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Monday, July 20 • 7:00pm - 8:00pm
P67: A computational model to inform presurgical evaluation of epilepsy from scalp EEG

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Virtual poster presentation/discussion in Zoom: 
Meeting ID: 970 6793 2885
Password: 206515
I have a professional account in zoom, so there won't be a limit on the duration and number of participants. 

Poster teaser in youtube! This poster is based on this paper.
You can ask me questions by email if you miss the poster: lopesm1@cardiff.ac.uk 

Marinho Lopes
, Leandro Junges, Luke Tait, John Terry, Eugenio Abela, Mark Richardson, Marc Goodfellow

Epilepsy affects an estimated fifty million people worldwide. Approximately one third do not respond to anti-epileptic medication and are therefore potential candidates for alternative treatments such as epilepsy surgery. Surgery aims to remove the epileptogenic zone (EZ), the brain area responsible for the generation of seizures. Epilepsy surgery is thus preceded by an evaluation to determine the location of the EZ. A number of brain imaging modalities may be used in this evaluation, namely scalp electroencephalography (EEG) and magnetic resonance imaging (MRI), possibly followed by invasive intracranial EEG. The effectiveness of intracranial EEG to inform epilepsy surgery depends on where electrodes are implanted. This decision is informed by noninvasive recording modalities such as scalp EEG. The decision is frequently not trivial because scalp EEG may provide inconclusive or even contradictory predictions of the EZ location. A poor hypothesis based on noninvasive data may lead to an incorrect placement of intracranial electrodes, which in turn may make surgery ill-advised and potentially unsuccessful if performed [1].

Here we propose a framework to interrogate scalp EEG and determine epilepsy lateralization to aid in electrode implantation [2]. We used eLORETA to map source activities from seizure epochs recorded from scalp EEG and obtained functional networks using the phase-locking value (PLV). The networks were then studied using a mathematical model of epilepsy (a modified theta model to represent a network of interacting neural masses [2,3]). By removing different regions of interest 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 considered 15 individuals from the EPILEPSIAE database and studied a total of 62 seizures. Results were assessed by taking into account actual intracranial implantations and postsurgical outcome. The framework proved useful in assessing epilepsy lateralization in 12 out of 15 individuals considered. These results show promise for the use of this framework to better interrogate scalp EEG and aid clinicians in presurgical assessment of people with epilepsy.


[1] Jayakar, P., et al. "Diagnostic utility of invasive EEG for epilepsy surgery: indications, modalities, and techniques." Epilepsia 57.11 (2016): 1735-1747. https://doi.org/10.1111/epi.13515

[2] Lopes, M. A., et al. "Computational modelling in source space from scalp EEG to inform presurgical evaluation of epilepsy." Clinical Neurophysiology 131.1 (2020): 225-234. https://doi.org/10.1016/j.clinph.2019.10.027

[3] Lopes, M. A., et al. "An optimal strategy for epilepsy surgery: Disruption of the rich-club?." PLoS computational biology 13.8 (2017). https://doi.org/10.1371/journal.pcbi.1005637

avatar for Marinho Lopes

Marinho Lopes

Research Fellow, Cardiff University
I am interested in using mathematical modelling and data analysis to help advance epilepsy diagnosis, prognosis, and treatment. My research is focused on using neural mass models and network theory to assess brain networks inferred from electrophysiological data (both scalp and intracranial... Read More →

Monday July 20, 2020 7:00pm - 8:00pm CEST
Slot 12