High-Density EEG Source Connectivity as a Complementary Tool for Identifying Epileptogenic Networks
Abstract number :
2.096
Submission category :
3. Neurophysiology / 3G. Computational Analysis & Modeling of EEG
Year :
2018
Submission ID :
501810
Source :
www.aesnet.org
Presentation date :
12/2/2018 4:04:48 PM
Published date :
Nov 5, 2018, 18:00 PM
Authors :
Aya Kabbara, University Rennes, LTSI – U1099; Anca Nica, CHU Rennes, Neurology Department; Univ Rennes; Arnaud J. Biraben, CHU Rennes, Neurology Departrment; Univ Rennes; Isabelle Merlet, University Rennes, LTSI – U1099; Fabrice Wendling, Univ
Rationale: In drug resistant epilepsies the alternative of surgical treatment is studied by noninvasive methods followed when needed by invasive workup. In order to improve the accuracy of the presurgical assessment, the clinical demand is high for non-invasive methods identifying the epileptogenic networks. Methods: We introduce a novel methodological framework to identify epileptogenic networks from scalp high-density electroencephalography (HD EEG). The proposed approach combines the emerging technique called ‘EEG source connectivity’ with graph theory.We studied the HD EEG data at rest in 18 patients with drug resistant epilepsy, regardless of the presence or absence of epileptiform activity. Their stereo-electroencephalography (SEEG) data were used to evaluate the accuracy of epileptogenic networks identified from scalp data. The method performance was quantified by its ability to identify pathological brain networks in the region explored by SEEG in epileptic patients. This quantification was done using hemispherical and lobar accuracies as well as the distance between depth-EEG electrode location and estimated networks. Results: Results showed that the proposed approach was able to predict the brain hemisphere (accuracy= 97±9%) and the lobe (accuracy=91±19%) where SEEG exploration was performed a posteriori (average distance= 13±11 mm). Results showed also the high advantage of network segregation measures (local functional connectivity) compared to global measures (p<0.001, corrected) in revealing epileptogenic networks. Conclusions: These results may promote the noninvasive HD EEG source connectivity as a complementary tool in pre-surgical evaluation in order to guide optimal depth-electrode placement and to improve the presurgical workup accuracy. Funding: This work has received a French government support granted to the CominLabs excellence laboratory and managed by the National Research Agency in the "Investing for the Future" program under reference ANR-10-LABX-07-01. It was also financed by the Rennes University Hospital (COREC Project named conneXion, 2012-14). The study was also funded by the National Council for Scientific Research (CNRS) in Lebanon.