Within and betweenperson correlates of the temporal dynamics of resting EEG microstates Abstract   Microstates reflect transient brain states resulting from the synchronous activity of brain networks that predominate in the broadband EEG. There has been increasing interest in how the functional organization of the brain varies across individuals, or the extent to which its spatiotemporal dynamics are state dependent. However, little research has examined within and betweenperson correlates of microstate temporal parameters in healthy populations. In the present study, neuroelectric activity recorded during eyesclosed rest and during simple visual fixation was segmented into a time series of transient microstate intervals. It was found that five datadriven microstate configurations explained the preponderance of topographic variance in the EEG time series of the 374 recordings (from 187 participants) included in the study. We observed that the temporal dynamics of microstates varied within individuals to a greater degree than they differed between persons, with withinperson factors explaining a large portion of the variance in mean microstate duration and occurrence rate. Nevertheless, several individual differences were found to predict the temporal dynamics of microstates. Of these, age and gender were the most reliable. These findings suggest that not only do the rich temporal dynamics of wholebrain neuronal networks vary considerably withinindividuals, but that microstates appear to differentiate persons based on trait individual differences. The current findings suggest that rather than focusing exclusively on betweenperson differences in microstates as measures of brain function, researchers should turn their attention towards understanding the factors contributing to withinperson variation.

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.