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

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...

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Veröffentlicht in:Human brain mapping 2023-01, Vol.44 (1), p.66-81
Hauptverfasser: Seedat, Zelekha A., Rier, Lukas, Gascoyne, Lauren E., Cook, Harry, Woolrich, Mark W., Quinn, Andrew J., Roberts, Timothy P. L., Furlong, Paul L., Armstrong, Caren, St. Pier, Kelly, Mullinger, Karen J., Marsh, Eric D., Brookes, Matthew J., Gaetz, William
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container_end_page 81
container_issue 1
container_start_page 66
container_title Human brain mapping
container_volume 44
creator Seedat, Zelekha A.
Rier, Lukas
Gascoyne, Lauren E.
Cook, Harry
Woolrich, Mark W.
Quinn, Andrew J.
Roberts, Timothy P. L.
Furlong, Paul L.
Armstrong, Caren
St. Pier, Kelly
Mullinger, Karen J.
Marsh, Eric D.
Brookes, Matthew J.
Gaetz, William
description 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. In this study, we use hidden Markov modeling (HMM) to create a unique statistical model of interictal brain activity for 10 pediatric epilepsy patients. This data‐driven model produces an output unique to each patient, and is used to localize the epileptogenic area(s). In two patients, where more than one focus is identified, the HMM provides additional information about the relationship between the epileptiform activity arising in those areas.
doi_str_mv 10.1002/hbm.26118
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subjects Brain
Brain Mapping - methods
Child
Convulsions & seizures
Data acquisition
Decision analysis
Decision making
Drug Resistant Epilepsy - surgery
Electroencephalography
Electroencephalography - methods
Epilepsy
Epilepsy - diagnostic imaging
Epilepsy - surgery
Etiology
hidden Markov model
Humans
interictal activity
Kurtosis
Localization
Magnetic fields
Magnetic resonance imaging
Magnetoencephalography
Magnetoencephalography - methods
Mapping
Markov chains
Mathematical models
Methods
Neurological diseases
Patients
Pediatrics
Performance assessment
Philadelphia
Probability distribution
Statistical models
Tomography
title Mapping Interictal activity in epilepsy using a hidden Markov model: A magnetoencephalography study
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