Default-mode network streams for coupling to language and control systems

Evan M. Gordon, Timothy O. Laumann, Scott Marek, Ryan V. Raut, Caterina Gratton, Dillan J. Newbold, Deanna J. Greene, Rebecca S. Coalson, Abraham Z. Snyder, Bradley L. Schlaggar, Steven E. Petersen, Nico U.F. Dosenbach, Steven M. Nelson

Research output: Contribution to journalArticlepeer-review

Abstract

The human brain is organized into large-scale networks identifiable using resting-state functional connectivity (RSFC). These functional networks correspond with broad cognitive domains; for example, the Default-mode network (DMN) is engaged during internally oriented cognition. However, functional networks may contain hierarchical substructures corresponding with more specific cognitive functions. Here, we used individual-specific precision RSFC to test whether network substructures could be identified in 10 healthy human brains. Across all subjects and networks, individualized network subdivisions were more valid-more internally homogeneous and better matching spatial patterns of task activation-than canonical networks. These measures of validity were maximized at a hierarchical scale that contained ∼83 subnetworks across the brain. At this scale, nine DMN subnetworks exhibited topographical similarity across subjects, suggesting that this approach identifies homologous neurobiological circuits across individuals. Some DMN subnetworks matched known features of brain organization corresponding with cognitive functions. Other subnetworks represented separate streams by which DMN couples with other canonical large-scale networks, including language and control networks. Together, this work provides a detailed organizational framework for studying the DMN in individual humans.

Original languageEnglish (US)
Pages (from-to)17308-17319
Number of pages12
JournalProceedings of the National Academy of Sciences of the United States of America
Volume117
Issue number29
DOIs
StatePublished - Jul 21 2020
Externally publishedYes

Bibliographical note

Funding Information:
We acknowledge Dr. Marc Raichle for his helpful comments and suggestions. This work was supported by US Department of Veterans Affairs Clinical Sciences Research and Development Service Grant 1IK2CX001680 (to E.M.G.); by NIH Grants F31NS110332 (to D.J.N.), NS088590 (to N.U.F.D.), TR000448 (to N.U.F.D.), MH1000872 (to T.O.L.), 1R25MH112473 (to T.O.L.), 5T32 MH100019-02 (to S.M.), and MH104592 (to D.J.G.), 1P30NS098577 (to the Neuroimaging Informatics and Analysis Center); the Kiwanis Neuroscience Research Foundation (N.U.F.D. and B.L.S.); Jacobs Foundation Grant 2016121703 (to N.U.F.D.); the Child Neurology Foundation (N.U.F.D.); the McDonnell Center for Systems Neuroscience (N.U.F.D. and B.L.S.); Mallinckrodt Institute of Radiology Grant 14-011 (to N.U.F.D.); the Hope Center for Neurological Disorders (N.U.F.D., B.L.S., and S.E.P.). The views expressed in this article are those of the authors and do not necessarily reflect the position or policy of the Department of Veterans Affairs or the US government.

Funding Information:
ACKNOWLEDGMENTS. We acknowledge Dr. Marc Raichle for his helpful comments and suggestions. This work was supported by US Department of Veterans Affairs Clinical Sciences Research and Development Service Grant 1IK2CX001680 (to E.M.G.); by NIH Grants F31NS110332 (to D.J.N.), NS088590 (to N.U.F.D.), TR000448 (to N.U.F.D.), MH1000872 (to T.O.L.), 1R25MH112473 (to T.O.L.), 5T32 MH100019-02 (to S.M.), and MH104592 (to D.J.G.), 1P30NS098577 (to the Neuroimaging Informatics and Analysis Center); the Kiwanis Neuroscience Research Foundation (N.U.F.D. and B.L.S.); Jacobs Foundation Grant 2016121703 (to N.U.F.D.); the Child Neurology Foundation (N.U.F.D.); the McDonnell Center for Systems Neuroscience (N.U.F.D. and B.L.S.); Mallinckrodt Institute of Radiology Grant 14-011 (to N.U.F.D.); the Hope Center for Neurological Disorders (N.U.F.D., B.L.S., and S.E.P.). The views expressed in this article are those of the authors and do not necessarily reflect the position or policy of the Department of Veterans Affairs or the US government.

Publisher Copyright:
© 2020 National Academy of Sciences. All rights reserved.

Keywords

  • Brain networks
  • Default network
  • FMRI
  • Functional connectivity
  • Individual variability

PubMed: MeSH publication types

  • Journal Article
  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't
  • Research Support, U.S. Gov't, Non-P.H.S.

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