title: Identification of Interictal Epileptic Networks from Dense-EEG.
abstract: Epilepsy is a network disease. The epileptic network usually involves spatially distributed brain regions. In this context, noninvasive M/EEG source connectivity is an emerging technique to identify functional brain networks at cortical level from noninvasive recordings. In this paper, we analyze the effect of the two key factors involved in EEG source connectivity processing: (i) the algorithm used in the solution of the EEG inverse problem and (ii) the method used in the estimation of the functional connectivity. We evaluate four inverse solutions algorithms (dSPM, wMNE, sLORETA and cMEM) and four connectivity measures (r2, h2, PLV, and MI) on data simulated from a combined biophysical/physiological model to generate realistic interictal epileptic spikes reflected in scalp EEG. We use a new network-based similarity index to compare between the network identified by each of the inverse/connectivity combination and the original network generated in the model. The method will be also applied on real data recorded from one epileptic patient who underwent a full presurgical evaluation for drug-resistant focal epilepsy. In simulated data, results revealed that the selection of the inverse/connectivity combination has a significant impact on the identified networks. Results suggested that nonlinear methods (nonlinear correlation coefficient, phase synchronization and mutual information) for measuring the connectivity are more efficient than the linear one (the cross correlation coefficient). The wMNE inverse solution showed higher performance than dSPM, cMEM and sLORETA. In real data, the combination (wMNE/PLV) led to a very good matching between the interictal epileptic network identified from noninvasive EEG recordings and the network obtained from connectivity analysis of intracerebral EEG recordings. These results suggest that source connectivity method, when appropriately configured, is able to extract highly relevant diagnostic information about networks involved in interictal epileptic spikes from non-invasive dense-EEG data.
authors.affiliation: INSERM, U1099, Rennes, 35000, France. mahmoud.hassan@univ-rennes1.fr.|LTSI, Université de Rennes 1, Rennes, 35000, France. mahmoud.hassan@univ-rennes1.fr.
authors.forename: Mahmoud
authors.identifier: 0000-0003-0307-5086
authors.initials: M
authors.lastname: Hassan
authors.affiliation: INSERM, U1099, Rennes, 35000, France.|LTSI, Université de Rennes 1, Rennes, 35000, France.
authors.forename: Isabelle
authors.initials: I
authors.lastname: Merlet
authors.affiliation: INSERM, U1099, Rennes, 35000, France.|LTSI, Université de Rennes 1, Rennes, 35000, France.|AZM Center-EDST, Lebanese University, Tripoli, Lebanon.
authors.forename: Ahmad
authors.initials: A
authors.lastname: Mheich
authors.affiliation: INSERM, U1099, Rennes, 35000, France.|LTSI, Université de Rennes 1, Rennes, 35000, France.|AZM Center-EDST, Lebanese University, Tripoli, Lebanon.
authors.forename: Aya
authors.initials: A
authors.lastname: Kabbara
authors.affiliation: INSERM, U1099, Rennes, 35000, France.|LTSI, Université de Rennes 1, Rennes, 35000, France.|Neurology Department, CHU, Rennes, 35000, France.
authors.forename: Arnaud
authors.initials: A
authors.lastname: Biraben
authors.affiliation: Neurology Department, CHU, Rennes, 35000, France.
authors.forename: Anca
authors.initials: A
authors.lastname: Nica
authors.affiliation: INSERM, U1099, Rennes, 35000, France.|LTSI, Université de Rennes 1, Rennes, 35000, France.
authors.forename: Fabrice
authors.initials: F
authors.lastname: Wendling
mesh_terms: D000465:Algorithms; D001921:Brain / Q000503:physiopathology*; D001931:Brain Mapping / Q000379:methods*; D004569:Electroencephalography / Q000379:methods*; D004827:Epilepsy / Q000503:physiopathology*; D006801:Humans; D009415:Nerve Net / Q000503:physiopathology*
keywords: Dense-EEG source connectivity; Epilepsy; Epileptic networks