Boston University, Department of Mathematics and Statistics, Boston, MA, USA.
Massachusetts General Hospital, Department of Neurology, Boston, MA, USA.
Brain. 2019 May 1;142(5):1296-1309. doi: 10.1093/brain/awz059.
In the past decade, brief bursts of fast oscillations in the ripple range have been identified in the scalp EEG as a promising non-invasive biomarker for epilepsy. However, investigation and clinical application of this biomarker have been limited because standard approaches to identify these brief, low amplitude events are difficult, time consuming, and subjective. Recent studies have demonstrated that ripples co-occurring with epileptiform discharges ('spike ripple events') are easier to detect than ripples alone and have greater pathological significance. Here, we used objective techniques to quantify spike ripples and test whether this biomarker predicts seizure risk in childhood epilepsy. We evaluated spike ripples in scalp EEG recordings from a prospective cohort of children with a self-limited epilepsy syndrome, benign epilepsy with centrotemporal spikes, and healthy control children. We compared the rate of spike ripples between children with epilepsy and healthy controls, and between children with epilepsy during periods of active disease (active, within 1 year of seizure) and after a period of sustained seizure-freedom (seizure-free, >1 year without seizure), using semi-automated and automated detection techniques. Spike ripple rate was higher in subjects with active epilepsy compared to healthy controls (P = 0.0018) or subjects with epilepsy who were seizure-free ON or OFF medication (P = 0.0018). Among epilepsy subjects with spike ripples, each month seizure-free decreased the odds of a spike ripple by a factor of 0.66 [95% confidence interval (0.47, 0.91), P = 0.021]. Comparing the diagnostic accuracy of the presence of at least one spike ripple versus a classic spike event to identify group, we found comparable sensitivity and negative predictive value, but greater specificity and positive predictive value of spike ripples compared to spikes (P = 0.016 and P = 0.006, respectively). We found qualitatively consistent results using a fully automated spike ripple detector, including comparison with an automated spike detector. We conclude that scalp spike ripple events identify disease and track with seizure risk in this epilepsy population, using both semi-automated and fully automated detection methods, and that this biomarker outperforms analysis of spikes alone in categorizing seizure risk. These data provide evidence that spike ripples are a specific non-invasive biomarker for seizure risk in benign epilepsy with centrotemporal spikes and support future work to evaluate the utility of this biomarker to guide medication trials and tapers in these children and predict seizure risk in other at-risk populations.
在过去的十年中，头皮 EEG 中的快速涟漪范围内的短暂爆发已被确定为癫痫的一种有前途的非侵入性生物标志物。然而，由于识别这些短暂、低幅度事件的标准方法困难、耗时且主观，因此对该生物标志物的研究和临床应用受到限制。最近的研究表明，与癫痫样放电（“尖峰涟漪事件”）同时发生的涟漪更容易检测，并且具有更大的病理意义。在这里，我们使用客观技术来量化尖峰涟漪，并测试该生物标志物是否可预测儿童癫痫的癫痫发作风险。我们评估了良性癫痫伴中央颞区棘波的前瞻性队列中儿童头皮 EEG 记录中的尖峰涟漪，并将癫痫发作儿童与健康对照组儿童进行比较。我们比较了癫痫发作儿童与健康对照组之间以及癫痫发作期间（活跃，在发作后 1 年内）和持续无发作（无发作，> 1 年无发作）之间的尖峰涟漪率，使用半自动和自动检测技术。与健康对照组或无药物治疗的无发作癫痫患者相比，活跃性癫痫患者的尖峰涟漪率更高（P = 0.0018）或癫痫发作患者（P = 0.0018）。在有尖峰涟漪的癫痫患者中，每月无发作会使尖峰涟漪的发生几率降低 0.66 倍（95%置信区间 0.47，0.91），P = 0.021。将至少存在一个尖峰涟漪与经典尖峰事件的存在相比，以识别组，我们发现尖峰涟漪具有相似的敏感性和阴性预测值，但特异性和阳性预测值均高于尖峰（P = 0.016 和 P = 0.006）。我们使用全自动尖峰涟漪检测器发现了定性一致的结果，包括与自动尖峰检测器的比较。我们的结论是，头皮尖峰涟漪事件可识别疾病，并使用半自动和全自动检测方法跟踪癫痫发作风险，并且该生物标志物在分类癫痫发作风险方面优于单独分析尖峰。这些数据为尖峰涟漪是良性癫痫伴中央颞区棘波的癫痫发作风险的特异性非侵入性生物标志物提供了证据，并支持进一步研究该生物标志物在指导这些儿童的药物试验和减量以及预测其他高危人群的癫痫发作风险方面的效用。
