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Interictal High Frequency Oscillations Detected with Simultaneous Magnetoencephalography and Electroencephalography as Biomarker of Pediatric Epilepsy
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Dynamic analysis on simultaneous iEEG-MEG data via hidden Markov model.

Siqi Zhang1, Chunyan Cao2, Andrew Quinn3

  • 1Key Laboratory of Child Development and Learning Science of Ministry of Education, School of Biological Sciences & Medical Engineering, Southeast University, Nanjing 210096, Jiangsu, China; Wellcome Centre for Human Neuroimaging, UCL Institute of Neurology, 12 Queen Square, London WC1N 3BG, UK.

Neuroimage
|March 4, 2021
PubMed
Summary

This study shows that hidden Markov model (HMM) states derived from magnetoencephalography (MEG) align with intracranial electroencephalography (iEEG) power changes in epilepsy patients. This correspondence allows for functional clustering of iEEG contacts, improving group-level analysis.

Keywords:
DynamicsHumanOscillationsResting state

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Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Biophysics

Background:

  • Intracranial electroencephalography (iEEG) is crucial for epilepsy surgery evaluation and understanding brain function.
  • Inconsistent electrode placement across subjects hinders group-level iEEG analysis.
  • Hidden Markov Models (HMM) applied to magnetoencephalography (MEG) reveal distinct brain states in resting-state data.

Purpose of the Study:

  • To investigate the correspondence between HMM-derived brain states from MEG and iEEG power changes.
  • To determine if this correspondence can enable functional clustering of iEEG contacts for group analysis.

Main Methods:

  • Simultaneous resting-state MEG and iEEG recordings from 11 epilepsy patients.
  • Time-delay embedded HMM applied to MEG data to identify brain states.
  • Time-frequency decomposition of iEEG data to analyze spectral power changes.
  • Correlation analysis between HMM state probabilities and iEEG power across frequency bands.

Main Results:

  • Five HMM states were identified from MEG, with two showing temporal activations in the theta/alpha band.
  • Significant correlations (p < 0.05) were found between iEEG power and HMM states in the theta/alpha band for most electrodes.
  • Functional correlations between iEEG electrodes and HMM states were spatially congruent (p = 5.6e-6).
  • HMM models derived from epilepsy patients were comparable to those from healthy subjects.

Conclusions:

  • Epilepsy does not prevent HMM analysis of interictal data, yielding states similar to healthy controls.
  • iEEG power changes correlate with HMM states in the alpha-theta band, linked to electrode location.
  • HMM analysis offers a promising method for identifying comparable iEEG channels across subjects for group analysis.