AN EDGE-CENTRIC PERSPECTIVE FOR BRAIN NETWORK COMMUNITIES

Thesis (Ph.D.) - Indiana University, Department of Psychological and Brain Sciences and Program in Neuroscience, 2021The brain is a complex system organized on multiple scales and operating in both a local and distributed manner. Individual neurons and brain regions participate in specific functions, while at the same time existing in the context of a larger network, supporting a range of different functionalities. Building brain networks comprised of distinct neural elements (nodes) and their interrelationships (edges), allows us to model the brain from both local and global perspectives, and to deploy a wide array of computational network tools. A popular network analysis approach is community detection, which aims to subdivide a network’s nodes into clusters that can used to represent and evaluate network organization. Prevailing community detection approaches applied to brain networks are designed to find densely interconnected sets of nodes, leading to the notion that the brain is organized in an exclusively modular manner. Furthermore, many brain network analyses tend to focus on the nodes, evidenced by the search for modular groupings of neural elements that might serve a common function. In this thesis, we describe the application of community detection algorithms that are sensitive to alternative cluster configurations, enhancing our understanding of brain network organization. We apply a framework called the stochastic block model, which we use to uncover evidence of non-modular organization in human anatomical brain networks across the life span, and in the informatically-collated rat cerebral cortex. We also propose a framework to cluster functional brain network edges in human data, which naturally results in an overlapping organization at the level of nodes that bridges canonical functional systems. These alternative methods utilize the connection patterns of brain network edges in ways that prevailing approaches do not. Thus, we motivate an alternative outlook which focuses on the importance of information provided by the brain’s interconnections, or edges. We call this an edge-centric perspective. The edge-centric approaches developed here offer new ways to characterize distributed brain organization and contribute to a fundamental change in perspective in our thinking about the brain

Diverging volumetric trajectories following pediatric traumatic brain injury.

Traumatic brain injury (TBI) is a significant public health concern, and can be especially disruptive in children, derailing on-going neuronal maturation in periods critical for cognitive development. There is considerable heterogeneity in post-injury outcomes, only partially explained by injury severity. Understanding the time course of recovery, and what factors may delay or promote recovery, will aid clinicians in decision-making and provide avenues for future mechanism-based therapeutics. We examined regional changes in brain volume in a pediatric/adolescent moderate-severe TBI (msTBI) cohort, assessed at two time points. Children were first assessed 2-5 months post-injury, and again 12 months later. We used tensor-based morphometry (TBM) to localize longitudinal volume expansion and reduction. We studied 21 msTBI patients (5 F, 8-18 years old) and 26 well-matched healthy control children, also assessed twice over the same interval. In a prior paper, we identified a subgroup of msTBI patients, based on interhemispheric transfer time (IHTT), with significant structural disruption of the white matter (WM) at 2-5 months post injury. We investigated how this subgroup (TBI-slow, N = 11) differed in longitudinal regional volume changes from msTBI patients (TBI-normal, N = 10) with normal WM structure and function. The TBI-slow group had longitudinal decreases in brain volume in several WM clusters, including the corpus callosum and hypothalamus, while the TBI-normal group showed increased volume in WM areas. Our results show prolonged atrophy of the WM over the first 18 months post-injury in the TBI-slow group. The TBI-normal group shows a different pattern that could indicate a return to a healthy trajectory

The reliability and heritability of cortical folds and their genetic correlations across hemispheres

Cortical folds help drive the parcellation of the human cortex into functionally specific regions. Variations in the length, depth, width, and surface area of these sulcal landmarks have been associated with disease, and may be genetically mediated. Before estimating the heritability of sulcal variation, the extent to which these metrics can be reliably extracted from in-vivo MRI must be established. Using four independent test-retest datasets, we found high reliability across the brain (intraclass correlation interquartile range: 0.65–0.85). Heritability estimates were derived for three family-based cohorts using variance components analysis and pooled (total N \u3e 3000); the overall sulcal heritability pattern was correlated to that derived for a large population cohort (N \u3e 9000) calculated using genomic complex trait analysis. Overall, sulcal width was the most heritable metric, and earlier forming sulci showed higher heritability. The inter-hemispheric genetic correlations were high, yet select sulci showed incomplete pleiotropy, suggesting hemisphere-specific genetic influences

Reply to: New Meta- and Mega-analyses of Magnetic Resonance Imaging Findings in Schizophrenia: Do They Really Increase Our Knowledge About the Nature of the Disease Process?

