Source localization of epileptic spikes using Multiple Sparse Priors

Autores
Fernandez Corazza, Mariano; Feng, Rui; Ma, Chengxin; Hu, Jie; Pan, Li; Luu, Phan; Tucker, Don
Año de publicación
2021
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Objective: To evaluate epileptic source estimation using multiple sparse priors (MSP) inverse method and high-resolution, individual electrical head models. Methods: Accurate source localization is dependent on accurate electrical head models and appropriate inverse solvers. Using high-resolution, individual electrical head models in fifteen epilepsy patients, with surgical resection and clinical outcome as criteria for accuracy, performance of MSP method was compared against standardized low-resolution brain electromagnetic tomography (sLORETA) and coherent maximum entropy on the mean (cMEM) methods. Results: The MSP method performed similarly to the sLORETA method and slightly better than the cMEM method in terms of success rate. The MSP and cMEM methods were more focal than sLORETA with the advantage of not requiring an arbitrary selection of a hyperparameter or thresholding of reconstructed current density values to determine focus. MSP and cMEM methods were better than sLORETA in terms of spatial dispersion. Conclusions: Results suggest that the three methods are complementary and could be used together. In practice, the MSP method will be easier to use and interpret compared to sLORETA, and slightly more accurate and faster than the cMEM method. Significance: Source localization of interictal spikes from dense-array electroencephalography data has been shown to be a reliable marker of epileptic foci and useful for pre-surgical planning. The advantages of MSP make it a useful complement to other inverse solvers in clinical practice.
Fil: Fernandez Corazza, Mariano. Universidad Nacional de La Plata; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones en Electrónica, Control y Procesamiento de Señales. Universidad Nacional de La Plata. Instituto de Investigaciones en Electrónica, Control y Procesamiento de Señales; Argentina
Fil: Feng, Rui. Huashan Hospital; China
Fil: Ma, Chengxin. Huashan Hospital; China
Fil: Hu, Jie. Huashan Hospital; China
Fil: Pan, Li. Huashan Hospital; China
Fil: Luu, Phan. Brain Electrophysiology Laboratory Company; Estados Unidos. Huashan Hospital; China
Fil: Tucker, Don. University of Oregon; Estados Unidos. Brain Electrophysiology Laboratory Company; Estados Unidos
Materia
ELECTROENCEPHALOGRAPHY (EEG)
EPILEPSY
SOURCE LOCALIZATION
BAYESIAN
MULTIPLE SPARSE PRIORS
SLORETA
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/173752
Acceder
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network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Source localization of epileptic spikes using Multiple Sparse PriorsFernandez Corazza, MarianoFeng, RuiMa, ChengxinHu, JiePan, LiLuu, PhanTucker, DonELECTROENCEPHALOGRAPHY (EEG)EPILEPSYSOURCE LOCALIZATIONBAYESIANMULTIPLE SPARSE PRIORSSLORETAhttps://purl.org/becyt/ford/2.6https://purl.org/becyt/ford/2Objective: To evaluate epileptic source estimation using multiple sparse priors (MSP) inverse method and high-resolution, individual electrical head models. Methods: Accurate source localization is dependent on accurate electrical head models and appropriate inverse solvers. Using high-resolution, individual electrical head models in fifteen epilepsy patients, with surgical resection and clinical outcome as criteria for accuracy, performance of MSP method was compared against standardized low-resolution brain electromagnetic tomography (sLORETA) and coherent maximum entropy on the mean (cMEM) methods. Results: The MSP method performed similarly to the sLORETA method and slightly better than the cMEM method in terms of success rate. The MSP and cMEM methods were more focal than sLORETA with the advantage of not requiring an arbitrary selection of a hyperparameter or thresholding of reconstructed current density values to determine focus. MSP and cMEM methods were better than sLORETA in terms of spatial dispersion. Conclusions: Results suggest that the three methods are complementary and could be used together. In practice, the MSP method will be easier to use and interpret compared to sLORETA, and slightly more accurate and faster than the cMEM method. Significance: Source localization of interictal spikes from dense-array electroencephalography data has been shown to be a reliable marker of epileptic foci and useful for pre-surgical