Defining a bio-electrical marker for the brain area responsible for initiating a seizure remains an unsolved problem. Fast gamma activity has been identified as the most specific marker for seizure onset, but conflicting results have been reported. In this study, we describe an alternative marker, based on an objective description of interictal to ictal transition, with the aim of identifying a time-frequency pattern or ‘fingerprint’ that can differentiate the epileptogenic zone from areas of propagation. Seventeen patients who underwent stereoelectroencephalography were included in the study. Each had seizure onset characterized by sustained gamma activity and were seizure-free after tailored resection or laser ablation. We postulated that the epileptogenic zone was always located inside the resection region based on seizure freedom following surgery. To characterize the ictal frequency pattern, we applied the Morlet wavelet transform to data from each pair of adjacent intracerebral electrode contacts. Based on a visual assessment of the time-frequency plots, we hypothesized that a specific time-frequency pattern in the epileptogenic zone should include a combination of (i) sharp transients or spikes; preceding (ii) multiband fast activity concurrent; with (iii) suppression of lower frequencies. To test this hypothesis, we developed software that automatically extracted each of these features from the time-frequency data. We then used a support vector machine to classify each contact-pair as being within epileptogenic zone or not, based on these features. Our machine learning system identified this pattern in 15 of 17 patients. The total number of identified contacts across all patients was 64, with 58 localized inside the resected area. Subsequent quantitative analysis showed strong correlation between maximum frequency of fast activity and suppression inside the resection but not outside. We did not observe significant discrimination power using only the maximum frequency or the timing of fast activity to differentiate contacts either between resected and non-resected regions or between contacts identified as epileptogenic versus non-epileptogenic. Instead of identifying a single frequency or a single timing trait, we observed the more complex pattern described above that distinguishes the epileptogenic zone. This pattern encompasses interictal to ictal transition and may extend until seizure end. Its time-frequency characteristics can be explained in light of recent models emphasizing the role of fast inhibitory interneurons acting on pyramidal cells as a prominent mechanism in seizure triggering. The pattern clearly differentiates the epileptogenic zone from areas of propagation and, as such, represents an epileptogenic zone ‘fingerprint’.

中文翻译：

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人癫痫病中癫痫发生区的指纹

定义负责引发癫痫发作的大脑区域的生物电标记仍然是一个尚未解决的问题。快速伽马活性已被确定为癫痫发作最具体的标志物，但已报道了相互矛盾的结果。在这项研究中，我们基于客观的间质到间质过渡描述了一种替代标记，目的是识别可以区分癫痫源性区域和传播区域的时频模式或“指纹”。该研究纳入了十七名接受立体脑电图检查的患者。每个患者都有以持续的伽马活动为特征的癫痫发作，并且在经过专门切除或激光消融后无癫痫发作。我们假定，基于手术后的癫痫发作自由度，癫痫发生区始终位于切除区域内。为了表征初始频率模式，我们将Morlet小波变换应用于来自每对相邻的脑内电极触点的数据。基于对时间-频率图的视觉评估，我们假设在癫痫发生区中的特定时间-频率模式应包括（i）尖锐的瞬变或尖峰的组合；之前（ii）多频段快速活动并发；（iii）抑制低频。为了验证这一假设，我们开发了可以从时频数据中自动提取每个功能的软件。然后，基于这些功能，我们使用支持向量机将每个接触对归为是否处于致癫痫区域内。我们的机器学习系统在17名患者中的15名患者中发现了这种模式。在所有患者中确定的接触总数为64，其中58个位于切除区域内。随后的定量分析显示，快速活动的最大频率与切除术内部而非外部的抑制之间有很强的相关性。我们没有观察到仅使用最大频率或快速活动的时间来区分已切除和未切除区域之间或被识别为癫痫发生与非癫痫发生的接触之间的接触的显着辨别力。而不是识别单个频率或单个时间性状，我们观察到上述区分癫痫发生区的更复杂的模式。这种模式包括从发作期到发作期的过渡，并且可能一直持续到癫痫发作结束为止。它的时频特性可以根据最近的模型加以解释，该模型强调快速抑制的中间神经元作用于锥体细胞是癫痫发作触发中的重要机制。该模式清楚地将癫痫源区与传播区域区分开，因此代表了癫痫区“指纹”。