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Resting brain dynamics at different timescales capture distinct aspects of human behavior
by
Van De Ville, Dimitri
, Kong, Ru
, Sabuncu, Mert R.
, Liégeois, Raphaël
, Li, Jingwei
, Orban, Csaba
, Yeo, B. T. Thomas
, Ge, Tian
in
59/36
/ 631/378
/ 631/378/116
/ 631/378/2649
/ 631/477/2811
/ Adult
/ Behavior
/ Behavior - physiology
/ Brain
/ Brain - diagnostic imaging
/ Brain - physiology
/ Brain mapping
/ Coding
/ Cognition - physiology
/ Cognitive ability
/ Connectome - methods
/ Correlation analysis
/ Functional magnetic resonance imaging
/ Healthy Volunteers
/ Human behavior
/ Humanities and Social Sciences
/ Humans
/ Image Processing, Computer-Assisted
/ Intelligence
/ Life satisfaction
/ Magnetic Resonance Imaging - methods
/ Models, Neurological
/ multidisciplinary
/ Nerve Net - physiology
/ Nervous system
/ Neural networks
/ Phenotypes
/ Rest - physiology
/ Science
/ Science (multidisciplinary)
/ Time Factors
/ Young Adult
2019
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Resting brain dynamics at different timescales capture distinct aspects of human behavior
by
Van De Ville, Dimitri
, Kong, Ru
, Sabuncu, Mert R.
, Liégeois, Raphaël
, Li, Jingwei
, Orban, Csaba
, Yeo, B. T. Thomas
, Ge, Tian
in
59/36
/ 631/378
/ 631/378/116
/ 631/378/2649
/ 631/477/2811
/ Adult
/ Behavior
/ Behavior - physiology
/ Brain
/ Brain - diagnostic imaging
/ Brain - physiology
/ Brain mapping
/ Coding
/ Cognition - physiology
/ Cognitive ability
/ Connectome - methods
/ Correlation analysis
/ Functional magnetic resonance imaging
/ Healthy Volunteers
/ Human behavior
/ Humanities and Social Sciences
/ Humans
/ Image Processing, Computer-Assisted
/ Intelligence
/ Life satisfaction
/ Magnetic Resonance Imaging - methods
/ Models, Neurological
/ multidisciplinary
/ Nerve Net - physiology
/ Nervous system
/ Neural networks
/ Phenotypes
/ Rest - physiology
/ Science
/ Science (multidisciplinary)
/ Time Factors
/ Young Adult
2019
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Resting brain dynamics at different timescales capture distinct aspects of human behavior
by
Van De Ville, Dimitri
, Kong, Ru
, Sabuncu, Mert R.
, Liégeois, Raphaël
, Li, Jingwei
, Orban, Csaba
, Yeo, B. T. Thomas
, Ge, Tian
in
59/36
/ 631/378
/ 631/378/116
/ 631/378/2649
/ 631/477/2811
/ Adult
/ Behavior
/ Behavior - physiology
/ Brain
/ Brain - diagnostic imaging
/ Brain - physiology
/ Brain mapping
/ Coding
/ Cognition - physiology
/ Cognitive ability
/ Connectome - methods
/ Correlation analysis
/ Functional magnetic resonance imaging
/ Healthy Volunteers
/ Human behavior
/ Humanities and Social Sciences
/ Humans
/ Image Processing, Computer-Assisted
/ Intelligence
/ Life satisfaction
/ Magnetic Resonance Imaging - methods
/ Models, Neurological
/ multidisciplinary
/ Nerve Net - physiology
/ Nervous system
/ Neural networks
/ Phenotypes
/ Rest - physiology
/ Science
/ Science (multidisciplinary)
/ Time Factors
/ Young Adult
2019
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Resting brain dynamics at different timescales capture distinct aspects of human behavior
Journal Article
Resting brain dynamics at different timescales capture distinct aspects of human behavior
2019
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Overview
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.
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).
Publisher
Nature Publishing Group UK,Nature Publishing Group,Nature Portfolio
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