LAUSR

&NA; Resting state brain activity has become a significant area of investigation in human neuroimaging. An important approach for understanding the dynamics of neuronal activity in the resting state is to use complementary imaging modalities. Electrophysiological recordings can access fast temporal dynamics, while functional magnetic resonance imaging (fMRI) studies reveal detailed spatial patterns. However, the relationship between these two measures is not fully established. In this study, we used simultaneously recorded electroencephalography (EEG) and fMRI, along with Hidden Markov Modelling, to investigate how network dynamics at fast sub‐second time‐scales, accessible with EEG, link to the slower time‐scales and higher spatial detail of fMRI. We found that the fMRI correlates of fast transient EEG dynamic networks show highly reproducible spatial patterns, and that their spatial organization exhibits strong similarity with traditional fMRI resting state networks maps. This further demonstrates the potential of electrophysiology as a tool for understanding the fast network dynamics that underlie fMRI resting state networks. HighlightsHidden Markov Modelling of resting‐state EEG and fMRI.Model conveys connectivity information within and transition rules between networks.fMRI correlates of HMM‐EEG networks resemble resting state network (RSN) maps.Subsecond EEG network activity underlies slow fMRI RSN fluctuations.Similar dynamic structure of HMM‐EEG and HMM‐fMRI networks suggests scale‐free behaviour.