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.

Effects of repetitive transcranial magnetic stimulation on restingstate connectivity: A systematic review Abstract   The brain is organized into networks that reorganize dynamically in response to cognitive demands and exogenous stimuli. In recent years, repetitive transcranial magnetic stimulation (rTMS) has gained increasing use as a noninvasive means to modulate cortical physiology, with effects both proximal to the stimulation site and in distal areas that are intrinsically connected to the proximal target. In light of these networklevel neuromodulatory effects, there has been a rapid growth in studies attempting to leverage information about network connectivity to improve neuromodulatory control and intervention outcomes. However, the mechanismsofaction of rTMS on networklevel effects remain poorly understood and is based primarily on heuristics from proximal stimulation findings. To help bridge this gap, the current paper presents a systematic review of 33 rTMS studies with baseline and postrTMS measures of fMRI restingstate functional connectivity (RSFC). Literature synthesis revealed variability across studies in stimulation parameters, studied populations, and connectivity analysis methodology. Despite this variability, it is observed that active rTMS induces significant changes on RSFC, but the prevalent lowfrequencyinhibitionhighfrequencyfacilitation heuristic endorsed for proximal rTMS effects does not fully describe distal connectivity findings. This review also points towards other important considerations, including that the majority of rTMSinduced changes were found outside the stimulated functional network, suggesting that rTMS effects tend to spread across networks. Future studies may therefore wish to adopt conventions and systematic frameworks, such as the Yeo functional connectivity parcellation atlas adopted here, to better characterize networklevel effect that contribute to the efficacy of these rapidly developing noninvasive interventions.