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Microstate and Omega Complexity Analyses of the Resting-state Electroencephalography
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Brain state dynamics differ between eyes open and eyes closed rest.

Brandon T Ingram1, Stephen D Mayhew2, Andrew P Bagshaw1

  • 1Centre for Human Brain Health, School of Psychology, University of Birmingham, Birmingham, UK.

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|July 11, 2024
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Summary

This study used hidden Markov models (HMMs) with simultaneous EEG-fMRI data to analyze brain states during rest. Multimodal analysis improved spatial definition and revealed similar temporal dynamics across different brain states.

Keywords:
EEG‐fMRIeyes closedeyes openhidden Markov modelresting‐state

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Area of Science:

  • Neuroscience
  • Cognitive Science
  • Computational Neuroscience

Background:

  • The human brain exhibits complex spatio-temporal activity patterns, termed brain states, even at rest.
  • Previous brain state analysis relied on unimodal data, limiting state definition and cross-modal understanding.
  • Concurrent electroencephalography-functional magnetic resonance imaging (EEG-fMRI) offers a multimodal approach to capture brain dynamics.

Purpose of the Study:

  • To apply hidden Markov models (HMMs) to concurrent EEG-fMRI resting-state data (eyes open/closed).
  • To investigate the spatial and temporal relationships of brain states identified from separate EEG and fMRI analyses.
  • To assess if fMRI data can enhance the spatial definition of EEG-derived brain states.

Main Methods:

  • Hidden Markov Model (HMM) applied to concurrent EEG-fMRI data during eyes-open and eyes-closed rest.
  • Separate HMM models trained on EEG and fMRI data.
  • General Linear Model (GLM) used to identify BOLD correlates of EEG states.
  • Sliding window analysis performed on state time courses to assess temporal dynamics.

Main Results:

  • Both EEG and fMRI HMM models distinguished between eyes-open and eyes-closed rest conditions.
  • fMRI model identified changes in visual and attention networks; EEG model detected alpha power increase.
  • fMRI data successfully inferred spatial properties of EEG states, yielding canonical alpha-BOLD correlations.
  • Sliding window analysis revealed distinct state occupancy dynamics and cross-modal temporal correlations.

Conclusions:

  • HMMs are effective for multimodal brain state analysis.
  • Concurrent EEG-fMRI provides a more comprehensive definition of brain states.
  • Brain states identified across different modalities exhibit similar temporal dynamics.