Quantitative approaches to guide epilepsy surgery from intracranial EEG.

Autor:	Bernabei JM; Department of Bioengineering, School of Engineering & Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA.; Center for Neuroengineering & Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA., Li A; Department of Computer Science, Columbia University, New York, NY 10027, USA., Revell AY; Department of Bioengineering, School of Engineering & Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA., Smith RJ; Department of Electrical and Computer Engineering, University of Alabama at Birmingham, Birmingham, AL 35294, USA.; Neuroengineering Program, University of Alabama at Birmingham, Birmingham, AL 35294, USA., Gunnarsdottir KM; Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA.; Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD 21218, USA., Ong IZ; Department of Bioengineering, School of Engineering & Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA., Davis KA; Center for Neuroengineering & Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA.; Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA., Sinha N; Center for Neuroengineering & Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA.; Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA., Sarma S; Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA.; Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD 21218, USA., Litt B; Department of Bioengineering, School of Engineering & Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA.; Center for Neuroengineering & Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA.; Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.; Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.
Jazyk:	angličtina
Zdroj:	Brain : a journal of neurology [Brain] 2023 Jun 01; Vol. 146 (6), pp. 2248-2258.
DOI:	10.1093/brain/awad007
Abstrakt:	Over the past 10 years, the drive to improve outcomes from epilepsy surgery has stimulated widespread interest in methods to quantitatively guide epilepsy surgery from intracranial EEG (iEEG). Many patients fail to achieve seizure freedom, in part due to the challenges in subjective iEEG interpretation. To address this clinical need, quantitative iEEG analytics have been developed using a variety of approaches, spanning studies of seizures, interictal periods, and their transitions, and encompass a range of techniques including electrographic signal analysis, dynamical systems modeling, machine learning and graph theory. Unfortunately, many methods fail to generalize to new data and are sensitive to differences in pathology and electrode placement. Here, we critically review selected literature on computational methods of identifying the epileptogenic zone from iEEG. We highlight shared methodological challenges common to many studies in this field and propose ways that they can be addressed. One fundamental common pitfall is a lack of open-source, high-quality data, which we specifically address by sharing a centralized high-quality, well-annotated, multicentre dataset consisting of >100 patients to support larger and more rigorous studies. Ultimately, we provide a road map to help these tools reach clinical trials and hope to improve the lives of future patients. (© The Author(s) 2023. Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)
Databáze:	MEDLINE
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