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Using structural connectivity to augment community structure in EEG functional connectivity

Type of publication Peer-reviewed
Publikationsform Original article (peer-reviewed)
Author Glomb Katharina, Mullier Emeline, Carboni Margherita, Rubega Maria, Iannotti Giannarita, Tourbier Sebastien, Seeber Martin, Vulliemoz Serge, Hagmann Patric,
Project Exploring brain communication pathways by combining diffusion based quantitative structural connectivity and EEG source imaging : application to physiological and epileptic networks
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Original article (peer-reviewed)

Journal Network Neuroscience
Volume (Issue) 4(3)
Page(s) 761 - 787
Title of proceedings Network Neuroscience
DOI 10.1162/netn_a_00147

Abstract

Recently, EEG recording techniques and source analysis have improved, making it feasible to tap into fast network dynamics. Yet, analyzing whole-cortex EEG signals in source space is not standard, partly because EEG suffers from volume conduction: Functional connectivity (FC) reflecting genuine functional relationships is impossible to disentangle from spurious FC introduced by volume conduction. Here, we investigate the relationship between white matter structural connectivity (SC) and large-scale network structure encoded in EEG-FC. We start by confirming that FC (power envelope correlations) is predicted by SC beyond the impact of Euclidean distance, in line with the assumption that SC mediates genuine FC. We then use information from white matter structural connectivity in order to smooth the EEG signal in the space spanned by graphs derived from SC. Thereby, FC between nearby, structurally connected brain regions increases while FC between nonconnected regions remains unchanged, resulting in an increase in genuine, SC-mediated FC. We analyze the induced changes in FC, assessing the resemblance between EEG-FC and volume-conduction- free fMRI-FC, and find that smoothing increases resemblance in terms of overall correlation and community structure. This result suggests that our method boosts genuine FC, an outcome that is of interest for many EEG network neuroscience questions.
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