaluated their method on both public LONI_LBPA40 and IXI datasets. To quantitatively evaluate the labeling accuracy, the authors use the dice similarity coefficient to measure the overlap degree. Their method achieves average overlaps of 82.56% on 54 regions of interest (ROIs) and 79.78% on 80 ROIs, respectively, which significantly outperform the baseline method (random forests), with the average overlaps of 72.48% on 54 ROIs and 72.09% on 80 ROIs, respectively. Conclusions: The proposed methods have achieved the highest labeling accuracy, compared to several state-of-the-art methods in the literature. PMID:26843260

Repeated mapping of cortical language sites by preoperative navigated transcranial magnetic stimulation compared to repeated intraoperative DCS mapping in awake craniotomy

PubMed Central

2014-01-01

Background Repetitive navigated transcranial magnetic stimulation (rTMS) was recently described for mapping of human language areas. However, its capability of detecting language plasticity in brain tumor patients was not proven up to now. Thus, this study was designed to evaluate such data in order to compare rTMS language mapping to language mapping during repeated awake surgery during follow-up in patients suffering from language-eloquent gliomas. Methods Three right-handed patients with left-sided gliomas (2 opercular glioblastomas, 1 astrocytoma WHO grade III of the angular gyrus) underwent preoperative language mapping by rTMS as well as intraoperative language mapping provided via direct cortical stimulation (DCS) for initial as well as for repeated Resection 7, 10, and 15 months later. Results Overall, preoperative rTMS was able to elicit clear language errors in all mappings. A good correlation between initial rTMS and DCS results was observed. As a consequence of brain plasticity, initial DCS and rTMS findings only corresponded with the results obtained during the second examination in one out of three patients thus suggesting changes of language organization in two of our three patients. Conclusions This report points out the usefulness but also the limitations of preoperative rTMS language mapping to detect plastic changes in language function or for long-term follow-up prior to DCS even in recurrent gliomas. However, DCS still has to be regarded as gold standard. PMID:24479694

Whole-Brain Mapping of Neuronal Activity in the Learned Helplessness Model of Depression

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

Kim, Yongsoo; Perova, Zinaida; Mirrione, Martine M.

Some individuals are resilient, whereas others succumb to despair in repeated stressful situations. The neurobiological mechanisms underlying such divergent behavioral responses remain unclear. Here, we employed an automated method for mapping neuronal activity in search of signatures of stress responses in the entire mouse brain. We used serial two-photon tomography to detect expression of c-FosGFP – a marker of neuronal activation – in c-fosGFP transgenic mice subjected to the learned helplessness (LH) procedure, a widely used model of stress-induced depression-like phenotype in laboratory animals. We found that mice showing “helpless” behavior had an overall brain-wide reduction in the level ofmore » neuronal activation compared with mice showing “resilient” behavior, with the exception of a few brain areas, including the locus coeruleus, that were more activated in the helpless mice. In addition, the helpless mice showed a strong trend of having higher similarity in whole-brain activity profile among individuals, suggesting that helplessness is represented by a more stereotypic brain-wide activation pattern. This latter effect was confirmed in rats subjected to the LH procedure, using 2-deoxy-2[18F]fluoro-D-glucose positron emission tomography to assess neural activity. In conclusion, our findings reveal distinct brain activity markings that correlate with adaptive and maladaptive behavioral responses tostress, and provide a framework for further studies investigating the contribution of specific brain regions to maladaptive stress responses.« less

Whole-Brain Mapping of Neuronal Activity in the Learned Helplessness Model of Depression

DOE PAGES

Kim, Yongsoo; Perova, Zinaida; Mirrione, Martine M.; ...

2016-02-03

Some individuals are resilient, whereas others succumb to despair in repeated stressful situations. The neurobiological mechanisms underlying such divergent behavioral responses remain unclear. Here, we employed an automated method for mapping neuronal activity in search of signatures of stress responses in the entire mouse brain. We used serial two-photon tomography to detect expression of c-FosGFP – a marker of neuronal activation – in c-fosGFP transgenic mice subjected to the learned helplessness (LH) procedure, a widely used model of stress-induced depression-like phenotype in laboratory animals. We found that mice showing “helpless” behavior had an overall brain-wide reduction in the level ofmore » neuronal activation compared with mice showing “resilient” behavior, with the exception of a few brain areas, including the locus coeruleus, that were more activated in the helpless mice. In addition, the helpless mice showed a strong trend of having higher similarity in whole-brain activity profile among individuals, suggesting that helplessness is represented by a more stereotypic brain-wide activation pattern. This latter effect was confirmed in rats subjected to the LH procedure, using 2-deoxy-2[18F]fluoro-D-glucose positron emission tomography to assess neural activity. In conclusion, our findings reveal distinct brain activity markings that correlate with adaptive and maladaptive behavioral responses tostress, and provide a framework for further studies investigating the contribution of specific brain regions to maladaptive stress responses.« less

High-resolution whole-brain DCE-MRI using constrained reconstruction: Prospective clinical evaluation in brain tumor patients

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

Guo, Yi, E-mail: yiguo@usc.edu; Zhu, Yinghua; Lingala, Sajan Goud

Purpose: To clinically evaluate a highly accelerated T1-weighted dynamic contrast-enhanced (DCE) MRI technique that provides high spatial resolution and whole-brain coverage via undersampling and constrained reconstruction with multiple sparsity constraints. Methods: Conventional (rate-2 SENSE) and experimental DCE-MRI (rate-30) scans were performed 20 minutes apart in 15 brain tumor patients. The conventional clinical DCE-MRI had voxel dimensions 0.9 × 1.3 × 7.0 mm{sup 3}, FOV 22 × 22 × 4.2 cm{sup 3}, and the experimental DCE-MRI had voxel dimensions 0.9 × 0.9 × 1.9 mm{sup 3}, and broader coverage 22 × 22 × 19 cm{sup 3}. Temporal resolution was 5 smore » for both protocols. Time-resolved images and blood–brain barrier permeability maps were qualitatively evaluated by two radiologists. Results: The experimental DCE-MRI scans showed no loss of qualitative information in any of the cases, while achieving substantially higher spatial resolution and whole-brain spatial coverage. Average qualitative scores (from 0 to 3) were 2.1 for the experimental scans and 1.1 for the conventional clinical scans. Conclusions: The proposed DCE-MRI approach provides clinically superior image quality with higher spatial resolution and coverage than currently available approaches. These advantages may allow comprehensive permeability mapping in the brain, which is especially valuable in the setting of large lesions or multiple lesions spread throughout the brain.« less

Whole-Brain Mapping of Neuronal Activity in the Learned Helplessness Model of Depression

PubMed Central

Kim, Yongsoo; Perova, Zinaida; Mirrione, Martine M.; Pradhan, Kith; Henn, Fritz A.; Shea, Stephen; Osten, Pavel; Li, Bo

2016-01-01

Some individuals are resilient, whereas others succumb to despair in repeated stressful situations. The neurobiological mechanisms underlying such divergent behavioral responses remain unclear. Here, we employed an automated method for mapping neuronal activity in search of signatures of stress responses in the entire mouse brain. We used serial two-photon tomography to detect expression of c-FosGFP – a marker of neuronal activation – in c-fosGFP transgenic mice subjected to the learned helplessness (LH) procedure, a widely used model of stress-induced depression-like phenotype in laboratory animals. We found that mice showing “helpless” behavior had an overall brain-wide reduction in the level of neuronal activation compared with mice showing “resilient” behavior, with the exception of a few brain areas, including the locus coeruleus, that were more activated in the helpless mice. In addition, the helpless mice showed a strong trend of having higher similarity in whole-brain activity profile among individuals, suggesting that helplessness is represented by a more stereotypic brain-wide activation pattern. This latter effect was confirmed in rats subjected to the LH procedure, using 2-deoxy-2[18F]fluoro-D-glucose positron emission tomography to assess neural activity. Our findings reveal distinct brain activity markings that correlate with adaptive and maladaptive behavioral responses to stress, and provide a framework for further studies investigating the contribution of specific brain regions to maladaptive stress responses. PMID:26869888

Quantitative Susceptibility Mapping by Inversion of a Perturbation Field Model: Correlation with Brain Iron in Normal Aging

PubMed Central

Poynton, Clare; Jenkinson, Mark; Adalsteinsson, Elfar; Sullivan, Edith V.; Pfefferbaum, Adolf; Wells, William

2015-01-01

There is increasing evidence that iron deposition occurs in specific regions of the brain in normal aging and neurodegenerative disorders such as Parkinson's, Huntington's, and Alzheimer's disease. Iron deposition changes the magnetic susceptibility of tissue, which alters the MR signal phase, and allows estimation of susceptibility differences using quantitative susceptibility mapping (QSM). We present a method for quantifying susceptibility by inversion of a perturbation model, or ‘QSIP’. The perturbation model relates phase to susceptibility using a kernel calculated in the spatial domain, in contrast to previous Fourier-based techniques. A tissue/air susceptibility atlas is used to estimate B0 inhomogeneity. QSIP estimates in young and elderly subjects are compared to postmortem iron estimates, maps of the Field-Dependent Relaxation Rate Increase (FDRI), and the L1-QSM method. Results for both groups showed excellent agreement with published postmortem data and in-vivo FDRI: statistically significant Spearman correlations ranging from Rho = 0.905 to Rho = 1.00 were obtained. QSIP also showed improvement over FDRI and L1-QSM: reduced variance in susceptibility estimates and statistically significant group differences were detected in striatal and brainstem nuclei, consistent with age-dependent iron accumulation in these regions. PMID:25248179

Quantitative Evaluation of 2 Scatter-Correction Techniques for 18F-FDG Brain PET/MRI in Regard to MR-Based Attenuation Correction.

PubMed

Teuho, Jarmo; Saunavaara, Virva; Tolvanen, Tuula; Tuokkola, Terhi; Karlsson, Antti; Tuisku, Jouni; Teräs, Mika

2017-10-01

In PET, corrections for photon scatter and attenuation are essential for visual and quantitative consistency. MR attenuation correction (MRAC) is generally conducted by image segmentation and assignment of discrete attenuation coefficients, which offer limited accuracy compared with CT attenuation correction. Potential inaccuracies in MRAC may affect scatter correction, because the attenuation image (μ-map) is used in single scatter simulation (SSS) to calculate the scatter estimate. We assessed the impact of MRAC to scatter correction using 2 scatter-correction techniques and 3 μ-maps for MRAC. Methods: The tail-fitted SSS (TF-SSS) and a Monte Carlo-based single scatter simulation (MC-SSS) algorithm implementations on the Philips Ingenuity TF PET/MR were used with 1 CT-based and 2 MR-based μ-maps. Data from 7 subjects were used in the clinical evaluation, and a phantom study using an anatomic brain phantom was conducted. Scatter-correction sinograms were evaluated for each scatter correction method and μ-map. Absolute image quantification was investigated with the phantom data. Quantitative assessment of PET images was performed by volume-of-interest and ratio image analysis. Results: MRAC did not result in large differences in scatter algorithm performance, especially with TF-SSS. Scatter sinograms and scatter fractions did not reveal large differences regardless of the μ-map used. TF-SSS showed slightly higher absolute quantification. The differences in volume-of-interest analysis between TF-SSS and MC-SSS were 3% at maximum in the phantom and 4% in the patient study. Both algorithms showed excellent correlation with each other with no visual differences between PET images. MC-SSS showed a slight dependency on the μ-map used, with a difference of 2% on average and 4% at maximum when a μ-map without bone was used. Conclusion: The effect of different MR-based μ-maps on the performance of scatter correction was minimal in non-time-of-flight 18 F-FDG PET/MR brain imaging. The SSS algorithm was not affected significantly by MRAC. The performance of the MC-SSS algorithm is comparable but not superior to TF-SSS, warranting further investigations of algorithm optimization and performance with different radiotracers and time-of-flight imaging. © 2017 by the Society of Nuclear Medicine and Molecular Imaging.

Group-regularized individual prediction: theory and application to pain.

PubMed

Lindquist, Martin A; Krishnan, Anjali; López-Solà, Marina; Jepma, Marieke; Woo, Choong-Wan; Koban, Leonie; Roy, Mathieu; Atlas, Lauren Y; Schmidt, Liane; Chang, Luke J; Reynolds Losin, Elizabeth A; Eisenbarth, Hedwig; Ashar, Yoni K; Delk, Elizabeth; Wager, Tor D

2017-01-15

Multivariate pattern analysis (MVPA) has become an important tool for identifying brain representations of psychological processes and clinical outcomes using fMRI and related methods. Such methods can be used to predict or 'decode' psychological states in individual subjects. Single-subject MVPA approaches, however, are limited by the amount and quality of individual-subject data. In spite of higher spatial resolution, predictive accuracy from single-subject data often does not exceed what can be accomplished using coarser, group-level maps, because single-subject patterns are trained on limited amounts of often-noisy data. Here, we present a method that combines population-level priors, in the form of biomarker patterns developed on prior samples, with single-subject MVPA maps to improve single-subject prediction. Theoretical results and simulations motivate a weighting based on the relative variances of biomarker-based prediction-based on population-level predictive maps from prior groups-and individual-subject, cross-validated prediction. Empirical results predicting pain using brain activity on a trial-by-trial basis (single-trial prediction) across 6 studies (N=180 participants) confirm the theoretical predictions. Regularization based on a population-level biomarker-in this case, the Neurologic Pain Signature (NPS)-improved single-subject prediction accuracy compared with idiographic maps based on the individuals' data alone. The regularization scheme that we propose, which we term group-regularized individual prediction (GRIP), can be applied broadly to within-person MVPA-based prediction. We also show how GRIP can be used to evaluate data quality and provide benchmarks for the appropriateness of population-level maps like the NPS for a given individual or study. Copyright © 2015 Elsevier Inc. All rights reserved.

Confidence-based ensemble for GBM brain tumor segmentation

NASA Astrophysics Data System (ADS)

Huo, Jing; van Rikxoort, Eva M.; Okada, Kazunori; Kim, Hyun J.; Pope, Whitney; Goldin, Jonathan; Brown, Matthew

2011-03-01

It is a challenging task to automatically segment glioblastoma multiforme (GBM) brain tumors on T1w post-contrast isotropic MR images. A semi-automated system using fuzzy connectedness has recently been developed for computing the tumor volume that reduces the cost of manual annotation. In this study, we propose a an ensemble method that combines multiple segmentation results into a final ensemble one. The method is evaluated on a dataset of 20 cases from a multi-center pharmaceutical drug trial and compared to the fuzzy connectedness method. Three individual methods were used in the framework: fuzzy connectedness, GrowCut, and voxel classification. The combination method is a confidence map averaging (CMA) method. The CMA method shows an improved ROC curve compared to the fuzzy connectedness method (p < 0.001). The CMA ensemble result is more robust compared to the three individual methods.

Mapping Language Function in the Brain: A Review of the Recent Literature.

ERIC Educational Resources Information Center

Crafton, Robert E.; Kido, Elissa

2000-01-01

Considers the potential importance of brain study for composition instruction, briefly describes functional imaging techniques, and reviews the findings of recent brain-mapping studies investigating the neurocognitive systems involved in language function. Presents a review of the recent literature and considers the possible implications of this…

SHARAKU: an algorithm for aligning and clustering read mapping profiles of deep sequencing in non-coding RNA processing.

PubMed

Tsuchiya, Mariko; Amano, Kojiro; Abe, Masaya; Seki, Misato; Hase, Sumitaka; Sato, Kengo; Sakakibara, Yasubumi

2016-06-15

Deep sequencing of the transcripts of regulatory non-coding RNA generates footprints of post-transcriptional processes. After obtaining sequence reads, the short reads are mapped to a reference genome, and specific mapping patterns can be detected called read mapping profiles, which are distinct from random non-functional degradation patterns. These patterns reflect the maturation processes that lead to the production of shorter RNA sequences. Recent next-generation sequencing studies have revealed not only the typical maturation process of miRNAs but also the various processing mechanisms of small RNAs derived from tRNAs and snoRNAs. We developed an algorithm termed SHARAKU to align two read mapping profiles of next-generation sequencing outputs for non-coding RNAs. In contrast with previous work, SHARAKU incorporates the primary and secondary sequence structures into an alignment of read mapping profiles to allow for the detection of common processing patterns. Using a benchmark simulated dataset, SHARAKU exhibited superior performance to previous methods for correctly clustering the read mapping profiles with respect to 5'-end processing and 3'-end processing from degradation patterns and in detecting similar processing patterns in deriving the shorter RNAs. Further, using experimental data of small RNA sequencing for the common marmoset brain, SHARAKU succeeded in identifying the significant clusters of read mapping profiles for similar processing patterns of small derived RNA families expressed in the brain. The source code of our program SHARAKU is available at http://www.dna.bio.keio.ac.jp/sharaku/, and the simulated dataset used in this work is available at the same link. Accession code: The sequence data from the whole RNA transcripts in the hippocampus of the left brain used in this work is available from the DNA DataBank of Japan (DDBJ) Sequence Read Archive (DRA) under the accession number DRA004502. yasu@bio.keio.ac.jp Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press.

MO-F-CAMPUS-J-04: Tissue Segmentation-Based MR Electron Density Mapping Method for MR-Only Radiation Treatment Planning of Brain

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

Yu, H; Lee, Y; Ruschin, M

2015-06-15

Purpose: Automatically derive electron density of tissues using MR images and generate a pseudo-CT for MR-only treatment planning of brain tumours. Methods: 20 stereotactic radiosurgery (SRS) patients’ T1-weighted MR images and CT images were retrospectively acquired. First, a semi-automated tissue segmentation algorithm was developed to differentiate tissues with similar MR intensities and large differences in electron densities. The method started with approximately 12 slices of manually contoured spatial regions containing sinuses and airways, then air, bone, brain, cerebrospinal fluid (CSF) and eyes were automatically segmented using edge detection and anatomical information including location, shape, tissue uniformity and relative intensity distribution.more » Next, soft tissues - muscle and fat were segmented based on their relative intensity histogram. Finally, intensities of voxels in each segmented tissue were mapped into their electron density range to generate pseudo-CT by linearly fitting their relative intensity histograms. Co-registered CT was used as a ground truth. The bone segmentations of pseudo-CT were compared with those of co-registered CT obtained by using a 300HU threshold. The average distances between voxels on external edges of the skull of pseudo-CT and CT in three axial, coronal and sagittal slices with the largest width of skull were calculated. The mean absolute electron density (in Hounsfield unit) difference of voxels in each segmented tissues was calculated. Results: The average of distances between voxels on external skull from pseudo-CT and CT were 0.6±1.1mm (mean±1SD). The mean absolute electron density differences for bone, brain, CSF, muscle and fat are 78±114 HU, and 21±8 HU, 14±29 HU, 57±37 HU, and 31±63 HU, respectively. Conclusion: The semi-automated MR electron density mapping technique was developed using T1-weighted MR images. The generated pseudo-CT is comparable to that of CT in terms of anatomical position of tissues and similarity of electron density assignment. This method can allow MR-only treatment planning.« less

In vivo Magnetic Resonance Imaging of Tumor Protease Activity

PubMed Central

Haris, Mohammad; Singh, Anup; Mohammed, Imran; Ittyerah, Ranjit; Nath, Kavindra; Nanga, Ravi Prakash Reddy; Debrosse, Catherine; Kogan, Feliks; Cai, Kejia; Poptani, Harish; Reddy, Damodar; Hariharan, Hari; Reddy, Ravinder

2014-01-01

Increased expression of cathepsins has diagnostic as well as prognostic value in several types of cancer. Here, we demonstrate a novel magnetic resonance imaging (MRI) method, which uses poly-L-glutamate (PLG) as an MRI probe to map cathepsin expression in vivo, in a rat brain tumor model. This noninvasive, high-resolution and non-radioactive method exploits the differences in the CEST signals of PLG in the native form and cathepsin mediated cleaved form. The method was validated in phantoms with known physiological concentrations, in tumor cells and in an animal model of brain tumor along with immunohistochemical analysis. Potential applications in tumor diagnosis and evaluation of therapeutic response are outlined. PMID:25124082

PSF mapping-based correction of eddy-current-induced distortions in diffusion-weighted echo-planar imaging.

PubMed

In, Myung-Ho; Posnansky, Oleg; Speck, Oliver

2016-05-01

To accurately correct diffusion-encoding direction-dependent eddy-current-induced geometric distortions in diffusion-weighted echo-planar imaging (DW-EPI) and to minimize the calibration time at 7 Tesla (T). A point spread function (PSF) mapping based eddy-current calibration method is newly presented to determine eddy-current-induced geometric distortions even including nonlinear eddy-current effects within the readout acquisition window. To evaluate the temporal stability of eddy-current maps, calibration was performed four times within 3 months. Furthermore, spatial variations of measured eddy-current maps versus their linear superposition were investigated to enable correction in DW-EPIs with arbitrary diffusion directions without direct calibration. For comparison, an image-based eddy-current correction method was additionally applied. Finally, this method was combined with a PSF-based susceptibility-induced distortion correction approach proposed previously to correct both susceptibility and eddy-current-induced distortions in DW-EPIs. Very fast eddy-current calibration in a three-dimensional volume is possible with the proposed method. The measured eddy-current maps are very stable over time and very similar maps can be obtained by linear superposition of principal-axes eddy-current maps. High resolution in vivo brain results demonstrate that the proposed method allows more efficient eddy-current correction than the image-based method. The combination of both PSF-based approaches allows distortion-free images, which permit reliable analysis in diffusion tensor imaging applications at 7T. © 2015 Wiley Periodicals, Inc.

Methods for the correction of vascular artifacts in PET O-15 water brain-mapping studies

NASA Astrophysics Data System (ADS)

Chen, Kewei; Reiman, E. M.; Lawson, M.; Yun, Lang-sheng; Bandy, D.; Palant, A.

1996-12-01

While positron emission tomographic (PET) measurements of regional cerebral blood flow (rCBF) can be used to map brain regions that are involved in normal and pathological human behaviors, measurements in the anteromedial temporal lobe can be confounded by the combined effects of radiotracer activity in neighboring arteries and partial-volume averaging. The authors now describe two simple methods to address this vascular artifact. One method utilizes the early frames of a dynamic PET study, while the other method utilizes a coregistered magnetic resonance image (MRI) to characterize the vascular region of interest (VROI). Both methods subsequently assign a common value to each pixel in the VROI for the control (baseline) scan and the activation scan. To study the vascular artifact and to demonstrate the ability of the proposed methods correcting the vascular artifact, four dynamic PET scans were performed in a single subject during the same behavioral state. For each of the four scans, a vascular scan containing vascular activity was computed as the summation of the images acquired 0-60 s after radiotracer administration, and a control scan containing minimal vascular activity was computed as the summation of the images acquired 20-80 s after radiotracer administration. t-score maps calculated from the four pairs of vascular and control scans were used to characterize regional blood flow differences related to vascular activity before and after the application of each vascular artifact correction method. Both methods eliminated the observed differences in vascular activity, as well as the vascular artifact observed in the anteromedial temporal lobes. Using PET data from a study of normal human emotion, these methods permitted the authors to identify rCBF increases in the anteromedial temporal lobe free from the potentially confounding, combined effects of vascular activity and partial-volume averaging.

Whole-central nervous system functional imaging in larval Drosophila

PubMed Central

Lemon, William C.; Pulver, Stefan R.; Höckendorf, Burkhard; McDole, Katie; Branson, Kristin; Freeman, Jeremy; Keller, Philipp J.

2015-01-01

Understanding how the brain works in tight concert with the rest of the central nervous system (CNS) hinges upon knowledge of coordinated activity patterns across the whole CNS. We present a method for measuring activity in an entire, non-transparent CNS with high spatiotemporal resolution. We combine a light-sheet microscope capable of simultaneous multi-view imaging at volumetric speeds 25-fold faster than the state-of-the-art, a whole-CNS imaging assay for the isolated Drosophila larval CNS and a computational framework for analysing multi-view, whole-CNS calcium imaging data. We image both brain and ventral nerve cord, covering the entire CNS at 2 or 5 Hz with two- or one-photon excitation, respectively. By mapping network activity during fictive behaviours and quantitatively comparing high-resolution whole-CNS activity maps across individuals, we predict functional connections between CNS regions and reveal neurons in the brain that identify type and temporal state of motor programs executed in the ventral nerve cord. PMID:26263051

Dynamic changes in the distribution and time course of blood-brain barrier-permeative nitroxides in the mouse head with EPR imaging: visualization of blood flow in a mouse model of ischemia.

PubMed

Emoto, Miho C; Sato-Akaba, Hideo; Hirata, Hiroshi; Fujii, Hirotada G

2014-09-01

Electron paramagnetic resonance (EPR) imaging using nitroxides as redox-sensitive probes is a powerful, noninvasive method that can be used under various physiological conditions to visualize changes in redox status that result from oxidative damage. Two blood-brain barrier-permeative nitroxides, 3-hydroxymethyl-2,2,5,5-tetramethylpyrrolidine-1-oxyl (HMP) and 3-methoxycarbonyl-2,2,5,5-tetramethylpyrrolidine-1-yloxy (MCP), have been widely used as redox-sensitive probes in the brains of small animals, but their in vivo distribution and properties have not yet been analyzed in detail. In this study, a custom-made continuous-wave three-dimensional (3D) EPR imager was used to obtain 3D EPR images of mouse heads using MCP or HMP. This EPR imager made it possible to take 3D EPR images reconstructed from data from 181 projections acquired every 60s. Using this improved EPR imager and magnetic resonance imaging, the distribution and reduction time courses of HMP and MCP were examined in mouse heads. EPR images of living mice revealed that HMP and MCP have different distributions and different time courses for entering the brain. Based on the pharmacokinetics of the reduction reactions of HMP and MCP in the mouse head, the half-lives of HMP and MCP were clearly and accurately mapped pixel by pixel. An ischemic mouse model was prepared, and the half-life of MCP was mapped in the mouse head. Compared to the half-life in control mice, the half-life of MCP in the ischemic model mouse brain was significantly increased, suggesting a shift in the redox balance. This in vivo EPR imaging method using BBB-permeative MCP is a useful noninvasive method for assessing changes in the redox status in mouse brains under oxidative stress. Copyright © 2014 Elsevier Inc. All rights reserved.

Revealing pathologies in the liquid crystalline structures of the brain by polarimetric studies (Presentation Recording)

NASA Astrophysics Data System (ADS)

Bakhshetyan, Karen; Melkonyan, Gurgen G.; Galstian, Tigran V.; Saghatelyan, Armen

2015-10-01

Natural or "self" alignment of molecular complexes in living tissue represents many similarities with liquid crystals (LC), which are anisotropic liquids. The orientational characteristics of those complexes may be related to many important functional parameters and their study may reveal important pathologies. The know-how, accumulated thanks to the study of LC materials, may thus be used to this end. One of the traditionally used methods, to characterize those materials, is the polarized light imaging (PLI) that allows for label-free analysis of anisotropic structures in the brain tissue and can be used, for example, for the analysis of myelinated fiber bundles. In the current work, we first attempted to apply the PLI on the mouse histological brain sections to create a map of anisotropic structures using cross-polarizer transmission light. Then we implemented the PLI for comparative study of histological sections of human postmortem brain samples under normal and pathological conditions, such as Parkinson's disease (PD). Imaging the coronal, sagittal and horizontal sections of mouse brain allowed us to create a false color-coded fiber orientation map under polarized light. In human brain datasets for both control and PD groups we measured the pixel intensities in myelin-rich subregions of internal capsule and normalized these to non-myelinated background signal from putamen and caudate nucleus. Quantification of intensities revealed a statistically significant reduction of fiber intensity of PD compared to control subjects (2.801 +/- 0.303 and 3.724 +/- 0.07 respectively; *p < 0.05). Our study confirms the validity of PLI method for visualizing myelinated axonal fibers. This relatively simple technique can become a promising tool for study of neurodegenerative diseases where labeling-free imaging is an important benefit.

High-resolution in vivo Wistar rodent brain atlas based on T1 weighted image

NASA Astrophysics Data System (ADS)

Huang, Su; Lu, Zhongkang; Huang, Weimin; Seramani, Sankar; Ramasamy, Boominathan; Sekar, Sakthivel; Guan, Cuntai; Bhakoo, Kishore

2016-03-01

Image based atlases for rats brain have a significant impact on pre-clinical research. In this project we acquired T1-weighted images from Wistar rodent brains with fine 59μm isotropical resolution for generation of the atlas template image. By applying post-process procedures using a semi-automatic brain extraction method, we delineated the brain tissues from source data. Furthermore, we applied a symmetric group-wise normalization method to generate an optimized template of T1 image of rodent brain, then aligned our template to the Waxholm Space. In addition, we defined several simple and explicit landmarks to corresponding our template with the well known Paxinos stereotaxic reference system. Anchoring at the origin of the Waxholm Space, we applied piece-wise linear transformation method to map the voxels of the template into the coordinates system in Paxinos' stereotoxic coordinates to facilitate the labelling task. We also cross-referenced our data with both published rodent brain atlas and image atlases available online, methodologically labelling the template to produce a Wistar brain atlas identifying more than 130 structures. Particular attention was paid to the cortex and cerebellum, as these areas encompass the most researched aspects of brain functions. Moreover, we adopted the structure hierarchy and naming nomenclature common to various atlases, so that the names and hierarchy structure presented in the atlas are readily recognised for easy use. It is believed the atlas will present a useful tool in rodent brain functional and pharmaceutical studies.

[Awake craniotomy for brain tumours].

PubMed

Milos, Peter; Metcalf, Kerstin; Vigren, Patrick; Lindehammar, Hans; Nilsson, Malin; Boström, Sverre

2016-10-11

Awake craniotomy for brain tumours  Awake neurosurgery is a useful method in lesions near eloquent brain areas, particularly low-grade gliomas.The aim is to maximise tumour resection and preserve neurological function. We performed 40 primary awake surgeries and 8 residual surgeries. Patients were operated awake throughout the procedure or with a laryngeal mask and general anaesthesia during the opening stage and then awake during intracerebral surgery. Language and motor function were mapped with direct cortical stimulation, motor evoked potential and standardised neurological testing. Radiologically, complete resection was achieved in 18 out of 40 patients in the primary surgeries. Full neurological recovery at three months was observed in 29 patients. Of the 11 patients with persisting neurological deficits at three months, symptoms were present preoperatively in 9 patients. We conclude that awake surgery, combined with intraoperative neurophysiological methods, is a safe method to improve treatment for low-grade gliomas.

Rapid simultaneous high-resolution mapping of myelin water fraction and relaxation times in human brain using BMC-mcDESPOT.

PubMed

Bouhrara, Mustapha; Spencer, Richard G

2017-02-15

A number of central nervous system (CNS) diseases exhibit changes in myelin content and magnetic resonance longitudinal, T 1 , and transverse, T 2 , relaxation times, which therefore represent important biomarkers of CNS pathology. Among the methods applied for measurement of myelin water fraction (MWF) and relaxation times, the multicomponent driven equilibrium single pulse observation of T 1 and T 2 (mcDESPOT) approach is of particular interest. mcDESPOT permits whole brain mapping of multicomponent T 1 and T 2 , with data acquisition accomplished within a clinically realistic acquisition time. Unfortunately, previous studies have indicated the limited performance of mcDESPOT in the setting of the modest signal-to-noise range of high-resolution mapping, required for the depiction of small structures and to reduce partial volume effects. Recently, we showed that a new Bayesian Monte Carlo (BMC) analysis substantially improved determination of MWF from mcDESPOT imaging data. However, our previous study was limited in that it did not discuss determination of relaxation times. Here, we extend the BMC analysis to the simultaneous determination of whole-brain MWF and relaxation times using the two-component mcDESPOT signal model. Simulation analyses and in-vivo human brain studies indicate the overall greater performance of this approach compared to the stochastic region contraction (SRC) algorithm, conventionally used to derive parameter estimates from mcDESPOT data. SRC estimates of the transverse relaxation time of the long T 2 fraction, T 2,l , and the longitudinal relaxation time of the short T 1 fraction, T 1,s , clustered towards the lower and upper parameter search space limits, respectively, indicating failure of the fitting procedure. We demonstrate that this effect is absent in the BMC analysis. Our results also showed improved parameter estimation for BMC as compared to SRC for high-resolution mapping. Overall we find that the combination of BMC analysis and mcDESPOT, BMC-mcDESPOT, shows excellent performance for accurate high-resolution whole-brain mapping of MWF and bi-component transverse and longitudinal relaxation times within a clinically realistic acquisition time. Published by Elsevier Inc.

Brain Modulyzer: Interactive Visual Analysis of Functional Brain Connectivity

DOE PAGES

Murugesan, Sugeerth; Bouchard, Kristopher; Brown, Jesse A.; ...

2016-05-09

Here, we present Brain Modulyzer, an interactive visual exploration tool for functional magnetic resonance imaging (fMRI) brain scans, aimed at analyzing the correlation between different brain regions when resting or when performing mental tasks. Brain Modulyzer combines multiple coordinated views—such as heat maps, node link diagrams, and anatomical views—using brushing and linking to provide an anatomical context for brain connectivity data. Integrating methods from graph theory and analysis, e.g., community detection and derived graph measures, makes it possible to explore the modular and hierarchical organization of functional brain networks. Providing immediate feedback by displaying analysis results instantaneously while changing parametersmore » gives neuroscientists a powerful means to comprehend complex brain structure more effectively and efficiently and supports forming hypotheses that can then be validated via statistical analysis. In order to demonstrate the utility of our tool, we also present two case studies—exploring progressive supranuclear palsy, as well as memory encoding and retrieval« less

Brain Modulyzer: Interactive Visual Analysis of Functional Brain Connectivity

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

Murugesan, Sugeerth; Bouchard, Kristopher; Brown, Jesse A.

Here, we present Brain Modulyzer, an interactive visual exploration tool for functional magnetic resonance imaging (fMRI) brain scans, aimed at analyzing the correlation between different brain regions when resting or when performing mental tasks. Brain Modulyzer combines multiple coordinated views—such as heat maps, node link diagrams, and anatomical views—using brushing and linking to provide an anatomical context for brain connectivity data. Integrating methods from graph theory and analysis, e.g., community detection and derived graph measures, makes it possible to explore the modular and hierarchical organization of functional brain networks. Providing immediate feedback by displaying analysis results instantaneously while changing parametersmore » gives neuroscientists a powerful means to comprehend complex brain structure more effectively and efficiently and supports forming hypotheses that can then be validated via statistical analysis. In order to demonstrate the utility of our tool, we also present two case studies—exploring progressive supranuclear palsy, as well as memory encoding and retrieval« less

Intraoperative Functional Ultrasound Imaging of Human Brain Activity.

PubMed

Imbault, Marion; Chauvet, Dorian; Gennisson, Jean-Luc; Capelle, Laurent; Tanter, Mickael

2017-08-04

The functional mapping of brain activity is essential to perform optimal glioma surgery and to minimize the risk of postoperative deficits. We introduce a new, portable neuroimaging modality of the human brain based on functional ultrasound (fUS) for deep functional cortical mapping. Using plane-wave transmissions at an ultrafast frame rate (1 kHz), fUS is performed during surgery to measure transient changes in cerebral blood volume with a high spatiotemporal resolution (250 µm, 1 ms). fUS identifies, maps and differentiates regions of brain activation during task-evoked cortical responses within the depth of a sulcus in both awake and anaesthetized patients.

Differences in Information Mapping Strategies in Left and Right Brain Learners.

ERIC Educational Resources Information Center

Hauck, LaVerne S., Jr.

The Information Mapping technique was used to present a learning packet, and its usefulness in helping right-brain cerebrally dominant students to achieve the same level of subject mastery as their left-brain counterparts was examined. Reading level, grade point average, and gender were also analyzed. Torrance's "Your Style of Learning and…

On the accuracy and reproducibility of a novel probabilistic atlas-based generation for calculation of head attenuation maps on integrated PET/MR scanners.

PubMed

Chen, Kevin T; Izquierdo-Garcia, David; Poynton, Clare B; Chonde, Daniel B; Catana, Ciprian

2017-03-01

To propose an MR-based method for generating continuous-valued head attenuation maps and to assess its accuracy and reproducibility. Demonstrating that novel MR-based photon attenuation correction methods are both accurate and reproducible is essential prior to using them routinely in research and clinical studies on integrated PET/MR scanners. Continuous-valued linear attenuation coefficient maps ("μ-maps") were generated by combining atlases that provided the prior probability of voxel positions belonging to a certain tissue class (air, soft tissue, or bone) and an MR intensity-based likelihood classifier to produce posterior probability maps of tissue classes. These probabilities were used as weights to generate the μ-maps. The accuracy of this probabilistic atlas-based continuous-valued μ-map ("PAC-map") generation method was assessed by calculating the voxel-wise absolute relative change (RC) between the MR-based and scaled CT-based attenuation-corrected PET images. To assess reproducibility, we performed pair-wise comparisons of the RC values obtained from the PET images reconstructed using the μ-maps generated from the data acquired at three time points. The proposed method produced continuous-valued μ-maps that qualitatively reflected the variable anatomy in patients with brain tumor and agreed well with the scaled CT-based μ-maps. The absolute RC comparing the resulting PET volumes was 1.76 ± 2.33 %, quantitatively demonstrating that the method is accurate. Additionally, we also showed that the method is highly reproducible, the mean RC value for the PET images reconstructed using the μ-maps obtained at the three visits being 0.65 ± 0.95 %. Accurate and highly reproducible continuous-valued head μ-maps can be generated from MR data using a probabilistic atlas-based approach.

A Hybrid Hierarchical Approach for Brain Tissue Segmentation by Combining Brain Atlas and Least Square Support Vector Machine

PubMed Central

Kasiri, Keyvan; Kazemi, Kamran; Dehghani, Mohammad Javad; Helfroush, Mohammad Sadegh

2013-01-01

In this paper, we present a new semi-automatic brain tissue segmentation method based on a hybrid hierarchical approach that combines a brain atlas as a priori information and a least-square support vector machine (LS-SVM). The method consists of three steps. In the first two steps, the skull is removed and the cerebrospinal fluid (CSF) is extracted. These two steps are performed using the toolbox FMRIB's automated segmentation tool integrated in the FSL software (FSL-FAST) developed in Oxford Centre for functional MRI of the brain (FMRIB). Then, in the third step, the LS-SVM is used to segment grey matter (GM) and white matter (WM). The training samples for LS-SVM are selected from the registered brain atlas. The voxel intensities and spatial positions are selected as the two feature groups for training and test. SVM as a powerful discriminator is able to handle nonlinear classification problems; however, it cannot provide posterior probability. Thus, we use a sigmoid function to map the SVM output into probabilities. The proposed method is used to segment CSF, GM and WM from the simulated magnetic resonance imaging (MRI) using Brainweb MRI simulator and real data provided by Internet Brain Segmentation Repository. The semi-automatically segmented brain tissues were evaluated by comparing to the corresponding ground truth. The Dice and Jaccard similarity coefficients, sensitivity and specificity were calculated for the quantitative validation of the results. The quantitative results show that the proposed method segments brain tissues accurately with respect to corresponding ground truth. PMID:24696800

Planar implantable sensor for in vivo measurement of cellular oxygen metabolism in brain tissue.

PubMed

Tsytsarev, Vassiliy; Akkentli, Fatih; Pumbo, Elena; Tang, Qinggong; Chen, Yu; Erzurumlu, Reha S; Papkovsky, Dmitri B

2017-04-01

Brain imaging methods are continually improving. Imaging of the cerebral cortex is widely used in both animal experiments and charting human brain function in health and disease. Among the animal models, the rodent cerebral cortex has been widely used because of patterned neural representation of the whiskers on the snout and relative ease of activating cortical tissue with whisker stimulation. We tested a new planar solid-state oxygen sensor comprising a polymeric film with a phosphorescent oxygen-sensitive coating on the working side, to monitor dynamics of oxygen metabolism in the cerebral cortex following sensory stimulation. Sensory stimulation led to changes in oxygenation and deoxygenation processes of activated areas in the barrel cortex. We demonstrate the possibility of dynamic mapping of relative changes in oxygenation in live mouse brain tissue with such a sensor. Oxygenation-based functional magnetic resonance imaging (fMRI) is very effective method for functional brain mapping but have high costs and limited spatial resolution. Optical imaging of intrinsic signal (IOS) does not provide the required sensitivity, and voltage-sensitive dye optical imaging (VSDi) has limited applicability due to significant toxicity of the voltage-sensitive dye. Our planar solid-state oxygen sensor imaging approach circumvents these limitations, providing a simple optical contrast agent with low toxicity and rapid application. The planar solid-state oxygen sensor described here can be used as a tool in visualization and real-time analysis of sensory-evoked neural activity in vivo. Further, this approach allows visualization of local neural activity with high temporal and spatial resolution. Copyright © 2017 Elsevier B.V. All rights reserved.

Phase congruency map driven brain tumour segmentation

NASA Astrophysics Data System (ADS)

Szilágyi, Tünde; Brady, Michael; Berényi, Ervin

2015-03-01

Computer Aided Diagnostic (CAD) systems are already of proven value in healthcare, especially for surgical planning, nevertheless much remains to be done. Gliomas are the most common brain tumours (70%) in adults, with a survival time of just 2-3 months if detected at WHO grades III or higher. Such tumours are extremely variable, necessitating multi-modal Magnetic Resonance Images (MRI). The use of Gadolinium-based contrast agents is only relevant at later stages of the disease where it highlights the enhancing rim of the tumour. Currently, there is no single accepted method that can be used as a reference. There are three main challenges with such images: to decide whether there is tumour present and is so localize it; to construct a mask that separates healthy and diseased tissue; and to differentiate between the tumour core and the surrounding oedema. This paper presents two contributions. First, we develop tumour seed selection based on multiscale multi-modal texture feature vectors. Second, we develop a method based on a local phase congruency based feature map to drive level-set segmentation. The segmentations achieved with our method are more accurate than previously presented methods, particularly for challenging low grade tumours.

Improved spatial coverage for brain 3D PRESS MRSI by automatic placement of outer-volume suppression saturation bands.

PubMed

Ozhinsky, Eugene; Vigneron, Daniel B; Nelson, Sarah J

2011-04-01

To develop a technique for optimizing coverage of brain 3D (1) H magnetic resonance spectroscopic imaging (MRSI) by automatic placement of outer-volume suppression (OVS) saturation bands (sat bands) and to compare the performance for point-resolved spectroscopic sequence (PRESS) MRSI protocols with manual and automatic placement of sat bands. The automated OVS procedure includes the acquisition of anatomic images from the head, obtaining brain and lipid tissue maps, calculating optimal sat band placement, and then using those optimized parameters during the MRSI acquisition. The data were analyzed to quantify brain coverage volume and data quality. 3D PRESS MRSI data were acquired from three healthy volunteers and 29 patients using protocols that included either manual or automatic sat band placement. On average, the automatic sat band placement allowed the acquisition of PRESS MRSI data from 2.7 times larger brain volumes than the conventional method while maintaining data quality. The technique developed helps solve two of the most significant problems with brain PRESS MRSI acquisitions: limited brain coverage and difficulty in prescription. This new method will facilitate routine clinical brain 3D MRSI exams and will be important for performing serial evaluation of response to therapy in patients with brain tumors and other neurological diseases. Copyright © 2011 Wiley-Liss, Inc.

