We present constrained source-based morphometry (SBM), a multivariate semiblind data-driven appro... more We present constrained source-based morphometry (SBM), a multivariate semiblind data-driven approach, to explore a possible brain-wide structural network in both gray matter (GM) and white matter (WM) associated with the functional default mode network (DMN). With this approach, we utilize seed regions associated with the DMN as constraints on GM maps and derive a joint GM and WM structural network automatically through a multivariate data-driven approach. In this article, we first provide a simulation to validate the constrained SBM approach. The approach was then applied to structural magnetic resonance imaging and diffusion tensor imaging data obtained from 102 healthy controls. Regions that have consistently reported to be associated with the DMN were used to create an a priori mask that was integrated within an independent component analysis framework to derive the structural network associated with the DMN. We identified a set of GM and corresponding WM regions contributing to a structural network underlying the functional DMN. The GM regions consisted mainly of the precuneus, superior and medial frontal gyri, middle temporal gyrus, hippocampus, cuneus, and cerebellum. The WM regions included the cingulum, corpus callosum, corona radiata, association fibers, and middle cerebellar peduncle. Significant gender differences in the relationship between intelligence quotient (IQ) and the identified structural network were observed. Our findings suggest that the functional DMN is underpinned by a corresponding brain-wide structural network. The constrained SBM approach is additionally applicable to a wide variety of problems identifying structural networks from seed regions.
Connectomics—the study of brain networks—provides a unique and valuable opportunity to study the ... more Connectomics—the study of brain networks—provides a unique and valuable opportunity to study the brain. However, research in human connectomics, accomplished via Magnetic Resonance Imaging (MRI), is a resource-intensive practice: typical analysis routines require impactful decision making and significant computational capabilities. Mitigating these issues requires the development of low-resource, easy to use, and flexible pipelines which can be applied across data with variable collection parameters. In response to these challenges, we have developed the MRI to Graphs (m2g) pipeline. m2g leverages functional and diffusion datasets to estimate connectomes reliably. To illustrate, m2g was used to process MRI data from 35 different studies (≈6,000 scans) from 15 sites without any manual intervention or parameter tuning. Every single scan yielded an estimated connectome that followed established properties, such as stronger ipsilateral than contralateral connections in structural connec...
Introduction Connectomics, the study of brain maps or graphs, is an emerging discipline of neuros... more Introduction Connectomics, the study of brain maps or graphs, is an emerging discipline of neuroscience, enabling new neurobiology questions to be asked and answered [1]. A hotly debated question relates to partitioning the brain into cortical regions, which are based on gross anatomical structures. The extent to which the brain connections are related to the classical partition remains largely unknown. Brain graphs generated by combining MRI data with that by different sensing modalities may consist of half million or more voxel-vertices, but only a fraction of which are labeled to 70 cortical regions [2]. Classifying or labeling the rest of the vertices is computationally challenging by the shear number of the voxels as well as by the high number of regions or classes. We present a fast and reliable approach for voxel-to-region classification based on a methodology by Sussman et al that offers theoretical assurance of asymptotic optimality in statistical sense [3].
Journal of the Royal Statistical Society: Series C (Applied Statistics)
This article focuses on the problem of predicting a response variable based on a network-valued p... more This article focuses on the problem of predicting a response variable based on a network-valued predictor. Our motivation is the development of interpretable and accurate predictive models for cognitive traits and neuro-psychiatric disorders based on an individual's brain connection network (connectome). Current methods reduce the complex, high-dimensional brain network into low-dimensional pre-specified features prior to applying standard predictive algorithms. Such methods are sensitive to feature choice and inevitably discard information. We instead propose a nonparametric Bayes class of models that utilize information from the entire adjacency matrix defining connections among brain regions in adaptively detecting predictive algorithms, while maintaining interpretability. The Bayesian Connectomics (BaCon) model class utilizes Poisson-Dirichlet processes to find a lower-dimensional, bidirectional (covariate, subject) pattern in the adjacency matrix. The small n, large p problem is transformed into a "small n, small q" problem, facilitating an effective stochastic search of the predictors. A spike-and-slab prior for the cluster predictors strikes a balance between regression model parsimony and flexibility, resulting in improved inferences and test case predictions. We describe basic properties of the BaCon model and develop efficient algorithms for posterior computation. The resulting methods are shown to outperform existing approaches and applied to a creative reasoning data set.
