APHM, Timone Hospital, Epileptology and Cerebral Rhythmology, Marseille, France.
CNRS, CRMBM, Aix Marseille University, Marseille, France.
Epilepsia. 2022 Aug;63(8):1942-1955. doi: 10.1111/epi.17310. Epub 2022 Jun 6.
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.
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 ) to the epileptogenic zone defined by clinical analysis incorporating the Epileptogenicity Index (EI) method (EZ ). In 28 operated patients, we compared the VEP results and clinical analysis with surgical outcome.
VEP showed a precision of 64% and a recall of 44% for EZ detection compared to EZ . 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 and EZ , there was a significantly higher number of regions defined as epileptogenic exclusively by VEP that remained nonresected in non-seizure-free patients.
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.
虚拟癫痫患者（VEP）是一种基于虚拟脑技术的大型脑建模方法，使用立体脑电图（SEEG）、解剖数据（磁共振成像[MRI]和连接）和计算神经元模型，为患者的癫痫发作提供计算机模拟。VEP 通过识别最有可能引发癫痫发作的区域，在耐药性癫痫的术前评估中具有潜在的兴趣。我们旨在评估 VEP 方法在估计致痫区和预测手术结果方面的性能。
回顾性地将 VEP 模型应用于 53 例药物难治性癫痫患者的队列中，这些患者有可获得的 SEEG、T1 加权 MRI 和弥散加权 MRI。使用精确召回来比较 VEP 确定的致痫区（EZ）与包含致痫性指数（EI）方法的临床分析定义的致痫区（EZ）。在 28 例接受手术的患者中，我们比较了 VEP 结果和临床分析与手术结果的关系。
与 EZ相比，VEP 对 EZ的检测精度为 64%，召回率为 44%。VEP 预测与临床结果的一致性更好，在无癫痫发作的患者中，精度更高（77%）。尽管 EZ 和 EZ 的完全切除与手术结果显著相关，但在无癫痫发作的患者中，仅由 VEP 定义为致痫的区域有更多的未切除区域。
VEP 是第一个估计致痫区网络范围和组织的计算模型。它的特点是在检测视觉分析和 EI 相结合定义的致痫区方面具有良好的精度。VEP 对改善手术预后的潜在影响仍有待开发。分析限制实际模型性能的因素对于其进一步发展至关重要。