Joint explorative analysis of neuroreceptor subsystems in the human brain: application to receptor-transporter correlation using PET data.

PubMed

Cselényi, Zsolt; Lundberg, Johan; Halldin, Christer; Farde, Lars; Gulyás, Balázs

2004-10-01

Positron emission tomography (PET) has proved to be a highly successful technique in the qualitative and quantitative exploration of the human brain's neurotransmitter-receptor systems. In recent years, the number of PET radioligands, targeted to different neuroreceptor systems of the human brain, has increased considerably. This development paves the way for a simultaneous analysis of different receptor systems and subsystems in the same individual. The detailed exploration of the versatility of neuroreceptor systems requires novel technical approaches, capable of operating on huge parametric image datasets. An initial step of such explorative data processing and analysis should be the development of novel exploratory data-mining tools to gain insight into the "structure" of complex multi-individual, multi-receptor data sets. For practical reasons, a possible and feasible starting point of multi-receptor research can be the analysis of the pre- and post-synaptic binding sites of the same neurotransmitter. In the present study, we propose an unsupervised, unbiased data-mining tool for this task and demonstrate its usefulness by using quantitative receptor maps, obtained with positron emission tomography, from five healthy subjects on (pre-synaptic) serotonin transporters (5-HTT or SERT) and (post-synaptic) 5-HT(1A) receptors. Major components of the proposed technique include the projection of the input receptor maps to a feature space, the quasi-clustering and classification of projected data (neighbourhood formation), trans-individual analysis of neighbourhood properties (trajectory analysis), and the back-projection of the results of trajectory analysis to normal space (creation of multi-receptor maps). The resulting multi-receptor maps suggest that complex relationships and tendencies in the relationship between pre- and post-synaptic transporter-receptor systems can be revealed and classified by using this method. As an example, we demonstrate the regional correlation of the serotonin transporter-receptor systems. These parameter-specific multi-receptor maps can usefully guide the researchers in their endeavour to formulate models of multi-receptor interactions and changes in the human brain.

Optimized MLAA for quantitative non-TOF PET/MR of the brain

NASA Astrophysics Data System (ADS)

Benoit, Didier; Ladefoged, Claes N.; Rezaei, Ahmadreza; Keller, Sune H.; Andersen, Flemming L.; Højgaard, Liselotte; Hansen, Adam E.; Holm, Søren; Nuyts, Johan

2016-12-01

For quantitative tracer distribution in positron emission tomography, attenuation correction is essential. In a hybrid PET/CT system the CT images serve as a basis for generation of the attenuation map, but in PET/MR, the MR images do not have a similarly simple relationship with the attenuation map. Hence attenuation correction in PET/MR systems is more challenging. Typically either of two MR sequences are used: the Dixon or the ultra-short time echo (UTE) techniques. However these sequences have some well-known limitations. In this study, a reconstruction technique based on a modified and optimized non-TOF MLAA is proposed for PET/MR brain imaging. The idea is to tune the parameters of the MLTR applying some information from an attenuation image computed from the UTE sequences and a T1w MR image. In this MLTR algorithm, an {αj} parameter is introduced and optimized in order to drive the algorithm to a final attenuation map most consistent with the emission data. Because the non-TOF MLAA is used, a technique to reduce the cross-talk effect is proposed. In this study, the proposed algorithm is compared to the common reconstruction methods such as OSEM using a CT attenuation map, considered as the reference, and OSEM using the Dixon and UTE attenuation maps. To show the robustness and the reproducibility of the proposed algorithm, a set of 204 [18F]FDG patients, 35 [11C]PiB patients and 1 [18F]FET patient are used. The results show that by choosing an optimized value of {αj} in MLTR, the proposed algorithm improves the results compared to the standard MR-based attenuation correction methods (i.e. OSEM using the Dixon or the UTE attenuation maps), and the cross-talk and the scale problem are limited.

Up-regulation of microtubule-associated protein 2 accompanying the filial imprinting of domestic chicks (Gallus gallus domesticus).

PubMed

Yamaguchi, Shinji; Fujii-Taira, Ikuko; Murakami, Akio; Hirose, Naoki; Aoki, Naoya; Izawa, Ei-Ichi; Fujimoto, Yasuyuki; Takano, Tatsuya; Matsushima, Toshiya; Homma, Koichi J

2008-06-15

Using cDNA microarrays, we have identified elsewhere the genes of microtubule-associated proteins as a group up-regulated in newly hatched chick brains after filial imprinting training. Here we show by in situ hybridization that the mRNA for the microtubule-associated protein 2 (MAP2) gene was enriched in the mesopallium and the hippocampus in the trained chick brain. The regionally specific enrichments of MAP2 mRNA were not observed in the brain of dark-reared or light-exposed chick as controls, implying an association between the degree of expression and the strength of the learned preference. In agreement with the gene expression, MAP2 protein was accumulated in the mesopallium of the trained chick brain, but not in the brains of the controls. The accumulation of MAP2 was found in the cytosol of neurons and co-localized with beta-tubulin, suggesting a change in microtubule assembly. Our results suggest a postnatal reorganization of cytoskeleton following filial imprinting.

Apparent diffusion coefficient mapping in medulloblastoma predicts non-infiltrative surgical planes.

PubMed

Marupudi, Neena I; Altinok, Deniz; Goncalves, Luis; Ham, Steven D; Sood, Sandeep

2016-11-01

An appropriate surgical approach for posterior fossa lesions is to start tumor removal from areas with a defined plane to where tumor is infiltrating the brainstem or peduncles. This surgical approach minimizes risk of damage to eloquent areas. Although magnetic resonance imaging (MRI) is the current standard preoperative imaging obtained for diagnosis and surgical planning of pediatric posterior fossa tumors, it offers limited information on the infiltrative planes between tumor and normal structures in patients with medulloblastomas. Because medulloblastomas demonstrate diffusion restriction on apparent diffusion coefficient map (ADC map) sequences, we investigated the role of ADC map in predicting infiltrative and non-infiltrative planes along the brain stem and/or cerebellar peduncles by medulloblastomas prior to surgery. Thirty-four pediatric patients with pathologically confirmed medulloblastomas underwent surgical resection at our facility from 2004 to 2012. An experienced pediatric neuroradiologist reviewed the brain MRIs/ADC map, assessing the planes between the tumor and cerebellar peduncles/brain stem. An independent evaluator documented surgical findings from operative reports for comparison to the radiographic findings. The radiographic findings were statistically compared to the documented intraoperative findings to determine predictive value of the test in identifying tumor infiltration of the brain stem cerebellar peduncles. Twenty-six patients had preoperative ADC mapping completed and thereby, met inclusion criteria. Mean age at time of surgery was 8.3 ± 4.6 years. Positive predictive value of ADC maps to predict tumor invasion of the brain stem and cerebellar peduncles ranged from 69 to 88 %; negative predictive values ranged from 70 to 89 %. Sensitivity approached 93 % while specificity approached 78 %. ADC maps are valuable in predicting the infiltrative and non-infiltrative planes along the tumor and brain stem interface in medulloblastomas. Inclusion and evaluation of ADC maps in preoperative evaluation can assist in surgical resection planning in patients with medulloblastoma.

Groupwise registration of MR brain images with tumors.

PubMed

Tang, Zhenyu; Wu, Yihong; Fan, Yong

2017-08-04

A novel groupwise image registration framework is developed for registering MR brain images with tumors. Our method iteratively estimates a normal-appearance counterpart for each tumor image to be registered and constructs a directed graph (digraph) of normal-appearance images to guide the groupwise image registration. Particularly, our method maps each tumor image to its normal appearance counterpart by identifying and inpainting brain tumor regions with intensity information estimated using a low-rank plus sparse matrix decomposition based image representation technique. The estimated normal-appearance images are groupwisely registered to a group center image guided by a digraph of images so that the total length of 'image registration paths' to be the minimum, and then the original tumor images are warped to the group center image using the resulting deformation fields. We have evaluated our method based on both simulated and real MR brain tumor images. The registration results were evaluated with overlap measures of corresponding brain regions and average entropy of image intensity information, and Wilcoxon signed rank tests were adopted to compare different methods with respect to their regional overlap measures. Compared with a groupwise image registration method that is applied to normal-appearance images estimated using the traditional low-rank plus sparse matrix decomposition based image inpainting, our method achieved higher image registration accuracy with statistical significance (p  =  7.02  ×  10 -9 ).

An SPM8-based Approach for Attenuation Correction Combining Segmentation and Non-rigid Template Formation: Application to Simultaneous PET/MR Brain Imaging

PubMed Central

Izquierdo-Garcia, David; Hansen, Adam E.; Förster, Stefan; Benoit, Didier; Schachoff, Sylvia; Fürst, Sebastian; Chen, Kevin T.; Chonde, Daniel B.; Catana, Ciprian

2014-01-01

We present an approach for head MR-based attenuation correction (MR-AC) based on the Statistical Parametric Mapping (SPM8) software that combines segmentation- and atlas-based features to provide a robust technique to generate attenuation maps (µ-maps) from MR data in integrated PET/MR scanners. Methods Coregistered anatomical MR and CT images acquired in 15 glioblastoma subjects were used to generate the templates. The MR images from these subjects were first segmented into 6 tissue classes (gray and white matter, cerebro-spinal fluid, bone and soft tissue, and air), which were then non-rigidly coregistered using a diffeomorphic approach. A similar procedure was used to coregister the anatomical MR data for a new subject to the template. Finally, the CT-like images obtained by applying the inverse transformations were converted to linear attenuation coefficients (LACs) to be used for AC of PET data. The method was validated on sixteen new subjects with brain tumors (N=12) or mild cognitive impairment (N=4) who underwent CT and PET/MR scans. The µ-maps and corresponding reconstructed PET images were compared to those obtained using the gold standard CT-based approach and the Dixon-based method available on the Siemens Biograph mMR scanner. Relative change (RC) images were generated in each case and voxel- and region of interest (ROI)-based analyses were performed. Results The leave-one-out cross-validation analysis of the data from the 15 atlas-generation subjects showed small errors in brain LACs (RC=1.38%±4.52%) compared to the gold standard. Similar results (RC=1.86±4.06%) were obtained from the analysis of the atlas-validation datasets. The voxel- and ROI-based analysis of the corresponding reconstructed PET images revealed quantification errors of 3.87±5.0% and 2.74±2.28%, respectively. The Dixon-based method performed substantially worse (the mean RC values were 13.0±10.25% and 9.38±4.97%, respectively). Areas closer to skull showed the largest improvement. Conclusion We have presented an SPM8-based approach for deriving the head µ-map from MR data to be used for PET AC in integrated PET/MR scanners. Its implementation is straightforward and only requires the morphological data acquired with a single MR sequence. The method is very accurate and robust, combining the strengths of both segmentation- and atlas-based approaches while minimizing their drawbacks. PMID:25278515

Brain Mapping of drug addiction in witdrawal condition based P300 Signals

NASA Astrophysics Data System (ADS)

Turnip, Arjon; Esti Kusumandari, Dwi; Hidayat, Teddy

2018-04-01

Drug abuse for a long time will slowly cause changes in brain structure and performance. These changes tend to occur in the front of the brain which is directly interfere the concentration and the decision-making process. In this study an experiment involving 10 drug users was performed. The process of recording data with EEG system is conducted during craving condition and 1 hour after taking methadone. From brain mapping results obtained that brain activity tend to occur in the upper layer of the brain during craving conditions and tend to be in the midle layer of the brain after one hour of taking methadone.

Three-dimensional functional magnetic resonance imaging of human brain on a clinical 1.5-T scanner.

PubMed Central

van Gelderen, P; Ramsey, N F; Liu, G; Duyn, J H; Frank, J A; Weinberger, D R; Moonen, C T

1995-01-01

Functional magnetic resonance imaging (fMRI) is a tool for mapping brain function that utilizes neuronal activity-induced changes in blood oxygenation. An efficient three-dimensional fMRI method is presented for imaging brain activity on conventional, widely available, 1.5-T scanners, without additional hardware. This approach uses large magnetic susceptibility weighting based on the echo-shifting principle combined with multiple gradient echoes per excitation. Motor stimulation, induced by self-paced finger tapping, reliably produced significant signal increase in the hand region of the contralateral primary motor cortex in every subject tested. Images Fig. 2 Fig. 3 PMID:7624341

Functional brain networks in Alzheimer's disease: EEG analysis based on limited penetrable visibility graph and phase space method

NASA Astrophysics Data System (ADS)

Wang, Jiang; Yang, Chen; Wang, Ruofan; Yu, Haitao; Cao, Yibin; Liu, Jing

2016-10-01

In this paper, EEG series are applied to construct functional connections with the correlation between different regions in order to investigate the nonlinear characteristic and the cognitive function of the brain with Alzheimer's disease (AD). First, limited penetrable visibility graph (LPVG) and phase space method map single EEG series into networks, and investigate the underlying chaotic system dynamics of AD brain. Topological properties of the networks are extracted, such as average path length and clustering coefficient. It is found that the network topology of AD in several local brain regions are different from that of the control group with no statistically significant difference existing all over the brain. Furthermore, in order to detect the abnormality of AD brain as a whole, functional connections among different brain regions are reconstructed based on similarity of clustering coefficient sequence (CCSS) of EEG series in the four frequency bands (delta, theta, alpha, and beta), which exhibit obvious small-world properties. Graph analysis demonstrates that for both methodologies, the functional connections between regions of AD brain decrease, particularly in the alpha frequency band. AD causes the graph index complexity of the functional network decreased, the small-world properties weakened, and the vulnerability increased. The obtained results show that the brain functional network constructed by LPVG and phase space method might be more effective to distinguish AD from the normal control than the analysis of single series, which is helpful for revealing the underlying pathological mechanism of the disease.

Optimizing the Usability of Brain-Computer Interfaces.

PubMed

Zhang, Yin; Chase, Steve M

2018-05-01

Brain-computer interfaces are in the process of moving from the laboratory to the clinic. These devices act by reading neural activity and using it to directly control a device, such as a cursor on a computer screen. An open question in the field is how to map neural activity to device movement in order to achieve the most proficient control. This question is complicated by the fact that learning, especially the long-term skill learning that accompanies weeks of practice, can allow subjects to improve performance over time. Typical approaches to this problem attempt to maximize the biomimetic properties of the device in order to limit the need for extensive training. However, it is unclear if this approach would ultimately be superior to performance that might be achieved with a nonbiomimetic device once the subject has engaged in extended practice and learned how to use it. Here we approach this problem using ideas from optimal control theory. Under the assumption that the brain acts as an optimal controller, we present a formal definition of the usability of a device and show that the optimal postlearning mapping can be written as the solution of a constrained optimization problem. We then derive the optimal mappings for particular cases common to most brain-computer interfaces. Our results suggest that the common approach of creating biomimetic interfaces may not be optimal when learning is taken into account. More broadly, our method provides a blueprint for optimal device design in general control-theoretic contexts.

Application of Intervention Mapping to the Development of a Complex Physical Therapist Intervention.

PubMed

Jones, Taryn M; Dear, Blake F; Hush, Julia M; Titov, Nickolai; Dean, Catherine M

2016-12-01

Physical therapist interventions, such as those designed to change physical activity behavior, are often complex and multifaceted. In order to facilitate rigorous evaluation and implementation of these complex interventions into clinical practice, the development process must be comprehensive, systematic, and transparent, with a sound theoretical basis. Intervention Mapping is designed to guide an iterative and problem-focused approach to the development of complex interventions. The purpose of this case report is to demonstrate the application of an Intervention Mapping approach to the development of a complex physical therapist intervention, a remote self-management program aimed at increasing physical activity after acquired brain injury. Intervention Mapping consists of 6 steps to guide the development of complex interventions: (1) needs assessment; (2) identification of outcomes, performance objectives, and change objectives; (3) selection of theory-based intervention methods and practical applications; (4) organization of methods and applications into an intervention program; (5) creation of an implementation plan; and (6) generation of an evaluation plan. The rationale and detailed description of this process are presented using an example of the development of a novel and complex physical therapist intervention, myMoves-a program designed to help individuals with an acquired brain injury to change their physical activity behavior. The Intervention Mapping framework may be useful in the development of complex physical therapist interventions, ensuring the development is comprehensive, systematic, and thorough, with a sound theoretical basis. This process facilitates translation into clinical practice and allows for greater confidence and transparency when the program efficacy is investigated. © 2016 American Physical Therapy Association.

Adaptive Neuromorphic Circuit for Stereoscopic Disparity Using Ocular Dominance Map

PubMed Central

Sharma, Sheena; Gupta, Priti; Markan, C. M.

2016-01-01

Stereopsis or depth perception is a critical aspect of information processing in the brain and is computed from the positional shift or disparity between the images seen by the two eyes. Various algorithms and their hardware implementation that compute disparity in real time have been proposed; however, most of them compute disparity through complex mathematical calculations that are difficult to realize in hardware and are biologically unrealistic. The brain presumably uses simpler methods to extract depth information from the environment and hence newer methodologies that could perform stereopsis with brain like elegance need to be explored. This paper proposes an innovative aVLSI design that leverages the columnar organization of ocular dominance in the brain and uses time-staggered Winner Take All (ts-WTA) to adaptively create disparity tuned cells. Physiological findings support the presence of disparity cells in the visual cortex and show that these cells surface as a result of binocular stimulation received after birth. Therefore, creating in hardware cells that can learn different disparities with experience not only is novel but also is biologically more realistic. These disparity cells, when allowed to interact diffusively on a larger scale, can be used to adaptively create stable topological disparity maps in silicon. PMID:27243029

Morphometric analysis and neuroanatomical mapping of the zebrafish brain.

PubMed

Gupta, Tripti; Marquart, Gregory D; Horstick, Eric J; Tabor, Kathryn M; Pajevic, Sinisa; Burgess, Harold A

2018-06-21

Large-scale genomic studies have recently identified genetic variants causative for major neurodevelopmental disorders, such as intellectual disability and autism. However, determining how underlying developmental processes are affected by these mutations remains a significant challenge in the field. Zebrafish is an established model system in developmental neurogenetics that may be useful in uncovering the mechanisms of these mutations. Here we describe the use of voxel-intensity, deformation field, and volume-based morphometric techniques for the systematic and unbiased analysis of gene knock-down and environmental exposure-induced phenotypes in zebrafish. We first present a computational method for brain segmentation based on transgene expression patterns to create a comprehensive neuroanatomical map. This map allowed us to disclose statistically significant changes in brain microstructure and composition in neurodevelopmental models. We demonstrate the effectiveness of morphometric techniques in measuring changes in the relative size of neuroanatomical subdivisions in atoh7 morphant larvae and in identifying phenotypes in larvae treated with valproic acid, a chemical demonstrated to increase the risk of autism in humans. These tools enable rigorous evaluation of the effects of gene mutations and environmental exposures on neural development, providing an entry point for cellular and molecular analysis of basic developmental processes as well as neurodevelopmental and neurodegenerative disorders. Published by Elsevier Inc.

A reliability study on brain activation during active and passive arm movements supported by an MRI-compatible robot.

PubMed

Estévez, Natalia; Yu, Ningbo; Brügger, Mike; Villiger, Michael; Hepp-Reymond, Marie-Claude; Riener, Robert; Kollias, Spyros

2014-11-01

In neurorehabilitation, longitudinal assessment of arm movement related brain function in patients with motor disability is challenging due to variability in task performance. MRI-compatible robots monitor and control task performance, yielding more reliable evaluation of brain function over time. The main goals of the present study were first to define the brain network activated while performing active and passive elbow movements with an MRI-compatible arm robot (MaRIA) in healthy subjects, and second to test the reproducibility of this activation over time. For the fMRI analysis two models were compared. In model 1 movement onset and duration were included, whereas in model 2 force and range of motion were added to the analysis. Reliability of brain activation was tested with several statistical approaches applied on individual and group activation maps and on summary statistics. The activated network included mainly the primary motor cortex, primary and secondary somatosensory cortex, superior and inferior parietal cortex, medial and lateral premotor regions, and subcortical structures. Reliability analyses revealed robust activation for active movements with both fMRI models and all the statistical methods used. Imposed passive movements also elicited mainly robust brain activation for individual and group activation maps, and reliability was improved by including additional force and range of motion using model 2. These findings demonstrate that the use of robotic devices, such as MaRIA, can be useful to reliably assess arm movement related brain activation in longitudinal studies and may contribute in studies evaluating therapies and brain plasticity following injury in the nervous system.

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.

Seventh Graders' Academic Achievement, Creativity, and Ability to Construct a Cross-Domain Concept Map--A Brain Function Perspective

ERIC Educational Resources Information Center

Yeh, Yu-Chu

2004-01-01

This study proposes an interactive model of "cross-domain" concept mapping with an emphasis on brain functions, and it further investigates the relationships between academic achievement, creative thinking, and cross-domain concept mapping. Sixty-nine seventh graders participated in this study which employed two 50-minute instructional…

The characteristic and changes of the event-related potentials (ERP) and brain topographic maps before and after treatment with rTMS in subjective tinnitus patients.

PubMed

Yang, Haidi; Xiong, Hao; Yu, Rongjun; Wang, Changming; Zheng, Yiqing; Zhang, Xueyuan

2013-01-01

To compare the event-related potentials (ERPs) and brain topographic maps characteristic and change in normal controls and subjective tinnitus patients before and after repetitive transcranial magnetic stimulation (rTMS) treatment. The ERPs and brain topographic maps elicited by target stimulus were compared before and after 1-week treatment with rTMS in 20 subjective tinnitus patients and 16 healthy controls. Before rTMS, target stimulus elicited a larger N1 component than the standard stimuli (repeating sounds)in control group but not in tinnitus patients. Instead, the tinnitus group pre-treatment exhibited larger amplitude of N1 in response to standard stimuli than to deviant stimuli. Furthermore tinnitus patients had smaller mismatch negativity (MMN) and late discriminative negativity (LDN)component at Fz compared with the control group. After rTMS treatment, tinnitus patients showed increased N1 response to deviant stimuli and larger MMN and LDN compared with pre-treatment. The topographic maps for the tinnitus group before rTMS -treatment demonstrated global asymmetry between the left and right cerebral hemispheres with more negative activities in left side and more positive activities in right side. In contrast, the brain topographic maps for patients after rTMS-treatment and controls seem roughly symmetrical. The ERP amplitudes and brain topographic maps in post-treatment patient group showed no significant difference with those in controls. The characterical changes in ERP and brain topographic maps in tinnitus patients maybe related with the electrophysiological mechanism of tinnitus induction and development. It can be used as an objective biomarker for the evaluation of auditory central in subjective tinnitus patients. These findings support the notion that rTMS treatment in tinnitus patients may exert a beneficial effect.

Automated glioblastoma segmentation based on a multiparametric structured unsupervised classification.

PubMed

Juan-Albarracín, Javier; Fuster-Garcia, Elies; Manjón, José V; Robles, Montserrat; Aparici, F; Martí-Bonmatí, L; García-Gómez, Juan M

2015-01-01

Automatic brain tumour segmentation has become a key component for the future of brain tumour treatment. Currently, most of brain tumour segmentation approaches arise from the supervised learning standpoint, which requires a labelled training dataset from which to infer the models of the classes. The performance of these models is directly determined by the size and quality of the training corpus, whose retrieval becomes a tedious and time-consuming task. On the other hand, unsupervised approaches avoid these limitations but often do not reach comparable results than the supervised methods. In this sense, we propose an automated unsupervised method for brain tumour segmentation based on anatomical Magnetic Resonance (MR) images. Four unsupervised classification algorithms, grouped by their structured or non-structured condition, were evaluated within our pipeline. Considering the non-structured algorithms, we evaluated K-means, Fuzzy K-means and Gaussian Mixture Model (GMM), whereas as structured classification algorithms we evaluated Gaussian Hidden Markov Random Field (GHMRF). An automated postprocess based on a statistical approach supported by tissue probability maps is proposed to automatically identify the tumour classes after the segmentations. We evaluated our brain tumour segmentation method with the public BRAin Tumor Segmentation (BRATS) 2013 Test and Leaderboard datasets. Our approach based on the GMM model improves the results obtained by most of the supervised methods evaluated with the Leaderboard set and reaches the second position in the ranking. Our variant based on the GHMRF achieves the first position in the Test ranking of the unsupervised approaches and the seventh position in the general Test ranking, which confirms the method as a viable alternative for brain tumour segmentation.

Non-invasive imaging of the levels and effects of glutathione on the redox status of mouse brain using electron paramagnetic resonance imaging.

PubMed

Emoto, Miho C; Matsuoka, Yuta; Yamada, Ken-Ichi; Sato-Akaba, Hideo; Fujii, Hirotada G

2017-04-15

Glutathione (GSH) is the most abundant non-protein thiol that buffers reactive oxygen species in the brain. GSH does not reduce nitroxides directly, but in the presence of ascorbates, addition of GSH increases ascorbate-induced reduction of nitroxides. In this study, we used electron paramagnetic resonance (EPR) imaging and the nitroxide imaging probe, 3-methoxycarbonyl-2,2,5,5-tetramethyl-piperidine-1-oxyl (MCP), to non-invasively obtain spatially resolved redox data from mouse brains depleted of GSH with diethyl maleate compared to control. Based on the pharmacokinetics of the reduction reaction of MCP in the mouse heads, the pixel-based rate constant of its reduction reaction was calculated as an index of the redox status in vivo and mapped as a "redox map". The obtained redox maps from control and GSH-depleted mouse brains showed a clear change in the brain redox status, which was due to the decreased levels of GSH in brains as measured by a biochemical assay. We observed a linear relationship between the reduction rate constant of MCP and the level of GSH for both control and GSH-depleted mouse brains. Using this relationship, the GSH level in the brain can be estimated from the redox map obtained with EPR imaging. Copyright © 2017 Elsevier Inc. All rights reserved.

Three-dimensional distribution of tyrosine hydroxylase, vasopressin and oxytocin neurones in the transparent postnatal mouse brain.

PubMed

Godefroy, D; Dominici, C; Hardin-Pouzet, H; Anouar, Y; Melik-Parsadaniantz, S; Rostène, W; Reaux-Le Goazigo, A

2017-12-01

Over the years, advances in immunohistochemistry techniques have been a critical step in detecting and mapping neuromodulatory substances in the central nervous system. The better quality and specificity of primary antibodies, new staining procedures and the spectacular development of imaging technologies have allowed such progress. Very recently, new methods permitting tissue transparency have been successfully used on brain tissues. In the present study, we combined whole-mount immunostaining for tyrosine hydroxylase (TH), oxytocin (OXT) and arginine vasopressin (AVP), with the iDISCO+ clearing method, light-sheet microscopy and semi-automated counting of three-dimensionally-labelled neurones to obtain a (3D) distribution of these neuronal populations in a 5-day postnatal (P5) mouse brain. Segmentation procedure and 3D reconstruction allowed us, with high resolution, to map TH staining of the various catecholaminergic cell groups and their ascending and descending fibre pathways. We show that TH pathways are present in the whole P5 mouse brain, similar to that observed in the adult rat brain. We also provide new information on the postnatal distribution of OXT and AVP immunoreactive cells in the mouse hypothalamus, and show that, compared to AVP neurones, OXT neurones in the supraoptic (SON) and paraventricular (PVN) nuclei are not yet mature in the early postnatal period. 3D semi-automatic quantitative analysis of the PVN reveals that OXT cell bodies are more numerous than AVP neurones, although their immunoreactive soma have a volume half smaller. More AVP nerve fibres compared to OXT were observed in the PVN and the retrochiasmatic area. In conclusion, the results of the present study demonstrate the utility and the potency of imaging large brain tissues with clearing procedures coupled to novel 3D imaging technologies to study, localise and quantify neurotransmitter substances involved in brain and neuroendocrine functions. © 2017 British Society for Neuroendocrinology.

EEG topography and tomography (LORETA) in the classification and evaluation of the pharmacodynamics of psychotropic drugs.

PubMed

Saletu, Bernd; Anderer, Peter; Saletu-Zyhlarz, Gerda M

2006-04-01

By multi-lead computer-assisted quantitative analyses of human scalp-recorded electroencephalogram (QEEG) in combination with certain statistical procedures (quantitative pharmaco-EEG) and mapping techniques (pharmaco-EEG mapping or topography), it is possible to classify psychotropic substances and objectively evaluate their bioavailability at the target organ, the human brain. Specifically, one may determine at an early stage of drug development whether a drug is effective on the central nervous system (CNS) compared with placebo, what its clinical efficacy will be like, at which dosage it acts, when it acts and the equipotent dosages of different galenic formulations. Pharmaco-EEG maps of neuroleptics, antidepressants, tranquilizers, hypnotics, psychostimulants and nootropics/cognition-enhancing drugs will be described. Methodological problems, as well as the relationships between acute and chronic drug effects, alterations in normal subjects and patients, CNS effects and therapeutic efficacy will be discussed. Imaging of drug effects on the regional brain electrical activity of healthy subjects by means of EEG tomography such as low-resolution electromagnetic tomography (LORETA) has been used for identifying brain areas predominantly involved in psychopharmacological action. This will be shown for the representative drugs of the four main psychopharmacological classes, such as 3 mg haloperidol for neuroleptics, 20 mg citalopram for antidepressants, 2 mg lorazepam for tranquilizers and 20 mg methylphenidate for psychostimulants. LORETA demonstrates that these psychopharmacological classes affect brain structures differently. By considering these differences between psychotropic drugs and placebo in normal subjects, as well as between mental disorder patients and normal controls, it may be possible to choose the optimum drug for a specific patient according to a key-lock principle, since the drug should normalize the deviant brain function. Thus, pharmaco-EEG topography and tomography are valuable methods in human neuropsychopharmacology, clinical psychiatry and neurology.

Brain serotonin transporter density and aggression in abstinent methamphetamine abusers.

PubMed

Sekine, Yoshimoto; Ouchi, Yasuomi; Takei, Nori; Yoshikawa, Etsuji; Nakamura, Kazuhiko; Futatsubashi, Masami; Okada, Hiroyuki; Minabe, Yoshio; Suzuki, Katsuaki; Iwata, Yasuhide; Tsuchiya, Kenji J; Tsukada, Hideo; Iyo, Masaomi; Mori, Norio

2006-01-01

In animals, methamphetamine is known to have a neurotoxic effect on serotonin neurons, which have been implicated in the regulation of mood, anxiety, and aggression. It remains unknown whether methamphetamine damages serotonin neurons in humans. To investigate the status of brain serotonin neurons and their possible relationship with clinical characteristics in currently abstinent methamphetamine abusers. Case-control analysis. A hospital research center. Twelve currently abstinent former methamphetamine abusers (5 women and 7 men) and 12 age-, sex-, and education-matched control subjects recruited from the community. The brain regional density of the serotonin transporter, a structural component of serotonin neurons, was estimated using positron emission tomography and trans-1,2,3,5,6,10-beta-hexahydro-6-[4-(methylthio)phenyl]pyrrolo-[2,1-a]isoquinoline ([(11)C](+)McN-5652). Estimates were derived from region-of-interest and statistical parametric mapping methods, followed by within-case analysis using the measures of clinical variables. The duration of methamphetamine use, the magnitude of aggression and depressive symptoms, and changes in serotonin transporter density represented by the [(11)C](+)McN-5652 distribution volume. Methamphetamine abusers showed increased levels of aggression compared with controls. Region-of-interest and statistical parametric mapping analyses revealed that the serotonin transporter density in global brain regions (eg, the midbrain, thalamus, caudate, putamen, cerebral cortex, and cerebellum) was significantly lower in methamphetamine abusers than in control subjects, and this reduction was significantly inversely correlated with the duration of methamphetamine use. Furthermore, statistical parametric mapping analyses indicated that the density in the orbitofrontal, temporal, and anterior cingulate areas was closely associated with the magnitude of aggression in methamphetamine abusers. Protracted abuse of methamphetamine may reduce the density of the serotonin transporter in the brain, leading to elevated aggression, even in currently abstinent abusers.

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

PubMed Central

2009-01-01

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

Mapping Resting-State Brain Networks in Conscious Animals

PubMed Central

Zhang, Nanyin; Rane, Pallavi; Huang, Wei; Liang, Zhifeng; Kennedy, David; Frazier, Jean A.; King, Jean

2010-01-01

In the present study we mapped brain functional connectivity in the conscious rat at the “resting state” based on intrinsic blood-oxygenation-level dependent (BOLD) fluctuations. The conscious condition eliminated potential confounding effects of anesthetic agents on the connectivity between brain regions. Indeed, using correlational analysis we identified multiple cortical and subcortical regions that demonstrated temporally synchronous variation with anatomically well-defined regions that are crucial to cognitive and emotional information processing including the prefrontal cortex (PFC), thalamus and retrosplenial cortex. The functional connectivity maps created were stringently validated by controlling for false positive detection of correlation, the physiologic basis of the signal source, as well as quantitatively evaluating the reproducibility of maps. Taken together, the present study has demonstrated the feasibility of assessing functional connectivity in conscious animals using fMRI and thus provided a convenient and non-invasive tool to systematically investigate the connectional architecture of selected brain networks in multiple animal models. PMID:20382183

From a meso- to micro-scale connectome: array tomography and mGRASP

PubMed Central

Rah, Jong-Cheol; Feng, Linqing; Druckmann, Shaul; Lee, Hojin; Kim, Jinhyun

2015-01-01

Mapping mammalian synaptic connectivity has long been an important goal of neuroscience because knowing how neurons and brain areas are connected underpins an understanding of brain function. Meeting this goal requires advanced techniques with single synapse resolution and large-scale capacity, especially at multiple scales tethering the meso- and micro-scale connectome. Among several advanced LM-based connectome technologies, Array Tomography (AT) and mammalian GFP-Reconstitution Across Synaptic Partners (mGRASP) can provide relatively high-throughput mapping synaptic connectivity at multiple scales. AT- and mGRASP-assisted circuit mapping (ATing and mGRASPing), combined with techniques such as retrograde virus, brain clearing techniques, and activity indicators will help unlock the secrets of complex neural circuits. Here, we discuss these useful new tools to enable mapping of brain circuits at multiple scales, some functional implications of spatial synaptic distribution, and future challenges and directions of these endeavors. PMID:26089781

Region specific optimization of continuous linear attenuation coefficients based on UTE (RESOLUTE): application to PET/MR brain imaging

NASA Astrophysics Data System (ADS)

Ladefoged, Claes N.; Benoit, Didier; Law, Ian; Holm, Søren; Kjær, Andreas; Højgaard, Liselotte; Hansen, Adam E.; Andersen, Flemming L.

2015-10-01

The reconstruction of PET brain data in a PET/MR hybrid scanner is challenging in the absence of transmission sources, where MR images are used for MR-based attenuation correction (MR-AC). The main challenge of MR-AC is to separate bone and air, as neither have a signal in traditional MR images, and to assign the correct linear attenuation coefficient to bone. The ultra-short echo time (UTE) MR sequence was proposed as a basis for MR-AC as this sequence shows a small signal in bone. The purpose of this study was to develop a new clinically feasible MR-AC method with patient specific continuous-valued linear attenuation coefficients in bone that provides accurate reconstructed PET image data. A total of 164 [18F]FDG PET/MR patients were included in this study, of which 10 were used for training. MR-AC was based on either standard CT (reference), UTE or our method (RESOLUTE). The reconstructed PET images were evaluated in the whole brain, as well as regionally in the brain using a ROI-based analysis. Our method segments air, brain, cerebral spinal fluid, and soft tissue voxels on the unprocessed UTE TE images, and uses a mapping of R2* values to CT Hounsfield Units (HU) to measure the density in bone voxels. The average error of our method in the brain was 0.1% and less than 1.2% in any region of the brain. On average 95% of the brain was within  ±10% of PETCT, compared to 72% when using UTE. The proposed method is clinically feasible, reducing both the global and local errors on the reconstructed PET images, as well as limiting the number and extent of the outliers.

Demyelination of subcortical nuclei in multiple sclerosis

NASA Astrophysics Data System (ADS)

Krutenkova, E.; Aitmagambetova, G.; Khodanovich, M.; Bowen, J.; Gangadharan, B.; Henson, L.; Mayadev, A.; Repovic, P.; Qian, P.; Yarnykh, V.

2016-02-01

Myelin containing in basal ganglia in multiple sclerosis patients was evaluated using new noninvasive quantitative MRI method fast whole brain macromolecular proton fraction mapping. Myelin level in globus pallidus and putamen significantly decreased in multiple sclerosis patients as compared with healthy control subjects but not in substantia nigra and caudate nucleus.

Towards robust deconvolution of low-dose perfusion CT: sparse perfusion deconvolution using online dictionary learning.

PubMed

Fang, Ruogu; Chen, Tsuhan; Sanelli, Pina C

2013-05-01

Computed tomography perfusion (CTP) is an important functional imaging modality in the evaluation of cerebrovascular diseases, particularly in acute stroke and vasospasm. However, the post-processed parametric maps of blood flow tend to be noisy, especially in low-dose CTP, due to the noisy contrast enhancement profile and the oscillatory nature of the results generated by the current computational methods. In this paper, we propose a robust sparse perfusion deconvolution method (SPD) to estimate cerebral blood flow in CTP performed at low radiation dose. We first build a dictionary from high-dose perfusion maps using online dictionary learning and then perform deconvolution-based hemodynamic parameters estimation on the low-dose CTP data. Our method is validated on clinical data of patients with normal and pathological CBF maps. The results show that we achieve superior performance than existing methods, and potentially improve the differentiation between normal and ischemic tissue in the brain. Copyright © 2013 Elsevier B.V. All rights reserved.

Towards robust deconvolution of low-dose perfusion CT: Sparse perfusion deconvolution using online dictionary learning

PubMed Central

Fang, Ruogu; Chen, Tsuhan; Sanelli, Pina C.

2014-01-01

Computed tomography perfusion (CTP) is an important functional imaging modality in the evaluation of cerebrovascular diseases, particularly in acute stroke and vasospasm. However, the post-processed parametric maps of blood flow tend to be noisy, especially in low-dose CTP, due to the noisy contrast enhancement profile and the oscillatory nature of the results generated by the current computational methods. In this paper, we propose a robust sparse perfusion deconvolution method (SPD) to estimate cerebral blood flow in CTP performed at low radiation dose. We first build a dictionary from high-dose perfusion maps using online dictionary learning and then perform deconvolution-based hemodynamic parameters estimation on the low-dose CTP data. Our method is validated on clinical data of patients with normal and pathological CBF maps. The results show that we achieve superior performance than existing methods, and potentially improve the differentiation between normal and ischemic tissue in the brain. PMID:23542422

Gradient-based reliability maps for ACM-based segmentation of hippocampus.

PubMed

Zarpalas, Dimitrios; Gkontra, Polyxeni; Daras, Petros; Maglaveras, Nicos

2014-04-01

Automatic segmentation of deep brain structures, such as the hippocampus (HC), in MR images has attracted considerable scientific attention due to the widespread use of MRI and to the principal role of some structures in various mental disorders. In this literature, there exists a substantial amount of work relying on deformable models incorporating prior knowledge about structures' anatomy and shape information. However, shape priors capture global shape characteristics and thus fail to model boundaries of varying properties; HC boundaries present rich, poor, and missing gradient regions. On top of that, shape prior knowledge is blended with image information in the evolution process, through global weighting of the two terms, again neglecting the spatially varying boundary properties, causing segmentation faults. An innovative method is hereby presented that aims to achieve highly accurate HC segmentation in MR images, based on the modeling of boundary properties at each anatomical location and the inclusion of appropriate image information for each of those, within an active contour model framework. Hence, blending of image information and prior knowledge is based on a local weighting map, which mixes gradient information, regional and whole brain statistical information with a multi-atlas-based spatial distribution map of the structure's labels. Experimental results on three different datasets demonstrate the efficacy and accuracy of the proposed method.

CT-based attenuation and scatter correction compared with uniform attenuation correction in brain perfusion SPECT imaging for dementia

NASA Astrophysics Data System (ADS)

Gillen, Rebecca; Firbank, Michael J.; Lloyd, Jim; O'Brien, John T.

2015-09-01

This study investigated if the appearance and diagnostic accuracy of HMPAO brain perfusion SPECT images could be improved by using CT-based attenuation and scatter correction compared with the uniform attenuation correction method. A cohort of subjects who were clinically categorized as Alzheimer’s Disease (n=38 ), Dementia with Lewy Bodies (n=29 ) or healthy normal controls (n=30 ), underwent SPECT imaging with Tc-99m HMPAO and a separate CT scan. The SPECT images were processed using: (a) correction map derived from the subject’s CT scan or (b) the Chang uniform approximation for correction or (c) no attenuation correction. Images were visually inspected. The ratios between key regions of interest known to be affected or spared in each condition were calculated for each correction method, and the differences between these ratios were evaluated. The images produced using the different corrections were noted to be visually different. However, ROI analysis found similar statistically significant differences between control and dementia groups and between AD and DLB groups regardless of the correction map used. We did not identify an improvement in diagnostic accuracy in images which were corrected using CT-based attenuation and scatter correction, compared with those corrected using a uniform correction map.

MRI-based methods for quantification of the cerebral metabolic rate of oxygen

PubMed Central

Rodgers, Zachary B; Detre, John A

2016-01-01

The brain depends almost entirely on oxidative metabolism to meet its significant energy requirements. As such, the cerebral metabolic rate of oxygen (CMRO2) represents a key measure of brain function. Quantification of CMRO2 has helped elucidate brain functional physiology and holds potential as a clinical tool for evaluating neurological disorders including stroke, brain tumors, Alzheimer’s disease, and obstructive sleep apnea. In recent years, a variety of magnetic resonance imaging (MRI)-based CMRO2 quantification methods have emerged. Unlike positron emission tomography – the current “gold standard” for measurement and mapping of CMRO2 – MRI is non-invasive, relatively inexpensive, and ubiquitously available in modern medical centers. All MRI-based CMRO2 methods are based on modeling the effect of paramagnetic deoxyhemoglobin on the magnetic resonance signal. The various methods can be classified in terms of the MRI contrast mechanism used to quantify CMRO2: T2*, T2′, T2, or magnetic susceptibility. This review article provides an overview of MRI-based CMRO2 quantification techniques. After a brief historical discussion motivating the need for improved CMRO2 methodology, current state-of-the-art MRI-based methods are critically appraised in terms of their respective tradeoffs between spatial resolution, temporal resolution, and robustness, all of critical importance given the spatially heterogeneous and temporally dynamic nature of brain energy requirements. PMID:27089912

Inter-Relations of Orthostatic Blood Pressure Change, Aortic Stiffness, and Brain Structure and Function in Young Adults.