The human brain and the neuronal networks comprising it are of immense interest to the scienti c ... more The human brain and the neuronal networks comprising it are of immense interest to the scienti c community. In this work, we focus on the structural connectivity of human brains, investigating sex di erences across male and female connectomes (brain-graphs) for the knowledge discovery problem Which brain regions exert di erences in connectivity across the two sexes? . One of our main ndings discloses the statistical di erence at the pars orbitalis of the connectome between sexes, which has been shown to function in language production. Moreover, we use these discriminative regions for the related learning problem Can we classify a given human connectome to belong to one of the sexes just by analyzing its connectivity structure? . We show that we can learn decision tree as well as support vector machine classi cation models for this task. We show that our models achieve up to 79% prediction accuracy with only a handful of brain regions as discriminating factors. Importantly, our resu...
Modern scientific discovery depends on collecting large heterogeneous datasets with many sources ... more Modern scientific discovery depends on collecting large heterogeneous datasets with many sources of variability, and applying domain-specific pipelines from which one can draw insight or clinical utility. For example, macroscale connectomics studies require complex pipelines to process raw functional or diffusion data and estimate connectomes. Individual studies tend to customize pipelines to their needs, raising concerns about their reproducibility, which add to a longer list of factors that may differ across studies and result in failures to replicate (including sampling, experimental design, and data acquisition protocols). Mitigating these issues requires multi-study datasets and the development of pipelines that can be applied across them. We developed NeuroData’s MRI to Graphs (NDMG) pipeline using several functional and diffusion studies, including the Consortium for Reliability and Reproducability, to estimate connectomes. Without any manual intervention or parameter tuning,...
Motivation: There is increasing interest in learning how human brain networks vary as a function ... more Motivation: There is increasing interest in learning how human brain networks vary as a function of a continuous trait, but flexible and efficient procedures to accomplish this goal are limited. We develop a Bayesian semiparametric model, which combines low-rank factorizations and flexible Gaussian process priors to learn changes in the conditional expectation of a network-valued random variable across the values of a continuous predictor, while including subject-specific random effects. Results: The formulation leads to a general framework for inference on changes in brain network structures across human traits, facilitating borrowing of information and coherently characterizing uncertainty. We provide an efficient Gibbs sampler for posterior computation along with simple procedures for inference, prediction and goodness-of-fit assessments. The model is applied to learn how human brain networks vary across individuals with different intelligence scores. Results provide interesting insights on the association between intelligence and brain connectivity, while demonstrating good predictive performance.
The brain's default network (DN) has been a topic of considerable empirical interest. In fMRI... more The brain's default network (DN) has been a topic of considerable empirical interest. In fMRI research, DN activity is associated with spontaneous and self-generated cognition, such as mind-wandering, episodic memory retrieval, future thinking, mental simulation, theory of mind reasoning, and creative cognition. Despite large literatures on developmental and disease-related influences on the DN, surprisingly little is known about the factors that impact normal variation in DN functioning. Using structural equation modeling and graph theoretical analysis of resting-state fMRI data, we provide evidence that Openness to Experience-a normally distributed personality trait reflecting a tendency to engage in imaginative, creative, and abstract cognitive processes-underlies efficiency of information processing within the DN. Across two studies, Openness predicted the global efficiency of a functional network comprised of DN nodes and corresponding edges. In Study 2, Openness remained a...
We present constrained source-based morphometry (SBM), a multivariate semiblind data-driven appro... more We present constrained source-based morphometry (SBM), a multivariate semiblind data-driven approach, to explore a possible brain-wide structural network in both gray matter (GM) and white matter (WM) associated with the functional default mode network (DMN). With this approach, we utilize seed regions associated with the DMN as constraints on GM maps and derive a joint GM and WM structural network automatically through a multivariate data-driven approach. In this article, we first provide a simulation to validate the constrained SBM approach. The approach was then applied to structural magnetic resonance imaging and diffusion tensor imaging data obtained from 102 healthy controls. Regions that have consistently reported to be associated with the DMN were used to create an a priori mask that was integrated within an independent component analysis framework to derive the structural network associated with the DMN. We identified a set of GM and corresponding WM regions contributing to...
