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Related Experiment Video

Updated: Dec 30, 2025

Functional Mapping with Simultaneous MEG and EEG
06:04

Functional Mapping with Simultaneous MEG and EEG

Published on: June 14, 2010

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Comparing MEG and high-density EEG for intrinsic functional connectivity mapping.

N Coquelet1, X De Tiège2, F Destoky1

  • 1Laboratoire de Cartographie fonctionnelle du Cerveau, UNI-ULB Neuroscience Institute, Université libre de Bruxelles (ULB), Brussels, Belgium.

Neuroimage
|January 24, 2020
PubMed

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Summary

Scalp electroencephalography (EEG) can reliably map human brain networks using resting-state functional connectivity (rsFC), similar to magnetoencephalography (MEG). This opens doors for widespread clinical applications of rsFC analyses.

Area of Science:

  • Neuroscience
  • Biophysics
  • Medical Imaging

Background:

  • Magnetoencephalography (MEG) and resting-state functional connectivity (rsFC) are used to study intrinsic brain networks.
  • Limited availability of MEG systems hinders clinical applications of electrophysiological rsFC.
  • High-density scalp electroencephalography (EEG) offers a potential alternative for rsFC studies.

Purpose of the Study:

  • To directly compare rsFC and connectome organization between simultaneously recorded MEG and EEG data.
  • To investigate the impact of head modeling precision on EEG rsFC estimation.
  • To assess the correspondence of rsFC state dynamics between MEG and EEG.

Main Methods:

  • Simultaneous recording of resting-state MEG and high-density EEG data.
Keywords:
ConnectomeElectroencephalographyEnvelope correlationMagnetoencephalographyResting-state networksState dynamics

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  • Calculation of band-limited power envelope correlation for rsFC.
  • Comparison of RSN topographies and whole-brain connectomes between MEG and EEG.
  • Evaluation of boundary and finite element head models for EEG rsFC.

    • Main Results:

    • Most RSN topographies were similar between MEG and EEG, with exceptions in the fronto-parietal network.
    • MEG showed lower sensitivity to frontal rsFC and higher sensitivity to parieto-occipital rsFC compared to EEG.
    • Differences in rsFC largely disappeared when accounting for relative sensor locations.
    • The default-mode network was the only RSN benefiting from advanced EEG head modeling.
    • rsFC state dynamics showed poor correspondence between MEG and EEG.

    Conclusions:

    • Static rsFC analysis of the human brain connectome can be performed effectively with scalp EEG, mirroring MEG findings.
    • EEG-based rsFC holds promise for widespread clinical applications due to its accessibility.
    • Dynamic rsFC analysis may require further methodological development for EEG to match MEG sensitivity.