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

Raphaël Liégeois; Jingwei Li; Ru Kong; Csaba Orban; Dimitri Van De Ville; Tian Ge; Mert R. Sabuncu; B. T. Thomas Yeo

doi:10.1038/s41467-019-10317-7

Nature Communications (May 2019)

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

Raphaël Liégeois,
Jingwei Li,
Ru Kong,
Csaba Orban,
Dimitri Van De Ville,
Tian Ge,
Mert R. Sabuncu,
B. T. Thomas Yeo

Affiliations

Raphaël Liégeois: Department of Electrical and Computer Engineering, Clinical Imaging Research Centre, N.1 Institute for Health and Memory Networks Program, National University of Singapore
Jingwei Li: Department of Electrical and Computer Engineering, Clinical Imaging Research Centre, N.1 Institute for Health and Memory Networks Program, National University of Singapore
Ru Kong: Department of Electrical and Computer Engineering, Clinical Imaging Research Centre, N.1 Institute for Health and Memory Networks Program, National University of Singapore
Csaba Orban: Department of Electrical and Computer Engineering, Clinical Imaging Research Centre, N.1 Institute for Health and Memory Networks Program, National University of Singapore
Dimitri Van De Ville: Institute of Bioengineering, Centre for Neuroprosthetics, École Polytechnique Fédérale de Lausanne
Tian Ge: Psychiatric & Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital
Mert R. Sabuncu: School of Electrical and Computer Engineering, Cornell University
B. T. Thomas Yeo: Department of Electrical and Computer Engineering, Clinical Imaging Research Centre, N.1 Institute for Health and Memory Networks Program, National University of Singapore

DOI: https://doi.org/10.1038/s41467-019-10317-7
Journal volume & issue: Vol. 10, no. 1
pp. 1 – 9

Abstract

Read online

An individual’s pattern of resting state brain connectivity, as measured with fMRI, has been shown to predict cognitive and behavioral traits. Here, the authors show that different traits are predicted by different time-scales of resting state activity (dynamic vs. static).

Published in Nature Communications

ISSN: 2041-1723 (Online)
Publisher: Nature Portfolio
Country of publisher: United Kingdom
LCC subjects: Science
Website: https://www.nature.com/ncomms/

About the journal