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.
Highlights
Linking human behavior to resting-state brain function is a central question in systems neuroscience
Subject-specific static functional connectivity (FC) markers were computed by averaging correlation matrices of functional magnetic resonance imaging (fMRI) time series across runs
Exploring how resting-state functional organization is linked to various behavioral traits is a central neuroimaging research question
Summary
Linking human behavior to resting-state brain function is a central question in systems neuroscience. The advent of large neuroimaging and behavioral datasets has allowed the further exploration of the FC behavioral counterparts, showing intricate contributions of cognitive, emotional, social, and demographic aspects[14] All these studies use static measures of FC, which reflect the average functional organization of entire neuroimaging recordings typically running over several minutes. The comparison of static and dynamic measures of FC has only been proposed in specific applications such as temporal lobe epilepsy[21] or the description of eating behaviors[22], and a comprehensive analysis exploring which types of behavioral features emerge from functional interactions at different timescales is missing We explore this question using a discovery dataset (N = 419). This model has been extensively used in genome-wide complex trait analyses and was recently applied to study the neuroanatomical signatures of traits such as cognitive or clinical measures[29]
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