Analyses of gray matter concentration (GMC) deficits in patients with schizophrenia (Sz) have ide... more Analyses of gray matter concentration (GMC) deficits in patients with schizophrenia (Sz) have identified robust changes throughout the cortex. We assessed the relationships between diagnosis, overall symptom severity, and patterns of gray matter in the largest aggregated structural imaging dataset to date. We performed both source-based morphometry (SBM) and voxel-based morphometry (VBM) analyses on GMC images from 784 Sz and 936 controls (Ct) across 23 scanning sites in Europe and the United States. After correcting for age, gender, site, and diagnosis by site interactions, SBM analyses showed 9 patterns of diagnostic differences. They comprised separate cortical, subcortical, and cerebellar regions. Seven patterns showed greater GMC in Ct than Sz, while 2 (brainstem and cerebellum) showed greater GMC for Sz. The greatest GMC deficit was in a single pattern comprising regions in the superior temporal gyrus, inferior frontal gyrus, and medial frontal cortex, which replicated over an...
2013 IEEE Global Conference on Signal and Information Processing, 2013
Graphs are quickly emerging as a leading abstraction for the representation of data. One importan... more Graphs are quickly emerging as a leading abstraction for the representation of data. One important application domain originates from an emerging discipline called "connectomics". Connectomics studies the brain as a graph; vertices correspond to neurons (or collections thereof) and edges correspond to structural or functional connections between them. To explore the variability of connectomes-to address both basic science questions regarding the structure of the brain, and medical health questions about psychiatry and neurology-one can study the topological properties of these brain-graphs. We define multivariate glocal graph invariants: these are features of the graph that capture various local and global topological properties of the graphs. We show that the collection of features can collectively be computed via a combination of daisy-chaining, sparse matrix representation and computations, and efficient approximations. Our custom open-source Python package serves as a back-end to a Web-service that we have created to enable researchers to upload graphs, and download the corresponding invariants in a number of different formats. Moreover, we built this package to support distributed processing on multicore machines. This is therefore an enabling technology for network science, lowering the barrier of entry by providing tools to biologists and analysts who otherwise lack these capabilities. As a demonstration, we run our code on 120 brain-graphs, each with approximately 16M vertices and up to 90M edges.
To further understanding of basic and complex cognitive functions, previous connectome research h... more To further understanding of basic and complex cognitive functions, previous connectome research has identified functional and structural connections of the human brain. Functional connectivity is often measured by using resting-state functional magnetic resonance imaging (rs-fMRI) and is generally interpreted as an indirect measure of neuronal activity. Gray matter (GM) primarily consists of neuronal and glia cell bodies; therefore, it is surprising that the majority of connectome research has excluded GM measures. Therefore, we propose that by exploring where GM corresponds to function would aid in the understanding of both structural and functional connectivity and in turn the human connectome. A cohort of 603 healthy participants underwent structural and functional scanning on the same 3 T scanner at the Mind Research Network. To investigate the spatial correspondence between structure and function, spatial independent component analysis (ICA) was applied separately to both GM density (GMD) maps and to rs-fMRI data. ICA of GM delineates structural components based on the covariation of GMD regions among subjects. For the rs-fMRI data, ICA identified spatial patterns with common temporal features. These decomposed structural and functional components were then compared by spatial correlation. Basal ganglia components exhibited the highest structural to resting-state functional spatial correlation (r = 0.59). Cortical components generally show correspondence between a single structural component and several resting-state functional components. We also studied relationships between the weights of different structural components and identified the precuneus as a hub in GMD structural network correlations. In addition, we analyzed relationships between component weights, age, and gender; concluding that age has a significant effect on structural components.
