Rapid dynamics of electrophysiological connectome states are heritable

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 cognitio...

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Published inHarvard 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
LanguageEnglish
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
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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 ( = 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.
AbstractList 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.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.
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.
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 (Author Summary: 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.
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.
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.
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. 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.
Author Wilson, Sylia
Hunt, Ruskin H.
Sadaghiani, Sepideh
Thomas, Kathleen M.
Iacono, William G.
Malone, Stephen M.
Harper, Jeremy
Jun, Suhnyoung
Alderson, Thomas H.
AuthorAffiliation Neuroscience Program, University of Illinois Urbana-Champaign, Champaign, IL, USA
Department of Psychology, University of Minnesota Twin Cities, Minneapolis, MN, USA
Department of Psychology, University of Illinois Urbana-Champaign, Champaign, IL, USA
Institute of Child Development, University of Minnesota Twin Cities, Minneapolis, MN, USA
Beckman Institute for Advanced Science and Technology, University of Illinois Urbana-Champaign, Champaign, IL, USA
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Issue 4
Keywords Electrophysiology
Dynamic functional connectivity
Heritability
Hidden Markov modeling
Twin study
Variance component modeling
Language English
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SubjectTerms 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
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Title Rapid dynamics of electrophysiological connectome states are heritable
URI https://direct.mit.edu/netn/article/doi/10.1162/netn_a_00391
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