Biomarkers for epileptogenesis after brain injury

总结 脑损伤和随后的癫痫之间的潜伏期持续数月至数年， 抗癫痫治疗的独特窗口。最新报道的有前途的实验性疾病修饰 在克服三个障碍之前，治疗方法不能进入临床试验：首先， 评估干预措施的有效性是必要的，但脑损伤后的癫痫很难量化： 首次癫痫发作的潜伏期较长且变化不定，早期癫痫发作通常是轻微的、罕见的和成簇的 在很长的簇间间隔之间。其次，由于损伤和癫痫之间的潜伏期较长，临床 审判需要相当长的时间，因此费用高得惊人。第三，因为~ 20%的适度 脑损伤患者发生癫痫，大多数治疗患者不能从长期受益 抗癫痫治疗，尽管暴露于风险和副作用。 这三个障碍可以通过足够准确的生物标志物来克服。我们最近展示了 早期电图癫痫样活动是脑损伤后癫痫的一个有希望的预测因素， 红藻氨酸在这里，我们提出解决关键的知识差距，关于电图生物标志物， 癫痫发生电图生物标志物的预测能力还没有评估后， 临床相关损伤，如创伤和缺氧缺血性损伤。的预测能力 电图生物标志物还没有系统地与传统物理标志物的预测能力进行比较。 损伤描述符，如损伤大小和位置。此外，尚未确定是否 结合电图和物理损伤参数将提高其预测能力。我们会委聘 完善的临床损伤模型，侧向液压冲击（LFP）创伤模型和Rice- P30大鼠Vannucci局灶性缺氧缺血模型。这些模型中癫痫的发病率接近 人类的经验，从而提供了一个更严格的测试这些生物标志物的预测能力 而不是红藻氨酸盐模型。此外，在这些模型中，癫痫发作的潜伏期足够长，以使得能够进行测试， 早期电图生物标志物的出现是否与自 损伤，或第一次癫痫发作前剩余的时间;这些关系的性质将显着 影响这些生物标志物的临床研究设计。在目标1中，我们将使用一种新型的微型遥测技术， 用于连续记录视频EEG以及经验证的无偏计算机检测的装置 算法来量化这些脑损伤模型中的早期癫痫样活动和癫痫发作。我们将优化 EEG采样并基于癫痫样电图活动和损伤开发最佳预测模型 描述符，然后在第二组动物中前瞻性地测试该模型。在目标2中，我们将使用 同样的方法来测试是否早期电描记癫痫样活动和损伤描述符预测 癫痫的严重程度，包括首次癫痫发作的潜伏期和癫痫发作频率，一旦癫痫完全发展。

Summary The latent period between brain injury and subsequent epilepsy is months to years in duration, providing a unique window for antiepileptogenic therapy. Recent reports of promising experimental disease-modifying therapies can't advance to clinical trials until three hurdles are overcome: First, quantification of epilepsy is necessary to assess efficacy of interventions, but epilepsy after brain injury is very difficult to quantify: the latency to first seizure is long and variable, and early seizures are often subtle, infrequent, and clustered between long inter-cluster intervals. Second, because of the long latency between injury and epilepsy, clinical trials need to be quite prolonged and therefore prohibitively expensive. Third, because ~ 20% of moderately brain-injured patients develop epilepsy, most of the treated patients could not benefit from long-term antiepileptogenic therapy, despite exposure to the risks and side effects. These three hurdles could be overcome with sufficiently accurate biomarkers. We recently demonstrated that early electrographic epileptiform activity is a promising predictor of epilepsy after brain injury induced by kainic acid. Here we propose to address critical knowledge gaps regarding electrographic biomarkers of epileptogenesis. The predictive power of electrographic biomarkers has not been assessed after more clinically relevant injuries such as trauma and hypoxic-ischemic injury. The predictive power of electrographic biomarkers has not been systematically compared to the predictive power of traditional physical descriptors of injury, such as lesion size and location. Furthermore, it has not been determined whether combining electrographic and physical-injury parameters would improve their predictive power. We will employ well-established models of clinical injuries, the lateral fluid percussion (LFP) trauma model and the Rice- Vannucci model of focal hypoxia-ischemia in P30 rats. The incidence of epilepsy in these models is close to the human experience, and thus provides a more rigorous test of the predictive power of these biomarkers than the kainate model. Further, the latency to seizures is sufficiently long in these models to enable testing as to whether the appearance of early electrographic biomarkers is more closely related to the time elapsed since the injury, or to the time remaining prior to the first seizure; the nature of these relationships will significantly impact the design of clinical studies of these biomarkers. In Aim 1, we will use a novel miniature telemetry device for continuous recording of video-EEG together with validated, unbiased computer detection algorithms to quantify early epileptiform activity and seizures in these brain injury models. We will optimize EEG sampling and develop the best predictive model based on epileptiform electrographic activity and injury descriptors, and then prospectively test this model in a second group of animals. In Aim 2, we will use the same approach to test whether early electrographic epileptiform activity and injury descriptors predict the severity of epilepsy, including latency to first seizure and seizure frequency, once epilepsy is fully developed.