This work was supported by National Institute of Biomedical Imaging and Bioengineering Grant No. U54EB020403 (to the ENIGMA consortium)

brainlife.io: A decentralized and open source cloud platform to support neuroscience research

Neuroscience research has expanded dramatically over the past 30 years by advancing standardization and tool development to support rigor and transparency. Consequently, the complexity of the data pipeline has also increased, hindering access to FAIR data analysis to portions of the worldwide research community. brainlife.io was developed to reduce these burdens and democratize modern neuroscience research across institutions and career levels. Using community software and hardware infrastructure, the platform provides open-source data standardization, management, visualization, and processing and simplifies the data pipeline. brainlife.io automatically tracks the provenance history of thousands of data objects, supporting simplicity, efficiency, and transparency in neuroscience research. Here brainlife.io's technology and data services are described and evaluated for validity, reliability, reproducibility, replicability, and scientific utility. Using data from 4 modalities and 3,200 participants, we demonstrate that brainlife.io's services produce outputs that adhere to best practices in modern neuroscience research

Smaller limbic structures are associated with greater immunosuppression in over 1000 HIV-infected adults across five continents: Findings from the ENIGMA-HIV Working Group

Background: Human immunodeficiency virus type-1 (HIV) infection can be controlled with combination antiretroviral therapy (cART), but neurocognitive impairment remains common even in chronic and treated HIV-infected (HIV+) cohorts. Identifying the neuroanatomical pathways associated with infection has the potential to delineate novel neuropathological processes underlying persisting deficits, yet individual neuroimaging studies have yielded inconsistent findings. The ENIGMA-HIV Working Group was established to harmonize data from diverse studies to identify the common effects of HIV-infection on brain structure. Methods: Data were pooled from 12 independent neuroHIV studies from Africa, Asia, Australia, Europe, and North America. Volume estimates for eight subcortical brain regions were extracted from T1-weighted MRI from 1,044 HIV+ adults (aged 22-81 years; 72.4% on cART; 70.3% male; 41.6% with detectable viral load (dVL)), to identify associations with plasma markers reflecting current immunosuppression (CD4+ T-cell count) or dVL. Follow-up analyses stratified data by cART status and sex. Bonferroni correction was used to determine statistical significance. Findings: LowercurrentCD4+ count was associated with smaller hippocampal (β= 20.3 mm3 per 100 cells/mm3; p = 0.0001) and thalamic volumes (β= 29.3; p = 0.003); in the subset of participants not on cART, it was associated with smaller putamen volumes (β= 65.1; p = 0.0009). On average, a dVL was associated with smaller hippocampal (Cohen’s d = 0.24; p = 0.0003) and amygdala volumes (d = 0.18; p = 0.0058).Interpretation: In HIV+ individuals across five continents, smaller limbic volumes were consistently associated with current plasma markers. As we assessed cohorts with different inclusion/exclusion criteria and demographic distributions, these deficits may represent a generalizable brain-signature of HIV infection in the cART era. Our findings support the importance of achieving viral suppression and immune restoration for maintaining brain health. Funding: This work was supported, in part, by NIH grant U54 EB020403

Association of Immunosuppression and Viral Load With Subcortical Brain Volume in an International Sample of People Living With HIV

