Abstract
Advances in neuroimaging have made it possible to reconstruct functional networks from the activity patterns of brain regions distributed across the cerebral cortex. Recent work has shown that flexible reconfiguration of human brain networks over short timescales supports cognitive flexibility and learning. However, modulating network flexibility to enhance learning requires an understanding of an as-yet unknown relationship between flexibility and brain state. Here, we investigate the relationship between network flexibility and affect, leveraging an unprecedented longitudinal data set. We demonstrate that indices associated with positive mood and surprise are both associated with network flexibility – positive mood portends a more flexible brain while increased levels of surprise portend a less flexible brain. In both cases, these relationships are driven predominantly by a subset of brain regions comprising the somatomotor system. Our results simultaneously suggest a network-level mechanism underlying learning deficits in mood disorders as well as a potential target – altering an individual’s mood or task novelty – to improve learning.
Highlights
Advances in neuroimaging have made it possible to reconstruct functional networks from the activity patterns of brain regions distributed across the cerebral cortex
One means of characterizing dynamic functional connectivity (FC) networks is by their community structure[12,13,14], which refers to decompositions of a network into densely-interconnected sub-networks or “communities”[15, 16]
To address the hypothesis that network flexibility is associated with mood, we analyzed data collected as part of the MyConnectome Project, which includes extensive longitudinal neuroimaging and behavioral data from a single participant acquired over a period of approximately one year[34, 35]
Summary
Advances in neuroimaging have made it possible to reconstruct functional networks from the activity patterns of brain regions distributed across the cerebral cortex. The community structure of dynamic networks, tracks the ongoing formation and dissolution of communities over time[20, 21] Such a measure makes it possible to identify the brain’s temporal core and periphery of flexible brain regions that tend to change their community assignment over time versus inflexible ones that maintain a consistent assignment[22]. Network flexibility has been shown to correlate with both learning rate and cognitive flexibility[12, 23] These abilities are not static but can vary considerably over time and as a function of an individual’s affective state. Learning often shows an “inverted-U” relationship with arousal, with optimal learning at moderate levels of arousal[24] Together, these findings imply that the influence of affective state on learning and cognition may involve modulations of brain network flexibility. A second potential driver of fluctuations www.nature.com/scientificreports/
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