An individual data-driven virtual resection model based on epileptic network dynamics in children with intractable epilepsy: a magnetoencephalography interictal activity application
Epilepsy surgery continues to be a recommended treatment for intractable (medication-resistant) epilepsy; however, 30–70% of epilepsy surgery patients can continue to have seizures. Surgical failures are often associated with incomplete resection or inaccurate localization of the epileptogenic zone....
| Autores: | , , , , , , , |
|---|---|
| Formato: | artículo |
| Fecha de publicación: | 2023 |
| País: | España |
| Recursos: | Universidad Complutense de Madrid (UCM) |
| Repositorio: | Docta Complutense |
| Idioma: | inglés |
| OAI Identifier: | oai:docta.ucm.es:20.500.14352/100916 |
| Acesso em linha: | https://hdl.handle.net/20.500.14352/100916 |
| Access Level: | acceso abierto |
| Palavra-chave: | 612.8 616.853-053.2 Magnetoencephalography interictal activity computational neurosurgery children epilepsy Neurociencias (Medicina) 2490 Neurociencias |
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An individual data-driven virtual resection model based on epileptic network dynamics in children with intractable epilepsy: a magnetoencephalography interictal activity applicationCuesta Prieto, PabloBruña Fernández, RicardoShah, EktaLaohathai, ChristopherGarcia-Tarodo, StephanieFunke, MichaelVon Allmen, GretchenMaestu Unturbe, Fernando612.8616.853-053.2Magnetoencephalographyinterictal activitycomputational neurosurgerychildren epilepsyNeurociencias (Medicina)2490 NeurocienciasEpilepsy surgery continues to be a recommended treatment for intractable (medication-resistant) epilepsy; however, 30–70% of epilepsy surgery patients can continue to have seizures. Surgical failures are often associated with incomplete resection or inaccurate localization of the epileptogenic zone. This retrospective study aims to improve surgical outcome through in silico testing of surgical hypotheses through a personalized computational neurosurgery model created from individualized patient’s magnetoencephalography recording and MRI. The framework assesses the extent of the epileptic network and evaluates underlying spike dynamics, resulting in identification of one single brain volume as a candidate for resection. Dynamic-locked networks were utilized for virtual cortical resection. This in silico protocol was tested in a cohort of 24 paediatric patients with focal drug-resistant epilepsy who underwent epilepsy surgery. Of 24 patients who were included in the analysis, 79% (19 of 24) of the models agreed with the patient's clinical surgery outcome and 21% (5 of 24) were considered as model failures (accuracy 0.79, sensitivity 0.77, specificity 0.82). Patients with unsuccessful surgery outcome typically showed a model cluster outside of the resected cavity, while those with successful surgery showed the cluster model within the cavity. Two of the model failures showed the cluster in the vicinity of the resected tissue and either a functional disconnection or lack of precision of the magnetoencephalography–MRI overlapping could explain the results. Two other cases were seizure free for 1 year but developed late recurrence. This is the first study that provides in silico personalized protocol for epilepsy surgery planning using magnetoencephalography spike network analysis. This model could provide complementary information to the traditional pre-surgical assessment methods and increase the proportion of patients achieving seizure-free outcome from surgery.Oxford University PressUniversidad Complutense de Madrid20232023-05-2520232023-05-25journal articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/20.500.14352/100916reponame:Docta Complutenseinstname:Universidad Complutense de Madrid (UCM)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2Attribution 4.0 Internationalhttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:docta.ucm.es:20.500.14352/1009162026-06-02T12:44:21Z |
| dc.title.none.fl_str_mv |
An individual data-driven virtual resection model based on epileptic network dynamics in children with intractable epilepsy: a magnetoencephalography interictal activity application |
| title |
An individual data-driven virtual resection model based on epileptic network dynamics in children with intractable epilepsy: a magnetoencephalography interictal activity application |
| spellingShingle |
An individual data-driven virtual resection model based on epileptic network dynamics in children with intractable epilepsy: a magnetoencephalography interictal activity application Cuesta Prieto, Pablo 612.8 616.853-053.2 Magnetoencephalography interictal activity computational neurosurgery children epilepsy Neurociencias (Medicina) 2490 Neurociencias |
| title_short |
An individual data-driven virtual resection model based on epileptic network dynamics in children with intractable epilepsy: a magnetoencephalography interictal activity application |
| title_full |
An individual data-driven virtual resection model based on epileptic network dynamics in children with intractable epilepsy: a magnetoencephalography interictal activity application |
| title_fullStr |
