Investigating the electrophysiological basis of resting state networks using magnetoencephalography

Abstract

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-oxygenationlevel-dependent (BOLD) signals from different brain areas. However, 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 characterize resting state brain networks independently using magnetoencephalography (MEG), a neuroimaging modality that bypasses the hemodynamic response and measures the magnetic fields associated with electrophysiological brain activity. The MEG data are analyzed using a unique combination of beamformer spatial filtering and independent component analysis (ICA) and require no prior assumptions about the spatial locations or patterns of the networks. 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 hemodynamic networks and demonstrates the potential of MEG as a tool for understanding the mechanisms that underlie RSNs and the nature of connectivity that binds network nodes.functional connectivity | neural oscillations I n recent years interest has grown in the study of connectivity between spatially separate functionally specific brain regions. The way in which separate areas synchronize to form networks is integral to information processing (1, 2). Abnormal communication between regions is thought to be the basis for a number of neurological pathologies (e.g., schizophrenia) (3). It follows that if we are to generate a complete understanding of brain function (and dysfunction), then elucidation of the role of brain networks will be critical. The majority of research in this area has been conducted using functional magnetic resonance imaging (fMRI). During the "resting state", blood-oxygenation-level-dependent (BOLD) fMRI signals originating in spatially separate brain regions are correlated in time (4-6). This correlation implies connectivity between those areas, even in the absence of a task. Temporally correlated BOLD signals have led to the discovery of a number of resting state networks (RSNs) that are consistent across time and subjects. These networks are known to have functional relevance and clinical significance (7,8). Whereas RSNs have also been investigated using noninvasive measures of electrophysiology [electroencephalography (EEG) (9) and magnetoencephalography (MEG) (10-12)], this investigation has been limited to analysis in sensor space or has relied on prior assumptions about spatial locations or patterns of the networks. To date, it has not been shown that MEG (or EEG) can independently measure the spatial pattern of RSNs in the manner that has been demonstrated in fMRI (13). This result would confirm a neural basis for t...

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