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

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

doi:10.1016/j.clinph.2019.10.027

Abstract

ObjectiveThe 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. MethodsWe 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. ResultsThe framework provided potentially useful information regarding epilepsy lateralization in 12 out of the 15 individuals (p=0.02, binomial test). ConclusionsOur results show promise for the use of this framework to better interrogate scalp EEG to determine epilepsy lateralization. SignificanceThe framework may aid clinicians in the decision process to define where to implant electrodes for intracranial monitoring.

Highlights

MethodsWe used three criteria to choose these individuals: (i) had both intracranial and scalp EEG recordings; (ii) received surgery; and (iii) had at least 12 months follow-up
We aimed to explore whether the same framework could yield useful information for presurgical evaluation when applied to source mapped data from scalp EEG using relevant regions of interest (ROIs)
The Node Ictogenicity (NI) framework applied to scalp EEG was unable to lateralize the epileptogenic zone, i.e. it identified regions in both hemispheres

Summary

Methods

We used three criteria to choose these individuals: (i) had both intracranial and scalp EEG recordings; (ii) received surgery; and (iii) had at least 12 months follow-up. We used these criteria so that we could compare predictions from scalp EEG with the placement of implanted electrodes and use postsurgical outcome as a validation for whether our predictions could have added value in presurgical evaluation. 5 individuals had a bilateral electrode implantation. Scalp EEG was recorded using the 10–20 system for electrode placement.

Results

Discussion

Conclusion