  Diverse cognitive processes set different demands on locally segregated and
globally integrated brain activity. However, it remains unclear how resting
brains configure their functional organization to balance the demands on
network segregation and integration to best serve cognition. 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. 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. This state allows for a balanced time dwelling at segregated
and integrated configurations, and highly flexible switching between them.
Furthermore, we employ structural equation modelling 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. More specifically,
stronger integration is associated with better general cognitive ability,
stronger segregation fosters crystallized intelligence and processing speed,
and individual's tendency towards balance supports better memory. 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.