PubMed

Cooper, Leroy L; Himali, Jayandra J; Torjesen, Alyssa; Tsao, Connie W; Beiser, Alexa; Hamburg, Naomi M; DeCarli, Charles; Vasan, Ramachandran S; Seshadri, Sudha; Pase, Matthew P; Mitchell, Gary F

2017-08-17

Relations of orthostatic change in blood pressure with brain structure and function have not been studied thoroughly, particularly in younger, healthier individuals. Elucidation of factors that contribute to early changes in brain integrity may lead to development of interventions that delay or prevent cognitive impairment. In a sample of the Framingham Heart Study Third Generation (N=2119; 53% women; mean age±SD, 47±8 years), we assessed orthostatic change in mean arterial pressure (MAP), aortic stiffness (carotid-femoral pulse wave velocity), neuropsychological function, and markers of subclinical brain injury on magnetic resonance imaging. Multivariable regression analyses were used to assess relations between orthostatic change in MAP and brain structural and neuropsychological outcomes. Greater orthostatic increase in MAP on standing was related to better Trails B-A performance among participants aged <49 years (β±SE, 0.062±0.029; P =0.031) and among participants with carotid-femoral pulse wave velocity <6.9 m/s (β±SE, 0.063±0.026; P =0.016). This relation was not significant among participants who were older or had stiffer aortas. Conversely, greater orthostatic increase in MAP was related to larger total brain volume among older participants (β±SE, 0.065±0.029; P =0.023) and among participants with carotid-femoral pulse wave velocity ≥6.9 m/s (β±SE, 0.078±0.031; P =0.011). Blunted orthostatic increase in MAP was associated with smaller brain volume among participants who were older or had stiffer aortas and with poorer executive function among persons who were younger or who had more-elastic aortas. Our findings suggest that the brain is sensitive to orthostatic change in MAP, with results dependent on age and aortic stiffness. © 2017 The Authors. Published on behalf of the American Heart Association, Inc., by Wiley.

Korea Brain Initiative: Integration and Control of Brain Functions.

PubMed

Jeong, Sung-Jin; Lee, Haejin; Hur, Eun-Mi; Choe, Youngshik; Koo, Ja Wook; Rah, Jong-Cheol; Lee, Kea Joo; Lim, Hyun-Ho; Sun, Woong; Moon, Cheil; Kim, Kyungjin

2016-11-02

This article introduces the history and the long-term goals of the Korea Brain Initiative, which is centered on deciphering the brain functions and mechanisms that mediate the integration and control of brain functions that underlie decision-making. The goal of this initiative is the mapping of a functional connectome with searchable, multi-dimensional, and information-integrated features. The project also includes the development of novel technologies and neuro-tools for integrated brain mapping. Beyond the scientific goals this grand endeavor will ultimately have socioeconomic ramifications that not only facilitate global collaboration in the neuroscience community, but also develop various brain science-related industrial and medical innovations. Copyright © 2016. Published by Elsevier Inc.

Large-scale brain networks in affective and social neuroscience: Towards an integrative functional architecture of the brain

PubMed Central

Barrett, Lisa Feldman; Satpute, Ajay

2013-01-01

Understanding how a human brain creates a human mind ultimately depends on mapping psychological categories and concepts to physical measurements of neural response. Although it has long been assumed that emotional, social, and cognitive phenomena are realized in the operations of separate brain regions or brain networks, we demonstrate that it is possible to understand the body of neuroimaging evidence using a framework that relies on domain general, distributed structure-function mappings. We review current research in affective and social neuroscience and argue that the emerging science of large-scale intrinsic brain networks provides a coherent framework for a domain-general functional architecture of the human brain. PMID:23352202

Post traumatic brain perfusion SPECT analysis using reconstructed ROI maps of radioactive microsphere derived cerebral blood flow and statistical parametric mapping.

PubMed

McGoron, Anthony J; Capille, Michael; Georgiou, Michael F; Sanchez, Pablo; Solano, Juan; Gonzalez-Brito, Manuel; Kuluz, John W

2008-02-29

Assessment of cerebral blood flow (CBF) by SPECT could be important in the management of patients with severe traumatic brain injury (TBI) because changes in regional CBF can affect outcome by promoting edema formation and intracranial pressure elevation (with cerebral hyperemia), or by causing secondary ischemic injury including post-traumatic stroke. The purpose of this study was to establish an improved method for evaluating regional CBF changes after TBI in piglets. The focal effects of moderate traumatic brain injury (TBI) on cerebral blood flow (CBF) by SPECT cerebral blood perfusion (CBP) imaging in an animal model were investigated by parallelized statistical techniques. Regional CBF was measured by radioactive microspheres and by SPECT 2 hours after injury in sham-operated piglets versus those receiving severe TBI by fluid-percussion injury to the left parietal lobe. Qualitative SPECT CBP accuracy was assessed against reference radioactive microsphere regional CBF measurements by map reconstruction, registration and smoothing. Cerebral hypoperfusion in the test group was identified at the voxel level using statistical parametric mapping (SPM). A significant area of hypoperfusion (P < 0.01) was found as a response to the TBI. Statistical mapping of the reference microsphere CBF data confirms a focal decrease found with SPECT and SPM. The suitability of SPM for application to the experimental model and ability to provide insight into CBF changes in response to traumatic injury was validated by the SPECT SPM result of a decrease in CBP at the left parietal region injury area of the test group. Further study and correlation of this characteristic lesion with long-term outcomes and auxiliary diagnostic modalities is critical to developing more effective critical care treatment guidelines and automated medical imaging processing techniques.

Mind Maps as a Lifelong Learning Tool

ERIC Educational Resources Information Center

Erdem, Aliye

2017-01-01

Mind map, which was developed by Tony Buzan as a note-taking technique, is an application which has the power of uncovering the thoughts which the brain has about a subject from different viewpoints and which activate the right and left lobes of the brain together as an alternative to linear thought. It is known that mind maps have benefits such…

Iron deposition quantification: Applications in the brain and liver.

PubMed

Yan, Fuhua; He, Naying; Lin, Huimin; Li, Ruokun

2018-06-13

Iron has long been implicated in many neurological and other organ diseases. It is known that over and above the normal increases in iron with age, in certain diseases there is an excessive iron accumulation in the brain and liver. MRI is a noninvasive means by which to image the various structures in the brain in three dimensions and quantify iron over the volume of the object of interest. The quantification of iron can provide information about the severity of iron-related diseases as well as quantify changes in iron for patient follow-up and treatment monitoring. This article provides an overview of current MRI-based methods for iron quantification, specifically for the brain and liver, including: signal intensity ratio, R 2 , R2*, R2', phase, susceptibility weighted imaging and quantitative susceptibility mapping (QSM). Although there are numerous approaches to measuring iron, R 2 and R2* are currently preferred methods in imaging the liver and QSM has become the preferred approach for imaging iron in the brain. 5 Technical Efficacy: Stage 5 J. Magn. Reson. Imaging 2018. © 2018 International Society for Magnetic Resonance in Medicine.

Improving accuracy of simultaneously reconstructed activity and attenuation maps using deep learning.

PubMed

Hwang, Donghwi; Kim, Kyeong Yun; Kang, Seung Kwan; Seo, Seongho; Paeng, Jin Chul; Lee, Dong Soo; Lee, Jae Sung

2018-02-15

Simultaneous reconstruction of activity and attenuation using the maximum likelihood reconstruction of activity and attenuation (MLAA) augmented by time-of-flight (TOF) information is a promising method for positron emission tomography (PET) attenuation correction. However, it still suffers from several problems, including crosstalk artifacts, slow convergence speed, and noisy attenuation maps (μ-maps). In this work, we developed deep convolutional neural networks (CNNs) to overcome these MLAA limitations, and we verified their feasibility using a clinical brain PET data set. Methods: We applied the proposed method to one of the most challenging PET cases for simultaneous image reconstruction ( 18 F-FP-CIT PET scans with highly specific binding to striatum of the brain). Three different CNN architectures (convolutional autoencoder (CAE), U-net, hybrid of CAE and U-net) were designed and trained to learn x-ray computed tomography (CT) derived μ-map (μ-CT) from the MLAA-generated activity distribution and μ-map (μ-MLAA). PET/CT data of 40 patients with suspected Parkinson's disease were employed for five-fold cross-validation. For the training of CNNs, 800,000 transverse PET slices and CTs augmented from 32 patient data sets were used. The similarity to μ-CT of the CNN-generated μ-maps (μ-CAE, μ-Unet, and μ-Hybrid) and μ-MLAA was compared using Dice similarity coefficients. In addition, we compared the activity concentration of specific (striatum) and non-specific binding regions (cerebellum and occipital cortex) and the binding ratios in the striatum in the PET activity images reconstructed using those μ-maps. Results: The CNNs generated less noisy and more uniform μ-maps than original μ-MLAA. Moreover, the air cavities and bones were better resolved in the proposed CNN outputs. In addition, the proposed deep learning approach was useful for mitigating the crosstalk problem in the MLAA reconstruction. The hybrid network of CAE and U-net yielded the most similar μ-maps to μ-CT (Dice similarity coefficient in the whole head = 0.79 in the bone and 0.72 in air cavities), resulting in only approximately 5% errors in activity and biding ratio quantification. Conclusion: The proposed deep learning approach is promising for accurate attenuation correction of activity distribution in TOF PET systems. Copyright © 2018 by the Society of Nuclear Medicine and Molecular Imaging, Inc.

Mutated Genes in Schizophrenia Map to Brain Networks

MedlinePlus

... Research Matters August 12, 2013 Mutated Genes in Schizophrenia Map to Brain Networks Schizophrenia networks in the prefrontal cortex area of the ... University of Washington Researchers found that people with schizophrenia have a high number of spontaneous mutations in ...

A versatile clearing agent for multi-modal brain imaging

PubMed Central

Costantini, Irene; Ghobril, Jean-Pierre; Di Giovanna, Antonino Paolo; Mascaro, Anna Letizia Allegra; Silvestri, Ludovico; Müllenbroich, Marie Caroline; Onofri, Leonardo; Conti, Valerio; Vanzi, Francesco; Sacconi, Leonardo; Guerrini, Renzo; Markram, Henry; Iannello, Giulio; Pavone, Francesco Saverio

2015-01-01

Extensive mapping of neuronal connections in the central nervous system requires high-throughput µm-scale imaging of large volumes. In recent years, different approaches have been developed to overcome the limitations due to tissue light scattering. These methods are generally developed to improve the performance of a specific imaging modality, thus limiting comprehensive neuroanatomical exploration by multi-modal optical techniques. Here, we introduce a versatile brain clearing agent (2,2′-thiodiethanol; TDE) suitable for various applications and imaging techniques. TDE is cost-efficient, water-soluble and low-viscous and, more importantly, it preserves fluorescence, is compatible with immunostaining and does not cause deformations at sub-cellular level. We demonstrate the effectiveness of this method in different applications: in fixed samples by imaging a whole mouse hippocampus with serial two-photon tomography; in combination with CLARITY by reconstructing an entire mouse brain with light sheet microscopy and in translational research by imaging immunostained human dysplastic brain tissue. PMID:25950610

PET/MRI for Oncologic Brain Imaging: A Comparison of Standard MR-Based Attenuation Corrections with a Model-Based Approach for the Siemens mMR PET/MR System.

PubMed

Rausch, Ivo; Rischka, Lucas; Ladefoged, Claes N; Furtner, Julia; Fenchel, Matthias; Hahn, Andreas; Lanzenberger, Rupert; Mayerhoefer, Marius E; Traub-Weidinger, Tatjana; Beyer, Thomas

2017-09-01

The aim of this study was to compare attenuation-correction (AC) approaches for PET/MRI in clinical neurooncology. Methods: Forty-nine PET/MRI brain scans were included: brain tumor studies using 18 F-fluoro-ethyl-tyrosine ( 18 F-FET) ( n = 31) and 68 Ga-DOTANOC ( n = 7) and studies of healthy subjects using 18 F-FDG ( n = 11). For each subject, MR-based AC maps (MR-AC) were acquired using the standard DIXON- and ultrashort echo time (UTE)-based approaches. A third MR-AC was calculated using a model-based, postprocessing approach to account for bone attenuation values (BD, noncommercial prototype software by Siemens Healthcare). As a reference, AC maps were derived from patient-specific CT images (CTref). PET data were reconstructed using standard settings after AC with all 4 AC methods. We report changes in diagnosis for all brain tumor patients and the following relative differences values (RDs [%]), with regards to AC-CTref: for 18 F-FET (A)-SUVs as well as volumes of interest (VOIs) defined by a 70% threshold of all segmented lesions and lesion-to-background ratios; for 68 Ga-DOTANOC (B)-SUVs as well as VOIs defined by a 50% threshold for all lesions and the pituitary gland; and for 18 F-FDG (C)-RD of SUVs of the whole brain and 10 anatomic regions segmented on MR images. Results: For brain tumor imaging (A and B), the standard PET-based diagnosis was not affected by any of the 3 MR-AC methods. For A, the average RDs of SUV mean were -10%, -4%, and -3% and of the VOIs 1%, 2%, and 7% for DIXON, UTE, and BD, respectively. Lesion-to-background ratios for all MR-AC methods were similar to that of CTref. For B, average RDs of SUV mean were -11%, -11%, and -3% and of the VOIs 1%, -4%, and -3%, respectively. In the case of 18 F-FDG PET/MRI (C), RDs for the whole brain were -11%, -8%, and -5% for DIXON, UTE, and BD, respectively. Conclusion: The diagnostic reading of PET/MR patients with brain tumors did not change with the chosen AC method. Quantitative accuracy of SUVs was clinically acceptable for UTE- and BD-AC for group A, whereas for group B BD was in accordance with CTref. Nevertheless, for the quantification of individual lesions large deviations to CTref can be observed independent of the MR-AC method used. © 2017 by the Society of Nuclear Medicine and Molecular Imaging.

Population-scale three-dimensional reconstruction and quantitative profiling of microglia arbors

PubMed Central

Rey-Villamizar, Nicolas; Merouane, Amine; Lu, Yanbin; Mukherjee, Amit; Trett, Kristen; Chong, Peter; Harris, Carolyn; Shain, William; Roysam, Badrinath

2015-01-01

Motivation: The arbor morphologies of brain microglia are important indicators of cell activation. This article fills the need for accurate, robust, adaptive and scalable methods for reconstructing 3-D microglial arbors and quantitatively mapping microglia activation states over extended brain tissue regions. Results: Thick rat brain sections (100–300 µm) were multiplex immunolabeled for IBA1 and Hoechst, and imaged by step-and-image confocal microscopy with automated 3-D image mosaicing, producing seamless images of extended brain regions (e.g. 5903 × 9874 × 229 voxels). An over-complete dictionary-based model was learned for the image-specific local structure of microglial processes. The microglial arbors were reconstructed seamlessly using an automated and scalable algorithm that exploits microglia-specific constraints. This method detected 80.1 and 92.8% more centered arbor points, and 53.5 and 55.5% fewer spurious points than existing vesselness and LoG-based methods, respectively, and the traces were 13.1 and 15.5% more accurate based on the DIADEM metric. The arbor morphologies were quantified using Scorcioni’s L-measure. Coifman’s harmonic co-clustering revealed four morphologically distinct classes that concord with known microglia activation patterns. This enabled us to map spatial distributions of microglial activation and cell abundances. Availability and implementation: Experimental protocols, sample datasets, scalable open-source multi-threaded software implementation (C++, MATLAB) in the electronic supplement, and website (www.farsight-toolkit.org). http://www.farsight-toolkit.org/wiki/Population-scale_Three-dimensional_Reconstruction_and_Quanti-tative_Profiling_of_Microglia_Arbors Contact: broysam@central.uh.edu Supplementary information: Supplementary data are available at Bioinformatics online. PMID:25701570

New developments in cerebral blood flow autoregulation analysis in preterm infants: a mechanistic approach.

PubMed

Riera, Joan; Cabañas, Fernando; Serrano, José Javier; Madero, Rosario; Pellicer, Adelina

2016-03-01

Impaired autoregulation capacity implies that changes in cerebral perfusion follow changes in blood pressure; however, no analytical method has explored such a signal causality relationship in infants. We sought to develop a method to assess cerebral autoregulation from a mechanistic point of view and explored the predictive capacity of the method to classify infants at risk for adverse outcomes. The partial directed coherence (PDC) method, which considers synchronicity and directionality of signal dependence across frequencies, was used to analyze the relationship between spontaneous changes in mean arterial pressure (MAP) and the cerebral tissue oxygenation index (TOI). PDCMAP>TOI indicated that changes in TOI were induced by MAP changes, and PDCTOI>MAP indicated the opposite. The PDCMAP>TOI and PDCTOI>MAP values differed. PDCMAP>TOI adjusted by gestational age predicted low superior vena cava flow (≤41 ml/kg per min), with an area under the receiver operating characteristic curve of 0.72 (95% CI: 0.63-0.81; P < 0.001), whereas PDCTOI>MAP did not. The adjusted pPDCMAP>TOI (the average value per patient) predicted severe intracranial hemorrhage and mortality. PDCMAP>TOI allows for a noninvasive physiological interpretation of the pressure autoregulation process in neonates. PDCMAP>TOI is a good classifier for infants at risk of brain hypoperfusion and adverse outcomes.

Effect of brain shift on the creation of functional atlases for deep brain stimulation surgery

PubMed Central

Pallavaram, Srivatsan; Remple, Michael S.; Neimat, Joseph S.; Kao, Chris; Konrad, Peter E.; D’Haese, Pierre-François

2011-01-01

Purpose In the recent past many groups have tried to build functional atlases of the deep brain using intra-operatively acquired information such as stimulation responses or micro-electrode recordings. An underlying assumption in building such atlases is that anatomical structures do not move between pre-operative imaging and intra-operative recording. In this study, we present evidences that this assumption is not valid. We quantify the effect of brain shift between pre-operative imaging and intra-operative recording on the creation of functional atlases using intra-operative somatotopy recordings and stimulation response data. Methods A total of 73 somatotopy points from 24 bilateral subthalamic nucleus (STN) implantations and 52 eye deviation stimulation response points from 17 bilateral STN implantations were used. These points were spatially normalized on a magnetic resonance imaging (MRI) atlas using a fully automatic non-rigid registration algorithm. Each implantation was categorized as having low, medium or large brain shift based on the amount of pneumocephalus visible on post-operative CT. The locations of somatotopy clusters and stimulation maps were analyzed for each category. Results The centroid of the large brain shift cluster of the somatotopy data (posterior, lateral, inferior: 3.06, 11.27, 5.36 mm) was found posterior, medial and inferior to that of the medium cluster (2.90, 13.57, 4.53 mm) which was posterior, medial and inferior to that of the low shift cluster (1.94, 13.92, 3.20 mm). The coordinates are referenced with respect to the mid-commissural point. Euclidean distances between the centroids were 1.68, 2.44 and 3.59 mm, respectively for low-medium, medium-large and low-large shift clusters. We found similar trends for the positions of the stimulation maps. The Euclidian distance between the highest probability locations on the low and medium-large shift maps was 4.06 mm. Conclusion The effect of brain shift in deep brain stimulation (DBS) surgery has been demonstrated using intra-operative somatotopy recordings as well as stimulation response data. The results not only indicate that considerable brain shift happens before micro-electrode recordings in DBS but also that brain shift affects the creation of accurate functional atlases. Therefore, care must be taken when building and using such atlases of intra-operative data and also when using intra-operative data to validate anatomical atlases. PMID:20033503

Mapping the pharmacological modulation of brain oxygen metabolism: The effects of caffeine on absolute CMRO2 measured using dual calibrated fMRI.

PubMed

Merola, Alberto; Germuska, Michael A; Warnert, Esther Ah; Richmond, Lewys; Helme, Daniel; Khot, Sharmila; Murphy, Kevin; Rogers, Peter J; Hall, Judith E; Wise, Richard G

2017-07-15

This study aims to map the acute effects of caffeine ingestion on grey matter oxygen metabolism and haemodynamics with a novel MRI method. Sixteen healthy caffeine consumers (8 males, age=24.7±5.1) were recruited to this randomised, double-blind, placebo-controlled study. Each participant was scanned on two days before and after the delivery of an oral caffeine (250mg) or placebo capsule. Our measurements were obtained with a newly proposed estimation approach applied to data from a dual calibration fMRI experiment that uses hypercapnia and hyperoxia to modulate brain blood flow and oxygenation. Estimates were based on a forward model that describes analytically the contributions of cerebral blood flow (CBF) and of the measured end-tidal partial pressures of CO 2 and O 2 to the acquired dual-echo GRE signal. The method allows the estimation of grey matter maps of: oxygen extraction fraction (OEF), CBF, CBF-related cerebrovascular reactivity (CVR) and cerebral metabolic rate of oxygen consumption (CMRO 2 ). Other estimates from a multi inversion time ASL acquisition (mTI-ASL), salivary samples of the caffeine concentration and behavioural measurements are also reported. We observed significant differences between caffeine and placebo on average across grey matter, with OEF showing an increase of 15.6% (SEM±4.9%, p<0.05) with caffeine, while CBF and CMRO 2 showed differences of -30.4% (SEM±1.6%, p<0.01) and -18.6% (SEM±2.9%, p<0.01) respectively with caffeine administration. The reduction in oxygen metabolism found is somehow unexpected, but consistent with a hypothesis of decreased energetic demand, supported by previous electrophysiological studies reporting reductions in spectral power with EEG. Moreover the maps of the physiological parameters estimated illustrate the spatial distribution of changes across grey matter enabling us to localise the effects of caffeine with voxel-wise resolution. CBF changes were widespread as reported by previous findings, while changes in OEF were found to be more restricted, leading to unprecedented mapping of significant CMRO 2 reductions mainly in frontal gyrus, parietal and occipital lobes. In conclusion, we propose the estimation framework based on our novel forward model with a dual calibrated fMRI experiment as a viable MRI method to map the effects of drugs on brain oxygen metabolism and haemodynamics with voxel-wise resolution. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

Brain perfusion evaluated by regional tissue oxygenation as a possible quality indicator of ongoing cardiopulmonary resuscitation. An experimental porcine cardiac arrest study.

PubMed

Bouček, Tomáš; Mlček, Mikuláš; Krupičková, Petra; Huptych, Michal; Belza, Tomáš; Kittnar, Otomar; Linhart, Aleš; Bělohlávek, Jan

2018-05-01

Relationship between regional tissue oxygenation (rSO 2 ) and microcirculatory changes during cardiac arrest (CA) are still unclear. Therefore, we designed an experimental study to correlate rSO 2 , microcirculation and systemic hemodynamic parameters in a porcine model of CA. Ventricular fibrillation was induced in 24 female pigs (50±3kg) and left for three minutes untreated followed by five minutes of mechanical CPR. Regional and peripheral saturations were assessed by near-infrared spectroscopy, sublingual microcirculation by Sidestream Dark Field technology and continuous hemodynamic parameters, including systemic blood pressure (MAP) and carotid blood flow (CF), during baseline, CA and CPR periods. The Wilcoxon Signed-Rank test, the Friedman test and the partial correlation method were used to compare these parameters. Brain and peripheral rSO 2 showed a gradual decrease during CA and only an increase of brain rSO 2 during mechanical CPR (34.5 to 42.5; p=0.0001), reflected by a rapid decrease of microcirculatory and hemodynamic parameters during CA and a slight increase during CPR. Peripheral rSO 2 was not changed significantly during CPR (38 to 38.5; p=0.09). We only found a moderate correlation of cerebral/peripheral rSO 2 to microcirculatory parameters (PVD: r=0.53/0.46; PPV: r=0.6/0.5 and MFI: r=0.64/0.52) and hemodynamic parameters (MAP: r=0.64/0.71 and CF: 0.71/0.67). Our experimental study confirmed that monitoring brain and peripheral rSO 2 is an easy-to-use method, well reflecting the hemodynamics during CA. However, only brain rSO 2 reflects the CPR efforts and might be used as a potential quality indicator for CPR.

An improved FSL-FIRST pipeline for subcortical gray matter segmentation to study abnormal brain anatomy using quantitative susceptibility mapping (QSM).

PubMed

Feng, Xiang; Deistung, Andreas; Dwyer, Michael G; Hagemeier, Jesper; Polak, Paul; Lebenberg, Jessica; Frouin, Frédérique; Zivadinov, Robert; Reichenbach, Jürgen R; Schweser, Ferdinand

2017-06-01

Accurate and robust segmentation of subcortical gray matter (SGM) nuclei is required in many neuroimaging applications. FMRIB's Integrated Registration and Segmentation Tool (FIRST) is one of the most popular software tools for automated subcortical segmentation based on T 1 -weighted (T1w) images. In this work, we demonstrate that FIRST tends to produce inaccurate SGM segmentation results in the case of abnormal brain anatomy, such as present in atrophied brains, due to a poor spatial match of the subcortical structures with the training data in the MNI space as well as due to insufficient contrast of SGM structures on T1w images. Consequently, such deviations from the average brain anatomy may introduce analysis bias in clinical studies, which may not always be obvious and potentially remain unidentified. To improve the segmentation of subcortical nuclei, we propose to use FIRST in combination with a special Hybrid image Contrast (HC) and Non-Linear (nl) registration module (HC-nlFIRST), where the hybrid image contrast is derived from T1w images and magnetic susceptibility maps to create subcortical contrast that is similar to that in the Montreal Neurological Institute (MNI) template. In our approach, a nonlinear registration replaces FIRST's default linear registration, yielding a more accurate alignment of the input data to the MNI template. We evaluated our method on 82 subjects with particularly abnormal brain anatomy, selected from a database of >2000 clinical cases. Qualitative and quantitative analyses revealed that HC-nlFIRST provides improved segmentation compared to the default FIRST method. Copyright © 2017 Elsevier Inc. All rights reserved.

Probing the brain with molecular fMRI.

PubMed

Ghosh, Souparno; Harvey, Peter; Simon, Jacob C; Jasanoff, Alan

2018-06-01

One of the greatest challenges of modern neuroscience is to incorporate our growing knowledge of molecular and cellular-scale physiology into integrated, organismic-scale models of brain function in behavior and cognition. Molecular-level functional magnetic resonance imaging (molecular fMRI) is a new technology that can help bridge these scales by mapping defined microscopic phenomena over large, optically inaccessible regions of the living brain. In this review, we explain how MRI-detectable imaging probes can be used to sensitize noninvasive imaging to mechanistically significant components of neural processing. We discuss how a combination of innovative probe design, advanced imaging methods, and strategies for brain delivery can make molecular fMRI an increasingly successful approach for spatiotemporally resolved studies of diverse neural phenomena, perhaps eventually in people. Copyright © 2018 Elsevier Ltd. All rights reserved.

Dissociable meta-analytic brain networks contribute to coordinated emotional processing.

PubMed

Riedel, Michael C; Yanes, Julio A; Ray, Kimberly L; Eickhoff, Simon B; Fox, Peter T; Sutherland, Matthew T; Laird, Angela R

2018-06-01

Meta-analytic techniques for mining the neuroimaging literature continue to exert an impact on our conceptualization of functional brain networks contributing to human emotion and cognition. Traditional theories regarding the neurobiological substrates contributing to affective processing are shifting from regional- towards more network-based heuristic frameworks. To elucidate differential brain network involvement linked to distinct aspects of emotion processing, we applied an emergent meta-analytic clustering approach to the extensive body of affective neuroimaging results archived in the BrainMap database. Specifically, we performed hierarchical clustering on the modeled activation maps from 1,747 experiments in the affective processing domain, resulting in five meta-analytic groupings of experiments demonstrating whole-brain recruitment. Behavioral inference analyses conducted for each of these groupings suggested dissociable networks supporting: (1) visual perception within primary and associative visual cortices, (2) auditory perception within primary auditory cortices, (3) attention to emotionally salient information within insular, anterior cingulate, and subcortical regions, (4) appraisal and prediction of emotional events within medial prefrontal and posterior cingulate cortices, and (5) induction of emotional responses within amygdala and fusiform gyri. These meta-analytic outcomes are consistent with a contemporary psychological model of affective processing in which emotionally salient information from perceived stimuli are integrated with previous experiences to engender a subjective affective response. This study highlights the utility of using emergent meta-analytic methods to inform and extend psychological theories and suggests that emotions are manifest as the eventual consequence of interactions between large-scale brain networks. © 2018 Wiley Periodicals, Inc.

Lesions causing freezing of gait localize to a cerebellar functional network

PubMed Central

Fasano, Alfonso; Laganiere, Simon E.; Lam, Susy; Fox, Michael D.

2016-01-01

Objective Freezing of gait is a disabling symptom in Parkinson’s disease and related disorders, but the brain regions involved in symptom generation remain unclear. Here we analyze brain lesions causing acute onset freezing of gait to identify regions causally involved in symptom generation. Methods Fourteen cases of lesion-induced freezing of gait were identified from the literature and lesions were mapped to a common brain atlas. Because lesion-induced symptoms can come from sites connected to the lesion location, not just the lesion location itself, we also identified brain regions functionally connected to each lesion location. This technique, termed lesion network mapping, has been recently shown to identify regions involved in symptom generation across a variety of lesion-induced disorders. Results Lesion location was heterogeneous and no single region could be considered necessary for symptom generation. However, over 90% (13/14) of lesions were functionally connected to a focal area in the dorsal medial cerebellum. This cerebellar area overlapped previously recognized regions that are activated by locomotor tasks, termed the cerebellar locomotor region. Connectivity to this region was specific to lesions causing freezing of gait compared to lesions causing other movement disorders (hemichorea or asterixis). Interpretation Lesions causing freezing of gait are located within a common functional network characterized by connectivity to the cerebellar locomotor region. These results based on causal brain lesions complement prior neuroimaging studies in Parkinson’s disease patients, advancing our understanding of the brain regions involved in freezing of gait. PMID:28009063

Control-display mapping in brain-computer interfaces.

PubMed

Thurlings, Marieke E; van Erp, Jan B F; Brouwer, Anne-Marie; Blankertz, Benjamin; Werkhoven, Peter

2012-01-01

Event-related potential (ERP) based brain-computer interfaces (BCIs) employ differences in brain responses to attended and ignored stimuli. When using a tactile ERP-BCI for navigation, mapping is required between navigation directions on a visual display and unambiguously corresponding tactile stimuli (tactors) from a tactile control device: control-display mapping (CDM). We investigated the effect of congruent (both display and control horizontal or both vertical) and incongruent (vertical display, horizontal control) CDMs on task performance, the ERP and potential BCI performance. Ten participants attended to a target (determined via CDM), in a stream of sequentially vibrating tactors. We show that congruent CDM yields best task performance, enhanced the P300 and results in increased estimated BCI performance. This suggests a reduced availability of attentional resources when operating an ERP-BCI with incongruent CDM. Additionally, we found an enhanced N2 for incongruent CDM, which indicates a conflict between visual display and tactile control orientations. Incongruency in control-display mapping reduces task performance. In this study, brain responses, task and system performance are related to (in)congruent mapping of command options and the corresponding stimuli in a brain-computer interface (BCI). Directional congruency reduces task errors, increases available attentional resources, improves BCI performance and thus facilitates human-computer interaction.

Twenty-first century brain banking. Processing brains for research: the Columbia University methods

PubMed Central

del Amaya, Maria Pilar; Keller, Christian E.

2007-01-01

Carefully categorized postmortem human brains are crucial for research. The lack of generally accepted methods for processing human postmortem brains for research persists. Thus, brain banking is essential; however, it cannot be achieved at the cost of the teaching mission of the academic institution by routing brains away from residency programs, particularly when the autopsy rate is steadily decreasing. A consensus must be reached whereby a brain can be utilizable for diagnosis, research, and teaching. The best diagnostic categorization possible must be secured and the yield of samples for basic investigation maximized. This report focuses on integrated, novel methods currently applied at the New York Brain Bank, Columbia University, New York, which are designed to reach accurate neuropathological diagnosis, optimize the yield of samples, and process fresh-frozen samples suitable for a wide range of modern investigations. The brains donated for research are processed as soon as possible after death. The prosector must have a good command of the neuroanatomy, neuropathology, and the protocol. One half of each brain is immersed in formalin for performing the thorough neuropathologic evaluation, which is combined with the teaching task. The contralateral half is extensively dissected at the fresh state. The anatomical origin of each sample is recorded using the map of Brodmann for the cortical samples. The samples are frozen at −160°C, barcode labeled, and ready for immediate disbursement once categorized diagnostically. A rigorous organization of freezer space, coupled to an electronic tracking system with its attached software, fosters efficient access for retrieval within minutes of any specific frozen samples in storage. This report describes how this achievement is feasible with emphasis on the actual processing of brains donated for research. PMID:17985145

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.

Imaging whole mouse brains with a dual resolution serial swept-source optical coherence tomography scanner

NASA Astrophysics Data System (ADS)

Lefebvre, Joël.; Castonguay, Alexandre; Lesage, Frédéric

2018-02-01

High resolution imaging of whole rodent brains using serial OCT scanners is a promising method to investigate microstructural changes in tissue related to the evolution of neuropathologies. Although micron to sub-micron sampling resolution can be obtained by using high numerical aperture objectives and dynamic focusing, such an imaging system is not adapted to whole brain imaging. This is due to the large amount of data it generates and the significant computational resources required for reconstructing such volumes. To address this limitation, a dual resolution serial OCT scanner was developed. The optical setup consists in a swept-source OCT made of two sample and reference arms, each arm being coupled with different microscope objectives (3X / 40X). Motorized flip mirrors were used to switch between each OCT arm, thus allowing low and high resolution acquisitions within the same sample. The low resolution OCT volumes acquired with the 3X arm were stitched together, providing a 3D map of the whole mouse brain. This brain can be registered to an OCT brain template to enable neurological structures localization. The high resolution volumes acquired with the 40X arm were also stitched together to create local high resolution 3D maps of the tissue microstructure. The 40X data can be acquired at any arbitrary location in the sample, thus limiting storage-heavy high resolution data to application restricted to specific regions of interest. By providing dual-resolution OCT data, this setup can be used to validate diffusion MRI with tissue microstructure derived metrics measured at any location in ex vivo brains.

Semi-automatic 10/20 Identification Method for MRI-Free Probe Placement in Transcranial Brain Mapping Techniques.

PubMed

Xiao, Xiang; Zhu, Hao; Liu, Wei-Jie; Yu, Xiao-Ting; Duan, Lian; Li, Zheng; Zhu, Chao-Zhe

2017-01-01

The International 10/20 system is an important head-surface-based positioning system for transcranial brain mapping techniques, e.g., fNIRS and TMS. As guidance for probe placement, the 10/20 system permits both proper ROI coverage and spatial consistency among multiple subjects and experiments in a MRI-free context. However, the traditional manual approach to the identification of 10/20 landmarks faces problems in reliability and time cost. In this study, we propose a semi-automatic method to address these problems. First, a novel head surface reconstruction algorithm reconstructs head geometry from a set of points uniformly and sparsely sampled on the subject's head. Second, virtual 10/20 landmarks are determined on the reconstructed head surface in computational space. Finally, a visually-guided real-time navigation system guides the experimenter to each of the identified 10/20 landmarks on the physical head of the subject. Compared with the traditional manual approach, our proposed method provides a significant improvement both in reliability and time cost and thus could contribute to improving both the effectiveness and efficiency of 10/20-guided MRI-free probe placement.

Three-dimensional MRI perfusion maps: a step beyond volumetric analysis in mental disorders

PubMed Central

Fabene, Paolo F; Farace, Paolo; Brambilla, Paolo; Andreone, Nicola; Cerini, Roberto; Pelizza, Luisa; Versace, Amelia; Rambaldelli, Gianluca; Birbaumer, Niels; Tansella, Michele; Sbarbati, Andrea

2007-01-01

A new type of magnetic resonance imaging analysis, based on fusion of three-dimensional reconstructions of time-to-peak parametric maps and high-resolution T1-weighted images, is proposed in order to evaluate the perfusion of selected volumes of interest. Because in recent years a wealth of data have suggested the crucial involvement of vascular alterations in mental diseases, we tested our new method on a restricted sample of schizophrenic patients and matched healthy controls. The perfusion of the whole brain was compared with that of the caudate nucleus by means of intrasubject analysis. As expected, owing to the encephalic vascular pattern, a significantly lower time-to-peak was observed in the caudate nucleus than in the whole brain in all healthy controls, indicating that the suggested method has enough sensitivity to detect subtle perfusion changes even in small volumes of interest. Interestingly, a less uniform pattern was observed in the schizophrenic patients. The latter finding needs to be replicated in an adequate number of subjects. In summary, the three-dimensional analysis method we propose has been shown to be a feasible tool for revealing subtle vascular changes both in normal subjects and in pathological conditions. PMID:17229290

The use of current source density as electrophysiological correlates in neuropsychiatric disorders: a review of human studies

PubMed Central

Kamarajan, Chella; Pandey, Ashwini K.; Chorlian, David B.; Porjesz, Bernice

2014-01-01

The use of current source density (CSD), the Laplacian of the scalp surface voltage, to map the electrical activity of the brain is a powerful method in studies of cognitive and affective phenomena. During the last few decades, mapping of CSD has been successfully applied to characterize several neuropsychiatric conditions such as alcoholism, schizophrenia, depression, anxiety disorders, childhood/developmental disorders, and neurological conditions (i.e., epilepsy and brain lesions) using electrophysiological data from resting state and during cognitive performance. Use of CSD and Laplacian measures has proven effective in elucidating topographic and activation differences between groups: i) patients with a specific diagnosis vs. healthy controls, ii) subjects at high risk for a specific diagnosis vs. low risk or normal controls, and iii) patients with specific symptom(s) vs. patients without these symptom(s). The present review outlines and summarizes the studies that have employed CSD measures in investigating several neuropsychiatric conditions. The advantages and potential of CSD-based methods in clinical and research applications along with some of the limitations inherent in the CSD-based methods are discussed in the review, as well as future directions to expand the implementation of CSD to other potential clinical applications. As CSD methods have proved to be more advantageous than using scalp potential data to understand topographic and source activations, its clinical applications offer promising potential, not only for a better understanding of a range of psychiatric conditions, but also for a variety of focal neurological disorders, including epilepsy and other conditions involving brain lesions and surgical interventions. PMID:25448264

Three-dimensional reconstruction of brain-wide wiring networks in Drosophila at single-cell resolution.

PubMed

Chiang, Ann-Shyn; Lin, Chih-Yung; Chuang, Chao-Chun; Chang, Hsiu-Ming; Hsieh, Chang-Huain; Yeh, Chang-Wei; Shih, Chi-Tin; Wu, Jian-Jheng; Wang, Guo-Tzau; Chen, Yung-Chang; Wu, Cheng-Chi; Chen, Guan-Yu; Ching, Yu-Tai; Lee, Ping-Chang; Lin, Chih-Yang; Lin, Hui-Hao; Wu, Chia-Chou; Hsu, Hao-Wei; Huang, Yun-Ann; Chen, Jing-Yi; Chiang, Hsin-Jung; Lu, Chun-Fang; Ni, Ru-Fen; Yeh, Chao-Yuan; Hwang, Jenn-Kang

2011-01-11

Animal behavior is governed by the activity of interconnected brain circuits. Comprehensive brain wiring maps are thus needed in order to formulate hypotheses about information flow and also to guide genetic manipulations aimed at understanding how genes and circuits orchestrate complex behaviors. To assemble this map, we deconstructed the adult Drosophila brain into approximately 16,000 single neurons and reconstructed them into a common standardized framework to produce a virtual fly brain. We have constructed a mesoscopic map and found that it consists of 41 local processing units (LPUs), six hubs, and 58 tracts covering the whole Drosophila brain. Despite individual local variation, the architecture of the Drosophila brain shows invariance for both the aggregation of local neurons (LNs) within specific LPUs and for the connectivity of projection neurons (PNs) between the same set of LPUs. An open-access image database, named FlyCircuit, has been constructed for online data archiving, mining, analysis, and three-dimensional visualization of all single neurons, brain-wide LPUs, their wiring diagrams, and neural tracts. We found that the Drosophila brain is assembled from families of multiple LPUs and their interconnections. This provides an essential first step in the analysis of information processing within and between neurons in a complete brain. Copyright © 2011 Elsevier Ltd. All rights reserved.

An SPM8-based approach for attenuation correction combining segmentation and nonrigid template formation: application to simultaneous PET/MR brain imaging.

PubMed

Izquierdo-Garcia, David; Hansen, Adam E; Förster, Stefan; Benoit, Didier; Schachoff, Sylvia; Fürst, Sebastian; Chen, Kevin T; Chonde, Daniel B; Catana, Ciprian

2014-11-01

We present an approach for head MR-based attenuation correction (AC) based on the Statistical Parametric Mapping 8 (SPM8) software, which combines segmentation- and atlas-based features to provide a robust technique to generate attenuation maps (μ maps) from MR data in integrated PET/MR scanners. Coregistered anatomic MR and CT images of 15 glioblastoma subjects were used to generate the templates. The MR images from these subjects were first segmented into 6 tissue classes (gray matter, white matter, cerebrospinal fluid, bone, soft tissue, and air), which were then nonrigidly coregistered using a diffeomorphic approach. A similar procedure was used to coregister the anatomic MR data for a new subject to the template. Finally, the CT-like images obtained by applying the inverse transformations were converted to linear attenuation coefficients to be used for AC of PET data. The method was validated on 16 new subjects with brain tumors (n = 12) or mild cognitive impairment (n = 4) who underwent CT and PET/MR scans. The μ maps and corresponding reconstructed PET images were compared with those obtained using the gold standard CT-based approach and the Dixon-based method available on the Biograph mMR scanner. Relative change (RC) images were generated in each case, and voxel- and region-of-interest-based analyses were performed. The leave-one-out cross-validation analysis of the data from the 15 atlas-generation subjects showed small errors in brain linear attenuation coefficients (RC, 1.38% ± 4.52%) compared with the gold standard. Similar results (RC, 1.86% ± 4.06%) were obtained from the analysis of the atlas-validation datasets. The voxel- and region-of-interest-based analysis of the corresponding reconstructed PET images revealed quantification errors of 3.87% ± 5.0% and 2.74% ± 2.28%, respectively. The Dixon-based method performed substantially worse (the mean RC values were 13.0% ± 10.25% and 9.38% ± 4.97%, respectively). Areas closer to the skull showed the largest improvement. We have presented an SPM8-based approach for deriving the head μ map from MR data to be used for PET AC in integrated PET/MR scanners. Its implementation is straightforward and requires only the morphologic data acquired with a single MR sequence. The method is accurate and robust, combining the strengths of both segmentation- and atlas-based approaches while minimizing their drawbacks. © 2014 by the Society of Nuclear Medicine and Molecular Imaging, Inc.

Distortion correction in EPI at ultra-high-field MRI using PSF mapping with optimal combination of shift detection dimension.

PubMed

Oh, Se-Hong; Chung, Jun-Young; In, Myung-Ho; Zaitsev, Maxim; Kim, Young-Bo; Speck, Oliver; Cho, Zang-Hee

2012-10-01

Despite its wide use, echo-planar imaging (EPI) suffers from geometric distortions due to off-resonance effects, i.e., strong magnetic field inhomogeneity and susceptibility. This article reports a novel method for correcting the distortions observed in EPI acquired at ultra-high-field such as 7 T. Point spread function (PSF) mapping methods have been proposed for correcting the distortions in EPI. The PSF shift map can be derived either along the nondistorted or the distorted coordinates. Along the nondistorted coordinates more information about compressed areas is present but it is prone to PSF-ghosting artifacts induced by large k-space shift in PSF encoding direction. In contrast, shift maps along the distorted coordinates contain more information in stretched areas and are more robust against PSF-ghosting. In ultra-high-field MRI, an EPI contains both compressed and stretched regions depending on the B0 field inhomogeneity and local susceptibility. In this study, we present a new geometric distortion correction scheme, which selectively applies the shift map with more information content. We propose a PSF-ghost elimination method to generate an artifact-free pixel shift map along nondistorted coordinates. The proposed method can correct the effects of the local magnetic field inhomogeneity induced by the susceptibility effects along with the PSF-ghost artifact cancellation. We have experimentally demonstrated the advantages of the proposed method in EPI data acquisitions in phantom and human brain using 7-T MRI. Copyright © 2011 Wiley Periodicals, Inc.

