Toggle Main Menu Toggle Search

Open Access padlockePrints

Interictal magnetoencephalography abnormalities to guide intracranial electrode implantation and predict surgical outcome

Lookup NU author(s): Dr Tom Owen, Vyte Janiukstyte, Dr Gerard HallORCiD, Professor Yujiang WangORCiD, Professor Peter TaylorORCiD

Downloads


Licence

This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).


Abstract

© The Author(s) 2023.Intracranial EEG is the gold standard technique for epileptogenic zone localization but requires a preconceived hypothesis of the location of the epileptogenic tissue. This placement is guided by qualitative interpretations of seizure semiology, MRI, EEG and other imaging modalities, such as magnetoencephalography. Quantitative abnormality mapping using magnetoencephalography has recently been shown to have potential clinical value. We hypothesized that if quantifiable magnetoencephalography abnormalities were sampled by intracranial EEG, then patients’ post-resection seizure outcome may be better. Thirty-two individuals with refractory neocortical epilepsy underwent magnetoencephalography and subsequent intracranial EEG recordings as part of presurgical evaluation. Eyes-closed resting-state interictal magnetoencephalography band power abnormality maps were derived from 70 healthy controls as a normative baseline. Magnetoencephalography abnormality maps were compared to intracranial EEG electrode implantation, with the spatial overlap of intracranial EEG electrode placement and cerebral magnetoencephalography abnormalities recorded. Finally, we assessed if the implantation of electrodes in abnormal tissue and subsequent resection of the strongest abnormalities determined by magnetoencephalography and intracranial EEG corresponded to surgical success. We used the area under the receiver operating characteristic curve as a measure of effect size. Intracranial electrodes were implanted in brain tissue with the most abnormal magnetoencephalography findings—in individuals that were seizure-free postoperatively (T = 3.9, P = 0.001) but not in those who did not become seizure-free. The overlap between magnetoencephalography abnormalities and electrode placement distinguished surgical outcome groups moderately well (area under the receiver operating characteristic curve = 0.68). In isolation, the resection of the strongest abnormalities as defined by magnetoencephalography and intracranial EEG separated surgical outcome groups well, area under the receiver operating characteristic curve = 0.71 and area under the receiver operating characteristic curve = 0.74, respectively. A model incorporating all three features separated surgical outcome groups best (area under the receiver operating characteristic curve = 0.80). Intracranial EEG is a key tool to delineate the epileptogenic zone and help render individuals seizure-free postoperatively. We showed that data-driven abnormality maps derived from resting-state magnetoencephalography recordings demonstrate clinical value and may help guide electrode placement in individuals with neocortical epilepsy. Additionally, our predictive model of postoperative seizure freedom, which leverages both magnetoencephalography and intracranial EEG recordings, could aid patient counselling of expected outcome.


Publication metadata

Author(s): Owen TW, Janiukstyte V, Hall GR, Chowdhury FA, Diehl B, McEvoy A, Miserocchi A, de Tisi J, Duncan JS, Rugg-Gunn F, Wang Y, Taylor PN

Publication type: Article

Publication status: Published

Journal: Brain Communications

Year: 2023

Volume: 5

Issue: 6

Online publication date: 25/10/2023

Acceptance date: 24/10/2023

Date deposited: 12/04/2024

ISSN (electronic): 2632-1297

Publisher: Oxford University Press

URL: https://doi.org/10.1093/braincomms/fcad292

DOI: 10.1093/braincomms/fcad292

Data Access Statement: Data and code to reproduce the main findings of the study are available at the following location: https://github.com/ cnnp-lab/Using_MEG_abnormalities_to_guide_intracranial_ electrode_implantation.


Altmetrics

Altmetrics provided by Altmetric


Funding

Funder referenceFunder name
EP/L015358/1EPSRC
MR/K005464/
MR/V026569/1
Wellcome Trust grant 218380

Share