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In recent years the study of resting state brain networks (RSNs) has become an important area of neuroimaging. The majority of studies have used functional magnetic resonance imaging (fMRI) to measure temporal correlation between blood-oxygenation- level–dependent (BOLD) signals from different brain areas. How- ever, BOLD is an indirect measure related to hemodynamics, and the electrophysiological basis of connectivity between spatially separate network nodes cannot be comprehensively assessed using this technique. In this paper we describe a means to charac- terize resting state brain networks independently using magneto- encephalography (MEG), a neuroimaging modality that bypasses the hemodynamic response and measures the magnetic fields as- sociated with electrophysiological brain activity. The MEG data are analyzed using a unique combination of beamformer spatial filter- ing and independent component analysis (ICA) and require no prior assumptions about the spatial locations or patterns of the net- works. This method results in RSNs with significant similarity in their spatial structure compared with RSNs derived independently using fMRI. This outcome confirms the neural basis of hemody- namic networks and demonstrates the potential of MEG as a tool for understanding the mechanisms that underlie RSNs and the na- ture of connectivity that binds network nodes.


Journal article


Proc Natl Acad Sci U S A

Publication Date





16783 - 16788