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https://doi.org/10.1038/s41467-019-10317-7
DC Field | Value | |
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dc.title | Resting brain dynamics at different timescales capture distinct aspects of human behavior | |
dc.contributor.author | Liégeois, R. | |
dc.contributor.author | Li, J. | |
dc.contributor.author | Kong, R. | |
dc.contributor.author | Orban, C. | |
dc.contributor.author | Van De Ville, D. | |
dc.contributor.author | Ge, T. | |
dc.contributor.author | Sabuncu, M.R. | |
dc.contributor.author | Yeo, B.T.T. | |
dc.date.accessioned | 2021-12-06T04:20:07Z | |
dc.date.available | 2021-12-06T04:20:07Z | |
dc.date.issued | 2019 | |
dc.identifier.citation | Liégeois, R., Li, J., Kong, R., Orban, C., Van De Ville, D., Ge, T., Sabuncu, M.R., Yeo, B.T.T. (2019). Resting brain dynamics at different timescales capture distinct aspects of human behavior. Nature Communications 10 (1) : 2317. ScholarBank@NUS Repository. https://doi.org/10.1038/s41467-019-10317-7 | |
dc.identifier.issn | 2041-1723 | |
dc.identifier.uri | https://scholarbank.nus.edu.sg/handle/10635/209514 | |
dc.description.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. © 2019, The Author(s). | |
dc.publisher | Nature Publishing Group | |
dc.rights | Attribution 4.0 International | |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | |
dc.source | Scopus OA2019 | |
dc.type | Article | |
dc.contributor.department | LIFE SCIENCES INSTITUTE | |
dc.contributor.department | ELECTRICAL AND COMPUTER ENGINEERING | |
dc.description.doi | 10.1038/s41467-019-10317-7 | |
dc.description.sourcetitle | Nature Communications | |
dc.description.volume | 10 | |
dc.description.issue | 1 | |
dc.description.page | 2317 | |
Appears in Collections: | Staff Publications Elements |
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