Analysis of Resting State according to the data of magnetoencephalography

Abstract Resting State (RS) is the basic state of human consciousness. Many neural networks of the brain in RS interact with each other. The same activity can be observed with a difficult cognitive load. Thus, RS reflects the whole dynamics of consciousness. Data, recorded through two methods: functional magnetic resonance imaging (fMRI) and magnetoencephalography (MEG) is generally used to image resting state network (RSN). The effective connection of RSN was visualized by fMRI, but the lack of this method is a poor temporal sensitivity. The MEG method makes it possible to get data with a good temporal resolution up to 1000 Hz. In this work we have analyzed the data, recorded at RS. Functional RSN, related to the motor and sensory regions of the brain, as well as the default and control networks, was identified. These functional networks were detected with the fMRI first, and are most well identified in the analysis of the BOLD signal.