Mapping causal functional contributions derived from the clinical assessment of brain damage after stroke

PubMed Central

Zavaglia, Melissa; Forkert, Nils D.; Cheng, Bastian; Gerloff, Christian; Thomalla, Götz; Hilgetag, Claus C.

2015-01-01

Lesion analysis reveals causal contributions of brain regions to mental functions, aiding the understanding of normal brain function as well as rehabilitation of brain-damaged patients. We applied a novel lesion inference technique based on game theory, Multi-perturbation Shapley value Analysis (MSA), to a large clinical lesion dataset. We used MSA to analyze the lesion patterns of 148 acute stroke patients together with their neurological deficits, as assessed by the National Institutes of Health Stroke Scale (NIHSS). The results revealed regional functional contributions to essential behavioral and cognitive functions as reflected in the NIHSS, particularly by subcortical structures. There were also side specific differences of functional contributions between the right and left hemispheric brain regions which may reflect the dominance of the left hemispheric syndrome aphasia in the NIHSS. Comparison of MSA to established lesion inference methods demonstrated the feasibility of the approach for analyzing clinical data and indicated its capability for objectively inferring functional contributions from multiple injured, potentially interacting sites, at the cost of having to predict the outcome of unknown lesion configurations. The analysis of regional functional contributions to neurological symptoms measured by the NIHSS contributes to the interpretation of this widely used standardized stroke scale in clinical practice as well as clinical trials and provides a first approximation of a ‘map of stroke’. PMID:26448908

A dataset of multiresolution functional brain parcellations in an elderly population with no or mild cognitive impairment.

PubMed

Tam, Angela; Dansereau, Christian; Badhwar, AmanPreet; Orban, Pierre; Belleville, Sylvie; Chertkow, Howard; Dagher, Alain; Hanganu, Alexandru; Monchi, Oury; Rosa-Neto, Pedro; Shmuel, Amir; Breitner, John; Bellec, Pierre

2016-12-01

We present group eight resolutions of brain parcellations for clusters generated from resting-state functional magnetic resonance images for 99 cognitively normal elderly persons and 129 patients with mild cognitive impairment, pooled from four independent datasets. This dataset was generated as part of the following study: Common Effects of Amnestic Mild Cognitive Impairment on Resting-State Connectivity Across Four Independent Studies (Tam et al., 2015) [1]. The brain parcellations have been registered to both symmetric and asymmetric MNI brain templates and generated using a method called bootstrap analysis of stable clusters (BASC) (Bellec et al., 2010) [2]. We present two variants of these parcellations. One variant contains bihemisphereic parcels (4, 6, 12, 22, 33, 65, 111, and 208 total parcels across eight resolutions). The second variant contains spatially connected regions of interest (ROIs) that span only one hemisphere (10, 17, 30, 51, 77, 199, and 322 total ROIs across eight resolutions). We also present maps illustrating functional connectivity differences between patients and controls for four regions of interest (striatum, dorsal prefrontal cortex, middle temporal lobe, and medial frontal cortex). The brain parcels and associated statistical maps have been publicly released as 3D volumes, available in .mnc and .nii file formats on figshare and on Neurovault. Finally, the code used to generate this dataset is available on Github.

Mapping causal functional contributions derived from the clinical assessment of brain damage after stroke.

PubMed

Zavaglia, Melissa; Forkert, Nils D; Cheng, Bastian; Gerloff, Christian; Thomalla, Götz; Hilgetag, Claus C

2015-01-01

Lesion analysis reveals causal contributions of brain regions to mental functions, aiding the understanding of normal brain function as well as rehabilitation of brain-damaged patients. We applied a novel lesion inference technique based on game theory, Multi-perturbation Shapley value Analysis (MSA), to a large clinical lesion dataset. We used MSA to analyze the lesion patterns of 148 acute stroke patients together with their neurological deficits, as assessed by the National Institutes of Health Stroke Scale (NIHSS). The results revealed regional functional contributions to essential behavioral and cognitive functions as reflected in the NIHSS, particularly by subcortical structures. There were also side specific differences of functional contributions between the right and left hemispheric brain regions which may reflect the dominance of the left hemispheric syndrome aphasia in the NIHSS. Comparison of MSA to established lesion inference methods demonstrated the feasibility of the approach for analyzing clinical data and indicated its capability for objectively inferring functional contributions from multiple injured, potentially interacting sites, at the cost of having to predict the outcome of unknown lesion configurations. The analysis of regional functional contributions to neurological symptoms measured by the NIHSS contributes to the interpretation of this widely used standardized stroke scale in clinical practice as well as clinical trials and provides a first approximation of a 'map of stroke'.

Mapping the Schizophrenia Genes by Neuroimaging: The Opportunities and the Challenges

PubMed Central

2018-01-01

Schizophrenia (SZ) is a heritable brain disease originating from a complex interaction of genetic and environmental factors. The genes underpinning the neurobiology of SZ are largely unknown but recent data suggest strong evidence for genetic variations, such as single nucleotide polymorphisms, making the brain vulnerable to the risk of SZ. Structural and functional brain mapping of these genetic variations are essential for the development of agents and tools for better diagnosis, treatment and prevention of SZ. Addressing this, neuroimaging methods in combination with genetic analysis have been increasingly used for almost 20 years. So-called imaging genetics, the opportunities of this approach along with its limitations for SZ research will be outlined in this invited paper. While the problems such as reproducibility, genetic effect size, specificity and sensitivity exist, opportunities such as multivariate analysis, development of multisite consortia for large-scale data collection, emergence of non-candidate gene (hypothesis-free) approach of neuroimaging genetics are likely to contribute to a rapid progress for gene discovery besides to gene validation studies that are related to SZ. PMID:29324666

IBMISPS (International Brain Mapping & Intraoperative Surgical Planning Symposium)

DTIC Science & Technology

2005-12-01

they received the 2005 Excellence in R, D & E award for their contribution in the feild of prosthetics and brain imaging. Excellence in Educational...specific bipolar magnetic gradient pulses which measure the velocity vector components of motion. Presented here are the development of dynamic MR...movies of quantitative velocity vector components, 30 frames per second. The 3 velocity vector maps with tensor analysis produced maps of the

Evolution of technetium-99m-HMPAO SPECT and brain mapping in a patient presenting with echolalia and palilalia.

PubMed

Dierckx, R A; Saerens, J; De Deyn, P P; Verslegers, W; Marien, P; Vandevivere, J

1991-08-01

A 78-yr-old woman presented with transient echolalia and palilalia. She had suffered from Parkinson's disease for 2 yr. Routine laboratory examination showed hypotonic hyponatremia, but was otherwise unremarkable. Brain mapping revealed a bifrontal delta focus, more pronounced on the right. Single photon emission computed tomography (SPECT) of the brain with technetium-99m labeled d,l hexamethylpropylene-amine oxime (99mTc-HMPAO), performed during the acute episode showed relative frontoparietal hypoactivity. Brain mapping performed after disappearance of the echolalia and palilalia, which persisted only for 1 day, was normal. By contrast, SPECT findings persisted for more than 3 wk. Features of particular interest in the presented patient are the extensive defects seen on brain SPECT despite the absence of morphologic lesions, the congruent electrophysiologic changes and their temporal relationship with the clinical evolution.

Hematocrit Measurement with R2* and Quantitative Susceptibility Mapping in Postmortem Brain.

PubMed

Walsh, A J; Sun, H; Emery, D J; Wilman, A H

2018-05-24

Noninvasive venous oxygenation quantification with MR imaging will improve the neurophysiologic investigation and the understanding of the pathophysiology in neurologic diseases. Available MR imaging methods are limited by sensitivity to flow and often require assumptions of the hematocrit level. In situ postmortem imaging enables evaluation of methods in a fully deoxygenated environment without flow artifacts, allowing direct calculation of hematocrit. This study compares 2 venous oxygenation quantification methods in in situ postmortem subjects. Transverse relaxation (R2*) mapping and quantitative susceptibility mapping were performed on a whole-body 4.7T MR imaging system. Intravenous measurements in major draining intracranial veins were compared between the 2 methods in 3 postmortem subjects. The quantitative susceptibility mapping technique was also applied in 10 healthy control subjects and compared with reference venous oxygenation values. In 2 early postmortem subjects, R2* mapping and quantitative susceptibility mapping measurements within intracranial veins had a significant and strong correlation ( R 2 = 0.805, P = .004 and R 2 = 0.836, P = .02). Higher R2* and susceptibility values were consistently demonstrated within gravitationally dependent venous segments during the early postmortem period. Hematocrit ranged from 0.102 to 0.580 in postmortem subjects, with R2* and susceptibility as large as 291 seconds -1 and 1.75 ppm, respectively. Measurements of R2* and quantitative susceptibility mapping within large intracranial draining veins have a high correlation in early postmortem subjects. This study supports the use of quantitative susceptibility mapping for evaluation of in vivo venous oxygenation and postmortem hematocrit concentrations. © 2018 by American Journal of Neuroradiology.

A Unique Procedure to Identify Cell Surface Markers Through a Spherical Self-Organizing Map Applied to DNA Microarray Analysis.

PubMed

Sugii, Yuh; Kasai, Tomonari; Ikeda, Masashi; Vaidyanath, Arun; Kumon, Kazuki; Mizutani, Akifumi; Seno, Akimasa; Tokutaka, Heizo; Kudoh, Takayuki; Seno, Masaharu

2016-01-01

To identify cell-specific markers, we designed a DNA microarray platform with oligonucleotide probes for human membrane-anchored proteins. Human glioma cell lines were analyzed using microarray and compared with normal and fetal brain tissues. For the microarray analysis, we employed a spherical self-organizing map, which is a clustering method suitable for the conversion of multidimensional data into two-dimensional data and displays the relationship on a spherical surface. Based on the gene expression profile, the cell surface characteristics were successfully mirrored onto the spherical surface, thereby distinguishing normal brain tissue from the disease model based on the strength of gene expression. The clustered glioma-specific genes were further analyzed by polymerase chain reaction procedure and immunocytochemical staining of glioma cells. Our platform and the following procedure were successfully demonstrated to categorize the genes coding for cell surface proteins that are specific to glioma cells. Our assessment demonstrates that a spherical self-organizing map is a valuable tool for distinguishing cell surface markers and can be employed in marker discovery studies for the treatment of cancer.

Reliability of resting-state microstate features in electroencephalography.

PubMed

Khanna, Arjun; Pascual-Leone, Alvaro; Farzan, Faranak

2014-01-01

Electroencephalographic (EEG) microstate analysis is a method of identifying quasi-stable functional brain states ("microstates") that are altered in a number of neuropsychiatric disorders, suggesting their potential use as biomarkers of neurophysiological health and disease. However, use of EEG microstates as neurophysiological biomarkers requires assessment of the test-retest reliability of microstate analysis. We analyzed resting-state, eyes-closed, 30-channel EEG from 10 healthy subjects over 3 sessions spaced approximately 48 hours apart. We identified four microstate classes and calculated the average duration, frequency, and coverage fraction of these microstates. Using Cronbach's α and the standard error of measurement (SEM) as indicators of reliability, we examined: (1) the test-retest reliability of microstate features using a variety of different approaches; (2) the consistency between TAAHC and k-means clustering algorithms; and (3) whether microstate analysis can be reliably conducted with 19 and 8 electrodes. The approach of identifying a single set of "global" microstate maps showed the highest reliability (mean Cronbach's α > 0.8, SEM ≈ 10% of mean values) compared to microstates derived by each session or each recording. There was notably low reliability in features calculated from maps extracted individually for each recording, suggesting that the analysis is most reliable when maps are held constant. Features were highly consistent across clustering methods (Cronbach's α > 0.9). All features had high test-retest reliability with 19 and 8 electrodes. High test-retest reliability and cross-method consistency of microstate features suggests their potential as biomarkers for assessment of the brain's neurophysiological health.

Mapping correlations between ventricular expansion and CSF amyloid and tau biomarkers in 240 subjects with Alzheimer’s disease, mild cognitive impairment and elderly controls

PubMed Central

Chou, Yi-Yu; Leporé, Natasha; Avedissian, Christina; Madsen, Sarah K.; Parikshak, Neelroop; Hua, Xue; Shaw, Leslie M.; Trojanowski, John Q.; Weiner, Michael W.; Toga, Arthur W.; Thompson, Paul M.

2009-01-01

Automated ventricular mapping with multi-atlas fluid image alignment reveals genetic effects in Alzheimer’s disease, NeuroImage 40(2): 615–630); with this method, we calculated minimal numbers of subjects needed to detect correlations between clinical scores and ventricular maps. We also assessed correlations between emerging CSF biomarkers of Alzheimer’s disease pathology and localizable deficits in the brain, in 80 AD, 80 mild cognitive impairment (MCI), and 80 healthy controls from the Alzheimer’s Disease Neuroimaging Initiative. Six expertly segmented images and their embedded parametric mesh surfaces were fluidly registered to each brain; segmentations were averaged within subjects to reduce errors. Surface-based statistical maps revealed powerful correlations between surface morphology and 4 variables: (1) diagnosis, (2) depression severity, (3) cognitive function at baseline, and (4) future cognitive decline over the following year. Cognitive function was assessed using the mini-mental state exam (MMSE), global and sum-of-boxes clinical dementia rating (CDR) scores, at baseline and 1-year follow-up. Lower CSF Aβ1–42 protein levels, a biomarker of AD pathology assessed in 138 of the 240 subjects, were correlated with lateral ventricular expansion. Using false discovery rate (FDR) methods, 40 and 120 subjects, respectively, were needed to discriminate AD and MCI from normal groups. 120 subjects were required to detect correlations between ventricular enlargement and MMSE, global CDR, sum-of-boxes CDR and clinical depression scores. Ventricular expansion maps correlate with pathological and cognitive measures in AD, and may be useful in future imaging-based clinical trials. PMID:19236926

Simultaneous imaging of neural activity in three dimensions

PubMed Central

Quirin, Sean; Jackson, Jesse; Peterka, Darcy S.; Yuste, Rafael

2014-01-01

We introduce a scanless optical method to image neuronal activity in three dimensions simultaneously. Using a spatial light modulator and a custom-designed phase mask, we illuminate and collect light simultaneously from different focal planes and perform calcium imaging of neuronal activity in vitro and in vivo. This method, combining structured illumination with volume projection imaging, could be used as a technological platform for brain activity mapping. PMID:24772066

Personalized Medicine in Veterans with Traumatic Brain Injuries

DTIC Science & Technology

2013-05-01

Pair-Group Method using Arithmetic averages ( UPGMA ) based on cosine correlation of row mean centered log2 signal values; this was the top 50%-tile...cluster- ing was performed by the UPGMA method using Cosine correlation as the similarity metric. For comparative purposes, clustered heat maps included...non-mTBI cases were subjected to unsupervised hierarchical clustering analysis using the UPGMA algorithm with cosine correlation as the similarity

R2* mapping for brain iron: associations with cognition in normal aging.

PubMed

Ghadery, Christine; Pirpamer, Lukas; Hofer, Edith; Langkammer, Christian; Petrovic, Katja; Loitfelder, Marisa; Schwingenschuh, Petra; Seiler, Stephan; Duering, Marco; Jouvent, Eric; Schmidt, Helena; Fazekas, Franz; Mangin, Jean-Francois; Chabriat, Hugues; Dichgans, Martin; Ropele, Stefan; Schmidt, Reinhold

2015-02-01

Brain iron accumulates during aging and has been associated with neurodegenerative disorders including Alzheimer's disease. Magnetic resonance (MR)-based R2* mapping enables the in vivo detection of iron content in brain tissue. We investigated if during normal brain aging iron load relates to cognitive impairment in region-specific patterns in a community-dwelling cohort of 336 healthy, middle aged, and older adults from the Austrian Stroke Prevention Family Study. MR imaging and R2* mapping in the basal ganglia and neocortex were done at 3T. Comprehensive neuropsychological testing assessed memory, executive function, and psychomotor speed. We found the highest iron concentration in the globus pallidus, and pallidal and putaminal iron was significantly and inversely associated with cognitive performance in all cognitive domains, except memory. These associations were iron load dependent. Vascular brain lesions and brain volume did not mediate the relationship between iron and cognitive performance. We conclude that higher R2*-determined iron in the basal ganglia correlates with cognitive impairment during brain aging independent of concomitant brain abnormalities. The prognostic significance of this finding needs to be determined. Copyright © 2015 Elsevier Inc. All rights reserved.

Diffusion Tensor Magnetic Resonance Imaging Strategies for Color Mapping of Human Brain Anatomy

PubMed Central

Boujraf, Saïd

2018-01-01

Background: A color mapping of fiber tract orientation using diffusion tensor imaging (DTI) can be prominent in clinical practice. The goal of this paper is to perform a comparative study of visualized diffusion anisotropy in the human brain anatomical entities using three different color-mapping techniques based on diffusion-weighted imaging (DWI) and DTI. Methods: The first technique is based on calculating a color map from DWIs measured in three perpendicular directions. The second technique is based on eigenvalues derived from the diffusion tensor. The last technique is based on three eigenvectors corresponding to sorted eigenvalues derived from the diffusion tensor. All magnetic resonance imaging measurements were achieved using a 1.5 Tesla Siemens Vision whole body imaging system. A single-shot DW echoplanar imaging sequence used a Stejskal–Tanner approach. Trapezoidal diffusion gradients are used. The slice orientation was transverse. The basic measurement yielded a set of 13 images. Each series consists of a single image without diffusion weighting, besides two DWIs for each of the next six noncollinear magnetic field gradient directions. Results: The three types of color maps were calculated consequently using the DWI obtained and the DTI. Indeed, we established an excellent similarity between the image data in the color maps and the fiber directions of known anatomical structures (e.g., corpus callosum and gray matter). Conclusions: In the meantime, rotationally invariant quantities such as the eigenvectors of the diffusion tensor reflected better, the real orientation found in the studied tissue. PMID:29928631

"Clinical brain profiling": a neuroscientific diagnostic approach for mental disorders.

PubMed

Peled, Abraham; Geva, Amir B

2014-10-01

Clinical brain profiling is an attempt to map a descriptive nosology in psychiatry to underlying constructs in neurobiology and brain dynamics. This paper briefly reviews the motivation behind clinical brain profiling (CBP) and presents some provisional validation using clinical assessments and meta-analyses of neuroscientific publications. The paper has four sections. In the first, we review the nature and motivation for clinical brain profiling. This involves a description of the key aspects of functional anatomy that can lead to psychopathology. These features constitute the dimensions or categories for a profile of brain disorders based upon pathophysiology. The second section describes a mapping or translation matrix that maps from symptoms and signs, of a descriptive sort, to the CBP dimensions that provide a more mechanistic explanation. We will describe how this mapping engenders archetypal diagnoses, referring readers to tables and figures. The third section addresses the construct validity of clinical brain profiling by establishing correlations between profiles based on clinical ratings of symptoms and signs under classical diagnostic categories with the corresponding profiles generated automatically using archetypal diagnoses. We then provide further validation by performing a cluster analysis on the symptoms and signs and showing how they correspond to the equivalent brain profiles based upon clinical and automatic diagnosis. In the fourth section, we address the construct validity of clinical brain profiling by looking for associations between pathophysiological mechanisms (such as connectivity and plasticity) and nosological diagnoses (such as schizophrenia and depression). Based upon the mechanistic perspective offered in the first section, we test some particular hypotheses about double dissociations using a meta-analysis of PubMed searches. The final section concludes with perspectives for the future and outstanding validation issues for clinical brain profiling. Copyright © 2014 Elsevier Ltd. All rights reserved.

Mapping common aphasia assessments to underlying cognitive processes and their neural substrates

PubMed Central

Lacey, Elizabeth H.; Skipper-Kallal, LM; Xing, S; Fama, ME; Turkeltaub, PE

2017-01-01

Background Understanding the relationships between clinical tests, the processes they measure, and the brain networks underlying them, is critical in order for clinicians to move beyond aphasia syndrome classification toward specification of individual language process impairments. Objective To understand the cognitive, language, and neuroanatomical factors underlying scores of commonly used aphasia tests. Methods 25 behavioral tests were administered to a group of 38 chronic left hemisphere stroke survivors and a high resolution MRI was obtained. Test scores were entered into a principal components analysis to extract the latent variables (factors) measured by the tests. Multivariate lesion-symptom mapping was used to localize lesions associated with the factor scores. Results The principal components analysis yielded four dissociable factors, which we labeled Word Finding/Fluency, Comprehension, Phonology/Working Memory Capacity, and Executive Function. While many tests loaded onto the factors in predictable ways, some relied heavily on factors not commonly associated with the tests. Lesion symptom mapping demonstrated discrete brain structures associated with each factor, including frontal, temporal, and parietal areas extending beyond the classical language network. Specific functions mapped onto brain anatomy largely in correspondence with modern neural models of language processing. Conclusions An extensive clinical aphasia assessment identifies four independent language functions, relying on discrete parts of the left middle cerebral artery territory. A better understanding of the processes underlying cognitive tests and the link between lesion and behavior may lead to improved aphasia diagnosis, and may yield treatments better targeted to an individual’s specific pattern of deficits and preserved abilities. PMID:28135902

Automated Glioblastoma Segmentation Based on a Multiparametric Structured Unsupervised Classification

PubMed Central

Juan-Albarracín, Javier; Fuster-Garcia, Elies; Manjón, José V.; Robles, Montserrat; Aparici, F.; Martí-Bonmatí, L.; García-Gómez, Juan M.

2015-01-01

Automatic brain tumour segmentation has become a key component for the future of brain tumour treatment. Currently, most of brain tumour segmentation approaches arise from the supervised learning standpoint, which requires a labelled training dataset from which to infer the models of the classes. The performance of these models is directly determined by the size and quality of the training corpus, whose retrieval becomes a tedious and time-consuming task. On the other hand, unsupervised approaches avoid these limitations but often do not reach comparable results than the supervised methods. In this sense, we propose an automated unsupervised method for brain tumour segmentation based on anatomical Magnetic Resonance (MR) images. Four unsupervised classification algorithms, grouped by their structured or non-structured condition, were evaluated within our pipeline. Considering the non-structured algorithms, we evaluated K-means, Fuzzy K-means and Gaussian Mixture Model (GMM), whereas as structured classification algorithms we evaluated Gaussian Hidden Markov Random Field (GHMRF). An automated postprocess based on a statistical approach supported by tissue probability maps is proposed to automatically identify the tumour classes after the segmentations. We evaluated our brain tumour segmentation method with the public BRAin Tumor Segmentation (BRATS) 2013 Test and Leaderboard datasets. Our approach based on the GMM model improves the results obtained by most of the supervised methods evaluated with the Leaderboard set and reaches the second position in the ranking. Our variant based on the GHMRF achieves the first position in the Test ranking of the unsupervised approaches and the seventh position in the general Test ranking, which confirms the method as a viable alternative for brain tumour segmentation. PMID:25978453

Expression of c-fos gene in central nervous system of adult medaka (Oryzias latipes) after hypergravity stimulation

NASA Astrophysics Data System (ADS)

Shimomura, S.; Ijiri, K.

The immediate-early genes serve as useful neurobiological tools for mapping brain activity induced by a sensory stimulation. In this study, we have examined brain activity related to gravity perception of medaka (Oryzias latipes) by use of c-fos. The gene, which is homologous to the c-fos genes of other vertebrates, was identified in medaka. Functionally important domains are highly conserved among all the vertebrate species analyzed. Intraperitoneal administration of kainic acid transiently induced the c-fos mRNAs in medaka brain. The results indicate that the expression of c-fos can be utilized as a suitable anatomical marker for the increased neural activities in the central nervous system of medaka. Fish were continuously exposed to 3G hypergravity by centrifugation. Investigation of c-fos mRNA expression showed that c-fos mRNA significantly increased 30 minutes after a start of 3G exposure. The distribution of its transcripts within brains was analyzed by an in situ hybridization method. The 3G-treated medakas displayed c-fos positive cells in their brainstem regions, which are related to vestibular function, such as torus semicircularis, posterior octavu nucleus, nucleus tangentialis and inferior olive. Our results established the method to trace the activated area in the fish brain following gravity stimulation. The method will be a useful tool for understanding gravity perception in the brain.

A novel approach for monitoring writing interferences during navigated transcranial magnetic stimulation mappings of writing related cortical areas.

PubMed

Rogić Vidaković, Maja; Gabelica, Dragan; Vujović, Igor; Šoda, Joško; Batarelo, Nikolina; Džimbeg, Andrija; Zmajević Schönwald, Marina; Rotim, Krešimir; Đogaš, Zoran

2015-11-30

It has recently been shown that navigated repetitive transcranial magnetic stimulation (nTMS) is useful in preoperative neurosurgical mapping of motor and language brain areas. In TMS mapping of motor cortices the evoked responses can be quantitatively monitored by electromyographic (EMG) recordings. No such setup exists for monitoring of writing during nTMS mappings of writing related cortical areas. We present a novel approach for monitoring writing during nTMS mappings of motor writing related cortical areas. To our best knowledge, this is the first demonstration of quantitative monitoring of motor evoked responses from hand by EMG, and of pen related activity during writing with our custom made pen, together with the application of chronometric TMS design and patterned protocol of rTMS. The method was applied in four healthy subjects participating in writing during nTMS mapping of the premotor cortical area corresponding to BA 6 and close to the superior frontal sulcus. The results showed that stimulation impaired writing in all subjects. The corresponding spectra of measured signal related to writing movements was observed in the frequency band 0-20 Hz. Magnetic stimulation affected writing by suppressing normal writing frequency band. The proposed setup for monitoring of writing provides additional quantitative data for monitoring and the analysis of rTMS induced writing response modifications. The setup can be useful for investigation of neurophysiologic mechanisms of writing, for therapeutic effects of nTMS, and in preoperative mapping of language cortical areas in patients undergoing brain surgery. Copyright © 2015 Elsevier B.V. All rights reserved.

Mapping human brain networks with cortico-cortical evoked potentials

PubMed Central

Keller, Corey J.; Honey, Christopher J.; Mégevand, Pierre; Entz, Laszlo; Ulbert, Istvan; Mehta, Ashesh D.

2014-01-01

The cerebral cortex forms a sheet of neurons organized into a network of interconnected modules that is highly expanded in humans and presumably enables our most refined sensory and cognitive abilities. The links of this network form a fundamental aspect of its organization, and a great deal of research is focusing on understanding how information flows within and between different regions. However, an often-overlooked element of this connectivity regards a causal, hierarchical structure of regions, whereby certain nodes of the cortical network may exert greater influence over the others. While this is difficult to ascertain non-invasively, patients undergoing invasive electrode monitoring for epilepsy provide a unique window into this aspect of cortical organization. In this review, we highlight the potential for cortico-cortical evoked potential (CCEP) mapping to directly measure neuronal propagation across large-scale brain networks with spatio-temporal resolution that is superior to traditional neuroimaging methods. We first introduce effective connectivity and discuss the mechanisms underlying CCEP generation. Next, we highlight how CCEP mapping has begun to provide insight into the neural basis of non-invasive imaging signals. Finally, we present a novel approach to perturbing and measuring brain network function during cognitive processing. The direct measurement of CCEPs in response to electrical stimulation represents a potentially powerful clinical and basic science tool for probing the large-scale networks of the human cerebral cortex. PMID:25180306

PORTR: Pre-Operative and Post-Recurrence Brain Tumor Registration

PubMed Central

Niethammer, Marc; Akbari, Hamed; Bilello, Michel; Davatzikos, Christos; Pohl, Kilian M.

2014-01-01

We propose a new method for deformable registration of pre-operative and post-recurrence brain MR scans of glioma patients. Performing this type of intra-subject registration is challenging as tumor, resection, recurrence, and edema cause large deformations, missing correspondences, and inconsistent intensity profiles between the scans. To address this challenging task, our method, called PORTR, explicitly accounts for pathological information. It segments tumor, resection cavity, and recurrence based on models specific to each scan. PORTR then uses the resulting maps to exclude pathological regions from the image-based correspondence term while simultaneously measuring the overlap between the aligned tumor and resection cavity. Embedded into a symmetric registration framework, we determine the optimal solution by taking advantage of both discrete and continuous search methods. We apply our method to scans of 24 glioma patients. Both quantitative and qualitative analysis of the results clearly show that our method is superior to other state-of-the-art approaches. PMID:24595340

Images Are Not the (Only) Truth: Brain Mapping, Visual Knowledge, and Iconoclasm.

ERIC Educational Resources Information Center

Beaulieu, Anne

2002-01-01

Debates the paradoxical nature of claims about the emerging contributions of functional brain mapping. Examines the various ways that images are deployed and rejected and highlights an approach that provides insight into the current demarcation of imaging. (Contains 68 references.) (DDR)

Broad Integration of Expression Maps and Co-Expression Networks Compassing Novel Gene Functions in the Brain

PubMed Central

Okamura-Oho, Yuko; Shimokawa, Kazuro; Nishimura, Masaomi; Takemoto, Satoko; Sato, Akira; Furuichi, Teiichi; Yokota, Hideo

2014-01-01

Using a recently invented technique for gene expression mapping in the whole-anatomy context, termed transcriptome tomography, we have generated a dataset of 36,000 maps of overall gene expression in the adult-mouse brain. Here, using an informatics approach, we identified a broad co-expression network that follows an inverse power law and is rich in functional interaction and gene-ontology terms. Our framework for the integrated analysis of expression maps and graphs of co-expression networks revealed that groups of combinatorially expressed genes, which regulate cell differentiation during development, were present in the adult brain and each of these groups was associated with a discrete cell types. These groups included non-coding genes of unknown function. We found that these genes specifically linked developmentally conserved groups in the network. A previously unrecognized robust expression pattern covering the whole brain was related to the molecular anatomy of key biological processes occurring in particular areas. PMID:25382412

Connecting long distance: semantic distance in analogical reasoning modulates frontopolar cortex activity.

PubMed

Green, Adam E; Kraemer, David J M; Fugelsang, Jonathan A; Gray, Jeremy R; Dunbar, Kevin N

2010-01-01

Solving problems often requires seeing new connections between concepts or events that seemed unrelated at first. Innovative solutions of this kind depend on analogical reasoning, a relational reasoning process that involves mapping similarities between concepts. Brain-based evidence has implicated the frontal pole of the brain as important for analogical mapping. Separately, cognitive research has identified semantic distance as a key characteristic of the kind of analogical mapping that can support innovation (i.e., identifying similarities across greater semantic distance reveals connections that support more innovative solutions and models). However, the neural substrates of semantically distant analogical mapping are not well understood. Here, we used functional magnetic resonance imaging (fMRI) to measure brain activity during an analogical reasoning task, in which we parametrically varied the semantic distance between the items in the analogies. Semantic distance was derived quantitatively from latent semantic analysis. Across 23 participants, activity in an a priori region of interest (ROI) in left frontopolar cortex covaried parametrically with increasing semantic distance, even after removing effects of task difficulty. This ROI was centered on a functional peak that we previously associated with analogical mapping. To our knowledge, these data represent a first empirical characterization of how the brain mediates semantically distant analogical mapping.

The role of long-range connectivity for the characterization of the functional-anatomical organization of the cortex.

PubMed

Knösche, Thomas R; Tittgemeyer, Marc

2011-01-01

This review focuses on the role of long-range connectivity as one element of brain structure that is of key importance for the functional-anatomical organization of the cortex. In this context, we discuss the putative guiding principles for mapping brain function and structure onto the cortical surface. Such mappings reveal a high degree of functional-anatomical segregation. Given that brain regions frequently maintain characteristic connectivity profiles and the functional repertoire of a cortical area is closely related to its anatomical connections, long-range connectivity may be used to define segregated cortical areas. This methodology is called connectivity-based parcellation. Within this framework, we investigate different techniques to estimate connectivity profiles with emphasis given to non-invasive methods based on diffusion magnetic resonance imaging (dMRI) and diffusion tractography. Cortical parcellation is then defined based on similarity between diffusion tractograms, and different clustering approaches are discussed. We conclude that the use of non-invasively acquired connectivity estimates to characterize the functional-anatomical organization of the brain is a valid, relevant, and necessary endeavor. Current and future developments in dMRI technology, tractography algorithms, and models of the similarity structure hold great potential for a substantial improvement and enrichment of the results of the technique.

[Awake craniotomy: analysis of complicated cases].

PubMed

Kulikov, A S; Kobyakov, G L; Gavrilov, A G; Lubnin, A Yu

2015-01-01

Awake craniotomy is recognized as method that can decrease the frequency of neurological complications after surgery for gliomas located near eloquent brain regions. Unfortunately good neurological outcome can't be ensured even by using of this technique. This paper discusses reasons and possible ways of prevention of such complications. 162 awake craniotomies were performed in our clinic. 152 of patients were discharged from the clinic with good outcome. In 10 (6%) cases sustained severe neurological deficit was noted. These complications were associated with anatomic or ischemic injury of subcortical pathways and internal capsule. Awake craniotomy is effective instrument of brain language mapping and prevention of neurological deterioration. Severe neurological complications of awake craniotomy are associated with underestimate neurosurgical risks, especially in terms of blood vessel injury and depth of resection. The main way of prevention of such complications is meticulous planning of operation and adequate using of mapping facilities.

The Human Brainnetome Atlas: A New Brain Atlas Based on Connectional Architecture.

PubMed

Fan, Lingzhong; Li, Hai; Zhuo, Junjie; Zhang, Yu; Wang, Jiaojian; Chen, Liangfu; Yang, Zhengyi; Chu, Congying; Xie, Sangma; Laird, Angela R; Fox, Peter T; Eickhoff, Simon B; Yu, Chunshui; Jiang, Tianzi

2016-08-01

The human brain atlases that allow correlating brain anatomy with psychological and cognitive functions are in transition from ex vivo histology-based printed atlases to digital brain maps providing multimodal in vivo information. Many current human brain atlases cover only specific structures, lack fine-grained parcellations, and fail to provide functionally important connectivity information. Using noninvasive multimodal neuroimaging techniques, we designed a connectivity-based parcellation framework that identifies the subdivisions of the entire human brain, revealing the in vivo connectivity architecture. The resulting human Brainnetome Atlas, with 210 cortical and 36 subcortical subregions, provides a fine-grained, cross-validated atlas and contains information on both anatomical and functional connections. Additionally, we further mapped the delineated structures to mental processes by reference to the BrainMap database. It thus provides an objective and stable starting point from which to explore the complex relationships between structure, connectivity, and function, and eventually improves understanding of how the human brain works. The human Brainnetome Atlas will be made freely available for download at http://atlas.brainnetome.org, so that whole brain parcellations, connections, and functional data will be readily available for researchers to use in their investigations into healthy and pathological states. © The Author 2016. Published by Oxford University Press.

Magnetic Resonance, Functional (fMRI) -- Brain

MedlinePlus

... thought, speech, movement and sensation, which is called brain mapping. help assess the effects of stroke, trauma, or degenerative disease (such as Alzheimer's) on brain function. monitor the growth and function of brain ...

Awake surgery between art and science. Part II: language and cognitive mapping

PubMed Central

Talacchi, Andrea; Santini, Barbara; Casartelli, Marilena; Monti, Alessia; Capasso, Rita; Miceli, Gabriele

Summary Direct cortical and subcortical stimulation has been claimed to be the gold standard for exploring brain function. In this field, efforts are now being made to move from intraoperative naming-assisted surgical resection towards the use of other language and cognitive tasks. However, before relying on new protocols and new techniques, we need a multi-staged system of evidence (low and high) relating to each step of functional mapping and its clinical validity. In this article we examine the possibilities and limits of brain mapping with the aid of a visual object naming task and various other tasks used to date. The methodological aspects of intraoperative brain mapping, as well as the clinical and operative settings, were discussed in Part I of this review. PMID:24139658

Use of pattern recognition for unaliasing simultaneously acquired slices in simultaneous multislice MR fingerprinting.

PubMed

Jiang, Yun; Ma, Dan; Bhat, Himanshu; Ye, Huihui; Cauley, Stephen F; Wald, Lawrence L; Setsompop, Kawin; Griswold, Mark A

2017-11-01

The purpose of this study is to accelerate an MR fingerprinting (MRF) acquisition by using a simultaneous multislice method. A multiband radiofrequency (RF) pulse was designed to excite two slices with different flip angles and phases. The signals of two slices were driven to be as orthogonal as possible. The mixed and undersampled MRF signal was matched to two dictionaries to retrieve T 1 and T 2 maps of each slice. Quantitative results from the proposed method were validated with the gold-standard spin echo methods in a phantom. T 1 and T 2 maps of in vivo human brain from two simultaneously acquired slices were also compared to the results of fast imaging with steady-state precession based MRF method (MRF-FISP) with a single-band RF excitation. The phantom results showed that the simultaneous multislice imaging MRF-FISP method quantified the relaxation properties accurately compared to the gold-standard spin echo methods. T 1 and T 2 values of in vivo brain from the proposed method also matched the results from the normal MRF-FISP acquisition. T 1 and T 2 values can be quantified at a multiband acceleration factor of two using our proposed acquisition even in a single-channel receive coil. Further acceleration could be achieved by combining this method with parallel imaging or iterative reconstruction. Magn Reson Med 78:1870-1876, 2017. © 2016 International Society for Magnetic Resonance in Medicine. © 2016 International Society for Magnetic Resonance in Medicine.

Viral-genetic tracing of the input-output organization of a central noradrenaline circuit.

PubMed

Schwarz, Lindsay A; Miyamichi, Kazunari; Gao, Xiaojing J; Beier, Kevin T; Weissbourd, Brandon; DeLoach, Katherine E; Ren, Jing; Ibanes, Sandy; Malenka, Robert C; Kremer, Eric J; Luo, Liqun

2015-08-06

Deciphering how neural circuits are anatomically organized with regard to input and output is instrumental in understanding how the brain processes information. For example, locus coeruleus noradrenaline (also known as norepinephrine) (LC-NE) neurons receive input from and send output to broad regions of the brain and spinal cord, and regulate diverse functions including arousal, attention, mood and sensory gating. However, it is unclear how LC-NE neurons divide up their brain-wide projection patterns and whether different LC-NE neurons receive differential input. Here we developed a set of viral-genetic tools to quantitatively analyse the input-output relationship of neural circuits, and applied these tools to dissect the LC-NE circuit in mice. Rabies-virus-based input mapping indicated that LC-NE neurons receive convergent synaptic input from many regions previously identified as sending axons to the locus coeruleus, as well as from newly identified presynaptic partners, including cerebellar Purkinje cells. The 'tracing the relationship between input and output' method (or TRIO method) enables trans-synaptic input tracing from specific subsets of neurons based on their projection and cell type. We found that LC-NE neurons projecting to diverse output regions receive mostly similar input. Projection-based viral labelling revealed that LC-NE neurons projecting to one output region also project to all brain regions we examined. Thus, the LC-NE circuit overall integrates information from, and broadcasts to, many brain regions, consistent with its primary role in regulating brain states. At the same time, we uncovered several levels of specificity in certain LC-NE sub-circuits. These tools for mapping output architecture and input-output relationship are applicable to other neuronal circuits and organisms. More broadly, our viral-genetic approaches provide an efficient intersectional means to target neuronal populations based on cell type and projection pattern.

Investigation of practical initial attenuation image estimates in TOF-MLAA reconstruction for PET/MR

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

Cheng, Ju-Chieh, E-mail: chengjuchieh@gmail.com; Y

Purpose: Time-of-flight joint attenuation and activity positron emission tomography reconstruction requires additional calibration (scale factors) or constraints during or post-reconstruction to produce a quantitative μ-map. In this work, the impact of various initializations of the joint reconstruction was investigated, and the initial average mu-value (IAM) method was introduced such that the forward-projection of the initial μ-map is already very close to that of the reference μ-map, thus reducing/minimizing the offset (scale factor) during the early iterations of the joint reconstruction. Consequently, the accuracy and efficiency of unconstrained joint reconstruction such as time-of-flight maximum likelihood estimation of attenuation and activity (TOF-MLAA)more » can be improved by the proposed IAM method. Methods: 2D simulations of brain and chest were used to evaluate TOF-MLAA with various initial estimates which include the object filled with water uniformly (conventional initial estimate), bone uniformly, the average μ-value uniformly (IAM magnitude initialization method), and the perfect spatial μ-distribution but with a wrong magnitude (initialization in terms of distribution). 3D GATE simulation was also performed for the chest phantom under a typical clinical scanning condition, and the simulated data were reconstructed with a fully corrected list-mode TOF-MLAA algorithm with various initial estimates. The accuracy of the average μ-values within the brain, chest, and abdomen regions obtained from the MR derived μ-maps was also evaluated using computed tomography μ-maps as the gold-standard. Results: The estimated μ-map with the initialization in terms of magnitude (i.e., average μ-value) was observed to reach the reference more quickly and naturally as compared to all other cases. Both 2D and 3D GATE simulations produced similar results, and it was observed that the proposed IAM approach can produce quantitative μ-map/emission when the corrections for physical effects such as scatter and randoms were included. The average μ-value obtained from MR derived μ-map was accurate within 5% with corrections for bone, fat, and uniform lungs. Conclusions: The proposed IAM-TOF-MLAA can produce quantitative μ-map without any calibration provided that there are sufficient counts in the measured data. For low count data, noise reduction and additional regularization/rescaling techniques need to be applied and investigated. The average μ-value within the object is prior information which can be extracted from MR and patient database, and it is feasible to obtain accurate average μ-value using MR derived μ-map with corrections as demonstrated in this work.« less

The heritability of the functional connectome is robust to common nonlinear registration methods

NASA Astrophysics Data System (ADS)

Hafzalla, George W.; Prasad, Gautam; Baboyan, Vatche G.; Faskowitz, Joshua; Jahanshad, Neda; McMahon, Katie L.; de Zubicaray, Greig I.; Wright, Margaret J.; Braskie, Meredith N.; Thompson, Paul M.

2016-03-01

Nonlinear registration algorithms are routinely used in brain imaging, to align data for inter-subject and group comparisons, and for voxelwise statistical analyses. To understand how the choice of registration method affects maps of functional brain connectivity in a sample of 611 twins, we evaluated three popular nonlinear registration methods: Advanced Normalization Tools (ANTs), Automatic Registration Toolbox (ART), and FMRIB's Nonlinear Image Registration Tool (FNIRT). Using both structural and functional MRI, we used each of the three methods to align the MNI152 brain template, and 80 regions of interest (ROIs), to each subject's T1-weighted (T1w) anatomical image. We then transformed each subject's ROIs onto the associated resting state functional MRI (rs-fMRI) scans and computed a connectivity network or functional connectome for each subject. Given the different degrees of genetic similarity between pairs of monozygotic (MZ) and same-sex dizygotic (DZ) twins, we used structural equation modeling to estimate the additive genetic influences on the elements of the function networks, or their heritability. The functional connectome and derived statistics were relatively robust to nonlinear registration effects.

