Static and dynamic functional connectome reveals reconfiguration profiles of whole-brain network across cognitive states

Heming Zhang, Chun Meng, Xin Di, Xiao Wu, Bharat Biswal

Research output: Contribution to journalArticlepeer-review

Abstract

Assessment of functional connectivity (FC) has revealed a great deal of knowledge about the macroscale spatiotemporal organization of the brain network. Recent studies found task-versus-rest network reconfigurations were crucial for cognitive functioning. However, brain network reconfiguration remains unclear among different cognitive states, considering both aggregate and time-resolved FC profiles. The current study utilized static FC (sFC, i.e., long timescale aggregate FC) and sliding window–based dynamic FC (dFC, i.e., short timescale time-varying FC) approaches to investigate the similarity and alterations of edge weights and network topology at different cognitive loads, particularly their relationships with specific cognitive process. Both dFC/sFC networks showed subtle but significant reconfigurations that correlated with task performance. At higher cognitive load, brain network reconfiguration displayed increased functional integration in the sFC-based aggregate network, but faster and larger variability of modular reorganization in the dFC-based time-varying network, suggesting difficult tasks require more integrated and flexible network reconfigurations. Moreover, sFC-based network reconfigurations mainly linked with the sensorimotor and low-order cognitive processes, but dFC-based network reconfigurations mainly linked with the highorder cognitive process. Our findings suggest that reconfiguration profiles of sFC/dFC networks provide specific information about cognitive functioning, which could potentially be used to study brain function and disorders.

Original languageEnglish (US)
Pages (from-to)1034-1050
Number of pages17
JournalNetwork Neuroscience
Volume7
Issue number3
DOIs
StatePublished - 2023

All Science Journal Classification (ASJC) codes

  • General Neuroscience
  • Computer Science Applications
  • Artificial Intelligence
  • Applied Mathematics

Keywords

  • Cognitive process
  • Drift diffusion model
  • Dynamic functional connectivity
  • Network reconfiguration
  • Static functional connectivity

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