A grouped beta process model for multivariate resting-state EEG microstate analysis on twins

Research output: Contribution to journalArticlepeer-review

Abstract

EEG microstate analysis investigates the collection of distinct temporal blocks that characterize the electrical activity of the brain. Brain activity within each microstate is stable, but activity switches rapidly between different microstates in a nonrandom way. We propose a Bayesian nonparametric model that concurrently estimates the number of microstates and their underlying behaviour. We use a Markov switching vector autoregressive (VAR) framework, where a hidden Markov model (HMM) controls the nonrandom state switching dynamics of the EEG activity and a VAR model defines the behaviour of all time points within a given state. We analyze the resting-state EEG data from twin pairs collected through the Minnesota Twin Family Study, consisting of 70 epochs per participant, where each epoch corresponds to 2 s of EEG data. We fit our model at the twin pair level, sharing information within epochs from the same participant and within epochs from the same twin pair. We capture within twin-pair similarity, using an Indian buffet process, to consider an infinite library of microstates, allowing each participant to select a finite number of states from this library. The state spaces of highly similar twins may completely overlap while dissimilar twins could select distinct state spaces. In this way, our Bayesian nonparametric model defines a sparse set of states that describe the EEG data. All epochs from a single participant use the same set of states and are assumed to adhere to the same state switching dynamics in the HMM model, enforcing within-participant similarity.

Original languageEnglish (US)
Pages (from-to)89-106
Number of pages18
JournalCanadian Journal of Statistics
Volume49
Issue number1
DOIs
StatePublished - Mar 2021

Bibliographical note

Funding Information:
Computational resources for this work were provided by the Minnesota Supercomputing Institute at the University of Minnesota Informatics Institute. The work on this paper by Stephen Malone and Mark Fiecas was supported by NIH grant R21AA026919‐01A. The original data collection of the data analyzed in this paper was funded by NIH grants R37 DA05147 and R37 AA09367.

Funding Information:
Computational resources for this work were provided by the Minnesota Supercomputing Institute at the University of Minnesota Informatics Institute. The work on this paper by Stephen Malone and Mark Fiecas was supported by NIH grant R21AA026919-01A. The original data collection of the data analyzed in this paper was funded by NIH grants R37 DA05147 and R37 AA09367.

Publisher Copyright:
© 2021 Statistical Society of Canada

Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.

Keywords

  • Bayesian nonparametric
  • microstate analysis
  • switching VAR
  • time series

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