Resting-state functional magnetic resonance imaging: the impact of regression analysis.

PubMed

Yeh, Chia-Jung; Tseng, Yu-Sheng; Lin, Yi-Ru; Tsai, Shang-Yueh; Huang, Teng-Yi

2015-01-01

To investigate the impact of regression methods on resting-state functional magnetic resonance imaging (rsfMRI). During rsfMRI preprocessing, regression analysis is considered effective for reducing the interference of physiological noise on the signal time course. However, it is unclear whether the regression method benefits rsfMRI analysis. Twenty volunteers (10 men and 10 women; aged 23.4 ± 1.5 years) participated in the experiments. We used node analysis and functional connectivity mapping to assess the brain default mode network by using five combinations of regression methods. The results show that regressing the global mean plays a major role in the preprocessing steps. When a global regression method is applied, the values of functional connectivity are significantly lower (P ≤ .01) than those calculated without a global regression. This step increases inter-subject variation and produces anticorrelated brain areas. rsfMRI data processed using regression should be interpreted carefully. The significance of the anticorrelated brain areas produced by global signal removal is unclear. Copyright © 2014 by the American Society of Neuroimaging.

Multiscale Reconstruction for Magnetic Resonance Fingerprinting

PubMed Central

Pierre, Eric Y.; Ma, Dan; Chen, Yong; Badve, Chaitra; Griswold, Mark A.

2015-01-01

Purpose To reduce acquisition time needed to obtain reliable parametric maps with Magnetic Resonance Fingerprinting. Methods An iterative-denoising algorithm is initialized by reconstructing the MRF image series at low image resolution. For subsequent iterations, the method enforces pixel-wise fidelity to the best-matching dictionary template then enforces fidelity to the acquired data at slightly higher spatial resolution. After convergence, parametric maps with desirable spatial resolution are obtained through template matching of the final image series. The proposed method was evaluated on phantom and in-vivo data using the highly-undersampled, variable-density spiral trajectory and compared with the original MRF method. The benefits of additional sparsity constraints were also evaluated. When available, gold standard parameter maps were used to quantify the performance of each method. Results The proposed approach allowed convergence to accurate parametric maps with as few as 300 time points of acquisition, as compared to 1000 in the original MRF work. Simultaneous quantification of T1, T2, proton density (PD) and B0 field variations in the brain was achieved in vivo for a 256×256 matrix for a total acquisition time of 10.2s, representing a 3-fold reduction in acquisition time. Conclusions The proposed iterative multiscale reconstruction reliably increases MRF acquisition speed and accuracy. PMID:26132462

On the relative merits of invasive and non-invasive pre-surgical brain mapping: New tools in ablative epilepsy surgery.

PubMed

Papanicolaou, Andrew C; Rezaie, Roozbeh; Narayana, Shalini; Choudhri, Asim F; Abbas-Babajani-Feremi; Boop, Frederick A; Wheless, James W

2018-05-01

Cortical Stimulation Mapping (CSM) and the Wada procedure have long been considered the gold standard for localizing motor and language-related cortical areas and for determining the language and memory-dominant hemisphere, respectively. In recent years, however, non-invasive methods such as magnetoencephalography (MEG), functional magnetic resonance imaging (fMRI), and transcranial magnetic stimulation (TMS) have emerged as promising alternatives to the aforementioned procedures, particularly in cases where the invasive localization of eloquent cortex has proven to be challenging. To illustrate this point, we will first introduce the evidence of the compatibility of invasive and non-invasive methods and subsequently outline the rationale and the conditions where the latter methods are applicable. Copyright © 2017 Elsevier B.V. All rights reserved.

Not single brain areas but a network is involved in language: Applications in presurgical planning.

PubMed

Alemi, Razieh; Batouli, Seyed Amir Hossein; Behzad, Ebrahim; Ebrahimpoor, Mitra; Oghabian, Mohammad Ali

2018-02-01

Language is an important human function, and is a determinant of the quality of life. In conditions such as brain lesions, disruption of the language function may occur, and lesion resection is a solution for that. Presurgical planning to determine the language-related brain areas would enhance the chances of language preservation after the operation; however, availability of a normative language template is essential. In this study, using data from 60 young individuals who were meticulously checked for mental and physical health, and using fMRI and robust imaging and data analysis methods, functional brain maps for the language production, perception and semantic were produced. The obtained templates showed that the language function should be considered as the product of the collaboration of a network of brain regions, instead of considering only few brain areas to be involved in that. This study has important clinical applications, and extends our knowledge on the neuroanatomy of the language function. Copyright © 2018 Elsevier B.V. All rights reserved.

Human brain lesion-deficit inference remapped.

PubMed

Mah, Yee-Haur; Husain, Masud; Rees, Geraint; Nachev, Parashkev

2014-09-01

Our knowledge of the anatomical organization of the human brain in health and disease draws heavily on the study of patients with focal brain lesions. Historically the first method of mapping brain function, it is still potentially the most powerful, establishing the necessity of any putative neural substrate for a given function or deficit. Great inferential power, however, carries a crucial vulnerability: without stronger alternatives any consistent error cannot be easily detected. A hitherto unexamined source of such error is the structure of the high-dimensional distribution of patterns of focal damage, especially in ischaemic injury-the commonest aetiology in lesion-deficit studies-where the anatomy is naturally shaped by the architecture of the vascular tree. This distribution is so complex that analysis of lesion data sets of conventional size cannot illuminate its structure, leaving us in the dark about the presence or absence of such error. To examine this crucial question we assembled the largest known set of focal brain lesions (n = 581), derived from unselected patients with acute ischaemic injury (mean age = 62.3 years, standard deviation = 17.8, male:female ratio = 0.547), visualized with diffusion-weighted magnetic resonance imaging, and processed with validated automated lesion segmentation routines. High-dimensional analysis of this data revealed a hidden bias within the multivariate patterns of damage that will consistently distort lesion-deficit maps, displacing inferred critical regions from their true locations, in a manner opaque to replication. Quantifying the size of this mislocalization demonstrates that past lesion-deficit relationships estimated with conventional inferential methodology are likely to be significantly displaced, by a magnitude dependent on the unknown underlying lesion-deficit relationship itself. Past studies therefore cannot be retrospectively corrected, except by new knowledge that would render them redundant. Positively, we show that novel machine learning techniques employing high-dimensional inference can nonetheless accurately converge on the true locus. We conclude that current inferences about human brain function and deficits based on lesion mapping must be re-evaluated with methodology that adequately captures the high-dimensional structure of lesion data. © The Author (2014). Published by Oxford University Press on behalf of the Guarantors of Brain.

Brain stem serotonin protects blood pressure in neonatal rats exposed to episodic anoxia.

PubMed

Yang, Hsiao T; Cummings, Kevin J

2013-12-01

In neonatal rodents, a loss of brain stem serotonin [5-hydroxytryptamine (5-HT)] in utero or at birth compromises anoxia-induced gasping and the recovery of heart rate (HR) and breathing with reoxygenation (i.e., autoresuscitation). How mean arterial pressure (MAP) is influenced after an acute loss of brain stem 5-HT content is unknown. We hypothesized that a loss of 5-HT for ∼1 day would compromise MAP during episodic anoxia. We injected 6-fluorotryptophan (20 mg/kg ip) into rat pups (postnatal days 9-10 or 11-13, n = 22 treated, 24 control), causing a ∼70% loss of brain stem 5-HT. Pups were exposed to a maximum of 15 anoxic episodes, separated by 5 min of room air to allow autoresuscitation. In younger pups, we measured breathing frequency and tidal volume using "head-out" plethysmography and HR from the electrocardiogram. In older pups, we used whole body plethysmography to detect gasping, while monitoring MAP. Gasp latency and the time required for respiratory, HR, and MAP recovery following each episode were determined. Despite normal gasp latency, breathing frequency and a larger tidal volume (P < 0.001), 5-HT-deficient pups survived one-half the number of episodes as controls (P < 0.001). The anoxia-induced decrease in MAP experienced by 5-HT-deficient pups was double that of controls (P = 0.017), despite the same drop in HR (P = 0.48). MAP recovery was delayed ∼10 s by 5-HT deficiency (P = 0.001). Our data suggest a loss of brain stem 5-HT leads to a pronounced, premature loss of MAP in response to episodic anoxia. These data may help explain why some sudden infant death syndrome cases die from what appears to be cardiovascular collapse during apparent severe hypoxia.

An analysis of logical thinking using mind mapping

NASA Astrophysics Data System (ADS)

Swestyani, S.; Masykuri, M.; Prayitno, B. A.; Rinanto, Y.; Widoretno, S.

2018-05-01

Brains can remember information in different forms, i.e images, symbols, sounds, and senses, and the information is connected by logical gate. This information needs imagination and association to construct new meaningful images. The purpose of this research was to describe a method of teaching which based on Tony Buzan’s mind mapping technique. This research showed how mind mapping could be used to measure students’ logical thinking and how mind mapping could promote students’ understanding in meaningful way. The test of mind mapping that involved 31 students of XI grade in SMA Batik 2 Surakarta was used as the data collecting method in this research. Then, the Ohassta’s mind mapping rubric was used to analyze the structure and content of mind mapping. The rubric includes four aspects, i.e knowledge, communication, thinking, and application. A qualitative analysis Miles and Hubberman’s was used to assess the obtained data. The result showed that the percentage of knowledge aspect was 53,23 %, communication aspect was 28,33 %, thinking aspect was 28,33 %, and knowledge aspect was 41,53 %. Mind mapping makes logical thinking visible so that the quality of learning that has occurred can be seen and explored. Using mind mapping in the course of teaching means that learning is no longer a complex and intractable process, measurable is not only by proxy but also by an observable phenomenon.

Forthergillian Lecture. Imaging human brain function.

PubMed

Frackowiak, R S

The non-invasive brain scanning techniques introduced a quarter of a century ago have become crucial for diagnosis in clinical neurology. They have also been used to investigate brain function and have provided information about normal activity and pathogenesis. They have been used to investigate functional specialization in the brain and how specialized areas communicate to generate complex integrated functions such as speech, memory, the emotions and so on. The phenomenon of brain plasticity is poorly understood and yet clinical neurologists are aware, from everyday observations, that spontaneous recovery from brain lesions is common. An improved understanding of the mechanisms of recovery may generate new therapeutic strategies and indicate ways of modulating mechanisms that promote plastic compensation for loss of function. The main methods used to investigate these issues are positron emission tomography and magnetic resonance imaging (M.R.I.). M.R.I. is also used to map brain structure. The techniques of functional brain mapping and computational morphometrics depend on high performance scanners and a validated set of analytic statistical procedures that generate reproducible data and meaningful inferences from brain scanning data. The motor system presents a good paradigm to illustrate advances made by scanning towards an understanding of plasticity at the level of brain areas. The normal motor system is organized in a nested hierarchy. Recovery from paralysis caused by internal capsule strokes involves functional reorganization manifesting itself as changed patterns of activity in the component brain areas of the normal motor system. The pattern of plastic modification depends in part on patterns of residual or disturbed connectivity after brain injury. Therapeutic manipulations in patients with Parkinson's disease using deep brain stimulation, dopaminergic agents or fetal mesencephalic transplantation provide a means to examine mechanisms underpinning plastic change. Other models of plastic change, such as normal visuospatial learning or re-establishing speech comprehension after cochlear implantation in the deaf illustrate how patterns of brain function adapt over time. Limitations of the scanning techniques and prospects for the future are discussed in relation to new developments in the neuroimaging field.

Focal Cortical Dysplasia (FCD) lesion analysis with complex diffusion approach.

PubMed

Rajan, Jeny; Kannan, K; Kesavadas, C; Thomas, Bejoy

2009-10-01

Identification of Focal Cortical Dysplasia (FCD) can be difficult due to the subtle MRI changes. Though sequences like FLAIR (fluid attenuated inversion recovery) can detect a large majority of these lesions, there are smaller lesions without signal changes that can easily go unnoticed by the naked eye. The aim of this study is to improve the visibility of focal cortical dysplasia lesions in the T1 weighted brain MRI images. In the proposed method, we used a complex diffusion based approach for calculating the FCD affected areas. Based on the diffused image and thickness map, a complex map is created. From this complex map; FCD areas can be easily identified. MRI brains of 48 subjects selected by neuroradiologists were given to computer scientists who developed the complex map for identifying the cortical dysplasia. The scientists were blinded to the MRI interpretation result of the neuroradiologist. The FCD could be identified in all the patients in whom surgery was done, however three patients had false positive lesions. More lesions were identified in patients in whom surgery was not performed and lesions were seen in few of the controls. These were considered as false positive. This computer aided detection technique using complex diffusion approach can help detect focal cortical dysplasia in patients with epilepsy.

Adult neurogenesis is necessary to refine and maintain circuit specificity.

PubMed

Cummings, Diana M; Snyder, Jason S; Brewer, Michelle; Cameron, Heather A; Belluscio, Leonardo

2014-10-08

The circuitry of the olfactory bulb contains a precise anatomical map that links isofunctional regions within each olfactory bulb. This intrabulbar map forms perinatally and undergoes activity-dependent refinement during the first postnatal weeks. Although this map retains its plasticity throughout adulthood, its organization is remarkably stable despite the addition of millions of new neurons to this circuit. Here we show that the continuous supply of new neuroblasts from the subventricular zone is necessary for both the restoration and maintenance of this precise central circuit. Using pharmacogenetic methods to conditionally ablate adult neurogenesis in transgenic mice, we find that the influx of neuroblasts is required for recovery of intrabulbar map precision after disruption due to sensory block. We further demonstrate that eliminating adult-born interneurons in naive animals leads to an expansion of tufted cell axons that is identical to the changes caused by sensory block, thus revealing an essential role for new neurons in circuit maintenance under baseline conditions. These findings show, for the first time, that inhibiting adult neurogenesis alters the circuitry of projection neurons in brain regions that receive new interneurons and points to a critical role for adult-born neurons in stabilizing a brain circuit that exhibits high levels of plasticity. Copyright © 2014 the authors 0270-6474/14/3413801-10$15.00/0.

MS lesion segmentation using a multi-channel patch-based approach with spatial consistency

NASA Astrophysics Data System (ADS)

Mechrez, Roey; Goldberger, Jacob; Greenspan, Hayit

2015-03-01

This paper presents an automatic method for segmentation of Multiple Sclerosis (MS) in Magnetic Resonance Images (MRI) of the brain. The approach is based on similarities between multi-channel patches (T1, T2 and FLAIR). An MS lesion patch database is built using training images for which the label maps are known. For each patch in the testing image, k similar patches are retrieved from the database. The matching labels for these k patches are then combined to produce an initial segmentation map for the test case. Finally a novel iterative patch-based label refinement process based on the initial segmentation map is performed to ensure spatial consistency of the detected lesions. A leave-one-out evaluation is done for each testing image in the MS lesion segmentation challenge of MICCAI 2008. Results are shown to compete with the state-of-the-art methods on the MICCAI 2008 challenge.

Understanding brain networks and brain organization

PubMed Central

Pessoa, Luiz

2014-01-01

What is the relationship between brain and behavior? The answer to this question necessitates characterizing the mapping between structure and function. The aim of this paper is to discuss broad issues surrounding the link between structure and function in the brain that will motivate a network perspective to understanding this question. As others in the past, I argue that a network perspective should supplant the common strategy of understanding the brain in terms of individual regions. Whereas this perspective is needed for a fuller characterization of the mind-brain, it should not be viewed as panacea. For one, the challenges posed by the many-to-many mapping between regions and functions is not dissolved by the network perspective. Although the problem is ameliorated, one should not anticipate a one-to-one mapping when the network approach is adopted. Furthermore, decomposition of the brain network in terms of meaningful clusters of regions, such as the ones generated by community-finding algorithms, does not by itself reveal “true” subnetworks. Given the hierarchical and multi-relational relationship between regions, multiple decompositions will offer different “slices” of a broader landscape of networks within the brain. Finally, I described how the function of brain regions can be characterized in a multidimensional manner via the idea of diversity profiles. The concept can also be used to describe the way different brain regions participate in networks. PMID:24819881

Decoding Dynamic Brain Patterns from Evoked Responses: A Tutorial on Multivariate Pattern Analysis Applied to Time Series Neuroimaging Data.

PubMed

Grootswagers, Tijl; Wardle, Susan G; Carlson, Thomas A

2017-04-01

Multivariate pattern analysis (MVPA) or brain decoding methods have become standard practice in analyzing fMRI data. Although decoding methods have been extensively applied in brain-computer interfaces, these methods have only recently been applied to time series neuroimaging data such as MEG and EEG to address experimental questions in cognitive neuroscience. In a tutorial style review, we describe a broad set of options to inform future time series decoding studies from a cognitive neuroscience perspective. Using example MEG data, we illustrate the effects that different options in the decoding analysis pipeline can have on experimental results where the aim is to "decode" different perceptual stimuli or cognitive states over time from dynamic brain activation patterns. We show that decisions made at both preprocessing (e.g., dimensionality reduction, subsampling, trial averaging) and decoding (e.g., classifier selection, cross-validation design) stages of the analysis can significantly affect the results. In addition to standard decoding, we describe extensions to MVPA for time-varying neuroimaging data including representational similarity analysis, temporal generalization, and the interpretation of classifier weight maps. Finally, we outline important caveats in the design and interpretation of time series decoding experiments.

Analyzing brain networks with PCA and conditional Granger causality.

PubMed

Zhou, Zhenyu; Chen, Yonghong; Ding, Mingzhou; Wright, Paul; Lu, Zuhong; Liu, Yijun

2009-07-01

Identifying directional influences in anatomical and functional circuits presents one of the greatest challenges for understanding neural computations in the brain. Granger causality mapping (GCM) derived from vector autoregressive models of data has been employed for this purpose, revealing complex temporal and spatial dynamics underlying cognitive processes. However, the traditional GCM methods are computationally expensive, as signals from thousands of voxels within selected regions of interest (ROIs) are individually processed, and being based on pairwise Granger causality, they lack the ability to distinguish direct from indirect connectivity among brain regions. In this work a new algorithm called PCA based conditional GCM is proposed to overcome these problems. The algorithm implements the following two procedures: (i) dimensionality reduction in ROIs of interest with principle component analysis (PCA), and (ii) estimation of the direct causal influences in local brain networks, using conditional Granger causality. Our results show that the proposed method achieves greater accuracy in detecting network connectivity than the commonly used pairwise Granger causality method. Furthermore, the use of PCA components in conjunction with conditional GCM greatly reduces the computational cost relative to the use of individual voxel time series. Copyright 2009 Wiley-Liss, Inc

Clinical Impact and Implication of Real-Time Oscillation Analysis for Language Mapping.

PubMed

Ogawa, Hiroshi; Kamada, Kyousuke; Kapeller, Christoph; Prueckl, Robert; Takeuchi, Fumiya; Hiroshima, Satoru; Anei, Ryogo; Guger, Christoph

2017-01-01

We developed a functional brain analysis system that enabled us to perform real-time task-related electrocorticography (ECoG) and evaluated its potential in clinical practice. We hypothesized that high gamma activity (HGA) mapping would provide better spatial and temporal resolution with high signal-to-noise ratios. Seven awake craniotomy patients were evaluated. ECoG was recorded during language tasks using subdural grids, and HGA (60-170 Hz) maps were obtained in real time. The patients also underwent electrocortical stimulation (ECS) mapping to validate the suspected functional locations on HGA mapping. The results were compared and calculated to assess the sensitivity and specificity of HGA mapping. For reference, bedside HGA-ECS mapping was performed in 5 epilepsy patients. HGA mapping demonstrated functional brain areas in real time and was comparable with ECS mapping. Sensitivity and specificity for the language area were 90.1% ± 11.2% and 90.0% ± 4.2%, respectively. Most HGA-positive areas were consistent with ECS-positive regions in both groups, and there were no statistical between-group differences. Although this study included a small number of subjects, it showed real-time HGA mapping with the same setting and tasks under different conditions. This study demonstrates the clinical feasibility of real-time HGA mapping. Real-time HGA mapping enabled simple and rapid detection of language functional areas in awake craniotomy. The mapping results were highly accurate, although the mapping environment was noisy. Further studies of HGA mapping may provide the potential to elaborate complex brain functions and networks. Copyright © 2016 Elsevier Inc. All rights reserved.

Mapping brain activity in gradient-echo functional MRI using principal component analysis

NASA Astrophysics Data System (ADS)

Khosla, Deepak; Singh, Manbir; Don, Manuel

1997-05-01

The detection of sites of brain activation in functional MRI has been a topic of immense research interest and many technique shave been proposed to this end. Recently, principal component analysis (PCA) has been applied to extract the activated regions and their time course of activation. This method is based on the assumption that the activation is orthogonal to other signal variations such as brain motion, physiological oscillations and other uncorrelated noises. A distinct advantage of this method is that it does not require any knowledge of the time course of the true stimulus paradigm. This technique is well suited to EPI image sequences where the sampling rate is high enough to capture the effects of physiological oscillations. In this work, we propose and apply tow methods that are based on PCA to conventional gradient-echo images and investigate their usefulness as tools to extract reliable information on brain activation. The first method is a conventional technique where a single image sequence with alternating on and off stages is subject to a principal component analysis. The second method is a PCA-based approach called the common spatial factor analysis technique (CSF). As the name suggests, this method relies on common spatial factors between the above fMRI image sequence and a background fMRI. We have applied these methods to identify active brain ares during visual stimulation and motor tasks. The results from these methods are compared to those obtained by using the standard cross-correlation technique. We found good agreement in the areas identified as active across all three techniques. The results suggest that PCA and CSF methods have good potential in detecting the true stimulus correlated changes in the presence of other interfering signals.

Mapping Prefrontal Cortex Functions in Human Infancy

ERIC Educational Resources Information Center

Grossmann, Tobias

2013-01-01

It has long been thought that the prefrontal cortex, as the seat of most higher brain functions, is functionally silent during most of infancy. This review highlights recent work concerned with the precise mapping (localization) of brain activation in human infants, providing evidence that prefrontal cortex exhibits functional activation much…

Family members' narratives of lifespace: Mapping changes before and after a brain injury causing driving disruption.

PubMed

Liang, Phyllis; Liddle, Jacki; Fleming, Jennifer; Gustafsson, Louise

2016-06-01

Family members of a person with an acquired brain injury often provide transport assistance during driving disruption with potential impacts on their own travel and participation within the community. The geographic area in which people travel and conduct their activities is known as lifespace. This study aimed to describe the quantitative changes in family members' lifespace after brain injury and understand their subjective experiences through interacting with maps during narratives. Mapping was embedded within in-depth semi-structured interviews with 15 family members. Two sets of maps were generated per participant showing the number of travel locations before and after brain injury. In the interviews, participants reflected on the perceived meaning of lifespace change. Qualitative data were analysed using a narrative approach. Quantitative data from the mapping revealed an increase in travel locations for nine participants, a decrease for five, and no change for one participant. Data analysis revealed four typologies which complemented and enriched the quantitative data: (i) I will do everything for him or her; (ii) Trying to fit all in; (iii) We spend all our time together now; (iv) I need to also care for myself. The findings describe the change in family members' lifespace after taking on the driver role following acquired brain injury. This study highlights the importance of understanding both quantitative and qualitative aspects of lifespace. The subjective experiences and consequences of lifespace changes are different from the impact on individuals with health conditions. Mapping in an interview as a tool has potential clinical utility. © 2016 Occupational Therapy Australia.

Automatic recognition of holistic functional brain networks using iteratively optimized convolutional neural networks (IO-CNN) with weak label initialization.

PubMed

Zhao, Yu; Ge, Fangfei; Liu, Tianming

2018-07-01

fMRI data decomposition techniques have advanced significantly from shallow models such as Independent Component Analysis (ICA) and Sparse Coding and Dictionary Learning (SCDL) to deep learning models such Deep Belief Networks (DBN) and Convolutional Autoencoder (DCAE). However, interpretations of those decomposed networks are still open questions due to the lack of functional brain atlases, no correspondence across decomposed or reconstructed networks across different subjects, and significant individual variabilities. Recent studies showed that deep learning, especially deep convolutional neural networks (CNN), has extraordinary ability of accommodating spatial object patterns, e.g., our recent works using 3D CNN for fMRI-derived network classifications achieved high accuracy with a remarkable tolerance for mistakenly labelled training brain networks. However, the training data preparation is one of the biggest obstacles in these supervised deep learning models for functional brain network map recognitions, since manual labelling requires tedious and time-consuming labours which will sometimes even introduce label mistakes. Especially for mapping functional networks in large scale datasets such as hundreds of thousands of brain networks used in this paper, the manual labelling method will become almost infeasible. In response, in this work, we tackled both the network recognition and training data labelling tasks by proposing a new iteratively optimized deep learning CNN (IO-CNN) framework with an automatic weak label initialization, which enables the functional brain networks recognition task to a fully automatic large-scale classification procedure. Our extensive experiments based on ABIDE-II 1099 brains' fMRI data showed the great promise of our IO-CNN framework. Copyright © 2018 Elsevier B.V. All rights reserved.

Warping of a computerized 3-D atlas to match brain image volumes for quantitative neuroanatomical and functional analysis

NASA Astrophysics Data System (ADS)

Evans, Alan C.; Dai, Weiqian; Collins, D. Louis; Neelin, Peter; Marrett, Sean

1991-06-01

We describe the implementation, experience and preliminary results obtained with a 3-D computerized brain atlas for topographical and functional analysis of brain sub-regions. A volume-of-interest (VOI) atlas was produced by manual contouring on 64 adjacent 2 mm-thick MRI slices to yield 60 brain structures in each hemisphere which could be adjusted, originally by global affine transformation or local interactive adjustments, to match individual MRI datasets. We have now added a non-linear deformation (warp) capability (Bookstein, 1989) into the procedure for fitting the atlas to the brain data. Specific target points are identified in both atlas and MRI spaces which define a continuous 3-D warp transformation that maps the atlas on to the individual brain image. The procedure was used to fit MRI brain image volumes from 16 young normal volunteers. Regional volume and positional variability were determined, the latter in such a way as to assess the extent to which previous linear models of brain anatomical variability fail to account for the true variation among normal individuals. Using a linear model for atlas deformation yielded 3-D fits of the MRI data which, when pooled across subjects and brain regions, left a residual mis-match of 6 - 7 mm as compared to the non-linear model. The results indicate a substantial component of morphometric variability is not accounted for by linear scaling. This has profound implications for applications which employ stereotactic coordinate systems which map individual brains into a common reference frame: quantitative neuroradiology, stereotactic neurosurgery and cognitive mapping of normal brain function with PET. In the latter case, the combination of a non-linear deformation algorithm would allow for accurate measurement of individual anatomic variations and the inclusion of such variations in inter-subject averaging methodologies used for cognitive mapping with PET.

Laser technique for anatomical-functional study of the medial prefrontal cortex of the brain

NASA Astrophysics Data System (ADS)

Sanchez-Huerta, Laura; Hernandez, Adan; Ayala, Griselda; Marroquin, Javier; Silva, Adriana B.; Khotiaintsev, Konstantin S.; Svirid, Vladimir A.; Flores, Gonzalo; Khotiaintsev, Sergei N.

1999-05-01

The brain represents one of the most complex systems that we know yet. In its study, non-destructive methods -- in particular, behavioral studies play an important role. By alteration of brain functioning (e.g. by pharmacological means) and observation of consequent behavior changes an important information on brain organization and functioning is obtained. For inducing local alterations, permanent brain lesions are employed. However, for correct results this technique has to be quasi-non-destructive, i.e. not to affect the normal brain function. Hence, the lesions should be very small, accurate and applied precisely over the structure (e.g. the brain nucleus) of interest. These specifications are difficult to meet with the existing techniques for brain lesions -- specifically, neurotoxical, mechanical and electrical means because they result in too extensive damage. In this paper, we present new laser technique for quasi-non- destructive anatomical-functional mapping in vivo of the medial prefrontal cortex (MPFC) of the rat. The technique is based on producing of small-size, well-controlled laser- induced lesions over some areas of the MPFC. The anesthetized animals are subjected to stereotactic surgery and certain points of the MPFC are exposed the confined radiation of the 10 W cw CO2 laser. Subsequent behavioral changes observed in neonatal and adult animals as well as histological data prove effectiveness of this technology for anatomical- functional studies of the brain by areas, and as a treatment method for some pathologies.

Molecular neuroanatomy: a generation of progress.

PubMed

Pollock, Jonathan D; Wu, Da-Yu; Satterlee, John S

2014-02-01

The neuroscience research landscape has changed dramatically over the past decade. Specifically, an impressive array of new tools and technologies have been generated, including but not limited to: brain gene expression atlases, genetically encoded proteins to monitor and manipulate neuronal activity, and new methods for imaging and mapping circuits. However, despite these technological advances, several significant challenges must be overcome to enable a better understanding of brain function and to develop cell type-targeted therapeutics to treat brain disorders. This review provides an overview of some of the tools and technologies currently being used to advance the field of molecular neuroanatomy, and also discusses emerging technologies that may enable neuroscientists to address these crucial scientific challenges over the coming decade. Published by Elsevier Ltd.

Functional Maps of Mechanosensory Features in the Drosophila Brain.

PubMed

Patella, Paola; Wilson, Rachel I

2018-04-23

Johnston's organ is the largest mechanosensory organ in Drosophila. It contributes to hearing, touch, vestibular sensing, proprioception, and wind sensing. In this study, we used in vivo 2-photon calcium imaging and unsupervised image segmentation to map the tuning properties of Johnston's organ neurons (JONs) at the site where their axons enter the brain. We then applied the same methodology to study two key brain regions that process signals from JONs: the antennal mechanosensory and motor center (AMMC) and the wedge, which is downstream of the AMMC. First, we identified a diversity of JON response types that tile frequency space and form a rough tonotopic map. Some JON response types are direction selective; others are specialized to encode amplitude modulations over a specific range (dynamic range fractionation). Next, we discovered that both the AMMC and the wedge contain a tonotopic map, with a significant increase in tonotopy-and a narrowing of frequency tuning-at the level of the wedge. Whereas the AMMC tonotopic map is unilateral, the wedge tonotopic map is bilateral. Finally, we identified a subregion of the AMMC/wedge that responds preferentially to the coherent rotation of the two mechanical organs in the same angular direction, indicative of oriented steady air flow (directional wind). Together, these maps reveal the broad organization of the primary and secondary mechanosensory regions of the brain. They provide a framework for future efforts to identify the specific cell types and mechanisms that underlie the hierarchical re-mapping of mechanosensory information in this system. Copyright © 2018 Elsevier Ltd. All rights reserved.

Global differential expression of genes located in the Down Syndrome Critical Region in normal human brain

PubMed Central

Montoya, Julio Cesar; Fajardo, Dianora; Peña, Angela; Sánchez, Adalberto; Domínguez, Martha C; Satizábal, José María

2014-01-01

Background: The information of gene expression obtained from databases, have made possible the extraction and analysis of data related with several molecular processes involving not only in brain homeostasis but its disruption in some neuropathologies; principally in Down syndrome and the Alzheimer disease. Objective: To correlate the levels of transcription of 19 genes located in the Down Syndrome Critical Region (DSCR) with their expression in several substructures of normal human brain. Methods: There were obtained expression profiles of 19 DSCR genes in 42 brain substructures, from gene expression values available at the database of the human brain of the Brain Atlas of the Allen Institute for Brain Sciences", (http://human.brain-map.org/). The co-expression patterns of DSCR genes in brain were calculated by using multivariate statistical methods. Results: Highest levels of gene expression were registered at caudate nucleus, nucleus accumbens and putamen among central areas of cerebral cortex. Increased expression levels of RCAN1 that encode by a protein involved in signal transduction process of the CNS were recorded for PCP4 that participates in the binding to calmodulin and TTC3; a protein that is associated with differentiation of neurons. That previously identified brain structures play a crucial role in the learning process, in different class of memory and in motor skills. Conclusion: The precise regulation of DSCR gene expression is crucial to maintain the brain homeostasis, especially in those areas with high levels of gene expression associated with a remarkable process of learning and cognition. PMID:25767303

Rapid and minimum invasive functional brain mapping by real-time visualization of high gamma activity during awake craniotomy.

PubMed

Ogawa, Hiroshi; Kamada, Kyousuke; Kapeller, Christoph; Hiroshima, Satoru; Prueckl, Robert; Guger, Christoph

2014-11-01

Electrocortical stimulation (ECS) is the gold standard for functional brain mapping during an awake craniotomy. The critical issue is to set aside enough time to identify eloquent cortices by ECS. High gamma activity (HGA) ranging between 80 and 120 Hz on electrocorticogram is assumed to reflect localized cortical processing. In this report, we used real-time HGA mapping and functional neuronavigation integrated with functional magnetic resonance imaging (fMRI) for rapid and reliable identification of motor and language functions. Four patients with intra-axial tumors in their dominant hemisphere underwent preoperative fMRI and lesion resection with an awake craniotomy. All patients showed significant fMRI activation evoked by motor and language tasks. During the craniotomy, we recorded electrocorticogram activity by placing subdural grids directly on the exposed brain surface. Each patient performed motor and language tasks and demonstrated real-time HGA dynamics in hand motor areas and parts of the inferior frontal gyrus. Sensitivity and specificity of HGA mapping were 100% compared with ECS mapping in the frontal lobe, which suggested HGA mapping precisely indicated eloquent cortices. We found different HGA dynamics of language tasks in frontal and temporal regions. Specificities of the motor and language-fMRI did not reach 85%. The results of HGA mapping was mostly consistent with those of ECS mapping, although fMRI tended to overestimate functional areas. This novel technique enables rapid and accurate identification of motor and frontal language areas. Furthermore, real-time HGA mapping sheds light on underlying physiological mechanisms related to human brain functions. Copyright © 2014 Elsevier Inc. All rights reserved.

High and ultra-high resolution metabolite mapping of the human brain using 1H FID MRSI at 9.4T.

PubMed

Nassirpour, Sahar; Chang, Paul; Henning, Anke

2018-03-01

Magnetic resonance spectroscopic imaging (MRSI) is a promising technique for mapping the spatial distribution of multiple metabolites in the human brain. These metabolite maps can be used as a diagnostic tool to gain insight into several biochemical processes and diseases in the brain. In comparison to lower field strengths, MRSI at ultra-high field strengths benefits from a higher signal to noise ratio (SNR) as well as higher chemical shift dispersion, and hence spectral resolution. This study combines the benefits of an ultra-high field magnet with the advantages of an ultra-short TE and TR single-slice FID-MRSI sequence (such as negligible J-evolution and loss of SNR due to T 2 relaxation effects) and presents the first metabolite maps acquired at 9.4T in the healthy human brain at both high (voxel size of 97.6µL) and ultra-high (voxel size of 24.4µL) spatial resolutions in a scan time of 11 and 46min respectively. In comparison to lower field strengths, more anatomically-detailed maps with higher SNR from a larger number of metabolites are shown. A total of 12 metabolites including glutamate (Glu), glutamine (Gln), N-acetyl-aspartyl-glutamate (NAAG), Gamma-aminobutyric acid (GABA) and glutathione (GSH) are reliably mapped. Comprehensive description of the methodology behind these maps is provided. Copyright © 2016 Elsevier Inc. All rights reserved.

Mapping of Arithmetic Processing by Navigated Repetitive Transcranial Magnetic Stimulation in Patients with Parietal Brain Tumors and Correlation with Postoperative Outcome.

PubMed

Ille, Sebastian; Drummer, Katharina; Giglhuber, Katrin; Conway, Neal; Maurer, Stefanie; Meyer, Bernhard; Krieg, Sandro M

2018-06-01

Preserving functionality is important during neurosurgical resection of brain tumors. Specialized centers also map further brain functions apart from motor and language functions, such as arithmetic processing (AP). The mapping of AP by navigated repetitive transcranial magnetic stimulation (nrTMS) in healthy volunteers has been reported. The present study aimed to correlate the results of mapping AP with functional patient outcomes. We included 26 patients with parietal brain tumors. Because of preoperative impairment of AP, mapping was not possible in 8 patients (31%). We stimulated 52 cortical sites by nrTMS while patients performed a calculation task. Preoperatively and postoperatively, patients underwent a standardized number-processing and calculation test (NPCT). Tumor resection was blinded to nrTMS results, and the change in NPCT performance was correlated to resected AP-positive spots as identified by nrTMS. The resection of AP-positive sites correlated with a worsening of the postoperative NPCT result in 12 cases. In 3 cases, no AP-positive sites were resected and the postoperative NPCT result was similar to or better than preoperatively. Also, in 3 cases, the postoperative NPCT result was better than preoperatively, although AP-positive sites were resected. Despite presenting only a few cases, nrTMS might be a useful tool for preoperative mapping of AP. However, the reliability of the present results has to be evaluated in a larger series and by intraoperative mapping data. Copyright © 2018 Elsevier Inc. All rights reserved.

Knowledge Representation and Data Mining of Neuronal Morphologies Using Neuroinformatics Tools and Formal Ontologies

ERIC Educational Resources Information Center

Polavaram, Sridevi

2016-01-01

Neuroscience can greatly benefit from using novel methods in computer science and informatics, which enable knowledge discovery in unexpected ways. Currently one of the biggest challenges in Neuroscience is to map the functional circuitry of the brain. The applications of this goal range from understanding structural reorganization of neurons to…

Electroencephalographic inverse localization of brain activity in acute traumatic brain injury as a guide to surgery, monitoring and treatment

PubMed Central

Irimia, Andrei; Goh, S.-Y. Matthew; Torgerson, Carinna M.; Stein, Nathan R.; Chambers, Micah C.; Vespa, Paul M.; Van Horn, John D.

2013-01-01

Objective To inverse-localize epileptiform cortical electrical activity recorded from severe traumatic brain injury (TBI) patients using electroencephalography (EEG). Methods Three acute TBI cases were imaged using computed tomography (CT) and multimodal magnetic resonance imaging (MRI). Semi-automatic segmentation was performed to partition the complete TBI head into 25 distinct tissue types, including 6 tissue types accounting for pathology. Segmentations were employed to generate a finite element method model of the head, and EEG activity generators were modeled as dipolar currents distributed over the cortical surface. Results We demonstrate anatomically faithful localization of EEG generators responsible for epileptiform discharges in severe TBI. By accounting for injury-related tissue conductivity changes, our work offers the most realistic implementation currently available for the inverse estimation of cortical activity in TBI. Conclusion Whereas standard localization techniques are available for electrical activity mapping in uninjured brains, they are rarely applied to acute TBI. Modern models of TBI-induced pathology can inform the localization of epileptogenic foci, improve surgical efficacy, contribute to the improvement of critical care monitoring and provide guidance for patient-tailored treatment. With approaches such as this, neurosurgeons and neurologists can study brain activity in acute TBI and obtain insights regarding injury effects upon brain metabolism and clinical outcome. PMID:24011495

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

PubMed

Sperber, Christoph; Karnath, Hans-Otto

2017-03-01

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

Advanced lesion symptom mapping analyses and implementation as BCBtoolkit.

PubMed

Foulon, Chris; Cerliani, Leonardo; Kinkingnéhun, Serge; Levy, Richard; Rosso, Charlotte; Urbanski, Marika; Volle, Emmanuelle; Thiebaut de Schotten, Michel

2018-03-01

Patients with brain lesions provide a unique opportunity to understand the functioning of the human mind. However, even when focal, brain lesions have local and remote effects that impact functionally and structurally connected circuits. Similarly, function emerges from the interaction between brain areas rather than their sole activity. For instance, category fluency requires the associations between executive, semantic, and language production functions. Here, we provide, for the first time, a set of complementary solutions for measuring the impact of a given lesion on the neuronal circuits. Our methods, which were applied to 37 patients with a focal frontal brain lesions, revealed a large set of directly and indirectly disconnected brain regions that had significantly impacted category fluency performance. The directly disconnected regions corresponded to areas that are classically considered as functionally engaged in verbal fluency and categorization tasks. These regions were also organized into larger directly and indirectly disconnected functional networks, including the left ventral fronto-parietal network, whose cortical thickness correlated with performance on category fluency. The combination of structural and functional connectivity together with cortical thickness estimates reveal the remote effects of brain lesions, provide for the identification of the affected networks, and strengthen our understanding of their relationship with cognitive and behavioral measures. The methods presented are available and freely accessible in the BCBtoolkit as supplementary software [1].

CT Perfusion in Acute Stroke: "Black Holes" on Time-to-Peak Image Maps Indicate Unsalvageable Brain.

PubMed

Meagher, Ruairi; Shankar, Jai Jai Shiva

2016-11-01

CT perfusion is becoming important in acute stroke imaging to determine optimal patient-management strategies. The purpose of this study was to examine the predictive value of time-to-peak image maps and, specifically, a phenomenon coined a "black hole" for assessing infarcted brain tissue at the time of scan. Acute stroke patients were screened for the presence of black holes and their follow-up imaging (noncontrast CT or MR) was reviewed to assess for infarcted brain tissue. Of the 23 patients with signs of acute ischemia on CT perfusion, all had black holes. The black holes corresponded with areas of infarcted brain on follow-up imaging (specificity 100%). Black holes demonstrated significantly lower cerebral blood volumes (P < .001) and cerebral blood flow (P < .001) compared to immediately adjacent tissue. Black holes on time-to-peak image maps represent areas of unsalvageable brain. Copyright © 2016 by the American Society of Neuroimaging.

Whole brain diffeomorphic metric mapping via integration of sulcal and gyral curves, cortical surfaces, and images

PubMed Central

Du, Jia; Younes, Laurent; Qiu, Anqi

2011-01-01

This paper introduces a novel large deformation diffeomorphic metric mapping algorithm for whole brain registration where sulcal and gyral curves, cortical surfaces, and intensity images are simultaneously carried from one subject to another through a flow of diffeomorphisms. To the best of our knowledge, this is the first time that the diffeomorphic metric from one brain to another is derived in a shape space of intensity images and point sets (such as curves and surfaces) in a unified manner. We describe the Euler–Lagrange equation associated with this algorithm with respect to momentum, a linear transformation of the velocity vector field of the diffeomorphic flow. The numerical implementation for solving this variational problem, which involves large-scale kernel convolution in an irregular grid, is made feasible by introducing a class of computationally friendly kernels. We apply this algorithm to align magnetic resonance brain data. Our whole brain mapping results show that our algorithm outperforms the image-based LDDMM algorithm in terms of the mapping accuracy of gyral/sulcal curves, sulcal regions, and cortical and subcortical segmentation. Moreover, our algorithm provides better whole brain alignment than combined volumetric and surface registration (Postelnicu et al., 2009) and hierarchical attribute matching mechanism for elastic registration (HAMMER) (Shen and Davatzikos, 2002) in terms of cortical and subcortical volume segmentation. PMID:21281722

B(0) homogeneity throughout the monkey brain is strongly improved in the sphinx position as compared to the supine position.

