%0 Journal Article %A Margherita Lai %A Matteo Demuru %A Arjan Hillebrand %A Matteo Fraschini %T A comparison between scalp- and source-reconstructed EEG networks %D 2017 %R 10.1101/121764 %J bioRxiv %P 121764 %X Modern network science is a fundamental tool for the understanding of brain organization. EEG recordings of brain activity can be used to reconstruct, and characterise, functional networks using a variety of connectivity metrics and measures of network topology. Unlike EEG source reconstruction techniques, scalp analysis does not allow to make inferences about interacting anatomical regions, yet this latter approach has not been abandoned. Although the two approaches use different assumptions, conclusions drawn regarding the (global) topology of the underlying networks should, ideally, not depend on the approach that is used. Our aim was to compare network measures, as defined by minimum spanning tree (MST) parameters, extracted from scalp and source EEG signals, using a variety of functional connectivity (FC) metrics. Eyes-closed resting-state EEG recordings from 109 subjects were analysed with amplitude- and phase-based FC metrics, both with and without correction for field spread and volume conduction/signal leakage. We found a strong correlation (0.849<rho<0.933) for the global mean connectivity between scalp- and source-level for all the FC metrics. In contrast, network topology was only weakly correlated. The strongest correlations (0.262<rho<0.346) were obtained for MST leaf fraction, but only for connectivity metrics that limit the effects of field spread and volume conduction/signal leakage. These findings suggest that the effects of field spread and volume conduction/leakage alter the estimated scalp EEG network organization, thereby limiting the interpretation of results of EEG scalp analysis. Finally, this study also suggests that the use of metrics that address the problem of zero lag correlations may give more reliable estimates of the topology of the underlying brain networks. %U https://www.biorxiv.org/content/biorxiv/early/2017/03/29/121764.full.pdf