Role of betaband restingstate functional connectivity as a predictor of motor learning ability Abstract   It has been suggested that restingstate functional connectivity (rsFC) between the primary motor area (M1) region of the brain and other brain regions may be a predictor of motor learning, although this suggestion is still controversial. In the work reported here, we investigated the relationship between M1 seedbased rsFC and motor learning. Fiftythree healthy volunteers undertook random buttonpress and sequential motor learning tasks. Fiveminute restingstate data acquisition was performed between the two tasks. Oscillatory neural activities during the random task and the rest period were measured using magnetoencephalography. M1 seedbased rsFC was calculated for the alpha and beta bands using amplitude envelope correlation, in which the seed location was defined as an M1 position with peak eventrelated desynchronization value. The relationship between rsFC and the performance of motor learning was examined using whole brain correlation analysis. The results showed that betaband restingstate crossnetwork connectivity between the sensorimotor network and the core network, particularly the theory of mind network, affected the performance of subsequent motor learning tasks. Good learners could be distinguished from poor learners by the strength of rsFC between the M1 and the left superior temporal gyrus, a part of the theory of mind network. These results suggest that crossnetwork connectivity between the sensorimotor network and the theory of mind network can be used as a predictor of motor learning performance.

Comparing MEG and highdensity EEG for intrinsic functional connectivity mapping Abstract   Magnetoencephalography (MEG) has been used in conjunction with restingstate functional connectivity (rsFC) based on bandlimited power envelope correlation to study the intrinsic human brain network organization into restingstate networks (RSNs). However, the limited availability of current MEG systems hampers the clinical applications of electrophysiological rsFC. Here, we directly compared wellknown RSNs as well as the wholebrain rsFC connectome together with its state dynamics, obtained from simultaneouslyrecorded MEG and highdensity scalp electroencephalography (EEG) restingstate data. We also examined the impact of head model precision on EEG rsFC estimation, by comparing results obtained with boundary and finite element head models. Results showed that most RSN topographies obtained with MEG and EEG are similar, except for the frontoparietal network. At the connectome level, sensitivity was lower to frontal rsFC and higher to parietooccipital rsFC with MEG compared to EEG. This was mostly due to inhomogeneity of MEG sensor locations relative to the scalp and significant MEGEEG differences disappeared when taking relative MEGEEG sensor locations into account. The defaultmode network was the only RSN requiring advanced head modeling in EEG, in which gray and white matter are distinguished. Importantly, comparison of rsFC state dynamics evidenced a poor correspondence between MEG and scalp EEG, suggesting sensitivity to different components of transient neural functional integration. This study therefore shows that the investigation of static rsFC based on the human brain connectome can be performed with scalp EEG in a similar way than with MEG, opening the avenue to widespread clinical applications of rsFC analyses.