PubMed

Valette, Julien; Guillermier, Martine; Boumezbeur, Fawzi; Poupon, Cyril; Amadon, Alexis; Hantraye, Philippe; Lebon, Vincent

2006-03-01

To map B(0) distortions throughout the monkey brain in the two positions commonly used for NMR studies (the prone sphinx position and the supine position) in order to test the hypothesis that B(0) homogeneity in the sphinx position is significantly improved as compared to the supine position. Three macaque monkeys were installed in the two positions in a 3T whole-body MR system without shim correction. B(0) maps were acquired using a 3D gradient double-echo sequence, and field dispersion throughout the brain was quantified. In addition, field maps and localized (1)H spectra were acquired after first-order shimming was performed. The field maps collected in the three animals were highly reproducible. B(0) dispersion throughout the brain was typically two to three times greater in the supine position than in the sphinx position. Although first-order shimming proved relatively more efficient in the supine position, B(0) dispersion still remained greater in the supine than in the sphinx position. These findings can be explained by the thickness of outer brain tissues. This work demonstrates that the sphinx position is highly favorable in terms of B(0) homogeneity. It should prove useful for NMR exploration of the monkey brain, particularly at high fields where B(0) inhomogeneity associated with susceptibility artifacts is increased. (c) 2006 Wiley-Liss, Inc.

Correspondence of the brain's functional architecture during activation and rest.

PubMed

Smith, Stephen M; Fox, Peter T; Miller, Karla L; Glahn, David C; Fox, P Mickle; Mackay, Clare E; Filippini, Nicola; Watkins, Kate E; Toro, Roberto; Laird, Angela R; Beckmann, Christian F

2009-08-04

Neural connections, providing the substrate for functional networks, exist whether or not they are functionally active at any given moment. However, it is not known to what extent brain regions are continuously interacting when the brain is "at rest." In this work, we identify the major explicit activation networks by carrying out an image-based activation network analysis of thousands of separate activation maps derived from the BrainMap database of functional imaging studies, involving nearly 30,000 human subjects. Independently, we extract the major covarying networks in the resting brain, as imaged with functional magnetic resonance imaging in 36 subjects at rest. The sets of major brain networks, and their decompositions into subnetworks, show close correspondence between the independent analyses of resting and activation brain dynamics. We conclude that the full repertoire of functional networks utilized by the brain in action is continuously and dynamically "active" even when at "rest."

Toward fully automated processing of dynamic susceptibility contrast perfusion MRI for acute ischemic cerebral stroke.

PubMed

Kim, Jinsuh; Leira, Enrique C; Callison, Richard C; Ludwig, Bryan; Moritani, Toshio; Magnotta, Vincent A; Madsen, Mark T

2010-05-01

We developed fully automated software for dynamic susceptibility contrast (DSC) MR perfusion-weighted imaging (PWI) to efficiently and reliably derive critical hemodynamic information for acute stroke treatment decisions. Brain MR PWI was performed in 80 consecutive patients with acute nonlacunar ischemic stroke within 24h after onset of symptom from January 2008 to August 2009. These studies were automatically processed to generate hemodynamic parameters that included cerebral blood flow and cerebral blood volume, and the mean transit time (MTT). To develop reliable software for PWI analysis, we used computationally robust algorithms including the piecewise continuous regression method to determine bolus arrival time (BAT), log-linear curve fitting, arrival time independent deconvolution method and sophisticated motion correction methods. An optimal arterial input function (AIF) search algorithm using a new artery-likelihood metric was also developed. Anatomical locations of the automatically determined AIF were reviewed and validated. The automatically computed BAT values were statistically compared with estimated BAT by a single observer. In addition, gamma-variate curve-fitting errors of AIF and inter-subject variability of AIFs were analyzed. Lastly, two observes independently assessed the quality and area of hypoperfusion mismatched with restricted diffusion area from motion corrected MTT maps and compared that with time-to-peak (TTP) maps using the standard approach. The AIF was identified within an arterial branch and enhanced areas of perfusion deficit were visualized in all evaluated cases. Total processing time was 10.9+/-2.5s (mean+/-s.d.) without motion correction and 267+/-80s (mean+/-s.d.) with motion correction on a standard personal computer. The MTT map produced with our software adequately estimated brain areas with perfusion deficit and was significantly less affected by random noise of the PWI when compared with the TTP map. Results of image quality assessment by two observers revealed that the MTT maps exhibited superior quality over the TTP maps (88% good rating of MTT as compared to 68% of TTP). Our software allowed fully automated deconvolution analysis of DSC PWI using proven efficient algorithms that can be applied to acute stroke treatment decisions. Our streamlined method also offers promise for further development of automated quantitative analysis of the ischemic penumbra. Copyright (c) 2009 Elsevier Ireland Ltd. All rights reserved.

Functional connectomics from resting-state fMRI

PubMed Central

Smith, Stephen M; Vidaurre, Diego; Beckmann, Christian F; Glasser, Matthew F; Jenkinson, Mark; Miller, Karla L; Nichols, Thomas E; Robinson, Emma; Salimi-Khorshidi, Gholamreza; Woolrich, Mark W; Barch, Deanna M; Uğurbil, Kamil; Van Essen, David C

2014-01-01

Spontaneous fluctuations in activity in different parts of the brain can be used to study functional brain networks. We review the use of resting-state functional MRI for the purpose of mapping the macroscopic functional connectome. After describing MRI acquisition and image processing methods commonly used to generate data in a form amenable to connectomics network analysis, we discuss different approaches for estimating network structure from that data. Finally, we describe new possibilities resulting from the high-quality rfMRI data being generated by the Human Connectome Project, and highlight some upcoming challenges in functional connectomics. PMID:24238796

High-throughput dual-colour precision imaging for brain-wide connectome with cytoarchitectonic landmarks at the cellular level

PubMed Central

Gong, Hui; Xu, Dongli; Yuan, Jing; Li, Xiangning; Guo, Congdi; Peng, Jie; Li, Yuxin; Schwarz, Lindsay A.; Li, Anan; Hu, Bihe; Xiong, Benyi; Sun, Qingtao; Zhang, Yalun; Liu, Jiepeng; Zhong, Qiuyuan; Xu, Tonghui; Zeng, Shaoqun; Luo, Qingming

2016-01-01

The precise annotation and accurate identification of neural structures are prerequisites for studying mammalian brain function. The orientation of neurons and neural circuits is usually determined by mapping brain images to coarse axial-sampling planar reference atlases. However, individual differences at the cellular level likely lead to position errors and an inability to orient neural projections at single-cell resolution. Here, we present a high-throughput precision imaging method that can acquire a co-localized brain-wide data set of both fluorescent-labelled neurons and counterstained cell bodies at a voxel size of 0.32 × 0.32 × 2.0 μm in 3 days for a single mouse brain. We acquire mouse whole-brain imaging data sets of multiple types of neurons and projections with anatomical annotation at single-neuron resolution. The results show that the simultaneous acquisition of labelled neural structures and cytoarchitecture reference in the same brain greatly facilitates precise tracing of long-range projections and accurate locating of nuclei. PMID:27374071

Novel Neuroimaging Methods to Understand How HIV Affects the Brain

PubMed Central

Thompson, Paul

2015-01-01

In much of the developed world, the HIV epidemic has largely been controlled by anti-retroviral treatment. Even so, there is growing concern that HIV-infected individuals may be at risk for accelerated brain aging, and a range of cognitive impairments. What promotes or resists these changes is largely unknown. There is also interest in discovering factors that promote resilience to HIV, and combat its adverse effects in children. Here we review recent developments in brain imaging that reveal how the virus affects the brain. We relate these brain changes to changes in blood markers, cognitive function, and other patient outcomes or symptoms, such as apathy or neuropathic pain. We focus on new and emerging techniques, including new variants of brain MRI. Diffusion tensor imaging, for example, can map the brain’s structural connections while fMRI can uncover functional connections. Finally, we suggest how large-scale global research alliances, such as ENIGMA, may resolve controversies over effects where evidence is now lacking. These efforts pool scans from tens of thousands of individuals, and offer a source of power not previously imaginable for brain imaging studies. PMID:25902966

78 FR 66611 - National Alzheimer's Disease Awareness Month, 2013

Federal Register 2010, 2011, 2012, 2013, 2014

2013-11-05

... Proclamation Alzheimer's disease is an irreversible and progressive brain disease that slowly erodes precious... year, I proposed the Brain Research through Advancing Innovative Neurotechnologies (BRAIN) Initiative, which aims to revolutionize our understanding of the human brain. By mapping the brain, we hope to...

Diffusion-tensor imaging of white matter tracts in patients with cerebral neoplasm.

PubMed

Witwer, Brian P; Moftakhar, Roham; Hasan, Khader M; Deshmukh, Praveen; Haughton, Victor; Field, Aaron; Arfanakis, Konstantinos; Noyes, Jane; Moritz, Chad H; Meyerand, M Elizabeth; Rowley, Howard A; Alexander, Andrew L; Badie, Behnam

2002-09-01

Preserving vital cerebral function while maximizing tumor resection is a principal goal in surgical neurooncology. Although functional magnetic resonance imaging has been useful in the localization of eloquent cerebral cortex, this method does not provide information about the white matter tracts that may be involved in invasive, intrinsic brain tumors. Recently, diffusion-tensor (DT) imaging techniques have been used to map white matter tracts in the normal brain. The aim of this study was to demonstrate the role of DT imaging in preoperative mapping of white matter tracts in relation to cerebral neoplasms. Nine patients with brain malignancies (one pilocytic astrocytoma, five oligodendrogliomas, one low-grade oligoastrocytoma, one Grade 4 astrocytoma, and one metastatic adenocarcinoma) underwent DT imaging examinations prior to tumor excision. Anatomical information about white matter tract location, orientation, and projections was obtained in every patient. Depending on the tumor type and location, evidence of white matter tract edema (two patients), infiltration (two patients), displacement (five patients), and disruption (two patients) could be assessed with the aid of DT imaging in each case. Diffusion-tensor imaging allowed for visualization of white matter tracts and was found to be beneficial in the surgical planning for patients with intrinsic brain tumors. The authors' experience with DT imaging indicates that anatomically intact fibers may be present in abnormal-appearing areas of the brain. Whether resection of these involved fibers results in subtle postoperative neurological deficits requires further systematic study.

High-field fMRI unveils orientation columns in humans.

PubMed

Yacoub, Essa; Harel, Noam; Ugurbil, Kâmil

2008-07-29

Functional (f)MRI has revolutionized the field of human brain research. fMRI can noninvasively map the spatial architecture of brain function via localized increases in blood flow after sensory or cognitive stimulation. Recent advances in fMRI have led to enhanced sensitivity and spatial accuracy of the measured signals, indicating the possibility of detecting small neuronal ensembles that constitute fundamental computational units in the brain, such as cortical columns. Orientation columns in visual cortex are perhaps the best known example of such a functional organization in the brain. They cannot be discerned via anatomical characteristics, as with ocular dominance columns. Instead, the elucidation of their organization requires functional imaging methods. However, because of insufficient sensitivity, spatial accuracy, and image resolution of the available mapping techniques, thus far, they have not been detected in humans. Here, we demonstrate, by using high-field (7-T) fMRI, the existence and spatial features of orientation- selective columns in humans. Striking similarities were found with the known spatial features of these columns in monkeys. In addition, we found that a larger number of orientation columns are devoted to processing orientations around 90 degrees (vertical stimuli with horizontal motion), whereas relatively similar fMRI signal changes were observed across any given active column. With the current proliferation of high-field MRI systems and constant evolution of fMRI techniques, this study heralds the exciting prospect of exploring unmapped and/or unknown columnar level functional organizations in the human brain.

Seeing the whole picture: A comprehensive imaging approach to functional mapping of circuits in behaving zebrafish.

PubMed

Feierstein, C E; Portugues, R; Orger, M B

2015-06-18

In recent years, the zebrafish has emerged as an appealing model system to tackle questions relating to the neural circuit basis of behavior. This can be attributed not just to the growing use of genetically tractable model organisms, but also in large part to the rapid advances in optical techniques for neuroscience, which are ideally suited for application to the small, transparent brain of the larval fish. Many characteristic features of vertebrate brains, from gross anatomy down to particular circuit motifs and cell-types, as well as conserved behaviors, can be found in zebrafish even just a few days post fertilization, and, at this early stage, the physical size of the brain makes it possible to analyze neural activity in a comprehensive fashion. In a recent study, we used a systematic and unbiased imaging method to record the pattern of activity dynamics throughout the whole brain of larval zebrafish during a simple visual behavior, the optokinetic response (OKR). This approach revealed the broadly distributed network of neurons that were active during the behavior and provided insights into the fine-scale functional architecture in the brain, inter-individual variability, and the spatial distribution of behaviorally relevant signals. Combined with mapping anatomical and functional connectivity, targeted electrophysiological recordings, and genetic labeling of specific populations, this comprehensive approach in zebrafish provides an unparalleled opportunity to study complete circuits in a behaving vertebrate animal. Copyright © 2014. Published by Elsevier Ltd.

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

Fusing DTI and FMRI Data: A Survey of Methods and Applications

PubMed Central

Zhu, Dajiang; Zhang, Tuo; Jiang, Xi; Hu, Xintao; Chen, Hanbo; Yang, Ning; Lv, Jinglei; Han, Junwei; Guo, Lei; Liu, Tianming

2014-01-01

The relationship between brain structure and function has been one of the centers of research in neuroimaging for decades. In recent years, diffusion tensor imaging (DTI) and functional magnetic resonance imaging (fMRI) techniques have been widely available and popular in cognitive and clinical neurosciences for examining the brain’s white matter (WM) micro-structures and gray matter (GM) functions, respectively. Given the intrinsic integration of WM/GM and the complementary information embedded in DTI/fMRI data, it is natural and well-justified to combine these two neuroimaging modalities together to investigate brain structure and function and their relationships simultaneously. In the past decade, there have been remarkable achievements of DTI/fMRI fusion methods and applications in neuroimaging and human brain mapping community. This survey paper aims to review recent advancements on methodologies and applications in incorporating multimodal DTI and fMRI data, and offer our perspectives on future research directions. We envision that effective fusion of DTI/fMRI techniques will play increasingly important roles in neuroimaging and brain sciences in the years to come. PMID:24103849

A PCR-Based Method for RNA Probes and Applications in Neuroscience.

PubMed

Hua, Ruifang; Yu, Shanshan; Liu, Mugen; Li, Haohong

2018-01-01

In situ hybridization (ISH) is a powerful technique that is used to detect the localization of specific nucleic acid sequences for understanding the organization, regulation, and function of genes. However, in most cases, RNA probes are obtained by in vitro transcription from plasmids containing specific promoter elements and mRNA-specific cDNA. Probes originating from plasmid vectors are time-consuming and not suitable for the rapid gene mapping. Here, we introduce a simplified method to prepare digoxigenin (DIG)-labeled non-radioactive RNA probes based on polymerase chain reaction (PCR) amplification and applications in free-floating mouse brain sections. Employing a transgenic reporter line, we investigate the expression of the somatostatin (SST) mRNA in the adult mouse brain. The method can be applied to identify the colocalization of SST mRNA and proteins including corticotrophin-releasing hormone (CRH) and protein kinase C delta type (PKC-δ) using double immunofluorescence, which is useful for understanding the organization of complex brain nuclei. Moreover, the method can also be incorporated with retrograde tracing to visualize the functional connection in the neural circuitry. Briefly, the PCR-based method for non-radioactive RNA probes is a useful tool that can be substantially utilized in neuroscience studies.

Nanoelectronics enabled chronic multimodal neural platform in a mouse ischemic model

PubMed Central

Luan, Lan; Sullender, Colin T.; Li, Xue; Zhao, Zhengtuo; Zhu, Hanlin; Wei, Xiaoling; Xie, Chong; Dunn, Andrew K.

2018-01-01

Background Despite significant advancements of optical imaging techniques for mapping hemodynamics in small animal models, it remains challenging to combine imaging with spatially resolved electrical recording of individual neurons especially for longitudinal studies. This is largely due to the strong invasiveness to the living brain from the penetrating electrodes and their limited compatibility with longitudinal imaging. New Method We implant arrays of ultraflexible nanoelectronic threads (NETs) in mice for neural recording both at the brain surface and intracortically, which maintain great tissue compatibility chronically. By mounting a cranial window atop of the NET arrays that allows for chronic optical access, we establish a multimodal platform that combines spatially resolved electrical recording of neural activity and laser speckle contrast imaging (LSCI) of cerebral blood flow (CBF) for longitudinal studies. Results We induce peri-infarct depolarizations (PIDs) by targeted photothrombosis, and show the ability to detect its occurrence and propagation through spatiotemporal variations in both extracellular potentials and CBF. We also demonstrate chronic tracking of single-unit neural activity and CBF over days after photothrombosis, from which we observe reperfusion and increased firing rates. Comparison with Existing Method(s) This multimodal platform enables simultaneous mapping of neural activity and hemodynamic parameters at the microscale for quantitative, longitudinal comparisons with minimal perturbation to the baseline neurophysiology. Conclusion The ability to spatiotemporally resolve and chronically track CBF and neural electrical activity in the same living brain region has broad applications for studying the interplay between neural and hemodynamic responses in health and in cerebrovascular and neurological pathologies. PMID:29203409

Dynamic multi-coil technique (DYNAMITE) shimming for echo-planar imaging of the human brain at 7 Tesla.

PubMed

Juchem, Christoph; Umesh Rudrapatna, S; Nixon, Terence W; de Graaf, Robin A

2015-01-15

Gradient-echo echo-planar imaging (EPI) is the primary method of choice in functional MRI and other methods relying on fast MRI to image brain activation and connectivity. However, the high susceptibility of EPI towards B0 magnetic field inhomogeneity poses serious challenges. Conventional magnetic field shimming with low-order spherical harmonic (SH) functions is capable of compensating shallow field distortions, but performs poorly for global brain shimming or on specific areas with strong susceptibility-induced B0 distortions such as the prefrontal cortex (PFC). Excellent B0 homogeneity has been demonstrated recently in the human brain at 7 Tesla with the DYNAmic Multi-coIl TEchnique (DYNAMITE) for magnetic field shimming (J Magn Reson (2011) 212:280-288). Here, we report the benefits of DYNAMITE shimming for multi-slice EPI and T2* mapping. A standard deviation of 13Hz was achieved for the residual B0 distribution in the human brain at 7 Tesla with DYNAMITE shimming and was 60% lower compared to conventional shimming that employs static zero through third order SH shapes. The residual field inhomogeneity with SH shimming led to an average 8mm shift at acquisition parameters commonly used for fMRI and was reduced to 1.5-3mm with DYNAMITE shimming. T2* values obtained from the prefrontal and temporal cortices with DYNAMITE shimming were 10-50% longer than those measured with SH shimming. The reduction of the confounding macroscopic B0 field gradients with DYNAMITE shimming thereby promises improved access to the relevant microscopic T2* effects. The combination of high spatial resolution and DYNAMITE shimming allows largely artifact-free EPI and T2* mapping throughout the brain, including prefrontal and temporal lobe areas. DYNAMITE shimming is expected to critically benefit a wide range of MRI applications that rely on excellent B0 magnetic field conditions including EPI-based fMRI to study various cognitive processes and assessing large-scale brain connectivity in vivo. As such, DYNAMITE shimming has the potential to replace conventional SH shim systems in human MR scanners. Copyright © 2014 Elsevier Inc. All rights reserved.

Dynamic Multi-Coil Technique (DYNAMITE) Shimming for Echo-Planar Imaging of the Human Brain at 7 Tesla

PubMed Central

Juchem, Christoph; Rudrapatna, S. Umesh; Nixon, Terence W.; de Graaf, Robin A.

2014-01-01

Gradient-echo echo-planar imaging (EPI) is the primary method of choice in functional MRI and other methods relying on fast MRI to image brain activation and connectivity. However, the high susceptibility of EPI towards B0 magnetic field inhomogeneity poses serious challenges. Conventional magnetic field shimming with low-order spherical harmonic (SH) functions is capable of compensating shallow field distortions, but performs poorly for global brain shimming or on specific areas with strong susceptibility-induced B0 distortions such as the prefrontal cortex (PFC). Excellent B0 homogeneity has been demonstrated recently in the human brain at 7 Tesla with the DYNAmic Multi-coIl TEchnique (DYNAMITE) for magnetic field shimming (Juchem et al., J Magn Reson (2011) 212:280-288). Here, we report the benefits of DYNAMITE shimming for multi-slice EPI and T2* mapping. A standard deviation of 13 Hz was achieved for the residual B0 distribution in the human brain at 7 Tesla with DYNAMITE shimming and was 60% lower compared to conventional shimming that employs static zero through third order SH shapes. The residual field inhomogeneity with SH shimming led to an average 8 mm shift at acquisition parameters commonly used for fMRI and was reduced to 1.5-3 mm with DYNAMITE shimming. T2* values obtained from the prefrontal and temporal cortices with DYNAMITE shimming were 10-50% longer than those measured with SH shimming. The reduction of the confounding macroscopic B0 field gradients with DYNAMITE shimming thereby promises improved access to the relevant microscopic T2* effects. The combination of high spatial resolution and DYNAMITE shimming allows largely artifact-free EPI and T2* mapping throughout the brain, including prefrontal and temporal lobe areas. DYNAMITE shimming is expected to critically benefit a wide range of MRI applications that rely on excellent B0 magnetic field conditions including EPI-based fMRI to study various cognitive processes and assessing large-scale brain connectivity in vivo. As such, DYNAMITE shimming has the potential to replace conventional SH shim systems in human MR scanners. PMID:25462795

A Bayesian approach to the creation of a study-customized neonatal brain atlas

PubMed Central

Zhang, Yajing; Chang, Linda; Ceritoglu, Can; Skranes, Jon; Ernst, Thomas; Mori, Susumu; Miller, Michael I.; Oishi, Kenichi

2014-01-01

Atlas-based image analysis (ABA), in which an anatomical “parcellation map” is used for parcel-by-parcel image quantification, is widely used to analyze anatomical and functional changes related to brain development, aging, and various diseases. The parcellation maps are often created based on common MRI templates, which allow users to transform the template to target images, or vice versa, to perform parcel-by-parcel statistics, and report the scientific findings based on common anatomical parcels. The use of a study-specific template, which represents the anatomical features of the study population better than common templates, is preferable for accurate anatomical labeling; however, the creation of a parcellation map for a study-specific template is extremely labor intensive, and the definitions of anatomical boundaries are not necessarily compatible with those of the common template. In this study, we employed a Volume-based Template Estimation (VTE) method to create a neonatal brain template customized to a study population, while keeping the anatomical parcellation identical to that of a common MRI atlas. The VTE was used to morph the standardized parcellation map of the JHU-neonate-SS atlas to capture the anatomical features of a study population. The resultant “study-customized” T1-weighted and diffusion tensor imaging (DTI) template, with three-dimensional anatomical parcellation that defined 122 brain regions, was compared with the JHU-neonate-SS atlas, in terms of the registration accuracy. A pronounced increase in the accuracy of cortical parcellation and superior tensor alignment were observed when the customized template was used. With the customized atlas-based analysis, the fractional anisotropy (FA) detected closely approximated the manual measurements. This tool provides a solution for achieving normalization-based measurements with increased accuracy, while reporting scientific findings in a consistent framework. PMID:25026155

Towards the utilization of EEG as a brain imaging tool.

PubMed

Michel, Christoph M; Murray, Micah M

2012-06-01

Recent advances in signal analysis have engendered EEG with the status of a true brain mapping and brain imaging method capable of providing spatio-temporal information regarding brain (dys)function. Because of the increasing interest in the temporal dynamics of brain networks, and because of the straightforward compatibility of the EEG with other brain imaging techniques, EEG is increasingly used in the neuroimaging community. However, the full capability of EEG is highly underestimated. Many combined EEG-fMRI studies use the EEG only as a spike-counter or an oscilloscope. Many cognitive and clinical EEG studies use the EEG still in its traditional way and analyze grapho-elements at certain electrodes and latencies. We here show that this way of using the EEG is not only dangerous because it leads to misinterpretations, but it is also largely ignoring the spatial aspects of the signals. In fact, EEG primarily measures the electric potential field at the scalp surface in the same way as MEG measures the magnetic field. By properly sampling and correctly analyzing this electric field, EEG can provide reliable information about the neuronal activity in the brain and the temporal dynamics of this activity in the millisecond range. This review explains some of these analysis methods and illustrates their potential in clinical and experimental applications. Copyright © 2011 Elsevier Inc. All rights reserved.

Functional MRI mapping of visual function and selective attention for performance assessment and presurgical planning using conjunctive visual search

PubMed Central

Parker, Jason G; Zalusky, Eric J; Kirbas, Cemil

2014-01-01

Background Accurate mapping of visual function and selective attention using fMRI is important in the study of human performance as well as in presurgical treatment planning of lesions in or near visual centers of the brain. Conjunctive visual search (CVS) is a useful tool for mapping visual function during fMRI because of its greater activation extent compared with high-capacity parallel search processes. Aims The purpose of this work was to develop and evaluate a CVS that was capable of generating consistent activation in the basic and higher level visual areas of the brain by using a high number of distractors as well as an optimized contrast condition. Materials and methods Images from 10 healthy volunteers were analyzed and brain regions of greatest activation and deactivation were determined using a nonbiased decomposition of the results at the hemisphere, lobe, and gyrus levels. The results were quantified in terms of activation and deactivation extent and mean z-statistic. Results The proposed CVS was found to generate robust activation of the occipital lobe, as well as regions in the middle frontal gyrus associated with coordinating eye movements and in regions of the insula associated with task-level control and focal attention. As expected, the task demonstrated deactivation patterns commonly implicated in the default-mode network. Further deactivation was noted in the posterior region of the cerebellum, most likely associated with the formation of optimal search strategy. Conclusion We believe the task will be useful in studies of visual and selective attention in the neuroscience community as well as in mapping visual function in clinical fMRI. PMID:24683515

Conduction aphasia as a function of the dominant posterior perisylvian cortex. Report of two cases.

PubMed

Quigg, Mark; Geldmacher, David S; Elias, W Jeff

2006-05-01

Assessment of eloquent functions during brain mapping usually relies on testing reading, speech, and comprehension to uncover transient deficits during electrical stimulation. These tests stem from findings predicted by the Geschwind-Wernicke hypothesis of receptive and expressive cortices connected by white matter tracts. Later work, however, has emphasized cortical mechanisms of language function. The authors report two cases that demonstrate that conduction aphasia is cortically mediated and can be inadequately assessed if not specifically evaluated during brain mapping. To determine the distribution of language on the dominant cortex, electrical cortical stimulation was performed in two cases by using implanted subdural electrodes during brain mapping before epilepsy surgery. A transient isolated deficit in repetition of language was reported during stimulation of the posterior portion of the dominant superior temporal gyrus in one patient and during stimulation of the supramarginal gyrus in the other patient. These cases demonstrate a localization of language repetition to the posterior perisylvian cortex. Brain mapping of this region should include assessment of verbal repetition to avoid potential deficits resembling conduction aphasia.

Four-dimensional optoacoustic temperature mapping in laser-induced thermotherapy

NASA Astrophysics Data System (ADS)

Oyaga Landa, Francisco Javier; Deán-Ben, Xosé Luís.; Sroka, Ronald; Razansky, Daniel

2018-02-01

Photoablative laser therapy is in common use for selective destruction of malignant masses, vascular and brain abnormalities. Tissue ablation and coagulation are irreversible processes occurring shortly after crossing a certain thermal exposure threshold. As a result, accurate mapping of the temperature field is essential for optimizing the outcome of these clinical interventions. Here we demonstrate four-dimensional optoacoustic temperature mapping of the entire photoablated region. Accuracy of the method is investigated in tissue-mimicking phantom experiments. Deviations of the volumetric optoacoustic temperature readings provided at 40ms intervals remained below 10% for temperature elevations above 3°C, as validated by simultaneous thermocouple measurements. The excellent spatio-temporal resolution of the new temperature monitoring approach aims at improving safety and efficacy of laser-based photothermal procedures.

Optimization of flow-sensitive alternating inversion recovery (FAIR) for perfusion functional MRI of rodent brain.

PubMed

Nasrallah, Fatima A; Lee, Eugene L Q; Chuang, Kai-Hsiang

2012-11-01

Arterial spin labeling (ASL) MRI provides a noninvasive method to image perfusion, and has been applied to map neural activation in the brain. Although pulsed labeling methods have been widely used in humans, continuous ASL with a dedicated neck labeling coil is still the preferred method in rodent brain functional MRI (fMRI) to maximize the sensitivity and allow multislice acquisition. However, the additional hardware is not readily available and hence its application is limited. In this study, flow-sensitive alternating inversion recovery (FAIR) pulsed ASL was optimized for fMRI of rat brain. A practical challenge of FAIR is the suboptimal global inversion by the transmit coil of limited dimensions, which results in low effective labeling. By using a large volume transmit coil and proper positioning to optimize the body coverage, the perfusion signal was increased by 38.3% compared with positioning the brain at the isocenter. An additional 53.3% gain in signal was achieved using optimized repetition and inversion times compared with a long TR. Under electrical stimulation to the forepaws, a perfusion activation signal change of 63.7 ± 6.3% can be reliably detected in the primary somatosensory cortices using single slice or multislice echo planar imaging at 9.4 T. This demonstrates the potential of using pulsed ASL for multislice perfusion fMRI in functional and pharmacological applications in rat brain. Copyright © 2012 John Wiley & Sons, Ltd.

On brain activity mapping: insights and lessons from Brain Decoding Project to map memory patterns in the hippocampus.

PubMed

Tsien, Joe Z; Li, Meng; Osan, Remus; Chen, Guifen; Lin, Longnian; Wang, Phillip Lei; Frey, Sabine; Frey, Julietta; Zhu, Dajiang; Liu, Tianming; Zhao, Fang; Kuang, Hui

2013-09-01

The BRAIN project recently announced by the president Obama is the reflection of unrelenting human quest for cracking the brain code, the patterns of neuronal activity that define who we are and what we are. While the Brain Activity Mapping proposal has rightly emphasized on the need to develop new technologies for measuring every spike from every neuron, it might be helpful to consider both the theoretical and experimental aspects that would accelerate our search for the organizing principles of the brain code. Here we share several insights and lessons from the similar proposal, namely, Brain Decoding Project that we initiated since 2007. We provide a specific example in our initial mapping of real-time memory traces from one part of the memory circuit, namely, the CA1 region of the mouse hippocampus. We show how innovative behavioral tasks and appropriate mathematical analyses of large datasets can play equally, if not more, important roles in uncovering the specific-to-general feature-coding cell assembly mechanism by which episodic memory, semantic knowledge, and imagination are generated and organized. Our own experiences suggest that the bottleneck of the Brain Project is not only at merely developing additional new technologies, but also the lack of efficient avenues to disseminate cutting edge platforms and decoding expertise to neuroscience community. Therefore, we propose that in order to harness unique insights and extensive knowledge from various investigators working in diverse neuroscience subfields, ranging from perception and emotion to memory and social behaviors, the BRAIN project should create a set of International and National Brain Decoding Centers at which cutting-edge recording technologies and expertise on analyzing large datasets analyses can be made readily available to the entire community of neuroscientists who can apply and schedule to perform cutting-edge research.

Constructing fMRI connectivity networks: a whole brain functional parcellation method for node definition.

PubMed

Maggioni, Eleonora; Tana, Maria Gabriella; Arrigoni, Filippo; Zucca, Claudio; Bianchi, Anna Maria

2014-05-15

Functional Magnetic Resonance Imaging (fMRI) is used for exploring brain functionality, and recently it was applied for mapping the brain connection patterns. To give a meaningful neurobiological interpretation to the connectivity network, it is fundamental to properly define the network framework. In particular, the choice of the network nodes may affect the final connectivity results and the consequent interpretation. We introduce a novel method for the intra subject topological characterization of the nodes of fMRI brain networks, based on a whole brain parcellation scheme. The proposed whole brain parcellation algorithm divides the brain into clusters that are homogeneous from the anatomical and functional point of view, each of which constitutes a node. The functional parcellation described is based on the Tononi's cluster index, which measures instantaneous correlation in terms of intrinsic and extrinsic statistical dependencies. The method performance and reliability were first tested on simulated data, then on a real fMRI dataset acquired on healthy subjects during visual stimulation. Finally, the proposed algorithm was applied to epileptic patients' fMRI data recorded during seizures, to verify its usefulness as preparatory step for effective connectivity analysis. For each patient, the nodes of the network involved in ictal activity were defined according to the proposed parcellation scheme and Granger Causality Analysis (GCA) was applied to infer effective connectivity. We showed that the algorithm 1) performed well on simulated data, 2) was able to produce reliable inter subjects results and 3) led to a detailed definition of the effective connectivity pattern. Copyright © 2014 Elsevier B.V. All rights reserved.

Analysis of Resting-State fMRI Topological Graph Theory Properties in Methamphetamine Drug Users Applying Box-Counting Fractal Dimension.

PubMed

Siyah Mansoory, Meysam; Oghabian, Mohammad Ali; Jafari, Amir Homayoun; Shahbabaie, Alireza

2017-01-01

Graph theoretical analysis of functional Magnetic Resonance Imaging (fMRI) data has provided new measures of mapping human brain in vivo. Of all methods to measure the functional connectivity between regions, Linear Correlation (LC) calculation of activity time series of the brain regions as a linear measure is considered the most ubiquitous one. The strength of the dependence obligatory for graph construction and analysis is consistently underestimated by LC, because not all the bivariate distributions, but only the marginals are Gaussian. In a number of studies, Mutual Information (MI) has been employed, as a similarity measure between each two time series of the brain regions, a pure nonlinear measure. Owing to the complex fractal organization of the brain indicating self-similarity, more information on the brain can be revealed by fMRI Fractal Dimension (FD) analysis. In the present paper, Box-Counting Fractal Dimension (BCFD) is introduced for graph theoretical analysis of fMRI data in 17 methamphetamine drug users and 18 normal controls. Then, BCFD performance was evaluated compared to those of LC and MI methods. Moreover, the global topological graph properties of the brain networks inclusive of global efficiency, clustering coefficient and characteristic path length in addict subjects were investigated too. Compared to normal subjects by using statistical tests (P<0.05), topological graph properties were postulated to be disrupted significantly during the resting-state fMRI. Based on the results, analyzing the graph topological properties (representing the brain networks) based on BCFD is a more reliable method than LC and MI.

Investigating Causality Between Interacting Brain Areas with Multivariate Autoregressive Models of MEG Sensor Data

PubMed Central

Michalareas, George; Schoffelen, Jan-Mathijs; Paterson, Gavin; Gross, Joachim

2013-01-01

Abstract In this work, we investigate the feasibility to estimating causal interactions between brain regions based on multivariate autoregressive models (MAR models) fitted to magnetoencephalographic (MEG) sensor measurements. We first demonstrate the theoretical feasibility of estimating source level causal interactions after projection of the sensor-level model coefficients onto the locations of the neural sources. Next, we show with simulated MEG data that causality, as measured by partial directed coherence (PDC), can be correctly reconstructed if the locations of the interacting brain areas are known. We further demonstrate, if a very large number of brain voxels is considered as potential activation sources, that PDC as a measure to reconstruct causal interactions is less accurate. In such case the MAR model coefficients alone contain meaningful causality information. The proposed method overcomes the problems of model nonrobustness and large computation times encountered during causality analysis by existing methods. These methods first project MEG sensor time-series onto a large number of brain locations after which the MAR model is built on this large number of source-level time-series. Instead, through this work, we demonstrate that by building the MAR model on the sensor-level and then projecting only the MAR coefficients in source space, the true casual pathways are recovered even when a very large number of locations are considered as sources. The main contribution of this work is that by this methodology entire brain causality maps can be efficiently derived without any a priori selection of regions of interest. Hum Brain Mapp, 2013. © 2012 Wiley Periodicals, Inc. PMID:22328419

Examining Neural Correlates of Psychopathology Using a Lesion-Based Approach.

PubMed

Calamia, Matthew; Markon, Kristian E; Sutterer, Matthew J; Tranel, Daniel

2018-06-22

Studies of individuals with focal brain damage have long been used to expand understanding of the neural basis of psychopathology. However, most previous studies were conducted using small sample sizes and relatively coarse methods for measuring psychopathology or mapping brain-behavior relationships. Here, we examined the factor structure and neural correlates of psychopathology in 232 individuals with focal brain damage, using their responses to the Minnesota Multiphasic Personality Inventory-2-Restructured Form (MMPI-2-RF). Factor analysis and voxel-based lesion symptom mapping were used to examine the structure and neural correlates of psychopathology in this sample. Consistent with existing MMPI-2-RF literature, separate internalizing, externalizing, and psychotic symptom dimensions were found. In addition, a somatic dimension likely reflecting neurological symptoms was identified. Damage to the medial temporal lobe, including the hippocampus, was associated with scales related to both internalizing problems and psychoticism. Damage to the medial temporal lobe and orbitofrontal cortex was associated with both a general distrust of others and beliefs that one is being personally targeted by others. These findings provide evidence for the critical role of dysfunction in specific frontal and temporal regions in the development of psychopathology. Copyright © 2018. Published by Elsevier Ltd.

Stable long-term chronic brain mapping at the single-neuron level.

PubMed

Fu, Tian-Ming; Hong, Guosong; Zhou, Tao; Schuhmann, Thomas G; Viveros, Robert D; Lieber, Charles M

2016-10-01

Stable in vivo mapping and modulation of the same neurons and brain circuits over extended periods is critical to both neuroscience and medicine. Current electrical implants offer single-neuron spatiotemporal resolution but are limited by such factors as relative shear motion and chronic immune responses during long-term recording. To overcome these limitations, we developed a chronic in vivo recording and stimulation platform based on flexible mesh electronics, and we demonstrated stable multiplexed local field potentials and single-unit recordings in mouse brains for at least 8 months without probe repositioning. Properties of acquired signals suggest robust tracking of the same neurons over this period. This recording and stimulation platform allowed us to evoke stable single-neuron responses to chronic electrical stimulation and to carry out longitudinal studies of brain aging in freely behaving mice. Such advantages could open up future studies in mapping and modulating changes associated with learning, aging and neurodegenerative diseases.

A non-linear regression method for CT brain perfusion analysis

NASA Astrophysics Data System (ADS)

Bennink, E.; Oosterbroek, J.; Viergever, M. A.; Velthuis, B. K.; de Jong, H. W. A. M.

2015-03-01

CT perfusion (CTP) imaging allows for rapid diagnosis of ischemic stroke. Generation of perfusion maps from CTP data usually involves deconvolution algorithms providing estimates for the impulse response function in the tissue. We propose the use of a fast non-linear regression (NLR) method that we postulate has similar performance to the current academic state-of-art method (bSVD), but that has some important advantages, including the estimation of vascular permeability, improved robustness to tracer-delay, and very few tuning parameters, that are all important in stroke assessment. The aim of this study is to evaluate the fast NLR method against bSVD and a commercial clinical state-of-art method. The three methods were tested against a published digital perfusion phantom earlier used to illustrate the superiority of bSVD. In addition, the NLR and clinical methods were also tested against bSVD on 20 clinical scans. Pearson correlation coefficients were calculated for each of the tested methods. All three methods showed high correlation coefficients (>0.9) with the ground truth in the phantom. With respect to the clinical scans, the NLR perfusion maps showed higher correlation with bSVD than the perfusion maps from the clinical method. Furthermore, the perfusion maps showed that the fast NLR estimates are robust to tracer-delay. In conclusion, the proposed fast NLR method provides a simple and flexible way of estimating perfusion parameters from CT perfusion scans, with high correlation coefficients. This suggests that it could be a better alternative to the current clinical and academic state-of-art methods.

Longitudinal atlas for normative human brain development and aging over the lifespan using quantitative susceptibility mapping.

PubMed

Zhang, Yuyao; Wei, Hongjiang; Cronin, Matthew J; He, Naying; Yan, Fuhua; Liu, Chunlei

2018-05-01

Longitudinal brain atlases play an important role in the study of human brain development and cognition. Existing atlases are mainly based on anatomical features derived from T1-and T2-weighted MRI. A 4D developmental quantitative susceptibility mapping (QSM) atlas may facilitate the estimation of age-related iron changes in deep gray matter nuclei and myelin changes in white matter. To this end, group-wise co-registered QSM templates were generated over various age intervals from age 1-83 years old. Registration was achieved by combining both T1-weighted and QSM images. Based on the proposed template, we created an accurate deep gray matter nuclei parcellation map (DGM map). Notably, we segmented thalamus into 5 sub-regions, i.e. the anterior nuclei, the median nuclei, the lateral nuclei, the pulvinar and the internal medullary lamina. Furthermore, we built a "whole brain QSM parcellation map" by combining existing cortical parcellation and white-matter atlases with the proposed DGM map. Based on the proposed QSM atlas, the segmentation accuracy of iron-rich nuclei using QSM is significantly improved, especially for children and adolescent subjects. The age-related progression of magnetic susceptibility in each of the deep gray matter nuclei, the hippocampus, and the amygdala was estimated. Our automated atlas-based analysis provided a systematic confirmation of previous findings on susceptibility progression with age resulting from manual ROI drawings in deep gray matter nuclei. The susceptibility development in the hippocampus and the amygdala follow an iron accumulation model; while in the thalamus sub-regions, the susceptibility development exhibits a variety of trends. It is envisioned that the newly developed 4D QSM atlas will serve as a template for studying brain iron deposition and myelination/demyelination in both normal aging and various brain diseases. Copyright © 2018 Elsevier Inc. All rights reserved.

A fine structure genomic map of the region of 12q13 containing SAS and CDK4

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

Linder, C.Y.; Elkahloun, A.G.; Su, Y.A.

1994-09-01

We have recently adapted a method, originally described by Rackwitz, to the rapid restriction mapping of multiple cosmid DNA samples. Linearization of the cosmids at the lambda cohesive site using lambda terminase is followed by partial digestion with selected restriction enzymes and hybridization to oligonucleotides specific for the right or left hand termini. Partial digestions are performed in a microtiter plate thus allowing up to 12 cosmid clones to be digested with one restriction enzyme. We have applied this rapid restriction mapping method to cosmids derived from a region of chromosome 12q13 that has recently been shown to be amplifiedmore » in a variety of cancers including malignant fibrous histiocytoma, fibrosarcoma, liposarcoma, osteosarcoma and brain tumors. A small segment of this amplification unit containing three genes, SAS (a membrane protein), CDK4 (a cyclin dependent kinase) and OS-9 (a recently described cDNA) has been analyzed with the system described above. This fine structure genomic map will be useful for completing the expression map of this region as well as characterizing its pattern of amplification in tumor specimens.« less

Brain-wide Maps Reveal Stereotyped Cell-Type-Based Cortical Architecture and Subcortical Sexual Dimorphism.

