![]() ![]() ![]() Resting brains are thus supposed to balance the segregation and integration ( 17, 20), so as to satisfy competing cognitive demands. Furthermore, previous studies suggest that healthy resting brains operate near a critical state to render the capability of rapidly exploring and switching in the brain’s state space with large operating repertoires ( 3, 13, 17– 19). Since diverse cognitive tasks differently demand on segregation and integration ( 3, 5, 7– 12), the brain’s functional organization at rest is expected to possess the intrinsic capability of supporting diverse cognitive processes. Emerging evidence suggests that smaller differences between functional patterns at rest versus task states can facilitate better cognitive performance ( 13– 16). Independently from specific task demands, the brain’s functional organization at rest can mirror relevant task-induced activity patterns and thus predict task performance ( 13, 14). Our findings provide a comprehensive and deep understanding of the brain’s functioning principles in supporting diverse functional demands and cognitive abilities and advance modern network neuroscience theories of human cognition. More specifically, stronger integration is associated with better general cognitive ability, stronger segregation fosters crystallized intelligence and processing speed, and an individual’s tendency toward balance supports better memory. Furthermore, we employ structural equation modeling to estimate general and domain-specific cognitive phenotypes from nine tasks and demonstrate that network segregation, integration, and their balance in resting brains predict individual differences in diverse cognitive phenotypes. This state allows for a balanced time dwelling at segregated and integrated configurations and highly flexible switching between them. In a large sample of healthy young adults ( n = 991), we combine the whole-brain resting state functional magnetic resonance imaging (fMRI) data with a mean-filed model on the structural network derived from diffusion tensor imaging and demonstrate that resting brain networks are on average close to a balanced state. Here we use an eigenmode-based approach to identify hierarchical modules in functional brain networks and quantify the functional balance between network segregation and integration. However, it remains an open question how resting brains configure their functional organization to balance the demands on network segregation and integration to best serve cognition. Diverse cognitive processes set different demands on locally segregated and globally integrated brain activity. ![]()
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