An individual data-driven virtual resection model based on epileptic network dynamics in children with intractable epilepsy: a magnetoencephalography interictal activity application |
| title_full_unstemmed |
An individual data-driven virtual resection model based on epileptic network dynamics in children with intractable epilepsy: a magnetoencephalography interictal activity application |
| title_sort |
An individual data-driven virtual resection model based on epileptic network dynamics in children with intractable epilepsy: a magnetoencephalography interictal activity application |
| dc.creator.none.fl_str_mv |
Cuesta Prieto, Pablo Bruña Fernández, Ricardo Shah, Ekta Laohathai, Christopher Garcia-Tarodo, Stephanie Funke, Michael Von Allmen, Gretchen Maestu Unturbe, Fernando |
| author |
Cuesta Prieto, Pablo |
| author_facet |
Cuesta Prieto, Pablo Bruña Fernández, Ricardo Shah, Ekta Laohathai, Christopher Garcia-Tarodo, Stephanie Funke, Michael Von Allmen, Gretchen Maestu Unturbe, Fernando |
| author_role |
author |
| author2 |
Bruña Fernández, Ricardo Shah, Ekta Laohathai, Christopher Garcia-Tarodo, Stephanie Funke, Michael Von Allmen, Gretchen Maestu Unturbe, Fernando |
| author2_role |
author author author author author author author |
| dc.contributor.none.fl_str_mv |
Universidad Complutense de Madrid |
| dc.subject.none.fl_str_mv |
612.8 616.853-053.2 Magnetoencephalography interictal activity computational neurosurgery children epilepsy Neurociencias (Medicina) 2490 Neurociencias |
| topic |
612.8 616.853-053.2 Magnetoencephalography interictal activity computational neurosurgery children epilepsy Neurociencias (Medicina) 2490 Neurociencias |
| description |
Epilepsy surgery continues to be a recommended treatment for intractable (medication-resistant) epilepsy; however, 30–70% of epilepsy surgery patients can continue to have seizures. Surgical failures are often associated with incomplete resection or inaccurate localization of the epileptogenic zone. This retrospective study aims to improve surgical outcome through in silico testing of surgical hypotheses through a personalized computational neurosurgery model created from individualized patient’s magnetoencephalography recording and MRI. The framework assesses the extent of the epileptic network and evaluates underlying spike dynamics, resulting in identification of one single brain volume as a candidate for resection. Dynamic-locked networks were utilized for virtual cortical resection. This in silico protocol was tested in a cohort of 24 paediatric patients with focal drug-resistant epilepsy who underwent epilepsy surgery. Of 24 patients who were included in the analysis, 79% (19 of 24) of the models agreed with the patient's clinical surgery outcome and 21% (5 of 24) were considered as model failures (accuracy 0.79, sensitivity 0.77, specificity 0.82). Patients with unsuccessful surgery outcome typically showed a model cluster outside of the resected cavity, while those with successful surgery showed the cluster model within the cavity. Two of the model failures showed the cluster in the vicinity of the resected tissue and either a functional disconnection or lack of precision of the magnetoencephalography–MRI overlapping could explain the results. Two other cases were seizure free for 1 year but developed late recurrence. This is the first study that provides in silico personalized protocol for epilepsy surgery planning using magnetoencephalography spike network analysis. This model could provide complementary information to the traditional pre-surgical assessment methods and increase the proportion of patients achieving seizure-free outcome from surgery. |
| publishDate |
2023 |
| dc.date.none.fl_str_mv |
2023 2023-05-25 2023 2023-05-25 |
| dc.type.none.fl_str_mv |
journal article http://purl.org/coar/resource_type/c_6501 VoR http://purl.org/coar/version/c_970fb48d4fbd8a85 |
| dc.type.openaire.fl_str_mv |
info:eu-repo/semantics/article |
| format |
article |
| dc.identifier.none.fl_str_mv |
https://hdl.handle.net/20.500.14352/100916 |
| url |
https://hdl.handle.net/20.500.14352/100916 |
| dc.language.none.fl_str_mv |
Inglés eng |
| language_invalid_str_mv |
Inglés |
| language |
eng |
| dc.rights.none.fl_str_mv |
open access http://purl.org/coar/access_right/c_abf2 Attribution 4.0 International http://creativecommons.org/licenses/by/4.0/ |
| dc.rights.openaire.fl_str_mv |
info:eu-repo/semantics/openAccess |
| rights_invalid_str_mv |
open access http://purl.org/coar/access_right/c_abf2 Attribution 4.0 International http://creativecommons.org/licenses/by/4.0/ |
| eu_rights_str_mv |
openAccess |
| dc.format.none.fl_str_mv |
application/pdf |
| dc.publisher.none.fl_str_mv |
Oxford University Press |
| publisher.none.fl_str_mv |
Oxford University Press |
| dc.source.none.fl_str_mv |
reponame:Docta Complutense instname:Universidad Complutense de Madrid (UCM) |
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Universidad Complutense de Madrid (UCM) |
| reponame_str |
Docta Complutense |
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Docta Complutense |
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| repository.mail.fl_str_mv |
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