PubMed

Kim, Yongsoo; Yang, Guangyu Robert; Pradhan, Kith; Venkataraju, Kannan Umadevi; Bota, Mihail; García Del Molino, Luis Carlos; Fitzgerald, Greg; Ram, Keerthi; He, Miao; Levine, Jesse Maurica; Mitra, Partha; Huang, Z Josh; Wang, Xiao-Jing; Osten, Pavel

2017-10-05

The stereotyped features of neuronal circuits are those most likely to explain the remarkable capacity of the brain to process information and govern behaviors, yet it has not been possible to comprehensively quantify neuronal distributions across animals or genders due to the size and complexity of the mammalian brain. Here we apply our quantitative brain-wide (qBrain) mapping platform to document the stereotyped distributions of mainly inhibitory cell types. We discover an unexpected cortical organizing principle: sensory-motor areas are dominated by output-modulating parvalbumin-positive interneurons, whereas association, including frontal, areas are dominated by input-modulating somatostatin-positive interneurons. Furthermore, we identify local cell type distributions with more cells in the female brain in 10 out of 11 sexually dimorphic subcortical areas, in contrast to the overall larger brains in males. The qBrain resource can be further mined to link stereotyped aspects of neuronal distributions to known and unknown functions of diverse brain regions. Copyright © 2017 Elsevier Inc. All rights reserved.

Impaired cerebral autoregulation and brain injury in newborns with hypoxic-ischemic encephalopathy treated with hypothermia.

PubMed

Massaro, An N; Govindan, R B; Vezina, Gilbert; Chang, Taeun; Andescavage, Nickie N; Wang, Yunfei; Al-Shargabi, Tareq; Metzler, Marina; Harris, Kari; du Plessis, Adre J

2015-08-01

Impaired cerebral autoregulation may contribute to secondary injury in newborns with hypoxic-ischemic encephalopathy (HIE). Continuous, noninvasive assessment of cerebral pressure autoregulation can be achieved with bedside near-infrared spectroscopy (NIRS) and systemic mean arterial blood pressure (MAP) monitoring. This study aimed to evaluate whether impaired cerebral autoregulation measured by NIRS-MAP monitoring during therapeutic hypothermia and rewarming relates to outcome in 36 newborns with HIE. Spectral coherence analysis between NIRS and MAP was used to quantify changes in the duration [pressure passivity index (PPI)] and magnitude (gain) of cerebral autoregulatory impairment. Higher PPI in both cerebral hemispheres and gain in the right hemisphere were associated with neonatal adverse outcomes [death or detectable brain injury by magnetic resonance imaging (MRI), P < 0.001]. NIRS-MAP monitoring of cerebral autoregulation can provide an ongoing physiological biomarker that may help direct care in perinatal brain injury. Copyright © 2015 the American Physiological Society.

Effects of acute nicotine on hemodynamics and binding of [11C]raclopride to dopamine D2,3 receptors in pig brain.

PubMed

Cumming, Paul; Rosa-Neto, Pedro; Watanabe, Hideaki; Smith, Donald; Bender, Dirk; Clarke, Paul B S; Gjedde, Albert

2003-07-01

Positive reinforcing properties of nicotine and the psychostimulants have been attributed to elevated dopamine release in the basal ganglia. It is well known that the specific binding of [(11)C]raclopride to dopamine D(2,3) receptors in living striatum is reduced by cocaine and amphetamines, revealing increased competition between endogenous dopamine and [(11)C]raclopride for dopamine D(2,3) receptors. However, the sensitivity of [(11)C]raclopride binding to nicotine-induced dopamine release is less well documented. In order to provide the basis for mapping effects of nicotine, we first optimized reference tissue methods for quantifying [(11)C]raclopride binding sites in striatum of living pigs (n = 16). In the same animals, the rate of cerebral blood flow (CBF) was mapped using [(15)O]water. Neither a low dose of nicotine (50 mu kg(-1), iv) nor a high dose of nicotine (500 microg kg(-1), iv) altered CBF in the pig brain, an important condition for calculating the binding of radioligands when using a reference tissue to estimate the free ligand concentration. The methods of Logan and of Lammertsma were compared using the cerebellum or the occipital cortex as reference tissues for calculating the binding potential (pB) of [(11)C]raclolpride in brain. Irrespective of the method used, the mean undrugged baseline pB in striatum (ca. 2.0) was significantly asymmetric, with highest binding in the left caudate and right putamen. Test-retest estimates of pB were stable. Subtraction of Logan pB maps revealed that the low dose of nicotine reduced the pB of [(11)C]raclopride by 10% in a cluster of voxels in the left anteroventral striatum, but this effect did not persist after correction for multiple comparisons. The high dose of nicotine (n = 9) acutely reduced pB by 10% bilaterally in the ventral striatum; 3 h after the high nicotine dose, the reductions had shifted dorsally and caudally into the caudate and putamen. Evidently, nicotine challenge enhances the competition between endogenous dopamine for [(11)C]raclopride binding sites with a complex temporal and spacial pattern in pig brain, initially presenting in the left ventral striatum.

Non-invasive imaging of the levels and effects of glutathione on the redox status of mouse brain using electron paramagnetic resonance imaging

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

Emoto, Miho C.; Department of Neurology, Sapporo Medical University School of Medicine, Sapporo, Hokkaido 060-8556; Matsuoka, Yuta

Glutathione (GSH) is the most abundant non-protein thiol that buffers reactive oxygen species in the brain. GSH does not reduce nitroxides directly, but in the presence of ascorbates, addition of GSH increases ascorbate-induced reduction of nitroxides. In this study, we used electron paramagnetic resonance (EPR) imaging and the nitroxide imaging probe, 3-methoxycarbonyl-2,2,5,5-tetramethyl-piperidine-1-oxyl (MCP), to non-invasively obtain spatially resolved redox data from mouse brains depleted of GSH with diethyl maleate compared to control. Based on the pharmacokinetics of the reduction reaction of MCP in the mouse heads, the pixel-based rate constant of its reduction reaction was calculated as an index ofmore » the redox status in vivo and mapped as a “redox map”. The obtained redox maps from control and GSH-depleted mouse brains showed a clear change in the brain redox status, which was due to the decreased levels of GSH in brains as measured by a biochemical assay. We observed a linear relationship between the reduction rate constant of MCP and the level of GSH for both control and GSH-depleted mouse brains. Using this relationship, the GSH level in the brain can be estimated from the redox map obtained with EPR imaging. - Highlights: • Redox status of glutathione-depleted mouse brain was examined with EPR imaging. • Redox status of mouse brain changed depending on glutathione (GSH) levels in brains. • Linear relationship between GSH levels and redox status in brains was found. • Using this relation, estimation of GSH levels in brains is possible from EPR images.« less

Rapid geodesic mapping of brain functional connectivity: implementation of a dedicated co-processor in a field-programmable gate array (FPGA) and application to resting state functional MRI.

PubMed

Minati, Ludovico; Cercignani, Mara; Chan, Dennis

2013-10-01

Graph theory-based analyses of brain network topology can be used to model the spatiotemporal correlations in neural activity detected through fMRI, and such approaches have wide-ranging potential, from detection of alterations in preclinical Alzheimer's disease through to command identification in brain-machine interfaces. However, due to prohibitive computational costs, graph-based analyses to date have principally focused on measuring connection density rather than mapping the topological architecture in full by exhaustive shortest-path determination. This paper outlines a solution to this problem through parallel implementation of Dijkstra's algorithm in programmable logic. The processor design is optimized for large, sparse graphs and provided in full as synthesizable VHDL code. An acceleration factor between 15 and 18 is obtained on a representative resting-state fMRI dataset, and maps of Euclidean path length reveal the anticipated heterogeneous cortical involvement in long-range integrative processing. These results enable high-resolution geodesic connectivity mapping for resting-state fMRI in patient populations and real-time geodesic mapping to support identification of imagined actions for fMRI-based brain-machine interfaces. Copyright © 2013 IPEM. Published by Elsevier Ltd. All rights reserved.

Blood pressure, brain structure, and cognition: opposite associations in men and women.

PubMed

Cherbuin, Nicolas; Mortby, Moyra E; Janke, Andrew L; Sachdev, Perminder S; Abhayaratna, Walter P; Anstey, Kaarin J

2015-02-01

Research on associations between blood pressure, brain structure, and cognitive function has produced somewhat inconsistent results. In part, this may be due to differences in age ranges studied and because of sex differences in physiology and/or exposure to risk factors, which may lead to different time course or patterns in cardiovascular disease progression. The aim of this study was to investigate the impact of sex on associations between blood pressure, regional cerebral volumes, and cognitive function in older individuals. In this cohort study, brachial blood pressure was measured twice at rest in 266 community-based individuals free of dementia aged 68-73 years who had also undergone a brain scan and a neuropsychological assessment. Associations between mean blood pressure (MAP), regional brain volumes, and cognition were investigated with voxel-wise regression analyses. Positive associations between MAP and regional volumes were detected in men, whereas negative associations were found in women. Similarly, there were sex differences in the brain-volume cognition relationship, with a positive relationship between regional brain volumes associated with MAP in men and a negative relationship in women. In this cohort of older individuals, higher MAP was associated with larger regional volume and better cognition in men, whereas opposite findings were demonstrated in women. These effects may be due to different lifetime risk exposure or because of physiological differences between men and women. Future studies investigating the relationship between blood pressure and brain structure or cognitive function should evaluate the potential for differential sex effects. © American Journal of Hypertension, Ltd 2014. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Mapping human brain networks with cortico-cortical evoked potentials.

PubMed

Keller, Corey J; Honey, Christopher J; Mégevand, Pierre; Entz, Laszlo; Ulbert, Istvan; Mehta, Ashesh D

2014-10-05

The cerebral cortex forms a sheet of neurons organized into a network of interconnected modules that is highly expanded in humans and presumably enables our most refined sensory and cognitive abilities. The links of this network form a fundamental aspect of its organization, and a great deal of research is focusing on understanding how information flows within and between different regions. However, an often-overlooked element of this connectivity regards a causal, hierarchical structure of regions, whereby certain nodes of the cortical network may exert greater influence over the others. While this is difficult to ascertain non-invasively, patients undergoing invasive electrode monitoring for epilepsy provide a unique window into this aspect of cortical organization. In this review, we highlight the potential for cortico-cortical evoked potential (CCEP) mapping to directly measure neuronal propagation across large-scale brain networks with spatio-temporal resolution that is superior to traditional neuroimaging methods. We first introduce effective connectivity and discuss the mechanisms underlying CCEP generation. Next, we highlight how CCEP mapping has begun to provide insight into the neural basis of non-invasive imaging signals. Finally, we present a novel approach to perturbing and measuring brain network function during cognitive processing. The direct measurement of CCEPs in response to electrical stimulation represents a potentially powerful clinical and basic science tool for probing the large-scale networks of the human cerebral cortex. © 2014 The Author(s) Published by the Royal Society. All rights reserved.

Prediction of CT Substitutes from MR Images Based on Local Diffeomorphic Mapping for Brain PET Attenuation Correction.

PubMed

Wu, Yao; Yang, Wei; Lu, Lijun; Lu, Zhentai; Zhong, Liming; Huang, Meiyan; Feng, Yanqiu; Feng, Qianjin; Chen, Wufan

2016-10-01

Attenuation correction is important for PET reconstruction. In PET/MR, MR intensities are not directly related to attenuation coefficients that are needed in PET imaging. The attenuation coefficient map can be derived from CT images. Therefore, prediction of CT substitutes from MR images is desired for attenuation correction in PET/MR. This study presents a patch-based method for CT prediction from MR images, generating attenuation maps for PET reconstruction. Because no global relation exists between MR and CT intensities, we propose local diffeomorphic mapping (LDM) for CT prediction. In LDM, we assume that MR and CT patches are located on 2 nonlinear manifolds, and the mapping from the MR manifold to the CT manifold approximates a diffeomorphism under a local constraint. Locality is important in LDM and is constrained by the following techniques. The first is local dictionary construction, wherein, for each patch in the testing MR image, a local search window is used to extract patches from training MR/CT pairs to construct MR and CT dictionaries. The k-nearest neighbors and an outlier detection strategy are then used to constrain the locality in MR and CT dictionaries. Second is local linear representation, wherein, local anchor embedding is used to solve MR dictionary coefficients when representing the MR testing sample. Under these local constraints, dictionary coefficients are linearly transferred from the MR manifold to the CT manifold and used to combine CT training samples to generate CT predictions. Our dataset contains 13 healthy subjects, each with T1- and T2-weighted MR and CT brain images. This method provides CT predictions with a mean absolute error of 110.1 Hounsfield units, Pearson linear correlation of 0.82, peak signal-to-noise ratio of 24.81 dB, and Dice in bone regions of 0.84 as compared with real CTs. CT substitute-based PET reconstruction has a regression slope of 1.0084 and R 2 of 0.9903 compared with real CT-based PET. In this method, no image segmentation or accurate registration is required. Our method demonstrates superior performance in CT prediction and PET reconstruction compared with competing methods. © 2016 by the Society of Nuclear Medicine and Molecular Imaging, Inc.

Multi-component intrinsic brain activities as a safe, alternative to cortical stimulation for sensori-motor mapping in neurosurgery.

PubMed

Neshige, Shuichiro; Matsuhashi, Masao; Kobayashi, Katsuya; Sakurai, Takeyo; Shimotake, Akihiro; Hitomi, Takefumi; Kikuchi, Takayuki; Yoshida, Kazumichi; Kunieda, Takeharu; Matsumoto, Riki; Takahashi, Ryosuke; Miyamoto, Susumu; Maruyama, Hirofumi; Matsumoto, Masayasu; Ikeda, Akio

2018-06-18

To assess the feasibility of multi-component electrocorticography (ECoG)-based mapping using "wide-spectrum, intrinsic-brain activities" for identifying the primary sensori-motor area (S1-M1) by comparing that using electrical cortical stimulation (ECS). We evaluated 14 epilepsy patients with 1514 subdural electrodes implantation covering the perirolandic cortices at Kyoto University Hospital between 2011 and 2016. We performed multi-component, ECoG-based mapping (band-pass filter, 0.016-300/600 Hz) involving combined analyses of the single components: movement-related cortical potential (<0.5-1 Hz), event-related synchronization (76-200 Hz), and event-related de-synchronization (8-24 Hz) to identify the S1-M1. The feasibility of multi-component mapping was assessed through comparisons with single-component mapping and ECS. Among 54 functional areas evaluation, ECoG-based maps showed significantly higher rate of localization concordances with ECS maps when the three single-component maps were consistent than when those were inconsistent with each other (p < 0.001 in motor, and p = 0.02 in sensory mappings). Multi-component mapping revealed high sensitivity (89-90%) and specificity (94-97%) as compared with ECS. Wide-spectrum, multi-component ECoG-based mapping is feasible, having high sensitivity/specificity relative to ECS. This safe (non-stimulus) mapping strategy, alternative to ECS, would allow clinicians to rule in/out the possibility of brain function prior to resection surgery. Copyright © 2018 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.

From brain maps to cognitive ontologies: informatics and the search for mental structure

PubMed Central

Poldrack, Russell A.; Yarkoni, Tal

2015-01-01

A major goal of cognitive neuroscience is to delineate how brain systems give rise to mental function. Here we review the increasingly large role informatics-driven approaches are playing in such efforts. We begin by reviewing a number of challenges conventional neuroimaging approaches face in trying to delineate brain-cognition mappings—for example, the difficulty in establishing the specificity of postulated associations. Next, we demonstrate how these limitations can potentially be overcome using complementary approaches that emphasize large-scale analysis—including meta-analytic methods that synthesize hundreds or thousands of studies at a time; latent-variable approaches that seek to extract structure from data in a bottom-up manner; and predictive modeling approaches capable of quantitatively inferring mental states from patterns of brain activity. We highlight the underappreciated but critical role for formal cognitive ontologies in helping to clarify, refine, and test theories of brain and cognitive function. Finally, we conclude with a speculative discussion of what future informatics developments may hold for cognitive neuroscience. PMID:26393866

Electroencephalography(EEG)-based instinctive brain-control of a quadruped locomotion robot.

PubMed

Jia, Wenchuan; Huang, Dandan; Luo, Xin; Pu, Huayan; Chen, Xuedong; Bai, Ou

2012-01-01

Artificial intelligence and bionic control have been applied in electroencephalography (EEG)-based robot system, to execute complex brain-control task. Nevertheless, due to technical limitations of the EEG decoding, the brain-computer interface (BCI) protocol is often complex, and the mapping between the EEG signal and the practical instructions lack of logic associated, which restrict the user's actual use. This paper presents a strategy that can be used to control a quadruped locomotion robot by user's instinctive action, based on five kinds of movement related neurophysiological signal. In actual use, the user drives or imagines the limbs/wrists action to generate EEG signal to adjust the real movement of the robot according to his/her own motor reflex of the robot locomotion. This method is easy for real use, as the user generates the brain-control signal through the instinctive reaction. By adopting the behavioral control of learning and evolution based on the proposed strategy, complex movement task may be realized by instinctive brain-control.

3D deformable image matching: a hierarchical approach over nested subspaces

NASA Astrophysics Data System (ADS)

Musse, Olivier; Heitz, Fabrice; Armspach, Jean-Paul

2000-06-01

This paper presents a fast hierarchical method to perform dense deformable inter-subject matching of 3D MR Images of the brain. To recover the complex morphological variations in neuroanatomy, a hierarchy of 3D deformations fields is estimated, by minimizing a global energy function over a sequence of nested subspaces. The nested subspaces, generated from a single scaling function, consist of deformation fields constrained at different scales. The highly non linear energy function, describing the interactions between the target and the source images, is minimized using a coarse-to-fine continuation strategy over this hierarchy. The resulting deformable matching method shows low sensitivity to local minima and is able to track large non-linear deformations, with moderate computational load. The performances of the approach are assessed both on simulated 3D transformations and on a real data base of 3D brain MR Images from different individuals. The method has shown efficient in putting into correspondence the principle anatomical structures of the brain. An application to atlas-based MRI segmentation, by transporting a labeled segmentation map on patient data, is also presented.

Comparing three-dimensional serial optical coherence tomography histology to MRI imaging in the entire mouse brain

NASA Astrophysics Data System (ADS)

Castonguay, Alexandre; Lefebvre, Joël; Pouliot, Philippe; Lesage, Frédéric

2018-01-01

An automated serial histology setup combining optical coherence tomography (OCT) imaging with vibratome sectioning was used to image eight wild type mouse brains. The datasets resulted in thousands of volumetric tiles resolved at a voxel size of (4.9×4.9×6.5) μm3 stitched back together to give a three-dimensional map of the brain from which a template OCT brain was obtained. To assess deformation caused by tissue sectioning, reconstruction algorithms, and fixation, OCT datasets were compared to both in vivo and ex vivo magnetic resonance imaging (MRI) imaging. The OCT brain template yielded a highly detailed map of the brain structure, with a high contrast in white matter fiber bundles and was highly resemblant to the in vivo MRI template. Brain labeling using the Allen brain framework showed little variation in regional brain volume among imaging modalities with no statistical differences. The high correspondence between the OCT template brain and its in vivo counterpart demonstrates the potential of whole brain histology to validate in vivo imaging.

Automated selection of brain regions for real-time fMRI brain-computer interfaces

NASA Astrophysics Data System (ADS)

Lührs, Michael; Sorger, Bettina; Goebel, Rainer; Esposito, Fabrizio

2017-02-01

Objective. Brain-computer interfaces (BCIs) implemented with real-time functional magnetic resonance imaging (rt-fMRI) use fMRI time-courses from predefined regions of interest (ROIs). To reach best performances, localizer experiments and on-site expert supervision are required for ROI definition. To automate this step, we developed two unsupervised computational techniques based on the general linear model (GLM) and independent component analysis (ICA) of rt-fMRI data, and compared their performances on a communication BCI. Approach. 3 T fMRI data of six volunteers were re-analyzed in simulated real-time. During a localizer run, participants performed three mental tasks following visual cues. During two communication runs, a letter-spelling display guided the subjects to freely encode letters by performing one of the mental tasks with a specific timing. GLM- and ICA-based procedures were used to decode each letter, respectively using compact ROIs and whole-brain distributed spatio-temporal patterns of fMRI activity, automatically defined from subject-specific or group-level maps. Main results. Letter-decoding performances were comparable to supervised methods. In combination with a similarity-based criterion, GLM- and ICA-based approaches successfully decoded more than 80% (average) of the letters. Subject-specific maps yielded optimal performances. Significance. Automated solutions for ROI selection may help accelerating the translation of rt-fMRI BCIs from research to clinical applications.

Postmortem 3-D brain hemisphere cortical tau and amyloid-β pathology mapping and quantification as a validation method of neuropathology imaging.

PubMed

Smid, Lojze M; Kepe, Vladimir; Vinters, Harry V; Bresjanac, Mara; Toyokuni, Tatsushi; Satyamurthy, Nagichettiar; Wong, Koon-Pong; Huang, Sung-Cheng; Silverman, Daniel H S; Miller, Karen; Small, Gary W; Barrio, Jorge R

2013-01-01

This work is aimed at correlating pre-mortem [18F]FDDNP positron emission tomography (PET) scan results in a patient with dementia with Lewy bodies (DLB), with cortical neuropathology distribution determined postmortem in three physical dimensions in whole brain coronal sections. Analysis of total amyloid-β (Aβ) distribution in frontal cortex and posterior cingulate gyrus confirmed its statistically significant correlation with cortical [18F]FDDNP PET binding values (distribution volume ratios, DVR) (p < 0.001, R = 0.97, R2 = 0.94). Neurofibrillary tangle (NFT) distribution correlated significantly with cortical [18F]FDDNP PET DVR in the temporal lobe (p < 0.001, R = 0.87, R2 = 0.76). Linear combination of Aβ and NFT densities was highly predictive of [18F]FDDNP PET DVR through all analyzed regions of interest (p < 0.0001, R = 0.92, R2 = 0.85), and both densities contributed significantly to the model. Lewy bodies were present at a much lower level than either Aβ or NFTs and did not significantly contribute to the in vivo signal. [18F]FDG PET scan results in this patient were consistent with the distinctive DLB pattern of hypometabolism. This work offers a mapping brain model applicable to all imaging probes for verification of imaging results with Aβ and/or tau neuropathology brain distribution using immunohistochemistry, fluorescence microscopy, and autoradiography.

Dynamic Mapping of Cortical Development before and after the Onset of Pediatric Bipolar Illness

ERIC Educational Resources Information Center

Gogtay, Nitin; Ordonez, Anna; Herman, David H.; Hayashi, Kiralee M.; Greenstein, Deanna; Vaituzis, Cathy; Lenane, Marge; Clasen, Liv; Sharp, Wendy; Giedd, Jay N.; Jung, David; Nugent, Tom F., III; Toga, Arthur W.; Leibenluft, Ellen; Thompson, Paul M.; Rapoport, Judith L.

2007-01-01

Background: There are, to date, no pre-post onset longitudinal imaging studies of bipolar disorder at any age. We report the first prospective study of cortical brain development in pediatric bipolar illness for 9 male children, visualized before and after illness onset. Method: We contrast this pattern with that observed in a matched group of…

Evaluation of multiple comparison correction procedures in drug assessment studies using LORETA maps.

PubMed

Alonso, Joan Francesc; Romero, Sergio; Mañanas, Miguel Ángel; Rojas, Mónica; Riba, Jordi; Barbanoj, Manel José

2015-10-01

The identification of the brain regions involved in the neuropharmacological action is a potential procedure for drug development. These regions are commonly determined by the voxels showing significant statistical differences after comparing placebo-induced effects with drug-elicited effects. LORETA is an electroencephalography (EEG) source imaging technique frequently used to identify brain structures affected by the drug. The aim of the present study was to evaluate different methods for the correction of multiple comparisons in the LORETA maps. These methods which have been commonly used in neuroimaging and also simulated studies have been applied on a real case of pharmaco-EEG study where the effects of increasing benzodiazepine doses on the central nervous system measured by LORETA were investigated. Data consisted of EEG recordings obtained from nine volunteers who received single oral doses of alprazolam 0.25, 0.5, and 1 mg, and placebo in a randomized crossover double-blind design. The identification of active regions was highly dependent on the selected multiple test correction procedure. The combined criteria approach known as cluster mass was useful to reveal that increasing drug doses led to higher intensity and spread of the pharmacologically induced changes in intracerebral current density.

Deformable Image Registration based on Similarity-Steered CNN Regression.

PubMed

Cao, Xiaohuan; Yang, Jianhua; Zhang, Jun; Nie, Dong; Kim, Min-Jeong; Wang, Qian; Shen, Dinggang

2017-09-01

Existing deformable registration methods require exhaustively iterative optimization, along with careful parameter tuning, to estimate the deformation field between images. Although some learning-based methods have been proposed for initiating deformation estimation, they are often template-specific and not flexible in practical use. In this paper, we propose a convolutional neural network (CNN) based regression model to directly learn the complex mapping from the input image pair (i.e., a pair of template and subject) to their corresponding deformation field. Specifically, our CNN architecture is designed in a patch-based manner to learn the complex mapping from the input patch pairs to their respective deformation field. First, the equalized active-points guided sampling strategy is introduced to facilitate accurate CNN model learning upon a limited image dataset. Then, the similarity-steered CNN architecture is designed, where we propose to add the auxiliary contextual cue, i.e., the similarity between input patches, to more directly guide the learning process. Experiments on different brain image datasets demonstrate promising registration performance based on our CNN model. Furthermore, it is found that the trained CNN model from one dataset can be successfully transferred to another dataset, although brain appearances across datasets are quite variable.

Mapping and mining interictal pathological gamma (30–100 Hz) oscillations with clinical intracranial EEG in patients with epilepsy

PubMed Central

Smart, Otis; Maus, Douglas; Marsh, Eric; Dlugos, Dennis; Litt, Brian; Meador, Kimford

2012-01-01

Localizing an epileptic network is essential for guiding neurosurgery and antiepileptic medical devices as well as elucidating mechanisms that may explain seizure-generation and epilepsy. There is increasing evidence that pathological oscillations may be specific to diseased networks in patients with epilepsy and that these oscillations may be a key biomarker for generating and indentifying epileptic networks. We present a semi-automated method that detects, maps, and mines pathological gamma (30–100 Hz) oscillations (PGOs) in human epileptic brain to possibly localize epileptic networks. We apply the method to standard clinical iEEG (<100 Hz) with interictal PGOs and seizures from six patients with medically refractory epilepsy. We demonstrate that electrodes with consistent PGO discharges do not always coincide with clinically determined seizure onset zone (SOZ) electrodes but at times PGO-dense electrodes include secondary seizure-areas (SS) or even areas without seizures (NS). In 4/5 patients with epilepsy surgery, we observe poor (Engel Class 4) post-surgical outcomes and identify more PGO-activity in SS or NS than in SOZ. Additional studies are needed to further clarify the role of PGOs in epileptic brain. PMID:23105174

Mapping Subcortical Brain Maturation during Adolescence: Evidence of Hemisphere-and Sex-Specific Longitudinal Changes

ERIC Educational Resources Information Center

Dennison, Meg; Whittle, Sarah; Yücel, Murat; Vijayakumar, Nandita; Kline, Alexandria; Simmons, Julian; Allen, Nicholas B.

2013-01-01

Early to mid-adolescence is an important developmental period for subcortical brain maturation, but longitudinal studies of these neurodevelopmental changes are lacking. The present study acquired repeated magnetic resonance images from 60 adolescent subjects (28 female) at ages 12.5 and 16.5 years to map changes in subcortical structure volumes.…

TH-CD-202-05: DECT Based Tissue Segmentation as Input to Monte Carlo Simulations for Proton Treatment Verification Using PET Imaging

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

Berndt, B; Wuerl, M; Dedes, G

Purpose: To improve agreement of predicted and measured positron emitter yields in patients, after proton irradiation for PET-based treatment verification, using a novel dual energy CT (DECT) tissue segmentation approach, overcoming known deficiencies from single energy CT (SECT). Methods: DECT head scans of 5 trauma patients were segmented and compared to existing decomposition methods with a first focus on the brain. For validation purposes, three brain equivalent solutions [water, white matter (WM) and grey matter (GM) – equivalent with respect to their reference carbon and oxygen contents and CT numbers at 90kVp and 150kVp] were prepared from water, ethanol, sucrosemore » and salt. The activities of all brain solutions, measured during a PET scan after uniform proton irradiation, were compared to Monte Carlo simulations. Simulation inputs were various solution compositions obtained from different segmentation approaches from DECT, SECT scans, and known reference composition. Virtual GM solution salt concentration corrections were applied based on DECT measurements of solutions with varying salt concentration. Results: The novel tissue segmentation showed qualitative improvements in %C for patient brain scans (ground truth unavailable). The activity simulations based on reference solution compositions agree with the measurement within 3–5% (4–8Bq/ml). These reference simulations showed an absolute activity difference between WM (20%C) and GM (10%C) to H2O (0%C) of 43 Bq/ml and 22 Bq/ml, respectively. Activity differences between reference simulations and segmented ones varied from −6 to 1 Bq/ml for DECT and −79 to 8 Bq/ml for SECT. Conclusion: Compared to the conventionally used SECT segmentation, the DECT based segmentation indicates a qualitative and quantitative improvement. In controlled solutions, a MC input based on DECT segmentation leads to better agreement with the reference. Future work will address the anticipated improvement of quantification accuracy in patients, comparing different tissue decomposition methods with an MR brain segmentation. Acknowledgement: DFG-MAP and HIT-Heidelberg Deutsche Forschungsgemeinschaft (MAP); Bundesministerium fur Bildung und Forschung (01IB13001)« less

Topographic brain mapping of emotion-related hemisphere asymmetries.

PubMed

Roschmann, R; Wittling, W

1992-03-01

The study used topographic brain mapping of visual evoked potentials to investigate emotion-related hemisphere asymmetries. The stimulus material consisted of color photographs of human faces, grouped into two emotion-related categories: normal faces (neutral stimuli) and faces deformed by dermatological diseases (emotional stimuli). The pictures were presented tachistoscopically to 20 adult right-handed subjects. Brain activity was recorded by 30 EEG electrodes with linked ears as reference. The waveforms were averaged separately with respect to each of the two stimulus conditions. Statistical analysis by means of significance probability mapping revealed significant differences between stimulus conditions for two periods of time, indicating right hemisphere superiority in emotion-related processing. The results are discussed in terms of a 2-stage-model of emotional processing in the cerebral hemispheres.

Artifact suppression and analysis of brain activities with electroencephalography signals.

PubMed

Rashed-Al-Mahfuz, Md; Islam, Md Rabiul; Hirose, Keikichi; Molla, Md Khademul Islam

2013-06-05

Brain-computer interface is a communication system that connects the brain with computer (or other devices) but is not dependent on the normal output of the brain (i.e., peripheral nerve and muscle). Electro-oculogram is a dominant artifact which has a significant negative influence on further analysis of real electroencephalography data. This paper presented a data adaptive technique for artifact suppression and brain wave extraction from electroencephalography signals to detect regional brain activities. Empirical mode decomposition based adaptive thresholding approach was employed here to suppress the electro-oculogram artifact. Fractional Gaussian noise was used to determine the threshold level derived from the analysis data without any training. The purified electroencephalography signal was composed of the brain waves also called rhythmic components which represent the brain activities. The rhythmic components were extracted from each electroencephalography channel using adaptive wiener filter with the original scale. The regional brain activities were mapped on the basis of the spatial distribution of rhythmic components, and the results showed that different regions of the brain are activated in response to different stimuli. This research analyzed the activities of a single rhythmic component, alpha with respect to different motor imaginations. The experimental results showed that the proposed method is very efficient in artifact suppression and identifying individual motor imagery based on the activities of alpha component.

Accelerated Brain DCE-MRI Using Iterative Reconstruction With Total Generalized Variation Penalty for Quantitative Pharmacokinetic Analysis: A Feasibility Study.

PubMed

Wang, Chunhao; Yin, Fang-Fang; Kirkpatrick, John P; Chang, Zheng

2017-08-01

To investigate the feasibility of using undersampled k-space data and an iterative image reconstruction method with total generalized variation penalty in the quantitative pharmacokinetic analysis for clinical brain dynamic contrast-enhanced magnetic resonance imaging. Eight brain dynamic contrast-enhanced magnetic resonance imaging scans were retrospectively studied. Two k-space sparse sampling strategies were designed to achieve a simulated image acquisition acceleration factor of 4. They are (1) a golden ratio-optimized 32-ray radial sampling profile and (2) a Cartesian-based random sampling profile with spatiotemporal-regularized sampling density constraints. The undersampled data were reconstructed to yield images using the investigated reconstruction technique. In quantitative pharmacokinetic analysis on a voxel-by-voxel basis, the rate constant K trans in the extended Tofts model and blood flow F B and blood volume V B from the 2-compartment exchange model were analyzed. Finally, the quantitative pharmacokinetic parameters calculated from the undersampled data were compared with the corresponding calculated values from the fully sampled data. To quantify each parameter's accuracy calculated using the undersampled data, error in volume mean, total relative error, and cross-correlation were calculated. The pharmacokinetic parameter maps generated from the undersampled data appeared comparable to the ones generated from the original full sampling data. Within the region of interest, most derived error in volume mean values in the region of interest was about 5% or lower, and the average error in volume mean of all parameter maps generated through either sampling strategy was about 3.54%. The average total relative error value of all parameter maps in region of interest was about 0.115, and the average cross-correlation of all parameter maps in region of interest was about 0.962. All investigated pharmacokinetic parameters had no significant differences between the result from original data and the reduced sampling data. With sparsely sampled k-space data in simulation of accelerated acquisition by a factor of 4, the investigated dynamic contrast-enhanced magnetic resonance imaging pharmacokinetic parameters can accurately estimate the total generalized variation-based iterative image reconstruction method for reliable clinical application.

Whole-Brain In-vivo Measurements of the Axonal G-Ratio in a Group of 37 Healthy Volunteers

PubMed Central

Mohammadi, Siawoosh; Carey, Daniel; Dick, Fred; Diedrichsen, Joern; Sereno, Martin I.; Reisert, Marco; Callaghan, Martina F.; Weiskopf, Nikolaus

2015-01-01

The g-ratio, quantifying the ratio between the inner and outer diameters of a fiber, is an important microstructural characteristic of fiber pathways and is functionally related to conduction velocity. We introduce a novel method for estimating the MR g-ratio non-invasively across the whole brain using high-fidelity magnetization transfer (MT) imaging and single-shell diffusion MRI. These methods enabled us to map the MR g-ratio in vivo across the brain's prominent fiber pathways in a group of 37 healthy volunteers and to estimate the inter-subject variability. Effective correction of susceptibility-related distortion artifacts was essential before combining the MT and diffusion data, in order to reduce partial volume and edge artifacts. The MR g-ratio is in good qualitative agreement with histological findings despite the different resolution and spatial coverage of MRI and histology. The MR g-ratio holds promise as an important non-invasive biomarker due to its microstructural and functional relevance in neurodegeneration. PMID:26640427

Atlas-Independent, Electrophysiological Mapping of the Optimal Locus of Subthalamic Deep Brain Stimulation for the Motor Symptoms of Parkinson Disease.

PubMed

Conrad, Erin C; Mossner, James M; Chou, Kelvin L; Patil, Parag G

2018-05-23

Deep brain stimulation (DBS) of the subthalamic nucleus (STN) improves motor symptoms of Parkinson disease (PD). However, motor outcomes can be variable, perhaps due to inconsistent positioning of the active contact relative to an unknown optimal locus of stimulation. Here, we determine the optimal locus of STN stimulation in a geometrically unconstrained, mathematically precise, and atlas-independent manner, using Unified Parkinson Disease Rating Scale (UPDRS) motor outcomes and an electrophysiological neuronal stimulation model. In 20 patients with PD, we mapped motor improvement to active electrode location, relative to the individual, directly MRI-visualized STN. Our analysis included a novel, unconstrained and computational electrical-field model of neuronal activation to estimate the optimal locus of DBS. We mapped the optimal locus to a tightly defined ovoid region 0.49 mm lateral, 0.88 mm posterior, and 2.63 mm dorsal to the anatomical midpoint of the STN. On average, this locus is 11.75 lateral, 1.84 mm posterior, and 1.08 mm ventral to the mid-commissural point. Our novel, atlas-independent method reveals a single, ovoid optimal locus of stimulation in STN DBS for PD. The methodology, here applied to UPDRS and PD, is generalizable to atlas-independent mapping of other motor and non-motor effects of DBS. © 2018 S. Karger AG, Basel.

Voxel-Based Lesion Symptom Mapping of Coarse Coding and Suppression Deficits in Patients With Right Hemisphere Damage

PubMed Central

Tompkins, Connie A.; Meigh, Kimberly M.; Prat, Chantel S.

2015-01-01

Purpose This study examined right hemisphere (RH) neuroanatomical correlates of lexical–semantic deficits that predict narrative comprehension in adults with RH brain damage. Coarse semantic coding and suppression deficits were related to lesions by voxel-based lesion symptom mapping. Method Participants were 20 adults with RH cerebrovascular accidents. Measures of coarse coding and suppression deficits were computed from lexical decision reaction times at short (175 ms) and long (1000 ms) prime-target intervals. Lesions were drawn on magnetic resonance imaging images and through normalization were registered on an age-matched brain template. Voxel-based lesion symptom mapping analysis was applied to build a general linear model at each voxel. Z score maps were generated for each deficit, and results were interpreted using automated anatomical labeling procedures. Results A deficit in coarse semantic activation was associated with lesions to the RH posterior middle temporal gyrus, dorsolateral prefrontal cortex, and lenticular nuclei. A maintenance deficit for coarsely coded representations involved the RH temporal pole and dorsolateral prefrontal cortex more medially. Ineffective suppression implicated lesions to the RH inferior frontal gyrus and subcortical regions, as hypothesized, along with the rostral temporal pole. Conclusion Beyond their scientific implications, these lesion–deficit correspondences may help inform the clinical diagnosis and enhance decisions about candidacy for deficit-focused treatment to improve narrative comprehension in individuals with RH damage. PMID:26425785

MRI segmentation using dialectical optimization.

PubMed

dos Santos, Wellington P; de Assis, Francisco M; de Souza, Ricardo E

2009-01-01

Biology, Psychology and Social Sciences are intrinsically connected to the very roots of the development of algorithms and methods in Computational Intelligence, as it is easily seen in approaches like genetic algorithms, evolutionary programming and particle swarm optimization. In this work we propose a new optimization method based on dialectics using fuzzy membership functions to model the influence of interactions between integrating poles in the status of each pole. Poles are the basic units composing dialectical systems. In order to validate our proposal we designed a segmentation method based on the optimization of k-means using dialectics for the segmentation of MR images. As a case study we used 181 MR synthetic multispectral images composed by proton density, T(1)- and T(2)-weighted synthetic brain images of 181 slices with 1 mm, resolution of 1 mm(3), for a normal brain and a noiseless MR tomographic system without field inhomogeneities, amounting a total of 543 images, generated by the simulator BrainWeb [2]. Our principal target here is comparing our proposal to k-means, fuzzy c-means, and Kohonen's self-organized maps, concerning the quantization error, we proved that our method can improved results obtained using k-means.

Corticostriatal connectivity fingerprints: Probability maps based on resting-state functional connectivity.

PubMed

Jaspers, Ellen; Balsters, Joshua H; Kassraian Fard, Pegah; Mantini, Dante; Wenderoth, Nicole

2017-03-01

Over the last decade, structure-function relationships have begun to encompass networks of brain areas rather than individual structures. For example, corticostriatal circuits have been associated with sensorimotor, limbic, and cognitive information processing, and damage to these circuits has been shown to produce unique behavioral outcomes in Autism, Parkinson's Disease, Schizophrenia and healthy ageing. However, it remains an open question how abnormal or absent connectivity can be detected at the individual level. Here, we provide a method for clustering gross morphological structures into subregions with unique functional connectivity fingerprints, and generate network probability maps usable as a baseline to compare individual cases against. We used connectivity metrics derived from resting-state fMRI (N = 100), in conjunction with hierarchical clustering methods, to parcellate the striatum into functionally distinct clusters. We identified three highly reproducible striatal subregions, across both hemispheres and in an independent replication dataset (N = 100) (dice-similarity values 0.40-1.00). Each striatal seed region resulted in a highly reproducible distinct connectivity fingerprint: the putamen showed predominant connectivity with cortical and cerebellar sensorimotor and language processing areas; the ventromedial striatum cluster had a distinct limbic connectivity pattern; the caudate showed predominant connectivity with the thalamus, frontal and occipital areas, and the cerebellum. Our corticostriatal probability maps agree with existing connectivity data in humans and non-human primates, and showed a high degree of replication. We believe that these maps offer an efficient tool to further advance hypothesis driven research and provide important guidance when investigating deviant connectivity in neurological patient populations suffering from e.g., stroke or cerebral palsy. Hum Brain Mapp 38:1478-1491, 2017. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

Scalable Joint Segmentation and Registration Framework for Infant Brain Images.

PubMed

Dong, Pei; Wang, Li; Lin, Weili; Shen, Dinggang; Wu, Guorong

2017-03-15

The first year of life is the most dynamic and perhaps the most critical phase of postnatal brain development. The ability to accurately measure structure changes is critical in early brain development study, which highly relies on the performances of image segmentation and registration techniques. However, either infant image segmentation or registration, if deployed independently, encounters much more challenges than segmentation/registration of adult brains due to dynamic appearance change with rapid brain development. In fact, image segmentation and registration of infant images can assists each other to overcome the above challenges by using the growth trajectories (i.e., temporal correspondences) learned from a large set of training subjects with complete longitudinal data. Specifically, a one-year-old image with ground-truth tissue segmentation can be first set as the reference domain. Then, to register the infant image of a new subject at earlier age, we can estimate its tissue probability maps, i.e., with sparse patch-based multi-atlas label fusion technique, where only the training images at the respective age are considered as atlases since they have similar image appearance. Next, these probability maps can be fused as a good initialization to guide the level set segmentation. Thus, image registration between the new infant image and the reference image is free of difficulty of appearance changes, by establishing correspondences upon the reasonably segmented images. Importantly, the segmentation of new infant image can be further enhanced by propagating the much more reliable label fusion heuristics at the reference domain to the corresponding location of the new infant image via the learned growth trajectories, which brings image segmentation and registration to assist each other. It is worth noting that our joint segmentation and registration framework is also flexible to handle the registration of any two infant images even with significant age gap in the first year of life, by linking their joint segmentation and registration through the reference domain. Thus, our proposed joint segmentation and registration method is scalable to various registration tasks in early brain development studies. Promising segmentation and registration results have been achieved for infant brain MR images aged from 2-week-old to 1-year-old, indicating the applicability of our method in early brain development study.

Predictive modeling of neuroanatomic structures for brain atrophy detection

NASA Astrophysics Data System (ADS)

Hu, Xintao; Guo, Lei; Nie, Jingxin; Li, Kaiming; Liu, Tianming

2010-03-01

In this paper, we present an approach of predictive modeling of neuroanatomic structures for the detection of brain atrophy based on cross-sectional MRI image. The underlying premise of applying predictive modeling for atrophy detection is that brain atrophy is defined as significant deviation of part of the anatomy from what the remaining normal anatomy predicts for that part. The steps of predictive modeling are as follows. The central cortical surface under consideration is reconstructed from brain tissue map and Regions of Interests (ROI) on it are predicted from other reliable anatomies. The vertex pair-wise distance between the predicted vertex and the true one within the abnormal region is expected to be larger than that of the vertex in normal brain region. Change of white matter/gray matter ratio within a spherical region is used to identify the direction of vertex displacement. In this way, the severity of brain atrophy can be defined quantitatively by the displacements of those vertices. The proposed predictive modeling method has been evaluated by using both simulated atrophies and MRI images of Alzheimer's disease.

