Defining functional areas in individual human brains using resting functional connectivity MRI

Alexander L. Cohen, Damien A. Fair, Nico U.F. Dosenbach, Francis M. Miezin, Donna Dierker, David C. Van Essen, Bradley L. Schlaggar, Steven E. Petersen

Research output: Contribution to journalArticlepeer-review

415 Scopus citations

Abstract

The cerebral cortex is anatomically organized at many physical scales starting at the level of single neurons and extending up to functional systems. Current functional magnetic resonance imaging (fMRI) studies often focus at the level of areas, networks, and systems. Except in restricted domains, (e.g., topographically-organized sensory regions), it is difficult to determine area boundaries in the human brain using fMRI. The ability to delineate functional areas non-invasively would enhance the quality of many experimental analyses allowing more accurate across-subject comparisons of independently identified functional areas. Correlations in spontaneous BOLD activity, often referred to as resting state functional connectivity (rs-fcMRI), are especially promising as a way to accurately localize differences in patterns of activity across large expanses of cortex. In the current report, we applied a novel set of image analysis tools to explore the utility of rs-fcMRI for defining wide-ranging functional area boundaries. We find that rs-fcMRI patterns show sharp transitions in correlation patterns and that these putative areal boundaries can be reliably detected in individual subjects as well as in group data. Additionally, combining surface-based analysis techniques with image processing algorithms allows automated mapping of putative areal boundaries across large expanses of cortex without the need for prior information about a region's function or topography. Our approach reliably produces maps of bounded regions appropriate in size and number for putative functional areas. These findings will hopefully stimulate further methodological refinements and validations.

Original languageEnglish (US)
Pages (from-to)45-57
Number of pages13
JournalNeuroImage
Volume41
Issue number1
DOIs
StatePublished - May 15 2008
Externally publishedYes

Bibliographical note

Funding Information:
The authors thank the participants in this study, as well as Jessica A. Church and Steven M. Nelson for logistical and editing assistance and John Harwell for assistance with CARET. This work was supported in part by a NSF/IGERT Program Fellowship (Cognitive, Computational, and Systems Neuroscience Pathway) to Alexander Cohen and a Washington University Chancellor's Fellowship and UNCF/Merck Graduate Science Research Dissertation Fellowship to Damien Fair; and by NIH NSADA (B.L.S.), NS32979 (S.E.P.), NS41255 (S.E.P.), and NS46424 (S.E.P.), The McDonnell Center for Higher Brain Function (S.E.P., B.L.S.), The Burroughs Wellcome Fund (B.L.S.), and The Charles A. Dana Foundation (B.L.S.).

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