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

Abstract

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 (EZVEP) to the epileptogenic zone defined by clinical analysis incorporating the Epileptogenicity Index (EI) method (EZC). 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 EZVEP detection compared to EZC. 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 EZC and EZVEP, 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.

VEP for VIP Care: Patient-Specific Modeling in Epilepsy Surgery

“…The highlighted study by Makhalova and colleagues is part of a growing body of work addressing these questions. 4 The authors examine the potential role of patient-specific modeling using the Virtual Epileptic Patient (VEP) in identifying EZ networks for surgery in DRE. 5 Virtual epileptic patient is a probabilistic modeling framework based on the Virtual Brain technology, described by the authors in a series of recent studies.…”

Brain sodium MRI-derived priors support the estimation of epileptogenic zones using personalized model-based methods in Epilepsy

“…Several prior knowledge can be incorporated such as plausible range of model parameters, dynamics of unobserved brain state, MRI lesions or even the clinical hypothesis of EZN for instance. The previous VEP priors were mainly based on SEEG recordings processing, providing the location of the initiation of the seizures (Hashemi et al, 2020;Makhalova et al, 2022;Vattikonda et al, 2021;H. E. Wang et al, 2022).…”

“…The VEP contains modules providing an estimation of the EZN, but also modules virtualizing surgery strategies (H. E. Wang et al, 2022). The EZN estimation via VEP has been validated not only while modeling seizures recorded with stereotactic-electroencephalography (SEEG) (Jirsa et al, 2017;Makhalova et al, 2022;H. E. Wang et al, 2022) but also on synthetic data (Hashemi et al, 2020;Vattikonda et al, 2021).…”

“…E. Wang et al, 2022) but also on synthetic data (Hashemi et al, 2020;Vattikonda et al, 2021). In addition, the VEP has been compared in a retrospective study of 53 patients to EI type quantification methods and to clinical analysis showing encouraging performances (Makhalova et al, 2022). The VEP provides the first fully nonlinear system analysis of whole brain NMM and works on the whole brain source spaces rather than the sensor recording spaces alone.…”

The role of epidemic spreading in seizure dynamics and epilepsy surgery

“…Further studies within this modelling framework also found a better match between the hypothesized EZ and propagation zone (i.e. the first regions to which ictal activity propagates to) for SF than NSF patients [42, 43]. On a virtual resection study, Sip et al [44] found that the effect of the resection in the model correlated with surgical outcome, so that patients with Engel score I and II presented a significantly larger effect of virtual resections in the model.…”