Evaluation of data-driven MEG analysis approaches for the extraction of fMRI resting state networks

The electrophysiological basis of resting state networks (RSN) is still under debate. In particular, no principled mechanism has been determined that is capable of explaining all RSN equally well. While magnetoencephalography (MEG) and electroencephalography (EEG) are the methods of choice to determine the electrophysiological basis of RSN, no standard analysis pipeline of RSN yet exists. In this paper, we compare the two main existing data-driven analysis strategies for extracting resting state networks from MEG data. The first approach extracts RSN through an independent component analysis (ICA) of the Hilbert envelope in different frequency bands. The second approach uses phase -amplitude coupling to determine the RSN. To evaluate the performance of these approaches, we compare the MEG-RSN to the functional magnetic resonance imaging (fMRI)-RSN from the same subjects. Overall, it was possible to extract the canonical fMRI RSN with MEG. The approach based on phase-amplitude coupling yielded the best correspondence to the fMRI-RSN. The Hilbert envelope-ICA produced different dominant frequency-bands underlying RSN for different ICA runs, suggesting the absence of a single dominant frequency underlying the RSN. Our results also suggest that individual RSN are not characterized by one single dominant frequency. Instead, the resting state networks seem to be based on a combination of the delta/theta phase and gamma amplitude.