Successful tactile based visual sensory substitution use functions independently of visual pathway integrity

PubMed Central

Lee, Vincent K.; Nau, Amy C.; Laymon, Charles; Chan, Kevin C.; Rosario, Bedda L.; Fisher, Chris

2014-01-01

Purpose: Neuronal reorganization after blindness is of critical interest because it has implications for the rational prescription of artificial vision devices. The purpose of this study was to distinguish the microstructural differences between perinatally blind (PB), acquired blind (AB), and normally sighted controls (SCs) and relate these differences to performance on functional tasks using a sensory substitution device (BrainPort). Methods: We enrolled 52 subjects (PB n = 11; AB n = 35; SC n = 6). All subjects spent 15 h undergoing BrainPort device training. Outcomes of light perception, motion, direction, temporal resolution, grating, and acuity were tested at baseline and after training. Twenty-six of the subjects were scanned with a three Tesla MRI scanner for diffusion tensor imaging (DTI), and with a positron emission tomography (PET) scanner for mapping regional brain glucose consumption during sensory substitution function. Non-parametric models were used to analyze fractional anisotropy (FA; a DTI measure of microstructural integrity) of the brain via region-of-interest (ROI) analysis and tract-based spatial statistics (TBSS). Results: At baseline, all subjects performed all tasks at chance level. After training, light perception, time resolution, location and grating acuity tasks improved significantly for all subject groups. ROI and TBSS analyses of FA maps show areas of statistically significant differences (p ≤ 0.025) in the bilateral optic radiations and some visual association connections between all three groups. No relationship was found between FA and functional performance with the BrainPort. Discussion: All subjects showed performance improvements using the BrainPort irrespective of nature and duration of blindness. Definite brain areas with significant microstructural integrity changes exist among PB, AB, and NC, and these variations are most pronounced in the visual pathways. However, the use of sensory substitution devices is feasible irrespective of microstructural integrity of the primary visual pathways between the eye and the brain. Therefore, tongue based devices devices may be usable for a broad array of non-sighted patients. PMID:24860473

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.

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.

Spectral mapping of brain functional connectivity from diffusion imaging.

PubMed

Becker, Cassiano O; Pequito, Sérgio; Pappas, George J; Miller, Michael B; Grafton, Scott T; Bassett, Danielle S; Preciado, Victor M

2018-01-23

Understanding the relationship between the dynamics of neural processes and the anatomical substrate of the brain is a central question in neuroscience. On the one hand, modern neuroimaging technologies, such as diffusion tensor imaging, can be used to construct structural graphs representing the architecture of white matter streamlines linking cortical and subcortical structures. On the other hand, temporal patterns of neural activity can be used to construct functional graphs representing temporal correlations between brain regions. Although some studies provide evidence that whole-brain functional connectivity is shaped by the underlying anatomy, the observed relationship between function and structure is weak, and the rules by which anatomy constrains brain dynamics remain elusive. In this article, we introduce a methodology to map the functional connectivity of a subject at rest from his or her structural graph. Using our methodology, we are able to systematically account for the role of structural walks in the formation of functional correlations. Furthermore, in our empirical evaluations, we observe that the eigenmodes of the mapped functional connectivity are associated with activity patterns associated with different cognitive systems.

Radiant thinking and the use of the mind map in nurse practitioner education.

PubMed

Spencer, Julie R; Anderson, Kelley M; Ellis, Kathryn K

2013-05-01

The concept of radiant thinking, which led to the concept of mind mapping, promotes all aspects of the brain working in synergy, with thought beginning from a central point. The mind map, which is a graphical technique to improve creative thinking and knowledge attainment, utilizes colors, images, codes, and dimensions to amplify and enhance key ideas. This technique augments the visualization of relationships and links between concepts, which aids in information acquisition, data retention, and overall comprehension. Faculty can promote students' use of the technique for brainstorming, organizing ideas, taking notes, learning collaboratively, presenting, and studying. These applications can be used in problem-based learning, developing plans of care, health promotion activities, synthesizing disease processes, and forming differential diagnoses. Mind mapping is a creative way for students to engage in a unique method of learning that can expand memory recall and help create a new environment for processing information. Copyright 2013, SLACK Incorporated.

Recent advances in parametric neuroreceptor mapping with dynamic PET: basic concepts and graphical analyses.

PubMed

Seo, Seongho; Kim, Su Jin; Lee, Dong Soo; Lee, Jae Sung

2014-10-01

Tracer kinetic modeling in dynamic positron emission tomography (PET) has been widely used to investigate the characteristic distribution patterns or dysfunctions of neuroreceptors in brain diseases. Its practical goal has progressed from regional data quantification to parametric mapping that produces images of kinetic-model parameters by fully exploiting the spatiotemporal information in dynamic PET data. Graphical analysis (GA) is a major parametric mapping technique that is independent on any compartmental model configuration, robust to noise, and computationally efficient. In this paper, we provide an overview of recent advances in the parametric mapping of neuroreceptor binding based on GA methods. The associated basic concepts in tracer kinetic modeling are presented, including commonly-used compartment models and major parameters of interest. Technical details of GA approaches for reversible and irreversible radioligands are described, considering both plasma input and reference tissue input models. Their statistical properties are discussed in view of parametric imaging.

Brain and Language.

ERIC Educational Resources Information Center

Damasio, Antonio R., Damasio, Hanna

1992-01-01

Discusses the advances made in understanding the brain structures responsible for language. Presents findings made using magnetic resonance imaging (MRI) and positron emission tomographic (PET) scans to study brain activity. These findings map the structures in the brain that manipulate concepts and those that turn concepts into words. (MCO)

Correspondence of the brain's functional architecture during activation and rest

PubMed Central

Smith, Stephen M.; Fox, Peter T.; Miller, Karla L.; Glahn, David C.; Fox, P. Mickle; Mackay, Clare E.; Filippini, Nicola; Watkins, Kate E.; Toro, Roberto; Laird, Angela R.; Beckmann, Christian F.

2009-01-01

Neural connections, providing the substrate for functional networks, exist whether or not they are functionally active at any given moment. However, it is not known to what extent brain regions are continuously interacting when the brain is “at rest.” In this work, we identify the major explicit activation networks by carrying out an image-based activation network analysis of thousands of separate activation maps derived from the BrainMap database of functional imaging studies, involving nearly 30,000 human subjects. Independently, we extract the major covarying networks in the resting brain, as imaged with functional magnetic resonance imaging in 36 subjects at rest. The sets of major brain networks, and their decompositions into subnetworks, show close correspondence between the independent analyses of resting and activation brain dynamics. We conclude that the full repertoire of functional networks utilized by the brain in action is continuously and dynamically “active” even when at “rest.” PMID:19620724

WE-FG-206-05: New Arterial Spin Labeling Method for Simultaneous Estimation of Arterial Cerebral Blood Volume, Cerebral Blood Flow and Arterial Transit Time

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

Johnston, M; Whitlow, C; Jung, Y

Purpose: To demonstrate the feasibility of a novel Arterial Spin Labeling (ASL) method for simultaneously measuring cerebral blood flow (CBF), arterial transit time (ATT), and arterial cerebral blood volume (aCBV) without the use of a contrast agent. Methods: A series of multi-TI ASL images were acquired from one healthy subject on a 3T Siemens Skyra, with the following parameters: PCASL labeling with variable TI [300, 400, 500, 600, 700, 800, 900, 1000, 1500, 2000, 2500, 3000, 3500, 4000] ms, labeling bolus 1400 ms when TI allows, otherwise 100 ms less than TI, TR was minimized for each TI, two sincmore » shaped pre-saturation pulses were applied in the imaging plane immediately before 2D EPI acquisition. 64×64×24 voxels, 5 mm slice thickness, 1 mm gap, full brain coverage, 6 averages per TI, no crusher gradients, 11 ms TE, scan time of 4:56. The perfusion weighted time-series was created for each voxel and fit to a novel model. The model has two components: 1) the traditional model developed by Buxton et al., accounting for CBF and ATT, and 2) a box car function characterizing the width of the labeling bolus, with variable timing and height in proportion to the aCBV. All three parameters were fit using a nonlinear fitting routine that constrained all parameters to be positive. The main purpose of the high-temporal resolution TI sampling for the first second of data acquisition was to precisely estimate the blood volume component for better detection of arrival time and magnitude of signal. Results: Whole brain maps of CBF, ATT, and aCBV were produced, and all three parameters maps are consistent with similar maps described in the literature. Conclusion: Simultaneous mapping of CBF, ATT, and aCBV is feasible with a clinically tractable scan time (under 5 minutes).« less

The neurolinguistic approach to awake surgery reviewed.

PubMed

De Witte, Elke; Mariën, Peter

2013-02-01

Intraoperative direct electrical stimulation (DES) is increasingly used in patients operated on for tumours in critical language areas. Although a positive impact of DES on postoperative linguistic outcome is generally advocated, the literature is only scantily documented with information about the linguistic methods applied in awake surgery. This article critically reviews the neurolinguistic procedures currently used in awake studies. Based on an extensive review of the literature, an overview is given of the language mapping techniques applied in brain tumour surgery. Studies investigating linguistic testing and outcome in awake surgery were analysed. Information about the timing of the assessment(s), the linguistic tasks, the linguistic stimuli and the indication for awake surgery was also discussed. Intraoperative DES remains the 'gold standard' for language mapping, but pre- and postoperative non-invasive mapping methods are important adjuncts. In the pre- and postoperative phase, standardised linguistic test batteries are generally used to assess language function. In the intraoperative phase, only naming and number counting are commonly applied. Most often no detailed information about the linguistic stimuli is provided and no standardised protocols measuring different linguistic levels have been described. Awake surgery with DES is a useful tool for preserving linguistic functions in patients undergoing surgery in critical brain regions. However, no studies exist that apply a well-balanced and standardised linguistic protocol to reliably identify the critical language zones. The availability of a standardised linguistic protocol might substantially increase intraoperative comfort and might improve outcome and quality of life. Copyright © 2012 Elsevier B.V. All rights reserved.

Evidence of demyelination in mild cognitive impairment and dementia using a direct and specific magnetic resonance imaging measure of myelin content.

PubMed

Bouhrara, Mustapha; Reiter, David A; Bergeron, Christopher M; Zukley, Linda M; Ferrucci, Luigi; Resnick, Susan M; Spencer, Richard G

2018-04-18

We investigated brain demyelination in aging, mild cognitive impairment (MCI), and dementia using magnetic resonance imaging of myelin. Brains of young and old controls and old subjects with MCI, Alzheimer's disease, or vascular dementia were scanned using our recently developed myelin water fraction (MWF) mapping technique, which provides greatly improved accuracy over previous comparable methods. Maps of MWF, a direct and specific myelin measure, and relaxation times and magnetization transfer ratio, indirect and nonspecific measures, were constructed. MCI subjects showed decreased MWF compared with old controls. Demyelination was greater in Alzheimer's disease or vascular dementia. As expected, decreased MWF was accompanied by decreased magnetization transfer ratio and increased relaxation times. The young subjects showed greater myelin content than the old subjects. We believe this to be the first demonstration of myelin loss in MCI, Alzheimer's disease, and vascular dementia using a method that provides a quantitative magnetic resonance imaging-based measure of myelin. Our findings add to the emerging evidence that myelination may represent an important biomarker for the pathology of MCI and dementia. This study supports the investigation of the role of myelination in MCI and dementia through use of this quantitative magnetic resonance imaging approach in clinical studies of disease progression, relationship of functional status to myelination status, and therapeutics. Furthermore, mapping MWF may permit myelin to serve as a therapeutic target in clinical trials. Copyright © 2018. Published by Elsevier Inc.

Simple and cost-effective hardware and software for functional brain mapping using intrinsic optical signal imaging.

PubMed

Harrison, Thomas C; Sigler, Albrecht; Murphy, Timothy H

2009-09-15

We describe a simple and low-cost system for intrinsic optical signal (IOS) imaging using stable LED light sources, basic microscopes, and commonly available CCD cameras. IOS imaging measures activity-dependent changes in the light reflectance of brain tissue, and can be performed with a minimum of specialized equipment. Our system uses LED ring lights that can be mounted on standard microscope objectives or video lenses to provide a homogeneous and stable light source, with less than 0.003% fluctuation across images averaged from 40 trials. We describe the equipment and surgical techniques necessary for both acute and chronic mouse preparations, and provide software that can create maps of sensory representations from images captured by inexpensive 8-bit cameras or by 12-bit cameras. The IOS imaging system can be adapted to commercial upright microscopes or custom macroscopes, eliminating the need for dedicated equipment or complex optical paths. This method can be combined with parallel high resolution imaging techniques such as two-photon microscopy.

Calcium-dependent molecular fMRI using a magnetic nanosensor.

PubMed

Okada, Satoshi; Bartelle, Benjamin B; Li, Nan; Breton-Provencher, Vincent; Lee, Jiyoung J; Rodriguez, Elisenda; Melican, James; Sur, Mriganka; Jasanoff, Alan

2018-06-01

Calcium ions are ubiquitous signalling molecules in all multicellular organisms, where they mediate diverse aspects of intracellular and extracellular communication over widely varying temporal and spatial scales 1 . Though techniques to map calcium-related activity at a high resolution by optical means are well established, there is currently no reliable method to measure calcium dynamics over large volumes in intact tissue 2 . Here, we address this need by introducing a family of magnetic calcium-responsive nanoparticles (MaCaReNas) that can be detected by magnetic resonance imaging (MRI). MaCaReNas respond within seconds to [Ca 2+ ] changes in the 0.1-1.0 mM range, suitable for monitoring extracellular calcium signalling processes in the brain. We show that the probes permit the repeated detection of brain activation in response to diverse stimuli in vivo. MaCaReNas thus provide a tool for calcium-activity mapping in deep tissue and offer a precedent for the development of further nanoparticle-based sensors for dynamic molecular imaging with MRI.

Pyrylium Salts as Reactive Matrices for MALDI-MS Imaging of Biologically Active Primary Amines

NASA Astrophysics Data System (ADS)

Shariatgorji, Mohammadreza; Nilsson, Anna; Källback, Patrik; Karlsson, Oskar; Zhang, Xiaoqun; Svenningsson, Per; Andren, Per E.

2015-06-01

Many neuroactive substances, including endogenous biomolecules, environmental compounds, and pharmaceuticals possess primary amine functional groups. Among these are catecholamine neurotransmitters (e.g., dopamine), many substituted phenethylamines (e.g., amphetamine), as well as amino acids and neuropeptides. In most cases, mass spectrometric (ESI and MALDI) analyses of trace amounts of such compounds are challenging because of their poor ionization properties. We present a method for chemical derivatization of primary amines by reaction with pyrylium salts that facilitates their detection by MALDI-MS and enables the imaging of primary amines in brain tissue sections. A screen of pyrylium salts revealed that the 2,4-diphenyl-pyranylium ion efficiently derivatizes primary amines and can be used as a reactive MALDI-MS matrix that induces both derivatization and desorption. MALDI-MS imaging with such matrix was used to map the localization of dopamine and amphetamine in brain tissue sections and to quantitatively map the distribution of the neurotoxin β- N-methylamino-L-alanine.

Distinguishing between direct and indirect directional couplings in large oscillator networks: Partial or non-partial phase analyses?

NASA Astrophysics Data System (ADS)

Rings, Thorsten; Lehnertz, Klaus

2016-09-01

We investigate the relative merit of phase-based methods for inferring directional couplings in complex networks of weakly interacting dynamical systems from multivariate time-series data. We compare the evolution map approach and its partialized extension to each other with respect to their ability to correctly infer the network topology in the presence of indirect directional couplings for various simulated experimental situations using coupled model systems. In addition, we investigate whether the partialized approach allows for additional or complementary indications of directional interactions in evolving epileptic brain networks using intracranial electroencephalographic recordings from an epilepsy patient. For such networks, both direct and indirect directional couplings can be expected, given the brain's connection structure and effects that may arise from limitations inherent to the recording technique. Our findings indicate that particularly in larger networks (number of nodes ≫10 ), the partialized approach does not provide information about directional couplings extending the information gained with the evolution map approach.

Gender differences in working memory networks: A BrainMap meta-analysis

PubMed Central

Hill, Ashley C.; Laird, Angela R.; Robinson, Jennifer L.

2014-01-01

Gender differences in psychological processes have been of great interest in a variety of fields. While the majority of research in this area has focused on specific differences in relation to test performance, this study sought to determine the underlying neurofunctional differences observed during working memory, a pivotal cognitive process shown to be predictive of academic achievement and intelligence. Using the BrainMap database, we performed a meta-analysis and applied activation likelihood estimation to our search set. Our results demonstrate consistent working memory networks across genders, but also provide evidence for gender-specific networks whereby females consistently activate more limbic (e.g., amygdala and hippocampus) and prefrontal structures (e.g., right inferior frontal gyrus), and males activate a distributed network inclusive of more parietal regions. These data provide a framework for future investigation using functional or effective connectivity methods to elucidate the underpinnings of gender differences in neural network recruitment during working memory tasks. PMID:25042764

Calcium-dependent molecular fMRI using a magnetic nanosensor

NASA Astrophysics Data System (ADS)

Okada, Satoshi; Bartelle, Benjamin B.; Li, Nan; Breton-Provencher, Vincent; Lee, Jiyoung J.; Rodriguez, Elisenda; Melican, James; Sur, Mriganka; Jasanoff, Alan

2018-06-01

Calcium ions are ubiquitous signalling molecules in all multicellular organisms, where they mediate diverse aspects of intracellular and extracellular communication over widely varying temporal and spatial scales1. Though techniques to map calcium-related activity at a high resolution by optical means are well established, there is currently no reliable method to measure calcium dynamics over large volumes in intact tissue2. Here, we address this need by introducing a family of magnetic calcium-responsive nanoparticles (MaCaReNas) that can be detected by magnetic resonance imaging (MRI). MaCaReNas respond within seconds to [Ca2+] changes in the 0.1-1.0 mM range, suitable for monitoring extracellular calcium signalling processes in the brain. We show that the probes permit the repeated detection of brain activation in response to diverse stimuli in vivo. MaCaReNas thus provide a tool for calcium-activity mapping in deep tissue and offer a precedent for the development of further nanoparticle-based sensors for dynamic molecular imaging with MRI.

Gender differences in working memory networks: a BrainMap meta-analysis.

PubMed

Hill, Ashley C; Laird, Angela R; Robinson, Jennifer L

2014-10-01

Gender differences in psychological processes have been of great interest in a variety of fields. While the majority of research in this area has focused on specific differences in relation to test performance, this study sought to determine the underlying neurofunctional differences observed during working memory, a pivotal cognitive process shown to be predictive of academic achievement and intelligence. Using the BrainMap database, we performed a meta-analysis and applied activation likelihood estimation to our search set. Our results demonstrate consistent working memory networks across genders, but also provide evidence for gender-specific networks whereby females consistently activate more limbic (e.g., amygdala and hippocampus) and prefrontal structures (e.g., right inferior frontal gyrus), and males activate a distributed network inclusive of more parietal regions. These data provide a framework for future investigations using functional or effective connectivity methods to elucidate the underpinnings of gender differences in neural network recruitment during working memory tasks. Copyright © 2014 Elsevier B.V. All rights reserved.

SPED light sheet microscopy: fast mapping of biological system structure and function

PubMed Central

Tomer, Raju; Lovett-Barron, Matthew; Kauvar, Isaac; Andalman, Aaron; Burns, Vanessa M.; Sankaran, Sethuraman; Grosenick, Logan; Broxton, Michael; Yang, Samuel; Deisseroth, Karl

2016-01-01

The goal of understanding living nervous systems has driven interest in high-speed and large field-of-view volumetric imaging at cellular resolution. Light-sheet microscopy approaches have emerged for cellular-resolution functional brain imaging in small organisms such as larval zebrafish, but remain fundamentally limited in speed. Here we have developed SPED light sheet microscopy, which combines large volumetric field-of-view via an extended depth of field with the optical sectioning of light sheet microscopy, thereby eliminating the need to physically scan detection objectives for volumetric imaging. SPED enables scanning of thousands of volumes-per-second, limited only by camera acquisition rate, through the harnessing of optical mechanisms that normally result in unwanted spherical aberrations. We demonstrate capabilities of SPED microscopy by performing fast sub-cellular resolution imaging of CLARITY mouse brains and cellular-resolution volumetric Ca2+ imaging of entire zebrafish nervous systems. Together, SPED light sheet methods enable high-speed cellular-resolution volumetric mapping of biological system structure and function. PMID:26687363

Quantitative Oxygenation Venography from MRI Phase

PubMed Central

Fan, Audrey P.; Bilgic, Berkin; Gagnon, Louis; Witzel, Thomas; Bhat, Himanshu; Rosen, Bruce R.; Adalsteinsson, Elfar

2014-01-01

Purpose To demonstrate acquisition and processing methods for quantitative oxygenation venograms that map in vivo oxygen saturation (SvO2) along cerebral venous vasculature. Methods Regularized quantitative susceptibility mapping (QSM) is used to reconstruct susceptibility values and estimate SvO2 in veins. QSM with ℓ1 and ℓ2 regularization are compared in numerical simulations of vessel structures with known magnetic susceptibility. Dual-echo, flow-compensated phase images are collected in three healthy volunteers to create QSM images. Bright veins in the susceptibility maps are vectorized and used to form a three-dimensional vascular mesh, or venogram, along which to display SvO2 values from QSM. Results Quantitative oxygenation venograms that map SvO2 along brain vessels of arbitrary orientation and geometry are shown in vivo. SvO2 values in major cerebral veins lie within the normal physiological range reported by 15O positron emission tomography. SvO2 from QSM is consistent with previous MR susceptometry methods for vessel segments oriented parallel to the main magnetic field. In vessel simulations, ℓ1 regularization results in less than 10% SvO2 absolute error across all vessel tilt orientations and provides more accurate SvO2 estimation than ℓ2 regularization. Conclusion The proposed analysis of susceptibility images enables reliable mapping of quantitative SvO2 along venograms and may facilitate clinical use of venous oxygenation imaging. PMID:24006229

Identification of ideal resuscitation pressure with concurrent traumatic brain injury in a rat model of hemorrhagic shock.

PubMed

Hu, Yi; Wu, Yue; Tian, Kunlun; Lan, Dan; Chen, Xiangyun; Xue, Mingying; Liu, Liangming; Li, Tao

2015-05-01

Traumatic brain injury (TBI) is often associated with uncontrolled hemorrhagic shock (UHS), which contributes significantly to the mortality of severe trauma. Studies have demonstrated that permissive hypotension resuscitation improves the survival for uncontrolled hemorrhage. What the ideal target mean arterial pressure (MAP) is for TBI with UHS remains unclear. With the rat model of TBI in combination with UHS, we investigated the effects of a series of target resuscitation pressures (MAP from 50-90 mm Hg) on animal survival, brain perfusion, and organ function before hemorrhage controlled. Rats in 50-, 60-, and 70-mm Hg target MAP groups had less blood loss and less fluid requirement, a better vital organ including mitochondrial function and better cerebral blood flow, and animal survival (8, 6, and 7 of 10, respectively) than 80- and 90-mm Hg groups. The 70-mm Hg group had a better cerebral blood flow and cerebral mitochondrial function than in 50- and 60-mm Hg groups. In contrast, 80- and 90-mm Hg groups resulted in an excessive hemodilution, a decreased blood flow, an increased brain water content, and more severe cerebral edema. A 50-mm Hg target MAP is not suitable for the resuscitation of TBI combined with UHS. A 70 mm Hg of MAP is the ideal target resuscitation pressure for this trauma, which can keep sufficient perfusion to the brain and keep good organ function including cerebral mitochondrial function. Copyright © 2015 Elsevier Inc. All rights reserved.

Phase-difference and spectroscopic imaging for monitoring of human brain temperature during cooling.

PubMed

Weis, Jan; Covaciu, Lucian; Rubertsson, Sten; Allers, Mats; Lunderquist, Anders; Ortiz-Nieto, Francisco; Ahlström, Håkan

2012-12-01

Decrease of the human brain temperature was induced by intranasal cooling. The main purpose of this study was to compare the two magnetic resonance methods for monitoring brain temperature changes during cooling: phase-difference and magnetic resonance spectroscopic imaging (MRSI) with high spatial resolution. Ten healthy volunteers were measured. Selective brain cooling was performed through nasal cavities using saline-cooled balloon catheters. MRSI was based on a radiofrequency spoiled gradient echo sequence. The spectral information was encoded by incrementing the echo time of the subsequent eight image records. Reconstructed voxel size was 1×1×5 mm(3). Relative brain temperature was computed from the positions of water spectral lines. Phase maps were obtained from the first image record of the MRSI sequence. Mild hypothermia was achieved in 15-20 min. Mean brain temperature reduction varied in the interval <-3.0; -0.6>°C and <-2.7; -0.7>°C as measured by the MRSI and phase-difference methods, respectively. Very good correlation was found in all locations between the temperatures measured by both techniques except in the frontal lobe. Measurements in the transversal slices were more robust to the movement artifacts than those in the sagittal planes. Good agreement was found between the MRSI and phase-difference techniques. Copyright © 2012 Elsevier Inc. All rights reserved.

Advanced lesion symptom mapping analyses and implementation as BCBtoolkit

PubMed Central

Foulon, Chris; Cerliani, Leonardo; Kinkingnéhun, Serge; Levy, Richard; Rosso, Charlotte; Urbanski, Marika

2018-01-01

Abstract Background Patients with brain lesions provide a unique opportunity to understand the functioning of the human mind. However, even when focal, brain lesions have local and remote effects that impact functionally and structurally connected circuits. Similarly, function emerges from the interaction between brain areas rather than their sole activity. For instance, category fluency requires the associations between executive, semantic, and language production functions. Findings Here, we provide, for the first time, a set of complementary solutions for measuring the impact of a given lesion on the neuronal circuits. Our methods, which were applied to 37 patients with a focal frontal brain lesions, revealed a large set of directly and indirectly disconnected brain regions that had significantly impacted category fluency performance. The directly disconnected regions corresponded to areas that are classically considered as functionally engaged in verbal fluency and categorization tasks. These regions were also organized into larger directly and indirectly disconnected functional networks, including the left ventral fronto-parietal network, whose cortical thickness correlated with performance on category fluency. Conclusions The combination of structural and functional connectivity together with cortical thickness estimates reveal the remote effects of brain lesions, provide for the identification of the affected networks, and strengthen our understanding of their relationship with cognitive and behavioral measures. The methods presented are available and freely accessible in the BCBtoolkit as supplementary software [1]. PMID:29432527

A Computer-Aided Analysis Method of SPECT Brain Images for Quantitative Treatment Monitoring: Performance Evaluations and Clinical Applications.

PubMed

Zheng, Xiujuan; Wei, Wentao; Huang, Qiu; Song, Shaoli; Wan, Jieqing; Huang, Gang

2017-01-01

The objective and quantitative analysis of longitudinal single photon emission computed tomography (SPECT) images are significant for the treatment monitoring of brain disorders. Therefore, a computer aided analysis (CAA) method is introduced to extract a change-rate map (CRM) as a parametric image for quantifying the changes of regional cerebral blood flow (rCBF) in longitudinal SPECT brain images. The performances of the CAA-CRM approach in treatment monitoring are evaluated by the computer simulations and clinical applications. The results of computer simulations show that the derived CRMs have high similarities with their ground truths when the lesion size is larger than system spatial resolution and the change rate is higher than 20%. In clinical applications, the CAA-CRM approach is used to assess the treatment of 50 patients with brain ischemia. The results demonstrate that CAA-CRM approach has a 93.4% accuracy of recovered region's localization. Moreover, the quantitative indexes of recovered regions derived from CRM are all significantly different among the groups and highly correlated with the experienced clinical diagnosis. In conclusion, the proposed CAA-CRM approach provides a convenient solution to generate a parametric image and derive the quantitative indexes from the longitudinal SPECT brain images for treatment monitoring.

Distributed XQuery-Based Integration and Visualization of Multimodality Brain Mapping Data

PubMed Central

Detwiler, Landon T.; Suciu, Dan; Franklin, Joshua D.; Moore, Eider B.; Poliakov, Andrew V.; Lee, Eunjung S.; Corina, David P.; Ojemann, George A.; Brinkley, James F.

2008-01-01

This paper addresses the need for relatively small groups of collaborating investigators to integrate distributed and heterogeneous data about the brain. Although various national efforts facilitate large-scale data sharing, these approaches are generally too “heavyweight” for individual or small groups of investigators, with the result that most data sharing among collaborators continues to be ad hoc. Our approach to this problem is to create a “lightweight” distributed query architecture, in which data sources are accessible via web services that accept arbitrary query languages but return XML results. A Distributed XQuery Processor (DXQP) accepts distributed XQueries in which subqueries are shipped to the remote data sources to be executed, with the resulting XML integrated by DXQP. A web-based application called DXBrain accesses DXQP, allowing a user to create, save and execute distributed XQueries, and to view the results in various formats including a 3-D brain visualization. Example results are presented using distributed brain mapping data sources obtained in studies of language organization in the brain, but any other XML source could be included. The advantage of this approach is that it is very easy to add and query a new source, the tradeoff being that the user needs to understand XQuery and the schemata of the underlying sources. For small numbers of known sources this burden is not onerous for a knowledgeable user, leading to the conclusion that the system helps to fill the gap between ad hoc local methods and large scale but complex national data sharing efforts. PMID:19198662

New Universal Nomenclature in Auriculotherapy.

PubMed

Alimi, David; Chelly, Jacques E

2018-01-01

To propose at the auriculotherapists a New Universal Nomenclature of Auriculotherapy, able to receive any mapping whatsoever. We built this proposition by using electronic database search to find the different formulations of Auricular Acupuncture Points (AAPs), by studying neuroradiology methods describing reliable and reproducible marks able to adapt to all brain morphologies, by studying the analysis of brain dissections, showed us the internal organization of the brain; and after having proved the neurophysiological correlations between auricular displays and their brain correspondences. Since the 1950s, the study of Auriculotherapy by Paul Nogier and his students regularly progressed. The World Health Organization recognized it in 1987 and developed the First International Nomenclature in 1990. The number of therapeutic zones of the ear, in proportion to the constant progress in neurophysiology, never stops growing. This growth presents a major problem: all the first classifications became inappropriate and unfit. We propose a Universal Nomenclature of Auriculotherapy which is a biomathematical model of the brain anatomic organization, with 189 areas on the lateral ear and 89 areas on the medial ear. The Universal Auriculotherapy Nomenclature we proposed to the World Federation of Chinese Medicine Societies and which approved it at its International Convention in September 2011 in London, gives accurate Cartesian Marks and is able to receive any mapping whatsoever. Dispatching around 57 countries (Europe, America, China, Russia and Africa) and 195 Acupuncture Societies, it will facilitate the work of auriculotherapists and allow a scientific progress of the subject worldwide. This progress will allow the largest number of people to have a common tool for education, research, and publications of the discipline.

Structural covariance mapping delineates medial and medio-lateral temporal networks in déjà vu.

PubMed

Shaw, Daniel Joel; Mareček, Radek; Brázdil, Milan

2016-12-01

Déjà vu (DV) is an eerie phenomenon experienced frequently as an aura of temporal lobe epilepsy, but also reported commonly by healthy individuals. The former pathological manifestation appears to result from aberrant neural activity among brain structures within the medial temporal lobes. Recent studies also implicate medial temporal brain structures in the non-pathological experience of DV, but as one element of a diffuse neuroanatomical correlate; it remains to be seen if neural activity among the medial temporal lobes also underlies this benign manifestation. The present study set out to investigate this. Due to its unpredictable and infrequent occurrence, however, non-pathological DV does not lend itself easily to functional neuroimaging. Instead, we draw on research showing that brain structure covaries among regions that interact frequently as nodes of functional networks. Specifically, we assessed whether grey-matter covariance among structures implicated in non-pathological DV differs according to the frequency with which the phenomenon is experienced. This revealed two diverging patterns of structural covariation: Among the first, comprised primarily of medial temporal structures and the caudate, grey-matter volume becomes more positively correlated with higher frequency of DV experience. The second pattern encompasses medial and lateral temporal structures, among which greater DV frequency is associated with more negatively correlated grey matter. Using a meta-analytic method of co-activation mapping, we demonstrate a higher probability of functional interactions among brain structures constituting the former pattern, particularly during memory-related processes. Our findings suggest that altered neural signalling within memory-related medial temporal brain structures underlies both pathological and non-pathological DV.

Mapping subcortical brain maturation during adolescence: evidence of hemisphere- and sex-specific longitudinal changes.

PubMed

Dennison, Meg; Whittle, Sarah; Yücel, Murat; Vijayakumar, Nandita; Kline, Alexandria; Simmons, Julian; Allen, Nicholas B

2013-09-01

Early to mid-adolescence is an important developmental period for subcortical brain maturation, but longitudinal studies of these neurodevelopmental changes are lacking. The present study acquired repeated magnetic resonance images from 60 adolescent subjects (28 female) at ages 12.5 and 16.5 years to map changes in subcortical structure volumes. Automated segmentation techniques optimized for longitudinal measurement were used to delineate volumes of the caudate, putamen, nucleus accumbens, pallidum, hippocampus, thalamus and the whole brain. Amygdala volumes were described using manual tracing methods. The results revealed heterogeneous maturation across the regions of interest (ROIs), and change was differentially moderated by sex and hemisphere. The caudate, thalamus and putamen declined in volume, more for females relative to males, and decreases in the putamen and thalamus were greater in the left hemisphere. The pallidum increased in size, but more so in the left hemisphere. While the left nucleus accumbens increased in size, the right accumbens decreased in size over the follow-up period. Increases in hippocampal volume were greater in the right hemisphere. While amygdala volume did not change over time, the left hemisphere was consistently larger than the right. These results suggest that subcortical brain development from early to middle adolescence is characterized by striking hemispheric specialization and sexual dimorphisms, and provide a framework for interpreting normal and abnormal changes in cognition, affect and behavior. Moreover, the differences in findings compared to previous cross-sectional research emphasize the importance of within-subject assessment of brain development during adolescence. © 2013 John Wiley & Sons Ltd.

Quantitative Susceptibility Mapping using Structural Feature based Collaborative Reconstruction (SFCR) in the Human Brain

PubMed Central

Cai, Congbo; Chen, Zhong; van Zijl, Peter C.M.

2017-01-01

The reconstruction of MR quantitative susceptibility mapping (QSM) from local phase measurements is an ill posed inverse problem and different regularization strategies incorporating a priori information extracted from magnitude and phase images have been proposed. However, the anatomy observed in magnitude and phase images does not always coincide spatially with that in susceptibility maps, which could give erroneous estimation in the reconstructed susceptibility map. In this paper, we develop a structural feature based collaborative reconstruction (SFCR) method for QSM including both magnitude and susceptibility based information. The SFCR algorithm is composed of two consecutive steps corresponding to complementary reconstruction models, each with a structural feature based l1 norm constraint and a voxel fidelity based l2 norm constraint, which allows both the structure edges and tiny features to be recovered, whereas the noise and artifacts could be reduced. In the M-step, the initial susceptibility map is reconstructed by employing a k-space based compressed sensing model incorporating magnitude prior. In the S-step, the susceptibility map is fitted in spatial domain using weighted constraints derived from the initial susceptibility map from the M-step. Simulations and in vivo human experiments at 7T MRI show that the SFCR method provides high quality susceptibility maps with improved RMSE and MSSIM. Finally, the susceptibility values of deep gray matter are analyzed in multiple head positions, with the supine position most approximate to the gold standard COSMOS result. PMID:27019480

Hippocampus Segmentation Based on Local Linear Mapping

PubMed Central

Pang, Shumao; Jiang, Jun; Lu, Zhentai; Li, Xueli; Yang, Wei; Huang, Meiyan; Zhang, Yu; Feng, Yanqiu; Huang, Wenhua; Feng, Qianjin

2017-01-01

We propose local linear mapping (LLM), a novel fusion framework for distance field (DF) to perform automatic hippocampus segmentation. A k-means cluster method is propose for constructing magnetic resonance (MR) and DF dictionaries. In LLM, we assume that the MR and DF samples are located on two nonlinear manifolds and the mapping from the MR manifold to the DF manifold is differentiable and locally linear. We combine the MR dictionary using local linear representation to present the test sample, and combine the DF dictionary using the corresponding coefficients derived from local linear representation procedure to predict the DF of the test sample. We then merge the overlapped predicted DF patch to obtain the DF value of each point in the test image via a confidence-based weighted average method. This approach enabled us to estimate the label of the test image according to the predicted DF. The proposed method was evaluated on brain images of 35 subjects obtained from SATA dataset. Results indicate the effectiveness of the proposed method, which yields mean Dice similarity coefficients of 0.8697, 0.8770 and 0.8734 for the left, right and bi-lateral hippocampus, respectively. PMID:28368016

Hippocampus Segmentation Based on Local Linear Mapping.

PubMed

Pang, Shumao; Jiang, Jun; Lu, Zhentai; Li, Xueli; Yang, Wei; Huang, Meiyan; Zhang, Yu; Feng, Yanqiu; Huang, Wenhua; Feng, Qianjin

2017-04-03

We propose local linear mapping (LLM), a novel fusion framework for distance field (DF) to perform automatic hippocampus segmentation. A k-means cluster method is propose for constructing magnetic resonance (MR) and DF dictionaries. In LLM, we assume that the MR and DF samples are located on two nonlinear manifolds and the mapping from the MR manifold to the DF manifold is differentiable and locally linear. We combine the MR dictionary using local linear representation to present the test sample, and combine the DF dictionary using the corresponding coefficients derived from local linear representation procedure to predict the DF of the test sample. We then merge the overlapped predicted DF patch to obtain the DF value of each point in the test image via a confidence-based weighted average method. This approach enabled us to estimate the label of the test image according to the predicted DF. The proposed method was evaluated on brain images of 35 subjects obtained from SATA dataset. Results indicate the effectiveness of the proposed method, which yields mean Dice similarity coefficients of 0.8697, 0.8770 and 0.8734 for the left, right and bi-lateral hippocampus, respectively.

Hippocampus Segmentation Based on Local Linear Mapping

NASA Astrophysics Data System (ADS)

Pang, Shumao; Jiang, Jun; Lu, Zhentai; Li, Xueli; Yang, Wei; Huang, Meiyan; Zhang, Yu; Feng, Yanqiu; Huang, Wenhua; Feng, Qianjin

2017-04-01

We propose local linear mapping (LLM), a novel fusion framework for distance field (DF) to perform automatic hippocampus segmentation. A k-means cluster method is propose for constructing magnetic resonance (MR) and DF dictionaries. In LLM, we assume that the MR and DF samples are located on two nonlinear manifolds and the mapping from the MR manifold to the DF manifold is differentiable and locally linear. We combine the MR dictionary using local linear representation to present the test sample, and combine the DF dictionary using the corresponding coefficients derived from local linear representation procedure to predict the DF of the test sample. We then merge the overlapped predicted DF patch to obtain the DF value of each point in the test image via a confidence-based weighted average method. This approach enabled us to estimate the label of the test image according to the predicted DF. The proposed method was evaluated on brain images of 35 subjects obtained from SATA dataset. Results indicate the effectiveness of the proposed method, which yields mean Dice similarity coefficients of 0.8697, 0.8770 and 0.8734 for the left, right and bi-lateral hippocampus, respectively.

Whole-Brain Mapping of Direct Inputs to and Axonal Projections from GABAergic Neurons in the Parafacial Zone.

PubMed

Su, Yun-Ting; Gu, Meng-Yang; Chu, Xi; Feng, Xiang; Yu, Yan-Qin

2018-06-01

The GABAergic neurons in the parafacial zone (PZ) play an important role in sleep-wake regulation and have been identified as part of a sleep-promoting center in the brainstem, but the long-range connections mediating this function remain poorly characterized. Here, we performed whole-brain mapping of both the inputs and outputs of the GABAergic neurons in the PZ of the mouse brain. We used the modified rabies virus EnvA-ΔG-DsRed combined with a Cre/loxP gene-expression strategy to map the direct monosynaptic inputs to the GABAergic neurons in the PZ, and found that they receive inputs mainly from the hypothalamic area, zona incerta, and parasubthalamic nucleus in the hypothalamus; the substantia nigra, pars reticulata and deep mesencephalic nucleus in the midbrain; and the intermediate reticular nucleus and medial vestibular nucleus (parvocellular part) in the pons and medulla. We also mapped the axonal projections of the PZ GABAergic neurons with adeno-associated virus, and defined the reciprocal connections of the PZ GABAergic neurons with their input and output nuclei. The newly-found inputs and outputs of the PZ were also listed compared with the literature. This cell-type-specific neuronal whole-brain mapping of the PZ GABAergic neurons may reveal the circuits underlying various functions such as sleep-wake regulation.

The study of the wonderful: the first topographical mapping of vision in the brain.

PubMed

Fishman, Ronald S

2008-12-01

The conception by René Descartes of the human brain, notorious as it is for placing the soul or mind in the pineal gland, had yet within it the basic idea of the brain as a highly organized mechanism with topographical sensory mapping and different functions localized in specific areas. Descartes was directly led to this idea by his appreciation of what the retinal image conceived by Johannes Kepler implied, not only for the nature of vision, but for the operation of the brain in general. The linkage between Kepler and Descartes is not widely appreciated but is one of the best examples of synergism in the history of science.

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

Potential predictors for the amount of intra-operative brain shift during deep brain stimulation surgery

NASA Astrophysics Data System (ADS)

Datteri, Ryan; Pallavaram, Srivatsan; Konrad, Peter E.; Neimat, Joseph S.; D'Haese, Pierre-François; Dawant, Benoit M.

2011-03-01

A number of groups have reported on the occurrence of intra-operative brain shift during deep brain stimulation (DBS) surgery. This has a number of implications for the procedure including an increased chance of intra-cranial bleeding and complications due to the need for more exploratory electrodes to account for the brain shift. It has been reported that the amount of pneumocephalus or air invasion into the cranial cavity due to the opening of the dura correlates with intraoperative brain shift. Therefore, pre-operatively predicting the amount of pneumocephalus expected during surgery is of interest toward accounting for brain shift. In this study, we used 64 DBS patients who received bilateral electrode implantations and had a post-operative CT scan acquired immediately after surgery (CT-PI). For each patient, the volumes of the pneumocephalus, left ventricle, right ventricle, third ventricle, white matter, grey matter, and cerebral spinal fluid were calculated. The pneumocephalus was calculated from the CT-PI utilizing a region growing technique that was initialized with an atlas-based image registration method. A multi-atlas-based image segmentation method was used to segment out the ventricles of each patient. The Statistical Parametric Mapping (SPM) software package was utilized to calculate the volumes of the cerebral spinal fluid (CSF), white matter and grey matter. The volume of individual structures had a moderate correlation with pneumocephalus. Utilizing a multi-linear regression between the volume of the pneumocephalus and the statistically relevant individual structures a Pearson's coefficient of r = 0.4123 (p = 0.0103) was found. This study shows preliminary results that could be used to develop a method to predict the amount of pneumocephalus ahead of the surgery.