We analyzed rs-fMRI whole-brain seed-correlation maps and the vector of EEG-coherence jointly, taking all image voxels and coherence values into account simultaneously using parallel ICA (Fusion ICA Toolbox: (Calhoun et al., 2006, Rachakonda et al., 2012)). Parallel ICA is described in detail in (Liu et al., 2009). Parallel ICA aims to identify independent components from the seed-correlation map and the EEG-coherence vector in addition to the relationship between them. Components extracted from the seed-correlation map can be interpreted as networks of brain regions with spatially similar seed-synchrony across subjects, but the strength of synchrony within each network may differ across subjects. Components extracted from the scalp-recorded EEG-coherence reflect frequency-specific EEG oscillations within networks, which are also expressed to different degrees across subjects. Extracted components are not sparse; components extracted from the seed-correlation map will have a contribution from every voxel in the map, and components extracted from the EEG-coherence will have a contribution from every electrode pair within every frequency band. The loading parameters for each component reflect the contribution of each network to that subject's whole brain seed-correlation map
