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Resting brain dynamics at different timescales capture distinct aspects of human behavior
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...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6534566/ https://www.ncbi.nlm.nih.gov/pubmed/31127095 http://dx.doi.org/10.1038/s41467-019-10317-7 |
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author | Liégeois, Raphaël Li, Jingwei Kong, Ru Orban, Csaba Van De Ville, Dimitri Ge, Tian Sabuncu, Mert R. Yeo, B. T. Thomas |
author_facet | Liégeois, Raphaël Li, Jingwei Kong, Ru Orban, Csaba Van De Ville, Dimitri Ge, Tian Sabuncu, Mert R. Yeo, B. T. Thomas |
author_sort | Liégeois, Raphaël |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-6534566 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-65345662019-05-28 Resting brain dynamics at different timescales capture distinct aspects of human behavior Liégeois, Raphaël Li, Jingwei Kong, Ru Orban, Csaba Van De Ville, Dimitri Ge, Tian Sabuncu, Mert R. Yeo, B. T. Thomas Nat Commun Article 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. Nature Publishing Group UK 2019-05-24 /pmc/articles/PMC6534566/ /pubmed/31127095 http://dx.doi.org/10.1038/s41467-019-10317-7 Text en © The Author(s) 2019 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Liégeois, Raphaël Li, Jingwei Kong, Ru Orban, Csaba Van De Ville, Dimitri Ge, Tian Sabuncu, Mert R. Yeo, B. T. Thomas Resting brain dynamics at different timescales capture distinct aspects of human behavior |
title | Resting brain dynamics at different timescales capture distinct aspects of human behavior |
title_full | Resting brain dynamics at different timescales capture distinct aspects of human behavior |
title_fullStr | Resting brain dynamics at different timescales capture distinct aspects of human behavior |
title_full_unstemmed | Resting brain dynamics at different timescales capture distinct aspects of human behavior |
title_short | Resting brain dynamics at different timescales capture distinct aspects of human behavior |
title_sort | resting brain dynamics at different timescales capture distinct aspects of human behavior |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6534566/ https://www.ncbi.nlm.nih.gov/pubmed/31127095 http://dx.doi.org/10.1038/s41467-019-10317-7 |
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