International audienceIMPORTANCE Despite more widely accessible combination antiretroviral therapy (cART), HIV-1 infection remains a global public health challenge. Even in treated patients with chronic HIV infection, neurocognitive impairment often persists, affecting quality of life. Identifying the neuroanatomical pathways associated with infection in vivo may delineate the neuropathologic processes underlying these deficits. However, published neuroimaging findings from relatively small, heterogeneous cohorts are inconsistent, limiting the generalizability of the conclusions drawnto date.OBJECTIVE To examine structural brain associations with the most commonly collected clinicalassessments of HIV burden (CD4+T-cell count and viral load), which are generalizable acrossdemographically and clinically diverse HIV-infected individuals worldwide.DESIGN, SETTING, AND PARTICIPANTS This cross-sectional study established the HIV WorkingGroup within the Enhancing Neuro Imaging Genetics Through Meta Analysis (ENIGMA) consortiumto pool and harmonize data from existing HIV neuroimaging studies. In total, data from 1295HIV-positive adults were contributed from 13 studies across Africa, Asia, Australia, Europe, and NorthAmerica. Regional and whole brain segmentations were extracted from data sets as contributingstudies joined the consortium on a rolling basis from November 1, 2014, to December 31, 2019.MAIN OUTCOMES AND MEASURES Volume estimates for 8 subcortical brain regions wereextracted from T1-weighted magnetic resonance images to identify associations with blood plasmamarkers of current immunosuppression (CD4+T-cell counts) or detectable plasma viral load (dVL) inHIV-positive participants. Post hoc sensitivity analyses stratified data by cART status.RESULTS After quality assurance, data from 1203 HIV-positive individuals (mean [SD] age, 45.7 [11.5]years; 880 [73.2%] male; 897 [74.6%] taking cART) remained. Lower current CD4+cell counts wereassociated with smaller hippocampal (mean [SE] β = 16.66 [4.72] mm3per 100 cells/mm3;P< .001)and thalamic (mean [SE] β = 32.24 [8.96] mm3per 100 cells/mm3;P< .001) volumes and largerventricles (mean [SE] β = −391.50 [122.58] mm3per 100 cells/mm3;P= .001); in participants nottaking cART, however, lowercurrent CD4+cell counts were associated with smaller putamen volumes(mean [SE] β = 57.34 [18.78] mm3per 100 cells/mm3;P= .003). A dVL was associated with smallerhippocampal volumes (d= −0.17;P= .005); in participants taking cART, dVL was also associated withsmaller amygdala volumes (d= −0.23;P= .004

Cortical brain abnormalities in 4474 individuals with schizophrenia and 5098 control subjects via the enhancing neuro Imaging genetics through meta analysis (ENIGMA) Consortium

BACKGROUND: The profile of cortical neuroanatomical abnormalities in schizophrenia is not fully understood, despite hundreds of published structural brain imaging studies. This study presents the first meta-analysis of cortical thickness and surface area abnormalities in schizophrenia conducted by the ENIGMA (Enhancing Neuro Imaging Genetics through Meta Analysis) Schizophrenia Working Group. METHODS: The study included data from 4474 individuals with schizophrenia (mean age, 32.3 years; range, 11-78 years; 66% male) and 5098 healthy volunteers (mean age, 32.8 years; range, 10-87 years; 53% male) assessed with standardized methods at 39 centers worldwide. RESULTS: Compared with healthy volunteers, individuals with schizophrenia have widespread thinner cortex (left/right hemisphere: Cohen's d = -0.530/-0.516) and smaller surface area (left/right hemisphere: Cohen's d = -0.251/-0.254), with the largest effect sizes for both in frontal and temporal lobe regions. Regional group differences in cortical thickness remained significant when statistically controlling for global cortical thickness, suggesting regional specificity. In contrast, effects for cortical surface area appear global. Case-control, negative, cortical thickness effect sizes were two to three times larger in individuals receiving antipsychotic medication relative to unmedicated individuals. Negative correlations between age and bilateral temporal pole thickness were stronger in individuals with schizophrenia than in healthy volunteers. Regional cortical thickness showed significant negative correlations with normalized medication dose, symptom severity, and duration of illness and positive correlations with age at onset. CONCLUSIONS: The findings indicate that the ENIGMA meta-analysis approach can achieve robust findings in clinical neuroscience studies; also, medication effects should be taken into account in future genetic association studies of cortical thickness in schizophrenia

multiAtlasTT_data

Multi-Atlas Transfer Tools for Neuroimaging (maTT)<br><br>This figshare repository contains the data used for the maTT project. The <b>atlas_data</b> folder here contains additional .gcs files that can be used for the maTT2 functionality. When running maTT2, the data here should be organized into each respective atlas folder (here: <i>https://github.com/faskowit/multiAtlasTT/tree/master/atlas_data</i>). When this tar.gz is unzipped in the multiAtlasTT base directory, it should automatically unzip into this structure.<br><br>Please check out the accompanying Github repository for details about using maTT and maTT2. <br><br>These gcs files were created after labeling the Mindboggle 101 individual subjects data with the maTT label transfer scripts, and then creating a GCS with these individual subject annotation files. Note: only light quality control was performed on the FreeSurfer outputs. Unlike the Mindboggle project, the annotation files were not carefully checked.<div><br></div><div>See also app-multiAtlasTT (<i>https://github.com/faskowit/app-multiAtlasTT</i>) for the brainlife.io version of this tool, which will automatically download the info you need from this data repo.</div><div><br>_{Any opinion, findings, and conclusions or recommendations expressed in this material are those of the authors(s) and do not necessarily reflect the views of the National Science Foundation.}<br><br><br></div