Fig 1.
The artefact-free raw data at the sensors level (top row) were fed into a minimum-norm localisation algorithm to obtain current densities at the cortex level (middle row). After filtering in 5 frequency bands (Delta & Theta, Alpha, Beta, Gamma1, Gamma2), the power of the cortical signals was computed by integrating over two-second-long windows. The end result at the cortical level was the power time courses in each of the 5 frequency bands (bottom row).
Fig 2.
Spatial ICA of the concatenated power dynamics at the cortical level.
Fig 3.
Three individual spatial ICA components.
For each component, the top part of the figure shows the values of the spatial distribution. The bottom part of the figure shows the power values of the corresponding component along time for the 5 frequency bands Delta & Theta, Alpha, Beta, Gamma1 and Gamma2.
Fig 4.
Hierarchical clustering of the individual spatial ICA components.
The individual spatial ICA components (left) were projected to the MNI colin27 template (right). A hierarchical clustering was then performed to identify the RSN_EEG.
Fig 5.
Two RSN_EEG and associated power distribution in each frequency band are shown. (a) RSN_EEG including somato-motor areas and (b) RSN_EEG including fronto-parietal areas.
Fig 6.
Stepwise regression performed between RSN_EEG and RSN_fMRI.
The results of the stepwise regression performed between RSN_EEG (a) and RSN_fMRI (b) are shown in (c).
Fig 7.
Association of EEG networks with fMRI networks.
The RSN_EEG overlapping with only one RSN_fMRI are shown in the left part of the figure (1 to 1 association). The RSN_EEG overlapping with several RSN_fMRI are shown in the right part of the figure (1 to N association).
Fig 8.
EEG networks not included in any fMRI network.