Rapid dynamics of electrophysiological connectome states are heritable

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

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

Time-varying changes in whole-brain connectivity patterns, or connectome state dynamics, are a prominent feature of brain activity with broad functional implications. While infraslow (<0.1 Hz) connectome dynamics have been extensively studied with fMRI, rapid dynamics highly relevant for cognition are poorly understood. Here, we asked whether rapid electrophysiological connectome dynamics constitute subject-specific brain traits and to what extent they are under genetic influence. Using source-localized EEG connectomes during resting state (N = 928, 473 females), we quantified the heritability of multivariate (multistate) features describing temporal or spatial characteristics of connectome dynamics. States switched rapidly every ∼60–500 ms. Temporal features were heritable, particularly Fractional Occupancy (in theta, alpha, beta, and gamma bands) and Transition Probability (in theta, alpha, and gamma bands), representing the duration spent in each state and the frequency of state switches, respectively. Genetic effects explained a substantial proportion of the phenotypic variance of these features: Fractional Occupancy in beta (44.3%) and gamma (39.8%) bands and Transition Probability in theta (38.4%), alpha (63.3%), beta (22.6%), and gamma (40%) bands. However, we found no evidence for the heritability of dynamic spatial features, specifically states’ Modularity and connectivity pattern. We conclude that genetic effects shape individuals’ connectome dynamics at rapid timescales, specifically states’ overall occurrence and sequencing.

Original languageEnglish (US)
Pages (from-to)1065-1088
Number of pages24
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

  • Dynamic functional connectivity
  • Electrophysiology
  • Heritability
  • Hidden Markov modeling
  • Twin study
  • Variance component modeling

PubMed: MeSH publication types

  • Journal Article

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