A strategy for using tissue water as a concentration standard in 1 H magnetic resonance spectrosc... more A strategy for using tissue water as a concentration standard in 1 H magnetic resonance spectroscopic imaging studies on the brain is presented, and the potential errors that may arise when the method is used are examined. The sensitivity of the method to errors in estimates of the different water compartment relaxation times is shown to be small at short echo times (TEs). Using data from healthy human subjects, it is shown that different image segmentation approaches that are commonly used to account for partial volume effects (SPM2, FSL's FAST, and K-means) lead to different estimates of metabolite levels, particularly in gray matter (GM), owing primarily to variability in the estimates of the cerebrospinal fluid (CSF) fraction. While consistency does not necessarily validate a method, a multispectral segmentation approach using FAST yielded the lowest intersubject variability in the estimates of GM metabolites. The mean GM and white matter (WM) levels of N-acetyl groups (NAc, primarily N-acetylaspartate), choline (Ch), and creatine (Cr) obtained in these subjects using the described method with FAST multispectral segmentation are reported: GM [
Regional gray matter (GM) abnormalities are well known to exist in patients with chronic schizoph... more Regional gray matter (GM) abnormalities are well known to exist in patients with chronic schizophrenia. Voxel-based morphometry (VBM) has been previously used on structural magnetic resonance images (MRI) data to characterize these abnormalities. Two multisite schizophrenia studies, the Functional Biomedical Informatics Research Network and the Mind Clinical Imaging Consortium, which include 9 data collection sites, are evaluating the efficacy of pooling structural imaging data across imaging centers. Such a pooling of data could yield the increased statistical power needed to elucidateeffects that may notbe seen with smaller samples. VBM analyses were performed to evaluate the consistency of patient versus control gray matter concentration (GMC) differences across the study sites, as well as the effects of combining multisite data. Integration of data from both studies yielded a large sample of 503 subjects, including 266 controls and 237 patients diagnosed with schizophrenia, schizoaffective or schizophreniform disorder. The data were analyzed using the combined sample, as well as analyzing each of the 2 multisite studies separately. A consistent pattern of reduced relative GMC in schizophrenia patients compared with controls was found across all study sites. Imaging center-specific effects were evaluated using a region of interest analysis. Overall, the findings support the use of VBM in combined multisite studies. This analysis of schizophrenics and controls from around the United States provides continued supporting evidence for GM deficits in the temporal lobes, anterior cingulate, and frontal regions in patients with schizophrenia spectrum disorders.
Primary brain tumours often occur near eloquent regions, affecting language, motor and memory cap... more Primary brain tumours often occur near eloquent regions, affecting language, motor and memory capacity, with awake mapping and tailored resection designed to preserve higher cognitive functioning. The effects of such tumours on subcortical structures, including the thalamus and basal ganglia, have been largely unexplored, in spite of the known importance of such structures to higher cognitive functioning. We sought to explore the effects of volume changes of subcortical structures on cognition, in 62 consecutive patients diagnosed with primary brain tumour and cavernous malformations, referred to our neurosurgical practice. We found right caudate to be highly predictive of intelligence, left pallidum of total neuropsychological function and right hippocampus of mood. Our study is the largest of its kind in exploring subcortical substrates of higher cognition in consecutive patients with brain tumours. This research supports prior literature, showing subcortical structures to be rela...
Resting-state functional magnetic resonance imaging (rsfMRI) is a promising task-free functional ... more Resting-state functional magnetic resonance imaging (rsfMRI) is a promising task-free functional imaging approach, which may complement or replace task-based fMRI (tfMRI) in patients who have difficulties performing required tasks. However, rsfMRI is highly sensitive to head movement and physiological noise, and validation relative to tfMRI and intraoperative electrocortical mapping is still necessary. In this study, we investigate (a) the feasibility of real-time rsfMRI for presurgical mapping of eloquent networks with monitoring of data quality in patients with brain tumors and (b) rsfMRI localization of eloquent cortex compared with tfMRI and intraoperative electrocortical stimulation (ECS) in retrospective analysis. Five brain tumor patients were studied with rsfMRI and tfMRI on a clinical 3T scanner using MultiBand(8)-echo planar imaging (EPI) with repetition time: 400 ms. Moving-averaged sliding-window correlation analysis with regression of motion parameters and signals from white matter and cerebrospinal fluid was used to map sensorimotor and language resting-state networks. Data quality monitoring enabled rapid optimization of scan protocols, early identification of task noncompliance, and head movement-related false-positive connectivity to determine scan continuation or repetition. Sensorimotor and language resting-state networks were identifiable within 1 min of scan time. The Euclidean distance between ECS and rsfMRI connectivity and task-activation in motor cortex, Broca's, and Wernicke's areas was 5-10 mm, with the exception of discordant rsfMRI and ECS localization of Wernicke's area in one patient due to possible cortical reorganization and/or altered neurovascular coupling. This study demonstrates the potential of real-time high-speed rsfMRI for presurgical mapping of eloquent cortex with realtime data quality control, and clinically acceptable concordance of rsfMRI with tfMRI and ECS localization. K E Y W O R D S electrocortical mapping, functional MRI, high-speed fMRI, presurgical mapping, real-time fMRI, resting-state fMRI, task-based fMRI Kishore Vakamudi and Stefan Posse are considered as joint first authors.
