Functional Brain Networks Are Dominated by Stable Group and Individual Factors, Not Cognitive or Daily Variation

Caterina Gratton, Timothy O. Laumann, Ashley N. Nielsen, Deanna J. Greene, Evan M. Gordon, Adrian W. Gilmore, Steven M. Nelson, Rebecca S. Coalson, Abraham Z. Snyder, Bradley L. Schlaggar, Nico U.F. Dosenbach, Steven E. Petersen

Research output: Contribution to journalArticlepeer-review

327 Scopus citations

Abstract

The organization of human brain networks can be measured by capturing correlated brain activity with fMRI. There is considerable interest in understanding how brain networks vary across individuals or neuropsychiatric populations or are altered during the performance of specific behaviors. However, the plausibility and validity of such measurements is dependent on the extent to which functional networks are stable over time or are state dependent. We analyzed data from nine high-quality, highly sampled individuals to parse the magnitude and anatomical distribution of network variability across subjects, sessions, and tasks. Critically, we find that functional networks are dominated by common organizational principles and stable individual features, with substantially more modest contributions from task-state and day-to-day variability. Sources of variation were differentially distributed across the brain and differentially linked to intrinsic and task-evoked sources. We conclude that functional networks are suited to measuring stable individual characteristics, suggesting utility in personalized medicine. Gratton et al. comprehensively measure individual, day-to-day, and task variance in functional brain networks, revealing that networks are dominated by stable individual factors, not cognitive content. These findings suggest utility of functional network measurements in personalized medicine.

Original languageEnglish (US)
Pages (from-to)439-452.e5
JournalNeuron
Volume98
Issue number2
DOIs
StatePublished - Apr 18 2018
Externally publishedYes

Bibliographical note

Funding Information:
This work is supported by NIH grants F32NS092290 (C.G.), NS088590 and TR000448 (N.U.F.D.), MH100872 (T.O.L.), MH104592 (D.J.G.), 1P30NS098577 (Neuroimaging Informatics and Analysis Center), and HD087011 (Intellectual and Developmental Disabilities Research Center at Washington University); Jacobs Foundation grant 2016121703 (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 (N.U.F.D.); the Hope Center for Neurological Disorders (N.U.F.D., B.L.S., S.E.P.); an American Psychological Association dissertation research award (A.W.G.); and Dart Neuroscience LLC . 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 U.S. government.

Funding Information:
This work is supported by NIH grants F32NS092290 (C.G.), NS088590 and TR000448 (N.U.F.D.), MH100872 (T.O.L.), MH104592 (D.J.G.), 1P30NS098577 (Neuroimaging Informatics and Analysis Center), and HD087011 (Intellectual and Developmental Disabilities Research Center at Washington University); Jacobs Foundation grant 2016121703 (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 (N.U.F.D.); the Hope Center for Neurological Disorders (N.U.F.D., B.L.S., S.E.P.); an American Psychological Association dissertation research award (A.W.G.); and Dart Neuroscience LLC. 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 U.S. government.

Publisher Copyright:
© 2018 Elsevier Inc.

Keywords

  • brain networks
  • fMRI
  • functional connectivity
  • individual differences

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