Segregation, integration, and balance of large-scale resting brain networks configure different cognitive abilities

Rong Wang, Mianxin Liu, Xinhong Cheng, Ying Wu, Andrea Hildebrandt*, Changsong Zhou*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)

Abstract

Diverse cognitive processes set different demands on locally segregated and globally integrated brain activity. 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. 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 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. 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. 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.

Original languageEnglish
Article numbere2022288118
Number of pages11
JournalProceedings of the National Academy of Sciences of the United States of America
Volume118
Issue number23
Early online date31 May 2021
DOIs
Publication statusPublished - 8 Jun 2021

Scopus Subject Areas

  • General

User-Defined Keywords

  • Functional brain network
  • Hierarchical modules
  • Human cognition
  • Segregation-integration balance
  • Structural equation modeling

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