EEG can be used to characterise functional networks using a variety of connectivity (FC) metrics. Unlike EEG source reconstruction, scalp analysis does not allow to make inferences about interacting regions, yet this latter approach has not been abandoned. Although the two approaches use diferent assumptions, conclusions drawn regarding the topology of the underlying networks should, ideally, not depend on the approach. The aim of the present work was to fnd an answer to the following questions: does scalp analysis provide a correct estimate of the network topology? how big are the distortions when using various pipelines in diferent experimental conditions? EEG recordings were analysed with amplitude- and phase-based metrics, founding a strong correlation for the global connectivity between scalp- and source-level. In contrast, network topology was only weakly correlated. The strongest correlations were obtained for MST leaf fraction, but only for FC metrics that limit the efects of volume conduction/signal leakage. These fndings suggest that these efects alter the estimated EEG network organization, limiting the interpretation of results of 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 underlying network topology.

A comparison between scalp- and source-reconstructed EEG networks

LAI, MARGHERITA;Matteo Demuru;HILLEBRAND, ARJAN;Matteo Fraschini
Ultimo
2018-01-01

Abstract

EEG can be used to characterise functional networks using a variety of connectivity (FC) metrics. Unlike EEG source reconstruction, scalp analysis does not allow to make inferences about interacting regions, yet this latter approach has not been abandoned. Although the two approaches use diferent assumptions, conclusions drawn regarding the topology of the underlying networks should, ideally, not depend on the approach. The aim of the present work was to fnd an answer to the following questions: does scalp analysis provide a correct estimate of the network topology? how big are the distortions when using various pipelines in diferent experimental conditions? EEG recordings were analysed with amplitude- and phase-based metrics, founding a strong correlation for the global connectivity between scalp- and source-level. In contrast, network topology was only weakly correlated. The strongest correlations were obtained for MST leaf fraction, but only for FC metrics that limit the efects of volume conduction/signal leakage. These fndings suggest that these efects alter the estimated EEG network organization, limiting the interpretation of results of 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 underlying network topology.
2018
Biomedical engineering; Dynamical systems;
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11584/249966
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