| Literature DB >> 31611415 |
Benjamin A Seitzman1, Caterina Gratton1,2,3, Timothy O Laumann4, Evan M Gordon5,6,7, Babatunde Adeyemo4, Ally Dworetsky4, Brian T Kraus2, Adrian W Gilmore8, Jeffrey J Berg8, Mario Ortega4, Annie Nguyen4, Deanna J Greene9,10, Kathleen B McDermott8,10, Steven M Nelson5,6,7, Christina N Lessov-Schlaggar9, Bradley L Schlaggar4,9,10,11,12,13, Nico U F Dosenbach4,11,12,14,15, Steven E Petersen4,8,10,13,15.
Abstract
Resting-state functional magnetic resonance imaging (fMRI) has provided converging descriptions of group-level functional brain organization. Recent work has revealed that functional networks identified in individuals contain local features that differ from the group-level description. We define these features as network variants. Building on these studies, we ask whether distributions of network variants reflect stable, trait-like differences in brain organization. Across several datasets of highly-sampled individuals we show that 1) variants are highly stable within individuals, 2) variants are found in characteristic locations and associate with characteristic functional networks across large groups, 3) task-evoked signals in variants demonstrate a link to functional variation, and 4) individuals cluster into subgroups on the basis of variant characteristics that are related to differences in behavior. These results suggest that distributions of network variants may reflect stable, trait-like, functionally relevant individual differences in functional brain organization.Entities:
Keywords: functional connectivity; individual differences; networks; resting-state
Mesh：
Year: 2019 PMID： 31611415 PMCID： PMC6842602 DOI： 10.1073/pnas.1902932116
Source DB: PubMed Journal: Proc Natl Acad Sci U S A ISSN： 0027-8424 Impact factor: 11.205