Data from: Large-scale functional networks identified from resting-state EEG using spatial ICA

Several methods have been applied to EEG or MEG signals to detect
functional networks. In recent works using MEG/EEG and fMRI data, temporal
ICA analysis has been used to extract spatial maps of resting-state
networks with or without an atlas-based parcellation of the cortex. Since
the links between the fMRI signal and the electromagnetic signals are not
fully established, and to avoid any bias, we examined whether EEG alone
was able to derive the spatial distribution and temporal characteristics
of functional networks. To do so, we propose a two-step original method:
1) An individual multi-frequency data analysis including EEG-based source
localisation and spatial independent component analysis, which allowed us
to characterize the resting-state networks. 2) A group-level analysis
involving a hierarchical clustering procedure to identify reproducible
large-scale networks across the population. Compared with large-scale
resting-state networks obtained with fMRI, the proposed EEG-based analysis
revealed smaller independent networks thanks to the high temporal
resolution of EEG, hence hierarchical organization of networks. The
comparison showed a substantial overlap between EEG and fMRI networks in
motor, premotor, sensory, frontal, and parietal areas. However, there were
mismatches between EEG-based and fMRI-based networks in temporal areas,
presumably resulting from a poor sensitivity of fMRI in these regions or
artefacts in the EEG signals. The proposed method opens the way for
studying the high temporal dynamics of networks at the source level thanks
to the high temporal resolution of EEG. It would then become possible to
study detailed measures of the dynamics of connectivity.