To further elucidate the relationship between microstates and RSNs, we developed a fully data-driven approach that combines information from simultaneously recorded, high-density EEG and BOLD-fMRI data. Using independent component analysis (ICA) of the combined EEG and fMRI data, we identified thirteen microstates and ten RSNs that are organized independently in their temporal and spatial characteristics, respectively. We hypothesized that the intrinsic brain networks that are active at rest would be reflected in both the EEG data and the fMRI data. To test this hypothesis, the rapid fluctuations associated with each microstate were correlated with the BOLD-fMRI signal associated with each RSN.
We found that each RSN was characterized further by a specific electrophysiological signature involving from one to a combination of several microstates. Moreover, by comparing the time course of EEG microstates to that of the whole-brain BOLD signal, on a multi-subject group level, we unraveled for the first time a set of microstate-associated networks that correspond to a range of previously described RSNs, including visual, sensorimotor, auditory, attention, frontal, visceromotor and default mode networks. These results extend our understanding of the electrophysiological signature of BOLD RSNs and demonstrate the intrinsic connection between the fast neuronal activity and slow hemodynamic fluctuations.