Mapping Interictal activity in epilepsy using a hidden Markov model: A magnetoencephalography study

Abstract

Epilepsy is a highly heterogeneous neurological disorder with variable etiology, manifestation, and response to treatment. It is imperative that new models of epileptiform brain activity account for this variability, to identify individual needs and allow clinicians to curate personalized care. Here, we use a hidden Markov model (HMM) to create a unique statistical model of interictal brain activity for 10 pediatric patients. We use magnetoencephalography (MEG) data acquired as part of standard clinical care for patients at the Children's Hospital of Philadelphia. These data are routinely analyzed using excess kurtosis mapping (EKM); however, as cases become more complex (extreme multifocal and/or polymorphic activity), they become harder to interpret with EKM. We assessed the performance of the HMM against EKM for three patient groups, with increasingly complicated presentation. The difference in localization of epileptogenic foci for the two methods was 7 ± 2 mm (mean ± SD over all 10 patients); and 94% ± 13% of EKM temporal markers were matched by an HMM state visit. The HMM localizes epileptogenic areas (in agreement with EKM) and provides additional information about the relationship between those areas. A key advantage over current methods is that the HMM is a data-driven model, so the output is tuned to each individual. Finally, the model output is intuitive, allowing a user (clinician) to review the result and manually select the HMM epileptiform state, offering multiple advantages over previous methods and allowing for broader implementation of MEG epileptiform analysis in surgical decision-making for patients with intractable epilepsy.

Publication DOI: https://doi.org/10.1002/hbm.26118
Divisions: College of Health & Life Sciences > School of Psychology
Additional Information: Copyright © 2022, The Authors. Human Brain Mapping published by Wiley Periodicals LLC. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
Uncontrolled Keywords: Neurology (clinical),Neurology,Radiology, Nuclear Medicine and imaging,Radiological and Ultrasound Technology,Anatomy
Publication ISSN: 1097-0193
Last Modified: 18 Jul 2024 07:19
Date Deposited: 09 Jan 2023 14:54
Full Text Link:
Related URLs: https://onlinel ... .1002/hbm.26118 (Publisher URL)
http://www.scop ... tnerID=8YFLogxK (Scopus URL)
PURE Output Type: Article
Published Date: 2023-01
Published Online Date: 2022-10-19
Accepted Date: 2022-09-26
Authors: Seedat, Zelekha A.
Rier, Lukas
Gascoyne, Lauren E.
Cook, Harry
Woolrich, Mark W.
Quinn, Andrew J.
Roberts, Timothy P. L.
Furlong, Paul L. (ORCID Profile 0000-0002-9840-8586)
Armstrong, Caren
St. Pier, Kelly
Mullinger, Karen J.
Marsh, Eric D.
Brookes, Matthew J.
Gaetz, William

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