Electroencephalographic Resting-State Networks: Source Localization of Microstates.

TLDR: It is demonstrated that the EEG RSNs can be estimated reliably by measuring the reproducibility of the findings, and the seven state-specific R SNs partly resemble and extend previous functional magnetic resonance imaging-based networks estimated as the hemodynamic correlates of four canonical EEG microstates.

Abstract: Using electroencephalography (EEG) to elucidate the spontaneous activation of brain resting-state networks (RSNs) is nontrivial as the signal of interest is of low amplitude and it is difficult to distinguish the underlying neural sources Using the principles of electric field topographical analysis, it is possible to estimate the meta-stable states of the brain (ie, the resting-state topographies, so-called microstates) We estimated seven resting-state topographies explaining the EEG data set with k-means clustering (N = 164, 256 electrodes) Using a method specifically designed to localize the sources of broadband EEG scalp topographies by matching sensor and source space temporal patterns, we demonstrated that we can estimate the EEG RSNs reliably by measuring the reproducibility of our findings After subtracting their mean from the seven EEG RSNs, we identified seven state-specific networks The mean map includes regions known to be densely anatomically and functionally connected (superi