TY - JOUR
T1 - Trait-like variants in human functional brain networks
AU - Seitzman, Benjamin A.
AU - Gratton, Caterina
AU - Laumann, Timothy O.
AU - Gordon, Evan M.
AU - Adeyemo, Babatunde
AU - Dworetsky, Ally
AU - Kraus, Brian T.
AU - Gilmore, Adrian W.
AU - Berg, Jeffrey J.
AU - Ortega, Mario
AU - Nguyen, Annie
AU - Greene, Deanna J.
AU - McDermott, Kathleen B.
AU - Nelson, Steven M.
AU - Lessov-Schlaggar, Christina N.
AU - Schlaggar, Bradley L.
AU - Dosenbach, Nico U.F.
AU - Petersen, Steven E.
N1 - Funding Information:
We thank Joshua S. Siegel and Deanna M. Barch for assistance with the Human Connectome Project data. This research was supported by NIH Grant T32NS073547 (to B.A.S.), NIH Grant F32NS092290 (to C.G.), NIH Grant R01MH118370 (to C.G.), NIH Grant R25MH112473 (to T.O.L.), NIH Grant T32NS047987 (to B.T.K.), National Science Foundation Graduate Research Fellowship Program Award DGE-1143954 (to A.W.G.), an American Psychological Association Dissertation Research Award (to A.W.G.), NIH Grant K01MH104592 (to D.J.G.), a Dart Neuroscience, LLC Grant (to K.B.M.), a McDonnell Foundation Collaborative Activity Award (to S.E.P.), NIH Grant R01NS32979 (to S.E.P.), NIH Grant R01NS06424 (to S.E.P.), and Career Development Award 1IK2CX001680 (to E.M.G.) from the US Department of Veterans Affairs Clinical Sciences Research and Development Service. The contents of this manuscript do not represent the views of the US Department of Veterans Affairs or the United States Government.
Funding Information:
ACKNOWLEDGMENTS. We thank Joshua S. Siegel and Deanna M. Barch for assistance with the Human Connectome Project data. This research was supported by NIH Grant T32NS073547 (to B.A.S.), NIH Grant F32NS092290 (to C.G.), NIH Grant R01MH118370 (to C.G.), NIH Grant R25MH112473 (to T.O.L.), NIH Grant T32NS047987 (to B.T.K.), National Science Foundation Graduate Research Fellowship Program Award DGE-1143954 (to A.W.G.), an American Psychological Association Dissertation Research Award (to A.W.G.), NIH Grant K01MH104592 (to D.J.G.), a Dart Neuroscience, LLC Grant (to K.B.M.), a McDonnell Foundation Collaborative Activity Award (to S.E.P.), NIH Grant R01NS32979 (to S.E.P.), NIH Grant R01NS06424 (to S.E.P.), and Career Development Award 1IK2CX001680 (to E.M.G.) from the US Department of Veterans Affairs Clinical Sciences Research and Development Service. The contents of this manuscript do not represent the views of the US Department of Veterans Affairs or the United States Government.
Publisher Copyright:
© 2019 National Academy of Sciences. All rights reserved.
PY - 2019
Y1 - 2019
N2 - 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.
AB - 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.
KW - Functional connectivity
KW - Individual differences
KW - Networks
KW - Resting-state
UR - http://www.scopus.com/inward/record.url?scp=85074448194&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85074448194&partnerID=8YFLogxK
U2 - 10.1073/pnas.1902932116
DO - 10.1073/pnas.1902932116
M3 - Article
C2 - 31611415
AN - SCOPUS:85074448194
SN - 0027-8424
VL - 116
SP - 22851
EP - 22861
JO - Proceedings of the National Academy of Sciences of the United States of America
JF - Proceedings of the National Academy of Sciences of the United States of America
IS - 45
ER -