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

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.
• 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.