Individual-specific features of brain systems identified with resting state functional correlations

Evan M. Gordon, Timothy O. Laumann, Babatunde Adeyemo, Adrian W. Gilmore, Steven M. Nelson, Nico U.F. Dosenbach, Steven E. Petersen

Research output: Contribution to journalArticlepeer-review

Abstract

Recent work has made important advances in describing the large-scale systems-level organization of human cortex by analyzing functional magnetic resonance imaging (fMRI) data averaged across groups of subjects. However, new findings have emerged suggesting that individuals’ cortical systems are topologically complex, containing small but reliable features that cannot be observed in group-averaged datasets, due in part to variability in the position of such features along the cortical sheet. This previous work has reported only specific examples of these individual-specific system features; to date, such features have not been comprehensively described. Here we used fMRI to identify cortical system features in individual subjects within three large cross-subject datasets and one highly sampled within-subject dataset. We observed system features that have not been previously characterized, but 1) were reliably detected across many scanning sessions within a single individual, and 2) could be matched across many individuals. In total, we identified forty-three system features that did not match group-average systems, but that replicated across three independent datasets. We described the size and spatial distribution of each non-group feature. We further observed that some individuals were missing specific system features, suggesting individual differences in the system membership of cortical regions. Finally, we found that individual-specific system features could be used to increase subject-to-subject similarity. Together, this work identifies individual-specific features of human brain systems, thus providing a catalog of previously unobserved brain system features and laying the foundation for detailed examinations of brain connectivity in individuals.

Original languageEnglish (US)
Pages (from-to)918-939
Number of pages22
JournalNeuroImage
Volume146
DOIs
StatePublished - Feb 1 2017
Externally publishedYes

Bibliographical note

Funding Information:
This work was supported by the National Institutes of Health (Grant nos. NS061144 and NS046424 to S.E.P.; MH100872 to T.O.L; NS088590 to N.U.F.D.; and MH091657 to David Van Essen); the McDonnell Foundation (Collaborative Action Award to S.E.P.); the Simons Foundation (Award 95177 to S.E.P.); the Mallinckrodt Institute of Radiology (Pilot Grant to N.U.F.D.); the Child Neurology Foundation (Scientific Research Award to N.U.F.D.); and the National Science Foundation Graduate Research Fellowship Program (Grant no. DGE-1143954 to A.W.G.). We would further like to acknowledge Bill Kelley and Jeremy Huckins for providing the Dartmouth Dataset; Matt Glasser for contributions to the procedures to generate cortical surfaces and map data to the surface; and Russell Poldrack for providing examples of high-quality single-subject data that motivated this investigation.

Publisher Copyright:
© 2016 Elsevier Inc.

Keywords

  • Brain systems
  • fMRI
  • Functional connectivity
  • Individual variability

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