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Computational modelling in source space from scalp EEG to inform presurgical evaluation of epilepsy

Lopes, Marinho A., Junges, Leandro, Tait, Luke, Terry, John R., Abela, Eugenio, Richardson, Mark P. and Goodfellow, Marc 2020. Computational modelling in source space from scalp EEG to inform presurgical evaluation of epilepsy. Clinical Neurophysiology 131 (1) , pp. 225-234. 10.1016/j.clinph.2019.10.027

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Abstract

Objective The effectiveness of intracranial electroencephalography (iEEG) to inform epilepsy surgery depends on where iEEG electrodes are implanted. This decision is informed by noninvasive recording modalities such as scalp EEG. Herein we propose a framework to interrogate scalp EEG and determine epilepsy lateralization to aid in electrode implantation. Methods We use eLORETA to map source activities from seizure epochs recorded from scalp EEG and consider 15 regions of interest (ROIs). Functional networks are then constructed using the phase-locking value and studied using a mathematical model. By removing different ROIs from the network and simulating their impact on the network’s ability to generate seizures in silico, the framework provides predictions of epilepsy lateralization. We consider 15 individuals from the EPILEPSIAE database and study a total of 62 seizures. Results were assessed by taking into account actual intracranial implantations and surgical outcome. Results The framework provided potentially useful information regarding epilepsy lateralization in 12 out of the 15 individuals ( p=0.02 , binomial test). Conclusions Our results show promise for the use of this framework to better interrogate scalp EEG to determine epilepsy lateralization.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Psychology
Cardiff University Brain Research Imaging Centre (CUBRIC)
Publisher: Elsevier
ISSN: 1388-2457
Date of First Compliant Deposit: 17 December 2019
Date of Acceptance: 26 October 2019
Last Modified: 18 Dec 2019 11:45
URI: http://orca.cf.ac.uk/id/eprint/127615

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