去同步指数：一种用于探索致痫网络的新型连通性方法
Desynchronization Index: a New Connectivity Approach for Exploring Epileptogenic Networks
AI总结：
本研究针对耐药性癫痫中致痫区定位准确率不足的问题，提出基于去同步指数的SEEG分析算法，在20例患者数据集上验证其性能优于现有致痫性指数，二者结合可进一步提升定位精度，为癫痫发作机制研究提供新视角。
摘要：
在耐药性癫痫中，立体脑电图（SEEG）监测是识别致痫区（EZ）的最有效技术之一，而EZ定位是癫痫手术的基本前提。尽管近期技术有所进步，SEEG记录的解读仍然存在难度，SEEG引导手术的成功率仍低于70%。在本研究中，我们开发了一种用于SEEG分析的新型计算框架，最终目标是提高EZ定位的准确性。具体而言，我们验证了如下假设：致痫区域在癫痫发作前倾向于表现出独立行为，因此会与邻近脑结构去同步。为此，我们设计了去同步指数（DI）算法，该算法将EZ识别为在发作转换期间与SEEG网络断开连接的通道子集。我们在包含20名患者的临床数据集上，将DI算法与EZ定位最常用的工具之一——致痫性指数（EI）进行对比评估，以SEEG监测结束时接受热凝的通道作为检测目标。结果显示，DI在ROC曲线下面积（AUC）方面优于EI（AUC=0.86对比AUC=0.83），而将两种算法整合到单一框架中可实现最佳性能（AUC=0.88）。总体而言，DI算法凸显了难以通过视觉检查发现的异常连通性模式，提高了EZ定位的准确性，并为癫痫发作的动力学机制提供了新见解。
英文摘要：
In drug-resistant epilepsy, Stereo-Electroencephalography (SEEG) monitoring is one of the most effective techniques to identify the Epileptogenic Zone (EZ), the fundamental prerequisite for epilepsy surgery. Despite recent technological advances, SEEG recordings remain difficult to interpret, and SEEG-guided surgery still achieves success rates below 70%. In this work, we develop a novel computational framework for SEEG analysis, with the ultimate aim of improving the accuracy in EZ definition. Specifically, we investigate the hypothesis that epileptogenic regions exhibit a tendency to behave independently and thus desynchronize from neighboring brain structures before seizure onset. To this end, we design the Desynchronization Index (DI), an algorithm that identifies the Epileptogenic Zone (EZ) as the subset of channels that disconnect from the SEEG network during the ictal transition. We evaluate the DI algorithm against Epileptogenicity Index (EI), one of the most common tools for EZ definition, on a clinical dataset of 20 patients, considering the channels that were thermocoagulated at the end of SEEG monitoring as the detection target. Our results show that DI overcomes EI in terms of area under the ROC curve (AUC=0.86 vs. AUC=0.83), while combining the two algorithms into a single framework leads to the best performance (AUC=0.88). Overall, the DI algorithm underscores anomalous connectivity patterns that are difficult to detect through visual inspection, improving the accuracy in the EZ definition and providing new insights into the dynamics of seizure generation.
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