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Nat Commun. 2019 May 24;10(1):2317. doi: 10.1038/s41467-019-10317-7.

Resting brain dynamics at different timescales capture distinct aspects of human behavior.

Author information

1
Department of Electrical and Computer Engineering, Clinical Imaging Research Centre, N.1 Institute for Health and Memory Networks Program, National University of Singapore, Singapore, 117583, Singapore. Raphael.Liegeois@epfl.ch.
2
Institute of Bioengineering, Centre for Neuroprosthetics, École Polytechnique Fédérale de Lausanne, 1015, Lausanne, Switzerland. Raphael.Liegeois@epfl.ch.
3
Department of Radiology and Medical Informatics, University of Geneva, 1205, Geneva, Switzerland. Raphael.Liegeois@epfl.ch.
4
Department of Electrical and Computer Engineering, Clinical Imaging Research Centre, N.1 Institute for Health and Memory Networks Program, National University of Singapore, Singapore, 117583, Singapore.
5
Institute of Bioengineering, Centre for Neuroprosthetics, École Polytechnique Fédérale de Lausanne, 1015, Lausanne, Switzerland.
6
Department of Radiology and Medical Informatics, University of Geneva, 1205, Geneva, Switzerland.
7
Psychiatric & Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, 02114, USA.
8
Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, 02129, USA.
9
School of Electrical and Computer Engineering, Cornell University, Ithaca, NY, 14853, USA.
10
Department of Electrical and Computer Engineering, Clinical Imaging Research Centre, N.1 Institute for Health and Memory Networks Program, National University of Singapore, Singapore, 117583, Singapore. Thomas.Yeo@nus.edu.sg.
11
Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, 02129, USA. Thomas.Yeo@nus.edu.sg.
12
Centre for Cognitive Neuroscience, Duke-NUS Medical School, Singapore, 169857, Singapore. Thomas.Yeo@nus.edu.sg.
13
NUS Graduate School for Integrative Sciences and Engineering, National University of Singapore, Singapore, 119077, Singapore. Thomas.Yeo@nus.edu.sg.

Abstract

Linking human behavior to resting-state brain function is a central question in systems neuroscience. In particular, the functional timescales at which different types of behavioral factors are encoded remain largely unexplored. The behavioral counterparts of static functional connectivity (FC), at the resolution of several minutes, have been studied but behavioral correlates of dynamic measures of FC at the resolution of a few seconds remain unclear. Here, using resting-state fMRI and 58 phenotypic measures from the Human Connectome Project, we find that dynamic FC captures task-based phenotypes (e.g., processing speed or fluid intelligence scores), whereas self-reported measures (e.g., loneliness or life satisfaction) are equally well explained by static and dynamic FC. Furthermore, behaviorally relevant dynamic FC emerges from the interconnections across all resting-state networks, rather than within or between pairs of networks. Our findings shed new light on the timescales of cognitive processes involved in distinct facets of behavior.

PMID:
31127095
PMCID:
PMC6534566
DOI:
10.1038/s41467-019-10317-7
[Indexed for MEDLINE]
Free PMC Article

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