planning. The advantages of MSP make it a useful complement to other inverse solvers in clinical practice.Fil: Fernandez Corazza, Mariano. Universidad Nacional de La Plata; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones en Electrónica, Control y Procesamiento de Señales. Universidad Nacional de La Plata. Instituto de Investigaciones en Electrónica, Control y Procesamiento de Señales; ArgentinaFil: Feng, Rui. Huashan Hospital; ChinaFil: Ma, Chengxin. Huashan Hospital; ChinaFil: Hu, Jie. Huashan Hospital; ChinaFil: Pan, Li. Huashan Hospital; ChinaFil: Luu, Phan. Brain Electrophysiology Laboratory Company; Estados Unidos. Huashan Hospital; ChinaFil: Tucker, Don. University of Oregon; Estados Unidos. Brain Electrophysiology Laboratory Company; Estados UnidosElsevier Science2021-02info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/173752Fernandez Corazza, Mariano; Feng, Rui; Ma, Chengxin; Hu, Jie; Pan, Li; et al.; Source localization of epileptic spikes using Multiple Sparse Priors; Elsevier Science; Clinical Neurophysiology; 132; 2; 2-2021; 586-5971388-2457CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1016/j.clinph.2020.10.030info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S1388245720305745info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-10T13:01:17Zoai:ri.conicet.gov.ar:11336/173752instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-10 13:01:17.344CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Source localization of epileptic spikes using Multiple Sparse Priors
title Source localization of epileptic spikes using Multiple Sparse Priors
spellingShingle Source localization of epileptic spikes using Multiple Sparse Priors
Fernandez Corazza, Mariano
ELECTROENCEPHALOGRAPHY (EEG)
EPILEPSY
SOURCE LOCALIZATION
BAYESIAN
MULTIPLE SPARSE PRIORS
SLORETA
title_short Source localization of epileptic spikes using Multiple Sparse Priors
title_full Source localization of epileptic spikes using Multiple Sparse Priors
title_fullStr Source localization of epileptic spikes using Multiple Sparse Priors
title_full_unstemmed Source localization of epileptic spikes using Multiple Sparse Priors
title_sort Source localization of epileptic spikes using Multiple Sparse Priors
dc.creator.none.fl_str_mv Fernandez Corazza, Mariano
Feng, Rui
Ma, Chengxin
Hu, Jie
Pan, Li
Luu, Phan
Tucker, Don
author Fernandez Corazza, Mariano
author_facet Fernandez Corazza, Mariano
Feng, Rui
Ma, Chengxin
Hu, Jie
Pan, Li
Luu, Phan
Tucker, Don
author_role author
author2 Feng, Rui
Ma, Chengxin
Hu, Jie
Pan, Li
Luu, Phan
Tucker, Don
author2_role author
author
author
author
author
author
dc.subject.none.fl_str_mv ELECTROENCEPHALOGRAPHY (EEG)
EPILEPSY
SOURCE LOCALIZATION
BAYESIAN
MULTIPLE SPARSE PRIORS
SLORETA
topic ELECTROENCEPHALOGRAPHY (EEG)
EPILEPSY
SOURCE LOCALIZATION
BAYESIAN
MULTIPLE SPARSE PRIORS
SLORETA
purl_subject.fl_str_mv https://purl.org/becyt/ford/2.6
https://purl.org/becyt/ford/2
dc.description.none.fl_txt_mv Objective: To evaluate epileptic source estimation using multiple sparse priors (MSP) inverse method and high-resolution, individual electrical head models. Methods: Accurate source localization is dependent on accurate electrical head models and appropriate inverse solvers. Using high-resolution, individual electrical head models in fifteen epilepsy patients, with surgical resection and clinical outcome as criteria for accuracy, performance of MSP method was compared against standardized low-resolution brain electromagnetic tomography (sLORETA) and coherent maximum entropy on the mean (cMEM) methods. Results: The MSP method performed similarly to the sLORETA method and slightly better than the cMEM method in terms of success rate. The MSP and cMEM methods were more focal than sLORETA with the advantage of not requiring an arbitrary selection of a hyperparameter or thresholding of reconstructed current density values to determine focus. MSP and cMEM methods were better than sLORETA in terms of spatial dispersion. Conclusions: Results suggest that the three methods are complementary and could be used together. In practice, the MSP method will be easier to use and interpret compared to sLORETA, and slightly more accurate and faster than the cMEM method. Significance: Source localization of interictal spikes from dense-array electroencephalography data has been shown to be a reliable marker of epileptic foci and useful for pre-surgical planning. The advantages of MSP make it a useful complement to other inverse solvers in clinical practice.
