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Clinical Validation of the Champagne Algorithm for Epilepsy Spike Localization

Magnetoencephalography (MEG) is increasingly used for presurgical planning in people with medically refractory focal epilepsy. Localization of interictal epileptiform activity, a surrogate for the seizure onset zone whose removal may prevent seizures, is challenging and depends on the use of multipl...

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Autores principales: Cai, Chang, Chen, Jessie, Findlay, Anne M., Mizuiri, Danielle, Sekihara, Kensuke, Kirsch, Heidi E., Nagarajan, Srikantan S.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8172809/
https://www.ncbi.nlm.nih.gov/pubmed/34093150
http://dx.doi.org/10.3389/fnhum.2021.642819
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author Cai, Chang
Chen, Jessie
Findlay, Anne M.
Mizuiri, Danielle
Sekihara, Kensuke
Kirsch, Heidi E.
Nagarajan, Srikantan S.
author_facet Cai, Chang
Chen, Jessie
Findlay, Anne M.
Mizuiri, Danielle
Sekihara, Kensuke
Kirsch, Heidi E.
Nagarajan, Srikantan S.
author_sort Cai, Chang
collection PubMed
description Magnetoencephalography (MEG) is increasingly used for presurgical planning in people with medically refractory focal epilepsy. Localization of interictal epileptiform activity, a surrogate for the seizure onset zone whose removal may prevent seizures, is challenging and depends on the use of multiple complementary techniques. Accurate and reliable localization of epileptiform activity from spontaneous MEG data has been an elusive goal. One approach toward this goal is to use a novel Bayesian inference algorithm—the Champagne algorithm with noise learning—which has shown tremendous success in source reconstruction, especially for focal brain sources. In this study, we localized sources of manually identified MEG spikes using the Champagne algorithm in a cohort of 16 patients with medically refractory epilepsy collected in two consecutive series. To evaluate the reliability of this approach, we compared the performance to equivalent current dipole (ECD) modeling, a conventional source localization technique that is commonly used in clinical practice. Results suggest that Champagne may be a robust, automated, alternative to manual parametric dipole fitting methods for localization of interictal MEG spikes, in addition to its previously described clinical and research applications.
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spelling pubmed-81728092021-06-04 Clinical Validation of the Champagne Algorithm for Epilepsy Spike Localization Cai, Chang Chen, Jessie Findlay, Anne M. Mizuiri, Danielle Sekihara, Kensuke Kirsch, Heidi E. Nagarajan, Srikantan S. Front Hum Neurosci Human Neuroscience Magnetoencephalography (MEG) is increasingly used for presurgical planning in people with medically refractory focal epilepsy. Localization of interictal epileptiform activity, a surrogate for the seizure onset zone whose removal may prevent seizures, is challenging and depends on the use of multiple complementary techniques. Accurate and reliable localization of epileptiform activity from spontaneous MEG data has been an elusive goal. One approach toward this goal is to use a novel Bayesian inference algorithm—the Champagne algorithm with noise learning—which has shown tremendous success in source reconstruction, especially for focal brain sources. In this study, we localized sources of manually identified MEG spikes using the Champagne algorithm in a cohort of 16 patients with medically refractory epilepsy collected in two consecutive series. To evaluate the reliability of this approach, we compared the performance to equivalent current dipole (ECD) modeling, a conventional source localization technique that is commonly used in clinical practice. Results suggest that Champagne may be a robust, automated, alternative to manual parametric dipole fitting methods for localization of interictal MEG spikes, in addition to its previously described clinical and research applications. Frontiers Media S.A. 2021-05-20 /pmc/articles/PMC8172809/ /pubmed/34093150 http://dx.doi.org/10.3389/fnhum.2021.642819 Text en Copyright © 2021 Cai, Chen, Findlay, Mizuiri, Sekihara, Kirsch and Nagarajan. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Human Neuroscience
Cai, Chang
Chen, Jessie
Findlay, Anne M.
Mizuiri, Danielle
Sekihara, Kensuke
Kirsch, Heidi E.
Nagarajan, Srikantan S.
Clinical Validation of the Champagne Algorithm for Epilepsy Spike Localization
title Clinical Validation of the Champagne Algorithm for Epilepsy Spike Localization
title_full Clinical Validation of the Champagne Algorithm for Epilepsy Spike Localization
title_fullStr Clinical Validation of the Champagne Algorithm for Epilepsy Spike Localization
title_full_unstemmed Clinical Validation of the Champagne Algorithm for Epilepsy Spike Localization
title_short Clinical Validation of the Champagne Algorithm for Epilepsy Spike Localization
title_sort clinical validation of the champagne algorithm for epilepsy spike localization
topic Human Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8172809/
https://www.ncbi.nlm.nih.gov/pubmed/34093150
http://dx.doi.org/10.3389/fnhum.2021.642819
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