Department of Neuroscience, University of Wisconsin, Madison, Wisconsin, United States of America.
Department of Biology, Beloit College, Beloit, Wisconsin, United States of America.
PLoS One. 2018 Nov 6;13(11):e0207158. doi: 10.1371/journal.pone.0207158. eCollection 2018.
Quantification of interictal spikes in EEG may provide insight on epilepsy disease burden, but manual quantification of spikes is time-consuming and subject to bias. We present a probability-based, automated method for the classification and quantification of interictal events, using EEG data from kainate- and saline-injected mice (C57BL/6J background) several weeks post-treatment. We first detected high-amplitude events, then projected event waveforms into Principal Components space and identified clusters of spike morphologies using a Gaussian Mixture Model. We calculated the odds-ratio of events from kainate- versus saline-treated mice within each cluster, converted these values to probability scores, P(kainate), and calculated an Hourly Epilepsy Index for each animal by summing the probabilities for events where the cluster P(kainate) > 0.5 and dividing the resultant sum by the record duration. This Index is predictive of whether an animal received an epileptogenic treatment (i.e., kainate), even if a seizure was never observed. We applied this method to an out-of-sample dataset to assess epileptiform spike morphologies in five kainate mice monitored for ~1 month. The magnitude of the Index increased over time in a subset of animals and revealed changes in the prevalence of epileptiform (P(kainate) > 0.5) spike morphologies. Importantly, in both data sets, animals that had electrographic seizures also had a high Index. This analysis is fast, unbiased, and provides information regarding the salience of spike morphologies for disease progression. Future refinement will allow a better understanding of the definition of interictal spikes in quantitative and unambiguous terms.
脑电的棘波定量分析可能有助于深入了解癫痫疾病负担，但棘波的手动定量分析既费时又容易产生偏差。我们提出了一种基于概率的、自动的棘波分类和定量方法，使用了几周后接受海人酸和盐水注射的（C57BL/6J 背景）小鼠的脑电数据。我们首先检测到高振幅事件，然后将事件波形投影到主成分空间，并使用高斯混合模型识别棘波形态的聚类。我们计算了每个簇中海人酸处理组与盐水处理组之间事件的优势比，将这些值转换为概率分数 P(kainate)，并通过对 P(kainate) > 0.5 的事件的概率求和，除以记录时长，为每个动物计算每小时癫痫指数。该指数可预测动物是否接受了致痫治疗（即海人酸），即使从未观察到癫痫发作。我们将该方法应用于一个样本外数据集，以评估 5 只接受海人酸监测约 1 个月的小鼠的癫痫样棘波形态。在一部分动物中，指数随时间增大，表明癫痫样（P(kainate) > 0.5）棘波形态的发生率发生了变化。重要的是，在两个数据集，发生电临床发作的动物的指数也很高。该分析快速、无偏倚，提供了关于棘波形态对疾病进展的重要性的信息。未来的改进将允许更好地理解定量和明确的棘波定义。
