On characterizing population commonalities and subject variations in brain networks.

TitleOn characterizing population commonalities and subject variations in brain networks.
Publication TypeJournal Article
Year of Publication2017
AuthorsGhanbari, Y, Bloy, L, Tunc, B, Shankar, V, Roberts, TPL, J Edgar, C, Schultz, RT, Verma, R
JournalMed Image Anal
Volume38
Pagination215-229
Date Published2017 05
ISSN1361-8423
KeywordsAdolescent, Algorithms, Autism Spectrum Disorder, Brain, Brain Mapping, Child, Female, Humans, Magnetic Resonance Imaging, Male, Neural Pathways
Abstract

Brain networks based on resting state connectivity as well as inter-regional anatomical pathways obtained using diffusion imaging have provided insight into pathology and development. Such work has underscored the need for methods that can extract sub-networks that can accurately capture the connectivity patterns of the underlying population while simultaneously describing the variation of sub-networks at the subject level. We have designed a multi-layer graph clustering method that extracts clusters of nodes, called 'network hubs', which display higher levels of connectivity within the cluster than to the rest of the brain. The method determines an atlas of network hubs that describes the population, as well as weights that characterize subject-wise variation in terms of within- and between-hub connectivity. This lowers the dimensionality of brain networks, thereby providing a representation amenable to statistical analyses. The applicability of the proposed technique is demonstrated by extracting an atlas of network hubs for a population of typically developing controls (TDCs) as well as children with autism spectrum disorder (ASD), and using the structural and functional networks of a population to determine the subject-level variation of these hubs and their inter-connectivity. These hubs are then used to compare ASD and TDCs. Our method is generalizable to any population whose connectivity (structural or functional) can be captured via non-negative network graphs.

DOI10.1016/j.media.2015.10.009
Alternate JournalMed Image Anal
PubMed ID26674972
PubMed Central IDPMC4887425
Grant ListR01 MH092862 / MH / NIMH NIH HHS / United States
R21 MH098010 / MH / NIMH NIH HHS / United States
RC1 MH088791 / MH / NIMH NIH HHS / United States
U54 HD086984 / HD / NICHD NIH HHS / United States
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