Nevertheless, given that fMRI BOLD activity is not a direct measure of neuronal activity, neuroscientists have become more and more interested in using electrophysiological techniques, as a stand alone or in combination with fMRI, to unravel the electrophysiological correlates of RSNs and their potential relation to brain oscillations (see e.g., Mantini et al., 2007). In the last decade, advancements in MEG data analysis have made it possible to rely on source space MEG connectivity to study RSNs. The richness of the MEG signal has offered several perspectives to look at RSNs (Larson-Prior et al., 2013) and, from that, to enhance the understanding of the putative mechanisms underlying their formation. The first studies (de Pasquale et al., 2010, 2012) used a signal analysis strategy aimed at extracting slow fluctuations of the MEG signal and investigating their temporal correlation, closely resembling the signal analysis strategy used for resting state fMRI data. Additionally, spatial Independent Component Analysis (ICA) has been used on resting state MEG (rsMEG) data to extract networks of coherent fluctuations, showing that MEG RSNs feature significant similarity in their spatial structure compared with fMRI RSNs as evidenced in Figure 5 (Brookes et al., 2011b).
