Trait-like variants in human functional brain networks

Benjamin A. Seitzman; Caterina Gratton; Timothy O. Laumann; Evan M. Gordon; Babatunde Adeyemo; Ally Dworetsky; Brian T. Kraus; Adrian W. Gilmore; Jeffrey J. Berg; Mario Ortega; Annie Nguyen; Deanna J. Greene; Kathleen B. McDermott; Steven M. Nelson; Christina N. Lessov-Schlaggar; Bradley L. Schlaggar; Nico U.F. Dosenbach; Steven E. Petersen

doi:10.1073/pnas.1902932116

Trait-like variants in human functional brain networks

Benjamin A. Seitzman, Caterina Gratton, Timothy O. Laumann, Evan M. Gordon, Babatunde Adeyemo, Ally Dworetsky, Brian T. Kraus, Adrian W. Gilmore, Jeffrey J. Berg, Mario Ortega, Annie Nguyen, Deanna J. Greene, Kathleen B. McDermott, Steven M. Nelson, Christina N. Lessov-Schlaggar, Bradley L. Schlaggar, Nico U.F. Dosenbach, Steven E. Petersen

Research output: Contribution to journal › Article › peer-review

109 Scopus citations

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.

Original language	English (US)
Pages (from-to)	22851-22861
Number of pages	11
Journal	Proceedings of the National Academy of Sciences of the United States of America
Volume	116
Issue number	45
DOIs	https://doi.org/10.1073/pnas.1902932116
State	Published - 2019
Externally published	Yes

Bibliographical note

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.

Keywords

Functional connectivity
Individual differences
Networks
Resting-state

Access

10.1073/pnas.1902932116

OpenUrl availability

Full text

Cite this

Seitzman, B. A., Gratton, C., Laumann, T. O., Gordon, E. M., Adeyemo, B., Dworetsky, A., Kraus, B. T., Gilmore, A. W., Berg, J. J., Ortega, M., Nguyen, A., Greene, D. J., McDermott, K. B., Nelson, S. M., Lessov-Schlaggar, C. N., Schlaggar, B. L., Dosenbach, N. U. F., & Petersen, S. E. (2019). Trait-like variants in human functional brain networks. Proceedings of the National Academy of Sciences of the United States of America, 116(45), 22851-22861. https://doi.org/10.1073/pnas.1902932116

Seitzman, BA, Gratton, C, Laumann, TO, Gordon, EM, Adeyemo, B, Dworetsky, A, Kraus, BT, Gilmore, AW, Berg, JJ, Ortega, M, Nguyen, A, Greene, DJ, McDermott, KB, Nelson, SM, Lessov-Schlaggar, CN, Schlaggar, BL, Dosenbach, NUF & Petersen, SE 2019, 'Trait-like variants in human functional brain networks', Proceedings of the National Academy of Sciences of the United States of America, vol. 116, no. 45, pp. 22851-22861. https://doi.org/10.1073/pnas.1902932116

@article{fe3ba50d5c4c4f8db4ff4fd610b6b169,

title = "Trait-like variants in human functional brain networks",

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.",

keywords = "Functional connectivity, Individual differences, Networks, Resting-state",

author = "Seitzman, {Benjamin A.} and Caterina Gratton and Laumann, {Timothy O.} and Gordon, {Evan M.} and Babatunde Adeyemo and Ally Dworetsky and Kraus, {Brian T.} and Gilmore, {Adrian W.} and Berg, {Jeffrey J.} and Mario Ortega and Annie Nguyen and Greene, {Deanna J.} and McDermott, {Kathleen B.} and Nelson, {Steven M.} and Lessov-Schlaggar, {Christina N.} and Schlaggar, {Bradley L.} and Dosenbach, {Nico U.F.} and Petersen, {Steven E.}",

note = "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: {\textcopyright} 2019 National Academy of Sciences. All rights reserved.",

year = "2019",

doi = "10.1073/pnas.1902932116",

language = "English (US)",

volume = "116",

pages = "22851--22861",

journal = "Proceedings of the National Academy of Sciences of the United States of America",

issn = "0027-8424",

publisher = "National Academy of Sciences",

number = "45",

}

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 -

Trait-like variants in human functional brain networks

Abstract

Bibliographical note

Keywords

Access

OpenUrl availability

Other files and links

Fingerprint

Cite this