We present constrained source-based morphometry (SBM), a multivariate semiblind data-driven appro... more We present constrained source-based morphometry (SBM), a multivariate semiblind data-driven approach, to explore a possible brain-wide structural network in both gray matter (GM) and white matter (WM) associated with the functional default mode network (DMN). With this approach, we utilize seed regions associated with the DMN as constraints on GM maps and derive a joint GM and WM structural network automatically through a multivariate data-driven approach. In this article, we first provide a simulation to validate the constrained SBM approach. The approach was then applied to structural magnetic resonance imaging and diffusion tensor imaging data obtained from 102 healthy controls. Regions that have consistently reported to be associated with the DMN were used to create an a priori mask that was integrated within an independent component analysis framework to derive the structural network associated with the DMN. We identified a set of GM and corresponding WM regions contributing to a structural network underlying the functional DMN. The GM regions consisted mainly of the precuneus, superior and medial frontal gyri, middle temporal gyrus, hippocampus, cuneus, and cerebellum. The WM regions included the cingulum, corpus callosum, corona radiata, association fibers, and middle cerebellar peduncle. Significant gender differences in the relationship between intelligence quotient (IQ) and the identified structural network were observed. Our findings suggest that the functional DMN is underpinned by a corresponding brain-wide structural network. The constrained SBM approach is additionally applicable to a wide variety of problems identifying structural networks from seed regions.
Connectomics—the study of brain networks—provides a unique and valuable opportunity to study the ... more Connectomics—the study of brain networks—provides a unique and valuable opportunity to study the brain. However, research in human connectomics, accomplished via Magnetic Resonance Imaging (MRI), is a resource-intensive practice: typical analysis routines require impactful decision making and significant computational capabilities. Mitigating these issues requires the development of low-resource, easy to use, and flexible pipelines which can be applied across data with variable collection parameters. In response to these challenges, we have developed the MRI to Graphs (m2g) pipeline. m2g leverages functional and diffusion datasets to estimate connectomes reliably. To illustrate, m2g was used to process MRI data from 35 different studies (≈6,000 scans) from 15 sites without any manual intervention or parameter tuning. Every single scan yielded an estimated connectome that followed established properties, such as stronger ipsilateral than contralateral connections in structural connec...
Introduction Connectomics, the study of brain maps or graphs, is an emerging discipline of neuros... more Introduction Connectomics, the study of brain maps or graphs, is an emerging discipline of neuroscience, enabling new neurobiology questions to be asked and answered [1]. A hotly debated question relates to partitioning the brain into cortical regions, which are based on gross anatomical structures. The extent to which the brain connections are related to the classical partition remains largely unknown. Brain graphs generated by combining MRI data with that by different sensing modalities may consist of half million or more voxel-vertices, but only a fraction of which are labeled to 70 cortical regions [2]. Classifying or labeling the rest of the vertices is computationally challenging by the shear number of the voxels as well as by the high number of regions or classes. We present a fast and reliable approach for voxel-to-region classification based on a methodology by Sussman et al that offers theoretical assurance of asymptotic optimality in statistical sense [3].
Journal of the Royal Statistical Society: Series C (Applied Statistics)
This article focuses on the problem of predicting a response variable based on a network-valued p... more This article focuses on the problem of predicting a response variable based on a network-valued predictor. Our motivation is the development of interpretable and accurate predictive models for cognitive traits and neuro-psychiatric disorders based on an individual's brain connection network (connectome). Current methods reduce the complex, high-dimensional brain network into low-dimensional pre-specified features prior to applying standard predictive algorithms. Such methods are sensitive to feature choice and inevitably discard information. We instead propose a nonparametric Bayes class of models that utilize information from the entire adjacency matrix defining connections among brain regions in adaptively detecting predictive algorithms, while maintaining interpretability. The Bayesian Connectomics (BaCon) model class utilizes Poisson-Dirichlet processes to find a lower-dimensional, bidirectional (covariate, subject) pattern in the adjacency matrix. The small n, large p problem is transformed into a "small n, small q" problem, facilitating an effective stochastic search of the predictors. A spike-and-slab prior for the cluster predictors strikes a balance between regression model parsimony and flexibility, resulting in improved inferences and test case predictions. We describe basic properties of the BaCon model and develop efficient algorithms for posterior computation. The resulting methods are shown to outperform existing approaches and applied to a creative reasoning data set.
