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Interictal intracranial electroencephalography for predicting surgical success: The importance of space and time

Lookup NU author(s): Dr Yujiang Wang, Nishant Sinha, Gabrielle Schroeder, Dr Sriharsha Ramaraju, Dr Peter Taylor

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


Abstract

© 2020 The Authors. Epilepsia published by Wiley Periodicals LLC on behalf of International League Against EpilepsyObjective: Predicting postoperative seizure freedom using functional correlation networks derived from interictal intracranial electroencephalography (EEG) has shown some success. However, there are important challenges to consider: (1) electrodes physically closer to each other naturally tend to be more correlated, causing a spatial bias; (2) implantation location and number of electrodes differ between patients, making cross-subject comparisons difficult; and (3) functional correlation networks can vary over time but are currently assumed to be static. Methods: In this study, we address these three challenges using intracranial EEG data from 55 patients with intractable focal epilepsy. Patients additionally underwent preoperative magnetic resonance imaging (MRI), intraoperative computed tomography, and postoperative MRI, allowing accurate localization of electrodes and delineation of the removed tissue. Results: We show that normalizing for spatial proximity between nearby electrodes improves prediction of postsurgery seizure outcomes. Moreover, patients with more extensive electrode coverage were more likely to have their outcome predicted correctly (area under the receiver operating characteristic curve > 0.9, P « 0.05) but not necessarily more likely to have a better outcome. Finally, our predictions are robust regardless of the time segment analyzed. Significance: Future studies should account for the spatial proximity of electrodes in functional network construction to improve prediction of postsurgical seizure outcomes. Greater coverage of both removed and spared tissue allows for predictions with higher accuracy.


Publication metadata

Author(s): Wang Y, Sinha N, Schroeder GM, Ramaraju S, McEvoy AW, Miserocchi A, de Tisi J, Chowdhury FA, Diehl B, Duncan JS, Taylor PN

Publication type: Article

Publication status: Published

Journal: Epilepsia

Year: 2020

Volume: 61

Issue: 7

Pages: 1417-1426

Print publication date: 01/07/2020

Online publication date: 26/06/2020

Acceptance date: 21/05/2020

Date deposited: 03/11/2020

ISSN (print): 0013-9580

ISSN (electronic): 1528-1167

Publisher: Blackwell Publishing Inc.

URL: https://doi.org/10.1111/epi.16580

DOI: 10.1111/epi.16580

PubMed id: 32589284


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