Rapid dynamics of electrophysiological connectome states are heritable

• Published in: Harvard data science review Vol. 8; no. 4; pp. 1065 - 1088
• Main Authors: Jun, Suhnyoung, Alderson, Thomas H., Malone, Stephen M., Harper, Jeremy, Hunt, Ruskin H., Thomas, Kathleen M., Iacono, William G., Wilson, Sylia, Sadaghiani, Sepideh
• Format: Journal Article
• Language: English
• Published: 255 Main Street, 9th Floor, Cambridge, Massachusetts 02142, USA MIT Press 10.12.2024; MIT Press Journals, The; The MIT Press
• Subjects: Brain; Cognition & reasoning; Cognitive ability; Dynamic functional connectivity; Dynamics; Electrophysiology; Heritability; Hidden Markov modeling; Markov chains; Multivariate analysis; Neural networks; Neurosciences; Theta rhythms; Time measurement; Transition probabilities; Twin study; Twins; Variance component modeling; Electrophysiology; Dynamic functional connectivity; Heritability; Hidden Markov modeling; Twin study; Variance component modeling
• ISSN: 2472-1751; 2472-1751; 2644-2353
• DOI: 10.1162/netn_a_00391

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 ( = 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. In this study, we investigate the genetic influence on rapid electrophysiological connectome dynamics. Using hidden Markov model on source-localized EEG data at rest, we obtained measures describing temporal trajectories and time-varying spatial characteristics of connectome states. Applying two heritability assessment methods to these multivariate, time-varying connectome dynamics features, we discovered that the duration (Fractional Occupancy) and frequency of state switches (Transition Probability) were heritable, particularly in theta, alpha, beta, and gamma bands. However, no genetic influence was observed on spatial features.