The human brain and the neuronal networks comprising it are of immense interest to the scienti c ... more The human brain and the neuronal networks comprising it are of immense interest to the scienti c community. In this work, we focus on the structural connectivity of human brains, investigating sex di erences across male and female connectomes (brain-graphs) for the knowledge discovery problem Which brain regions exert di erences in connectivity across the two sexes? . One of our main ndings discloses the statistical di erence at the pars orbitalis of the connectome between sexes, which has been shown to function in language production. Moreover, we use these discriminative regions for the related learning problem Can we classify a given human connectome to belong to one of the sexes just by analyzing its connectivity structure? . We show that we can learn decision tree as well as support vector machine classi cation models for this task. We show that our models achieve up to 79% prediction accuracy with only a handful of brain regions as discriminating factors. Importantly, our resu...
Modern scientific discovery depends on collecting large heterogeneous datasets with many sources ... more Modern scientific discovery depends on collecting large heterogeneous datasets with many sources of variability, and applying domain-specific pipelines from which one can draw insight or clinical utility. For example, macroscale connectomics studies require complex pipelines to process raw functional or diffusion data and estimate connectomes. Individual studies tend to customize pipelines to their needs, raising concerns about their reproducibility, which add to a longer list of factors that may differ across studies and result in failures to replicate (including sampling, experimental design, and data acquisition protocols). Mitigating these issues requires multi-study datasets and the development of pipelines that can be applied across them. We developed NeuroData’s MRI to Graphs (NDMG) pipeline using several functional and diffusion studies, including the Consortium for Reliability and Reproducability, to estimate connectomes. Without any manual intervention or parameter tuning,...
Motivation: There is increasing interest in learning how human brain networks vary as a function ... more Motivation: There is increasing interest in learning how human brain networks vary as a function of a continuous trait, but flexible and efficient procedures to accomplish this goal are limited. We develop a Bayesian semiparametric model, which combines low-rank factorizations and flexible Gaussian process priors to learn changes in the conditional expectation of a network-valued random variable across the values of a continuous predictor, while including subject-specific random effects. Results: The formulation leads to a general framework for inference on changes in brain network structures across human traits, facilitating borrowing of information and coherently characterizing uncertainty. We provide an efficient Gibbs sampler for posterior computation along with simple procedures for inference, prediction and goodness-of-fit assessments. The model is applied to learn how human brain networks vary across individuals with different intelligence scores. Results provide interesting insights on the association between intelligence and brain connectivity, while demonstrating good predictive performance.
The brain's default network (DN) has been a topic of considerable empirical interest. In fMRI... more The brain's default network (DN) has been a topic of considerable empirical interest. In fMRI research, DN activity is associated with spontaneous and self-generated cognition, such as mind-wandering, episodic memory retrieval, future thinking, mental simulation, theory of mind reasoning, and creative cognition. Despite large literatures on developmental and disease-related influences on the DN, surprisingly little is known about the factors that impact normal variation in DN functioning. Using structural equation modeling and graph theoretical analysis of resting-state fMRI data, we provide evidence that Openness to Experience-a normally distributed personality trait reflecting a tendency to engage in imaginative, creative, and abstract cognitive processes-underlies efficiency of information processing within the DN. Across two studies, Openness predicted the global efficiency of a functional network comprised of DN nodes and corresponding edges. In Study 2, Openness remained a...
We present constrained source-based morphometry (SBM), a multivariate semiblind data-driven appro... more We present constrained source-based morphometry (SBM), a multivariate semiblind data-driven approach, to explore a possible brain-wide structural network in both gray matter (GM) and white matter (WM) associated with the functional default mode network (DMN). With this approach, we utilize seed regions associated with the DMN as constraints on GM maps and derive a joint GM and WM structural network automatically through a multivariate data-driven approach. In this article, we first provide a simulation to validate the constrained SBM approach. The approach was then applied to structural magnetic resonance imaging and diffusion tensor imaging data obtained from 102 healthy controls. Regions that have consistently reported to be associated with the DMN were used to create an a priori mask that was integrated within an independent component analysis framework to derive the structural network associated with the DMN. We identified a set of GM and corresponding WM regions contributing to...
