Dollomaja Borana, Wang Huifang E, Guye Maxime, Makhalova Julia, Bartolomei Fabrice, Jirsa Viktor K
Institut de Neurosciences des Systèmes (INS) UMR1106, INSERM, Aix-Marseille Université, Marseille, France.
CRMBM, CNRS, Aix-Marseille Université, Marseille, France.
PLoS Comput Biol. 2025 Apr 11;21(4):e1012911. doi: 10.1371/journal.pcbi.1012911. eCollection 2025 Apr.
Epilepsy is a prevalent brain disorder, characterized by sudden, abnormal brain activity, making it difficult to live with. One-third of people with epilepsy do not respond to anti-epileptic drugs. Drug-resistant epilepsy is treated with brain surgery. Successful surgical treatment relies on identifying brain regions responsible for seizure onset, known as epileptogenic zones (EZ). Despite various methods for EZ estimation, evaluating their efficacy remains challenging due to a lack of ground truth for empirical data. To address this, we generated and evaluated a cohort of 30 virtual epilepsy patients, using patient-specific anatomical and functional data from 30 real drug-resistant epilepsy patients. This personalized modeling approach, based on each patient's brain data, is called a virtual brain twin. For each virtual patient, we provided data that included anatomically parcellated brain regions, structural connectivity, reconstructed intracranial electrodes, simulated brain activity at both the brain region and electrode levels, and key parameters of the virtual brain twin. These key parameters, which include the EZ hypothesis, serve as the ground truth for simulated brain activity. For each virtual brain twin, we generated synthetic spontaneous seizures, stimulation-induced seizures and interictal activity. We systematically evaluated these simulated brain signals by quantitatively comparing them against their corresponding empirical intracranial recordings. Simulated signals based on patient-specific EZ captured spatio-temporal seizure generation and propagation. Through in-silico exploration of stimulation parameters, we also demonstrated the role of patient-specific stimulation location and amplitude in reproducing empirically stimulated seizures. The virtual epileptic cohort is openly available, and can be used to systematically evaluate methods for the estimation of EZ or source localization using ground truth EZ parameters and source signals.
癫痫是一种常见的脑部疾病，其特征是大脑突然出现异常活动，给患者的生活带来诸多不便。三分之一的癫痫患者对抗癫痫药物没有反应。耐药性癫痫通过脑部手术进行治疗。成功的手术治疗依赖于确定引发癫痫发作的脑区，即癫痫源区（EZ）。尽管有多种估计EZ的方法，但由于缺乏实证数据的地面真值，评估这些方法的有效性仍然具有挑战性。为了解决这个问题，我们利用30名真实耐药性癫痫患者的特定患者解剖和功能数据，生成并评估了一个由30名虚拟癫痫患者组成的队列。这种基于每个患者脑部数据的个性化建模方法被称为虚拟脑双胞胎。对于每个虚拟患者，我们提供的数据包括解剖学上分割的脑区、结构连接性、重建的颅内电极、脑区和电极水平的模拟脑活动，以及虚拟脑双胞胎的关键参数。这些关键参数，包括EZ假设，作为模拟脑活动的地面真值。对于每个虚拟脑双胞胎，我们生成了合成的自发性癫痫发作、刺激诱发的癫痫发作和发作间期活动。我们通过将这些模拟脑信号与相应的经验性颅内记录进行定量比较，系统地评估了它们。基于患者特定EZ的模拟信号捕捉到了癫痫发作的时空产生和传播。通过对刺激参数的计算机模拟探索，我们还展示了患者特定的刺激位置和幅度在再现经验性刺激癫痫发作中的作用。虚拟癫痫队列是公开可用的，可用于使用地面真值EZ参数和源信号系统地评估EZ估计或源定位的方法。
