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Magnetoencephalography (MEG) has been used in conjunction with resting-state functional connectivity (rsFC) based on band-limited power envelope correlation to study the intrinsic human brain network organization into resting-state networks (RSNs). However, the limited availability of current MEG systems hampers the clinical applications of electrophysiological rsFC. Here, we directly compared well-known RSNs as well as the whole-brain rsFC connectome together with its state dynamics, obtained from simultaneously-recorded MEG and high-density scalp electroencephalography (EEG) resting-state data. We also examined the impact of head model precision on EEG rsFC estimation, by comparing results obtained with boundary and finite element head models. Results showed that most RSN topographies obtained with MEG and EEG are similar, except for the fronto-parietal network. At the connectome level, sensitivity was lower to frontal rsFC and higher to parieto-occipital rsFC with MEG compared to EEG. This was mostly due to inhomogeneity of MEG sensor locations relative to the scalp and significant MEG-EEG differences disappeared when taking relative MEG-EEG sensor locations into account. The default-mode network was the only RSN requiring advanced head modeling in EEG, in which gray and white matter are distinguished. Importantly, comparison of rsFC state dynamics evidenced a poor correspondence between MEG and scalp EEG, suggesting sensitivity to different components of transient neural functional integration. This study therefore shows that the investigation of static rsFC based on the human brain connectome can be performed with scalp EEG in a similar way than with MEG, opening the avenue to widespread clinical applications of rsFC analyses.

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