Analyses of gray matter concentration (GMC) deficits in patients with schizophrenia (Sz) have ide... more Analyses of gray matter concentration (GMC) deficits in patients with schizophrenia (Sz) have identified robust changes throughout the cortex. We assessed the relationships between diagnosis, overall symptom severity, and patterns of gray matter in the largest aggregated structural imaging dataset to date. We performed both source-based morphometry (SBM) and voxel-based morphometry (VBM) analyses on GMC images from 784 Sz and 936 controls (Ct) across 23 scanning sites in Europe and the United States. After correcting for age, gender, site, and diagnosis by site interactions, SBM analyses showed 9 patterns of diagnostic differences. They comprised separate cortical, subcortical, and cerebellar regions. Seven patterns showed greater GMC in Ct than Sz, while 2 (brainstem and cerebellum) showed greater GMC for Sz. The greatest GMC deficit was in a single pattern comprising regions in the superior temporal gyrus, inferior frontal gyrus, and medial frontal cortex, which replicated over an...
2013 IEEE Global Conference on Signal and Information Processing, 2013
Graphs are quickly emerging as a leading abstraction for the representation of data. One importan... more Graphs are quickly emerging as a leading abstraction for the representation of data. One important application domain originates from an emerging discipline called "connectomics". Connectomics studies the brain as a graph; vertices correspond to neurons (or collections thereof) and edges correspond to structural or functional connections between them. To explore the variability of connectomes-to address both basic science questions regarding the structure of the brain, and medical health questions about psychiatry and neurology-one can study the topological properties of these brain-graphs. We define multivariate glocal graph invariants: these are features of the graph that capture various local and global topological properties of the graphs. We show that the collection of features can collectively be computed via a combination of daisy-chaining, sparse matrix representation and computations, and efficient approximations. Our custom open-source Python package serves as a back-end to a Web-service that we have created to enable researchers to upload graphs, and download the corresponding invariants in a number of different formats. Moreover, we built this package to support distributed processing on multicore machines. This is therefore an enabling technology for network science, lowering the barrier of entry by providing tools to biologists and analysts who otherwise lack these capabilities. As a demonstration, we run our code on 120 brain-graphs, each with approximately 16M vertices and up to 90M edges.
To further understanding of basic and complex cognitive functions, previous connectome research h... more To further understanding of basic and complex cognitive functions, previous connectome research has identified functional and structural connections of the human brain. Functional connectivity is often measured by using resting-state functional magnetic resonance imaging (rs-fMRI) and is generally interpreted as an indirect measure of neuronal activity. Gray matter (GM) primarily consists of neuronal and glia cell bodies; therefore, it is surprising that the majority of connectome research has excluded GM measures. Therefore, we propose that by exploring where GM corresponds to function would aid in the understanding of both structural and functional connectivity and in turn the human connectome. A cohort of 603 healthy participants underwent structural and functional scanning on the same 3 T scanner at the Mind Research Network. To investigate the spatial correspondence between structure and function, spatial independent component analysis (ICA) was applied separately to both GM density (GMD) maps and to rs-fMRI data. ICA of GM delineates structural components based on the covariation of GMD regions among subjects. For the rs-fMRI data, ICA identified spatial patterns with common temporal features. These decomposed structural and functional components were then compared by spatial correlation. Basal ganglia components exhibited the highest structural to resting-state functional spatial correlation (r = 0.59). Cortical components generally show correspondence between a single structural component and several resting-state functional components. We also studied relationships between the weights of different structural components and identified the precuneus as a hub in GMD structural network correlations. In addition, we analyzed relationships between component weights, age, and gender; concluding that age has a significant effect on structural components.
