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

MA Lopes; L Junges; L Tait; JR Terry; E Abela; MP Richardson; M Goodfellow

doi:10.1016/j.clinph.2019.10.027

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

MA Lopes, L Junges, L Tait, JR Terry, E Abela, MP Richardson, M Goodfellow

Metabolism and Systems Research

Research output: Contribution to journal › Article › peer-review

9 Citations (Scopus)

195 Downloads (Pure)

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 (풑=0.02, binomial test).

Conclusions: Our results show promise for the use of this framework to better interrogate scalp EEG to determine epilepsy lateralization.

Significance: The framework may aid clinicians in the decision process to define where to implant electrodes for intracranial monitoring.

Original language	English
Pages (from-to)	225-234
Number of pages	10
Journal	Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology
Volume	131
Issue number	1
Early online date	22 Nov 2019
DOIs	https://doi.org/10.1016/j.clinph.2019.10.027
Publication status	Published - Jan 2020

Keywords

Epilepsy lateralization
Epilepsy surgery
Epileptogenic zone
Neural mass model
Scalp EEG
Source mapping

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Lopes, MA., Junges, L., Tait, L., Terry, JR., Abela, E., Richardson, MP., & Goodfellow, M. (2020). Computational modelling in source space from scalp EEG to inform presurgical evaluation of epilepsy. Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology, 131(1), 225-234. Advance online publication. https://doi.org/10.1016/j.clinph.2019.10.027

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title = "Computational modelling in source space from scalp EEG to inform presurgical evaluation of epilepsy",

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{\textquoteright}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 (풑=0.02, binomial test).Conclusions: Our results show promise for the use of this framework to better interrogate scalp EEG to determine epilepsy lateralization.Significance: The framework may aid clinicians in the decision process to define where to implant electrodes for intracranial monitoring.",

keywords = "Epilepsy lateralization, Epilepsy surgery, Epileptogenic zone, Neural mass model, Scalp EEG, Source mapping",

author = "MA Lopes and L Junges and L Tait and JR Terry and E Abela and MP Richardson and M Goodfellow",

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Lopes, MA, Junges, L, Tait, L, Terry, JR, Abela, E, Richardson, MP & Goodfellow, M 2020, 'Computational modelling in source space from scalp EEG to inform presurgical evaluation of epilepsy', Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology, vol. 131, no. 1, pp. 225-234. https://doi.org/10.1016/j.clinph.2019.10.027

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AU - Lopes, MA

AU - Junges, L

AU - Tait, L

AU - Terry, JR

AU - Abela, E

AU - Richardson, MP

AU - Goodfellow, M

PY - 2020/1

Y1 - 2020/1

N2 - 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 (풑=0.02, binomial test).Conclusions: Our results show promise for the use of this framework to better interrogate scalp EEG to determine epilepsy lateralization.Significance: The framework may aid clinicians in the decision process to define where to implant electrodes for intracranial monitoring.

AB - 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 (풑=0.02, binomial test).Conclusions: Our results show promise for the use of this framework to better interrogate scalp EEG to determine epilepsy lateralization.Significance: The framework may aid clinicians in the decision process to define where to implant electrodes for intracranial monitoring.

KW - Epilepsy lateralization

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KW - Neural mass model

KW - Scalp EEG

KW - Source mapping

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JO - Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology

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

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