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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 application.
Cuesta, Pablo; Bruña, Ricardo; Shah, Ekta; Laohathai, Christopher; Garcia-Tarodo, Stephanie; Funke, Michael; Von Allmen, Gretchen; Maestú, Fernando.
Afiliación
  • Cuesta P; Department of Radiology, Rehabilitation and Physiotherapy, Universidad Complutense de Madrid, Madrid, 28040, Spain.
  • Bruña R; Center for Cognitive and Computational Neuroscience, Complutense University of Madrid, Madrid, 28040, Spain.
  • Shah E; Instituto de Investigación Sanitaria San Carlos (IdISSC), Madrid, 28040, Spain.
  • Laohathai C; Department of Radiology, Rehabilitation and Physiotherapy, Universidad Complutense de Madrid, Madrid, 28040, Spain.
  • Garcia-Tarodo S; Instituto de Investigación Sanitaria San Carlos (IdISSC), Madrid, 28040, Spain.
  • Funke M; Department of Pediatrics, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA.
  • Von Allmen G; Department of Neurology, Saint Louis University, Saint Louis, MO 63110, USA.
  • Maestú F; Département de la femme, de l'enfant et de l'adolescent, Hôpital des Enfants - Hôpitaux Universitaires de Genève, Geneva, 1211 Genève 14, Switzerland.
Brain Commun ; 5(3): fcad168, 2023.
Article en En | MEDLINE | ID: mdl-37274829
ABSTRACT
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.
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Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Tipo de estudio: Guideline / Observational_studies / Risk_factors_studies Idioma: En Revista: Brain Commun Año: 2023 Tipo del documento: Article País de afiliación: España

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Tipo de estudio: Guideline / Observational_studies / Risk_factors_studies Idioma: En Revista: Brain Commun Año: 2023 Tipo del documento: Article País de afiliación: España