Virtual epileptic patient brain modeling: Relationships with seizure onset and surgical outcome

OBJECTIVE: The virtual epileptic patient (VEP) is a large‐scale brain modeling method based on virtual brain technology, using stereoelectroencephalography (SEEG), anatomical data (magnetic resonance imaging [MRI] and connectivity), and a computational neuronal model to provide computer simulations of a patient's seizures. VEP has potential interest in the presurgical evaluation of drug‐resistant epilepsy by identifying regions most likely to generate seizures. We aimed to assess the performance of the VEP approach in estimating the epileptogenic zone and in predicting surgical outcome. METHODS: VEP modeling was retrospectively applied in a cohort of 53 patients with pharmacoresistant epilepsy and available SEEG, T1‐weighted MRI, and diffusion‐weighted MRI. Precision recall was used to compare the regions identified as epileptogenic by VEP (EZ(VEP)) to the epileptogenic zone defined by clinical analysis incorporating the Epileptogenicity Index (EI) method (EZ(C)). In 28 operated patients, we compared the VEP results and clinical analysis with surgical outcome. RESULTS: VEP showed a precision of 64% and a recall of 44% for EZ(VEP) detection compared to EZ(C). There was a better concordance of VEP predictions with clinical results, with higher precision (77%) in seizure‐free compared to non‐seizure‐free patients. Although the completeness of resection was significantly correlated with surgical outcome for both EZ(C) and EZ(VEP), there was a significantly higher number of regions defined as epileptogenic exclusively by VEP that remained nonresected in non‐seizure‐free patients. SIGNIFICANCE: VEP is the first computational model that estimates the extent and organization of the epileptogenic zone network. It is characterized by good precision in detecting epileptogenic regions as defined by a combination of visual analysis and EI. The potential impact of VEP on improving surgical prognosis remains to be exploited. Analysis of factors limiting the performance of the actual model is crucial for its further development.