Schönberger Jan, Knopf Anja, Klotz Kerstin Alexandra, Dümpelmann Matthias, Schulze-Bonhage Andreas, Jacobs Julia
Epilepsy Center, Medical Center, University of Freiburg, 79106 Freiburg, Germany.
Department of Neuropediatrics and Muscle Disorders, Medical Center, University of Freiburg, 79106 Freiburg, Germany.
Brain Sci. 2021 Apr 24;11(5):538. doi: 10.3390/brainsci11050538.
Ripple oscillations (80-250 Hz) are a promising biomarker of epileptic activity, but are also involved in memory consolidation, which impairs their value as a diagnostic tool. Distinguishing physiologic from epileptic ripples has been particularly challenging because usually, invasive recordings are only performed in patients with refractory epilepsy. Here, we identified 'healthy' brain areas based on electrical stimulation and hypothesized that these regions specifically generate 'pure' ripples not coupled to spikes. Intracranial electroencephalography (EEG) recorded with subdural grid electrodes was retrospectively analyzed in 19 patients with drug-resistant focal epilepsy. Interictal spikes and ripples were automatically detected in slow-wave sleep using the publicly available Delphos software. We found that rates of spikes, ripples and ripples coupled to spikes ('spike-ripples') were higher inside the seizure-onset zone ( < 0.001). A comparison of receiver operating characteristic curves revealed that spike-ripples slightly delineated the seizure-onset zone channels, but did this significantly better than spikes ( < 0.001). Ripples were more frequent in the eloquent neocortex than in the remaining non-seizure onset zone areas ( < 0.001). This was due to the higher rates of 'pure' ripples ( < 0.001; median rates 3.3/min vs. 1.4/min), whereas spike-ripple rates were not significantly different ( = 0.87). 'Pure' ripples identified 'healthy' channels significantly better than chance ( < 0.001). Our findings suggest that, in contrast to epileptic spike-ripples, 'pure' ripples are mainly physiological. They may be considered, in addition to electrical stimulation, to delineate eloquent cortex in pre-surgical patients. Since we applied open source software for detection, our approach may be generally suited to tackle a variety of research questions in epilepsy and cognitive science.
涟漪振荡（80 - 250赫兹）是癫痫活动一种很有前景的生物标志物，但也参与记忆巩固过程，这削弱了它们作为诊断工具的价值。区分生理性涟漪和癫痫性涟漪一直极具挑战性，因为通常仅对难治性癫痫患者进行侵入性记录。在此，我们基于电刺激确定了“健康”脑区，并假设这些区域专门产生未与尖峰耦合的“纯”涟漪。对19例耐药性局灶性癫痫患者使用硬膜下网格电极记录的颅内脑电图（EEG）进行了回顾性分析。使用公开可用的德尔福斯软件在慢波睡眠中自动检测发作间期尖峰和涟漪。我们发现，发作起始区内尖峰、涟漪以及与尖峰耦合的涟漪（“尖峰 - 涟漪”）的发生率更高（<0.001）。接受者操作特征曲线比较显示，尖峰 - 涟漪略微界定了发作起始区通道，但比尖峰的界定效果显著更好（<0.001）。在明确的新皮质中，涟漪比其余非发作起始区区域更频繁（<0.001）。这是由于“纯”涟漪的发生率更高（<0.001；中位数发生率分别为3.3次/分钟和1.4次/分钟），而尖峰 - 涟漪的发生率无显著差异（=0.87）。“纯”涟漪对“健康”通道的识别显著优于随机水平（<0.001）。我们的研究结果表明，与癫痫性尖峰 - 涟漪不同，“纯”涟漪主要是生理性的。除电刺激外，它们可能有助于在术前患者中界定明确的皮质。由于我们应用开源软件进行检测，我们的方法可能普遍适用于解决癫痫和认知科学中的各种研究问题。
