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

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Computational modelling in source space from scalp EEG to inform presurgical evaluation of epilepsy. / Lopes, MA; Junges, L; Tait, L; Terry, JR; Abela, E; Richardson, MP; Goodfellow, M.

In: Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology, Vol. 131, No. 1, 01.2020, p. 225-234.

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@article{48593eacde0e4c0f94d8cae8a75af173,
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 surgery, Source mapping, Scalp EEG, Neural mass model, Epileptogenic zone, Epilepsy lateralization",
author = "MA Lopes and L Junges and L Tait and JR Terry and E Abela and MP Richardson and M Goodfellow",
year = "2020",
month = jan,
doi = "10.1016/j.clinph.2019.10.027",
language = "English",
volume = "131",
pages = "225--234",
journal = "Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology",
number = "1",

}

RIS

TY - JOUR

T1 - Computational modelling in source space from scalp EEG to inform presurgical evaluation of epilepsy

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 surgery

KW - Source mapping

KW - Scalp EEG

KW - Neural mass model

KW - Epileptogenic zone

KW - Epilepsy lateralization

UR - http://europepmc.org/abstract/med/31812920

U2 - 10.1016/j.clinph.2019.10.027

DO - 10.1016/j.clinph.2019.10.027

M3 - Article

C2 - 31812920

VL - 131

SP - 225

EP - 234

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

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

IS - 1

ER -