Milikovsky Dan Z, Weissberg Itai, Kamintsky Lyn, Lippmann Kristina, Schefenbauer Osnat, Frigerio Federica, Rizzi Massimo, Sheintuch Liron, Zelig Daniel, Ofer Jonathan, Vezzani Annamaria, Friedman Alon
Departments of Physiology and Cell Biology, Cognitive and Brain Sciences, Zlotowski Center for Neuroscience, Ben-Gurion University of the Negev, Beer-Sheva 84105, Israel.
Department of Medical Neuroscience, Dalhousie University, Halifax, Nova Scotia B3H 4R2, Canada.
J Neurosci. 2017 Apr 26;37(17):4450-4461. doi: 10.1523/JNEUROSCI.2446-16.2017. Epub 2017 Mar 22.
Postinjury epilepsy (PIE) is a devastating sequela of various brain insults. While recent studies offer novel insights into the mechanisms underlying epileptogenesis and discover potential preventive treatments, the lack of PIE biomarkers hinders the clinical implementation of such treatments. Here we explored the biomarker potential of different electrographic features in five models of PIE. Electrocorticographic or intrahippocampal recordings of epileptogenesis (from the insult to the first spontaneous seizure) from two laboratories were analyzed in three mouse and two rat PIE models. Time, frequency, and fractal and nonlinear properties of the signals were examined, in addition to the daily rate of epileptiform spikes, the relative power of five frequency bands (theta, alpha, beta, low gamma, and high gamma) and the dynamics of these features over time. During the latent pre-seizure period, epileptiform spikes were more frequent in epileptic compared with nonepileptic rodents; however, this feature showed limited predictive power due to high inter- and intra-animal variability. While nondynamic rhythmic representation failed to predict epilepsy, the dynamics of the theta band were found to predict PIE with a sensitivity and specificity of >90%. Moreover, theta dynamics were found to be inversely correlated with the latency period (and thus predict the onset of seizures) and with the power change of the high-gamma rhythm. In addition, changes in theta band power during epileptogenesis were associated with altered locomotor activity and distorted circadian rhythm. These results suggest that changes in theta band during the epileptogenic period may serve as a diagnostic biomarker for epileptogenesis, able to predict the future onset of spontaneous seizures. Postinjury epilepsy is an unpreventable and devastating disorder that develops following brain injuries, such as traumatic brain injury and stroke, and is often associated with neuropsychiatric comorbidities. As PIE affects as many as 20% of brain-injured patients, reliable biomarkers are imperative before any preclinical therapeutics can find clinical translation. We demonstrate the capacity to predict the epileptic outcome in five different models of PIE, highlighting theta rhythm dynamics as a promising biomarker for epilepsy. Our findings prompt the exploration of theta dynamics (using repeated electroencephalographic recordings) as an epilepsy biomarker in brain injury patients.
创伤后癫痫（PIE）是各种脑损伤的严重后遗症。虽然最近的研究为癫痫发生的潜在机制提供了新的见解，并发现了潜在的预防性治疗方法，但缺乏PIE生物标志物阻碍了这些治疗方法的临床应用。在此，我们在五种PIE模型中探索了不同脑电图特征作为生物标志物的潜力。在三个小鼠和两个大鼠PIE模型中，分析了来自两个实验室的癫痫发生过程（从损伤到首次自发性发作）的皮质脑电图或海马内记录。除了癫痫样棘波的每日发生率、五个频段（θ、α、β、低γ和高γ）的相对功率以及这些特征随时间的动态变化外，还检查了信号的时间、频率、分形和非线性特性。在癫痫发作前的潜伏期，癫痫啮齿动物的癫痫样棘波比非癫痫啮齿动物更频繁；然而，由于动物间和动物内的高度变异性，这一特征的预测能力有限。虽然非动态节律表现无法预测癫痫，但发现θ频段的动态变化能够以大于90%的敏感性和特异性预测PIE。此外，发现θ动态变化与潜伏期呈负相关（从而预测癫痫发作的开始），并与高γ节律的功率变化相关。此外，癫痫发生过程中θ频段功率的变化与运动活动改变和昼夜节律紊乱有关。这些结果表明，癫痫发生期θ频段的变化可能作为癫痫发生的诊断生物标志物，能够预测未来自发性癫痫发作的发生。创伤后癫痫是一种在脑损伤（如创伤性脑损伤和中风）后发生的不可预防的严重疾病，通常与神经精神共病有关。由于PIE影响多达20%的脑损伤患者，在任何临床前治疗方法能够实现临床转化之前，可靠的生物标志物至关重要。我们证明了在五种不同的PIE模型中预测癫痫结局的能力，突出了θ节律动态变化作为一种有前景的癫痫生物标志物。我们的发现促使人们探索将θ动态变化（使用重复脑电图记录）作为脑损伤患者癫痫的生物标志物。
