This paper investigates the electrophysiological basis of resting state networks (RSNs) using magnetoencephalography (MEG). The authors describe a method that combines beamformer spatial filtering and independent component analysis (ICA) to characterize RSNs independently from functional magnetic resonance imaging (fMRI). They demonstrate that MEG data can reveal RSNs with significant spatial similarity to those identified using fMRI, confirming the neural basis of hemodynamic networks. The study uses 5-minute resting state MEG measurements from 10 individuals, filtering the data into specific frequency bands and projecting it into source space. Temporal independent component analysis (ICA) is applied to the Hilbert envelope signals to identify temporally independent time signals (TICs) from spatially separate networks. The spatial maps of these TICs are compared with those derived from fMRI, showing significant agreement across modalities. Seed-based correlation analysis further supports these findings, revealing reasonable similarity between fMRI and MEG networks. The results confirm that neural oscillations play a key role in synchronizing electrical brain activity across spatially separate regions, and suggest that MEG has the potential to provide deeper insights into the mechanisms underlying RSNs and their connectivity.This paper investigates the electrophysiological basis of resting state networks (RSNs) using magnetoencephalography (MEG). The authors describe a method that combines beamformer spatial filtering and independent component analysis (ICA) to characterize RSNs independently from functional magnetic resonance imaging (fMRI). They demonstrate that MEG data can reveal RSNs with significant spatial similarity to those identified using fMRI, confirming the neural basis of hemodynamic networks. The study uses 5-minute resting state MEG measurements from 10 individuals, filtering the data into specific frequency bands and projecting it into source space. Temporal independent component analysis (ICA) is applied to the Hilbert envelope signals to identify temporally independent time signals (TICs) from spatially separate networks. The spatial maps of these TICs are compared with those derived from fMRI, showing significant agreement across modalities. Seed-based correlation analysis further supports these findings, revealing reasonable similarity between fMRI and MEG networks. The results confirm that neural oscillations play a key role in synchronizing electrical brain activity across spatially separate regions, and suggest that MEG has the potential to provide deeper insights into the mechanisms underlying RSNs and their connectivity.