Cognitive abilities are associated with rapid dynamics of electrophysiological connectome states

Suhnyoung Jun, Steve Malone, Thomas H. Alderson, Jeremy B Harper, Ruskin H Hunt, Kathleen M. Thomas, Sylia Wilson, William G Iacono, Sepideh Sadaghiani

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Time-varying changes in whole-brain connectivity patterns, or connectome state dynamics, hold significant implications for cognition. However, connectome dynamics at fast (>1 Hz) timescales highly relevant to cognition are poorly understood due to the dominance of inherently slow fMRI in connectome studies. Here, we investigated the behavioral significance of rapid electrophysiological connectome dynamics using source-localized EEG connectomes during resting state (N = 926, 473 females). We focused on dynamic connectome features pertinent to individual differences, specifically those with established heritability: Fractional Occupancy (i.e., the overall duration spent in each recurrent connectome state) in beta and gamma bands and Transition Probability (i.e., the frequency of state switches) in theta, alpha, beta, and gamma bands. Canonical correlation analysis found a significant relationship between the heritable phenotypes of subsecond connectome dynamics and cognition. Specifically, principal components of Transition Probabilities in alpha (followed by theta and gamma bands) and a cognitive factor representing visuospatial processing (followed by verbal and auditory working memory) most notably contributed to the relationship. We conclude that rapid connectome state transitions shape individuals’ cognitive abilities and traits. Such subsecond connectome dynamics may inform about behavioral function and dysfunction and serve as endophenotypes for cognitive abilities.

Original languageEnglish (US)
Pages (from-to)1089-1104
Number of pages16
JournalNetwork Neuroscience
Volume8
Issue number4
DOIs
StatePublished - Dec 10 2024

Bibliographical note

Publisher Copyright:
© 2024 Massachusetts Institute of Technology. Published under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.

Keywords

  • Canonical correlation analysis
  • Cognition
  • Dynamic functional connectivity
  • Electrophysiology
  • Hidden Markov modeling
  • Individual differences

PubMed: MeSH publication types

  • Journal Article

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