RT Journal Article
T1 An individual data-driven virtual resection model based on epileptic network dynamics in children with intractable epilepsy: a magnetoencephalography interictal activity application
A1 Cuesta Prieto, Pablo
A1 Bruña Fernández, Ricardo
A1 Shah, Ekta
A1 Laohathai, Christopher
A1 Garcia-Tarodo, Stephanie
A1 Funke, Michael
A1 Von Allmen, Gretchen
A1 Maestu Unturbe, Fernando
AB 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.
PB Oxford University Press
SN 2632-1297
YR 2023
FD 2023-05-25
LK https://hdl.handle.net/20.500.14352/100916
UL https://hdl.handle.net/20.500.14352/100916
LA eng
NO Pablo Cuesta, Ricardo Bruña, Ekta Shah, Christopher Laohathai, Stephanie Garcia-Tarodo, Michael Funke, Gretchen Von Allmen, Fernando Maestú, An individual data-driven virtual resection model based on epileptic network dynamics in children with intractable epilepsy: a magnetoencephalography interictal activity application, Brain Communications, Volume 5, Issue 3, 2023, fcad168, https://doi.org/10.1093/braincomms/fcad168
DS Docta Complutense
RD 18 jul 2024