This study investigates the electrophysiological basis of resting state networks (RSNs) using magnetoencephalography (MEG). The research compares MEG data with functional magnetic resonance imaging (fMRI) data to assess the spatial structure and connectivity of RSNs. MEG measures magnetic fields associated with brain activity, bypassing the hemodynamic response, and provides a direct measure of electrophysiological activity. The study uses a combination of beamformer spatial filtering and independent component analysis (ICA) to analyze MEG data, which does not require prior assumptions about network locations or patterns. This method results in RSNs with significant similarity in spatial structure to those derived from fMRI, confirming the neural basis of hemodynamic networks and demonstrating the potential of MEG as a tool for understanding RSNs and their connectivity. The study shows that RSNs measured using fMRI are mirrored in MEG data, indicating that MEG can independently measure the spatial pattern of RSNs. However, the ill-posed inverse problem in MEG makes separating real from spurious connectivity challenging. The study also shows that MEG data, when processed with ICA, reveal significant similarity between RSNs derived from MEG and fMRI data, suggesting that ICA offers advantages over seed-based correlation approaches. The results confirm that neural oscillations mediate functional connectivity between network nodes and that RSNs have electrophysiological underpinnings. The study also highlights the importance of considering frequency dependence in RSNs, with correlations strongest in the β-band. The findings suggest that MEG offers a promising method for investigating the electrophysiological basis of RSNs and their connectivity, providing insights into the mechanisms underlying brain function and dysfunction. The study concludes that MEG has the potential to enhance our understanding of RSNs and their role in neurological conditions.This study investigates the electrophysiological basis of resting state networks (RSNs) using magnetoencephalography (MEG). The research compares MEG data with functional magnetic resonance imaging (fMRI) data to assess the spatial structure and connectivity of RSNs. MEG measures magnetic fields associated with brain activity, bypassing the hemodynamic response, and provides a direct measure of electrophysiological activity. The study uses a combination of beamformer spatial filtering and independent component analysis (ICA) to analyze MEG data, which does not require prior assumptions about network locations or patterns. This method results in RSNs with significant similarity in spatial structure to those derived from fMRI, confirming the neural basis of hemodynamic networks and demonstrating the potential of MEG as a tool for understanding RSNs and their connectivity. The study shows that RSNs measured using fMRI are mirrored in MEG data, indicating that MEG can independently measure the spatial pattern of RSNs. However, the ill-posed inverse problem in MEG makes separating real from spurious connectivity challenging. The study also shows that MEG data, when processed with ICA, reveal significant similarity between RSNs derived from MEG and fMRI data, suggesting that ICA offers advantages over seed-based correlation approaches. The results confirm that neural oscillations mediate functional connectivity between network nodes and that RSNs have electrophysiological underpinnings. The study also highlights the importance of considering frequency dependence in RSNs, with correlations strongest in the β-band. The findings suggest that MEG offers a promising method for investigating the electrophysiological basis of RSNs and their connectivity, providing insights into the mechanisms underlying brain function and dysfunction. The study concludes that MEG has the potential to enhance our understanding of RSNs and their role in neurological conditions.