Fil: Fernandez Corazza, Mariano. Universidad Nacional de La Plata; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones en Electrónica, Control y Procesamiento de Señales. Universidad Nacional de La Plata. Instituto de Investigaciones en Electrónica, Control y Procesamiento de Señales; Argentina
Fil: Feng, Rui. Huashan Hospital; China
Fil: Ma, Chengxin. Huashan Hospital; China
Fil: Hu, Jie. Huashan Hospital; China
Fil: Pan, Li. Huashan Hospital; China
Fil: Luu, Phan. Brain Electrophysiology Laboratory Company; Estados Unidos. Huashan Hospital; China
Fil: Tucker, Don. University of Oregon; Estados Unidos. Brain Electrophysiology Laboratory Company; Estados Unidos
description Objective: To evaluate epileptic source estimation using multiple sparse priors (MSP) inverse method and high-resolution, individual electrical head models. Methods: Accurate source localization is dependent on accurate electrical head models and appropriate inverse solvers. Using high-resolution, individual electrical head models in fifteen epilepsy patients, with surgical resection and clinical outcome as criteria for accuracy, performance of MSP method was compared against standardized low-resolution brain electromagnetic tomography (sLORETA) and coherent maximum entropy on the mean (cMEM) methods. Results: The MSP method performed similarly to the sLORETA method and slightly better than the cMEM method in terms of success rate. The MSP and cMEM methods were more focal than sLORETA with the advantage of not requiring an arbitrary selection of a hyperparameter or thresholding of reconstructed current density values to determine focus. MSP and cMEM methods were better than sLORETA in terms of spatial dispersion. Conclusions: Results suggest that the three methods are complementary and could be used together. In practice, the MSP method will be easier to use and interpret compared to sLORETA, and slightly more accurate and faster than the cMEM method. Significance: Source localization of interictal spikes from dense-array electroencephalography data has been shown to be a reliable marker of epileptic foci and useful for pre-surgical planning. The advantages of MSP make it a useful complement to other inverse solvers in clinical practice.
publishDate 2021
dc.date.none.fl_str_mv 2021-02
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/173752
Fernandez Corazza, Mariano; Feng, Rui; Ma, Chengxin; Hu, Jie; Pan, Li; et al.; Source localization of epileptic spikes using Multiple Sparse Priors; Elsevier Science; Clinical Neurophysiology; 132; 2; 2-2021; 586-597
1388-2457
CONICET Digital
CONICET
url http://hdl.handle.net/11336/173752
identifier_str_mv Fernandez Corazza, Mariano; Feng, Rui; Ma, Chengxin; Hu, Jie; Pan, Li; et al.; Source localization of epileptic spikes using Multiple Sparse Priors; Elsevier Science; Clinical Neurophysiology; 132; 2; 2-2021; 586-597
1388-2457
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1016/j.clinph.2020.10.030
info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S1388245720305745
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Elsevier Science
publisher.none.fl_str_mv Elsevier Science
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 12.48226

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