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- W4313279569 abstract "Drug-refractory epilepsy (DRE) may be treated by surgical intervention. Intracranial EEG has been widely used to localize the epileptogenic zone (EZ). Most studies of epileptic network focus on the features of EZ nodes, such as centrality and degrees. It is difficult to apply those features to the treatment of individual patients. In this study, we proposed a spatial neighbor expansion approach for EZ localization based on a neural computational model and epileptic network reconstruction. The virtual resection method was also used to validate the effectiveness of our approach. The electrocorticography (ECoG) data from 11 patients with DRE were analyzed in this study. Both interictal data and surgical resection regions were used. The results showed that the rate of consistency between the localized regions and the surgical resections in patients with good outcomes was higher than that in patients with poor outcomes. The average deviation distance of the localized region for patients with good outcomes and poor outcomes were 15 mm and 36 mm, respectively. Outcome prediction showed that the patients with poor outcomes could be improved when the brain regions localized by the proposed approach were treated. This study provides a quantitative analysis tool for patient-specific measures for potential surgical treatment of epilepsy.药物难治性癫痫可通过手术进行治疗，颅内脑电图已广泛应用于致痫区定位。目前，多数癫痫网络分析依赖于致痫区节点的属性，比如中心性、出入度等，上述特征有一定片面性，且难以应用到个体化患者治疗中。本文提出了一种新的致痫区定位方法，通过引入神经计算模型，重构动态化的癫痫网络，结合虚拟切除和空间邻域扩张方法，来实现致痫区定位。本文分析了11位难治性癫痫患者的皮层脑电图（ECoG）数据，利用患者发作间期数据和手术切除区域和疗效进行分析。结果表明，在术后疗效好的患者中，定位区域与手术区域的一致率明显高于术后疗效差的患者。术后效果好的患者和效果差的患者定位区域的平均偏离距离分别为15 mm和36 mm。预后分析显示，以本文定位区域进行手术切除，当前疗效差的患者能获得更好的治疗效果。本文方法能够为难治性癫痫患者手术治疗提供个体化方案，为癫痫外科提供一种量化的分析工具。." @default.
- W4313279569 created "2023-01-06" @default.
- W4313279569 creator A5073661331 @default.
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- W4313279569 date "2022-12-25" @default.
- W4313279569 modified "2023-09-29" @default.
- W4313279569 title "[Localization of epileptogenic zone based on reconstruction of dynamical epileptic network and virtual resection]." @default.
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- W4313279569 doi "https://doi.org/10.7507/1001-5515.202205048" @default.
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