A strategy for using tissue water as a concentration standard in 1 H magnetic resonance spectrosc... more A strategy for using tissue water as a concentration standard in 1 H magnetic resonance spectroscopic imaging studies on the brain is presented, and the potential errors that may arise when the method is used are examined. The sensitivity of the method to errors in estimates of the different water compartment relaxation times is shown to be small at short echo times (TEs). Using data from healthy human subjects, it is shown that different image segmentation approaches that are commonly used to account for partial volume effects (SPM2, FSL's FAST, and K-means) lead to different estimates of metabolite levels, particularly in gray matter (GM), owing primarily to variability in the estimates of the cerebrospinal fluid (CSF) fraction. While consistency does not necessarily validate a method, a multispectral segmentation approach using FAST yielded the lowest intersubject variability in the estimates of GM metabolites. The mean GM and white matter (WM) levels of N-acetyl groups (NAc, primarily N-acetylaspartate), choline (Ch), and creatine (Cr) obtained in these subjects using the described method with FAST multispectral segmentation are reported: GM [
Regional gray matter (GM) abnormalities are well known to exist in patients with chronic schizoph... more Regional gray matter (GM) abnormalities are well known to exist in patients with chronic schizophrenia. Voxel-based morphometry (VBM) has been previously used on structural magnetic resonance images (MRI) data to characterize these abnormalities. Two multisite schizophrenia studies, the Functional Biomedical Informatics Research Network and the Mind Clinical Imaging Consortium, which include 9 data collection sites, are evaluating the efficacy of pooling structural imaging data across imaging centers. Such a pooling of data could yield the increased statistical power needed to elucidateeffects that may notbe seen with smaller samples. VBM analyses were performed to evaluate the consistency of patient versus control gray matter concentration (GMC) differences across the study sites, as well as the effects of combining multisite data. Integration of data from both studies yielded a large sample of 503 subjects, including 266 controls and 237 patients diagnosed with schizophrenia, schizoaffective or schizophreniform disorder. The data were analyzed using the combined sample, as well as analyzing each of the 2 multisite studies separately. A consistent pattern of reduced relative GMC in schizophrenia patients compared with controls was found across all study sites. Imaging center-specific effects were evaluated using a region of interest analysis. Overall, the findings support the use of VBM in combined multisite studies. This analysis of schizophrenics and controls from around the United States provides continued supporting evidence for GM deficits in the temporal lobes, anterior cingulate, and frontal regions in patients with schizophrenia spectrum disorders.
Primary brain tumours often occur near eloquent regions, affecting language, motor and memory cap... more Primary brain tumours often occur near eloquent regions, affecting language, motor and memory capacity, with awake mapping and tailored resection designed to preserve higher cognitive functioning. The effects of such tumours on subcortical structures, including the thalamus and basal ganglia, have been largely unexplored, in spite of the known importance of such structures to higher cognitive functioning. We sought to explore the effects of volume changes of subcortical structures on cognition, in 62 consecutive patients diagnosed with primary brain tumour and cavernous malformations, referred to our neurosurgical practice. We found right caudate to be highly predictive of intelligence, left pallidum of total neuropsychological function and right hippocampus of mood. Our study is the largest of its kind in exploring subcortical substrates of higher cognition in consecutive patients with brain tumours. This research supports prior literature, showing subcortical structures to be rela...
Resting-state functional magnetic resonance imaging (rsfMRI) is a promising task-free functional ... more Resting-state functional magnetic resonance imaging (rsfMRI) is a promising task-free functional imaging approach, which may complement or replace task-based fMRI (tfMRI) in patients who have difficulties performing required tasks. However, rsfMRI is highly sensitive to head movement and physiological noise, and validation relative to tfMRI and intraoperative electrocortical mapping is still necessary. In this study, we investigate (a) the feasibility of real-time rsfMRI for presurgical mapping of eloquent networks with monitoring of data quality in patients with brain tumors and (b) rsfMRI localization of eloquent cortex compared with tfMRI and intraoperative electrocortical stimulation (ECS) in retrospective analysis. Five brain tumor patients were studied with rsfMRI and tfMRI on a clinical 3T scanner using MultiBand(8)-echo planar imaging (EPI) with repetition time: 400 ms. Moving-averaged sliding-window correlation analysis with regression of motion parameters and signals from white matter and cerebrospinal fluid was used to map sensorimotor and language resting-state networks. Data quality monitoring enabled rapid optimization of scan protocols, early identification of task noncompliance, and head movement-related false-positive connectivity to determine scan continuation or repetition. Sensorimotor and language resting-state networks were identifiable within 1 min of scan time. The Euclidean distance between ECS and rsfMRI connectivity and task-activation in motor cortex, Broca's, and Wernicke's areas was 5-10 mm, with the exception of discordant rsfMRI and ECS localization of Wernicke's area in one patient due to possible cortical reorganization and/or altered neurovascular coupling. This study demonstrates the potential of real-time high-speed rsfMRI for presurgical mapping of eloquent cortex with realtime data quality control, and clinically acceptable concordance of rsfMRI with tfMRI and ECS localization. K E Y W O R D S electrocortical mapping, functional MRI, high-speed fMRI, presurgical mapping, real-time fMRI, resting-state fMRI, task-based fMRI Kishore Vakamudi and Stefan Posse are considered as joint first authors.
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Papers by Rex Jung