Tracking pre-seizure dynamics to predict and control seizures

癫痫发作是不可预测的事件，会显著降低生活质量。 预测下一次癫痫发作的时间既可以让人们做好准备， 癫痫及其护理人员，并可能有助于癫痫发作的治疗。 癫痫动物模型的建立为研究癫痫的脑功能提供了一个新的途径 癫痫发作前的活动使用小鼠和大鼠模型， 我们发现了一种特殊的丘脑 每次癫痫发作前几秒钟的神经尖峰活动。这本小说 电生理特征发生在没有任何明显的癫痫样症状的情况下， 脑电图活动。我们建议找出负责 使用高密度多通道硅探针记录癫痫发作前的活动 广泛地跨越了鼠标中的神经生成网络。我们还将测量 钙离子水平，神经活动的读数，在神经元细胞体和它们的 使用荧光钙指示剂（GCaMP）和多光子 显微镜捕捉癫痫发作前活动的高度互补成分 高空间分辨率。神经活动数据将与 EEG、运动信号、感觉诱发反应和瞳孔直径， 创建一个全面的多模式的扣押前活动流。这 信息将被输入到无偏见的机器学习方法中， 预测算法我们将直接测试耦合强度， 丘脑皮层癫痫发作前网络进行网络水平和有针对性的 急性脑切片中的单细胞记录。为了确定一个具体的作用前- 癫痫发作网络在产生癫痫发作，我们将测试是否化学遗传或 癫痫发作前关键网络元件的光遗传学沉默减少了癫痫发作 发生率或严重程度。最后，我们将测试我们是否可以使用 信号进行实时干预，并在癫痫发作之前预防癫痫发作。 总之，这些实验将为一种新的治疗方法提供概念证明。 方法：针对缉获前状态，以改进缉获控制。

Epileptic seizures are unpredictable events that significantly reduce quality of life. Predicting when the next seizure would occur could both prepare persons with epilepsy and their caregivers, and potentially aid in the treatment of seizures. Animal models of epilepsy provide an opportunity to explore the nature of brain activity in the period leading up to seizures. Using both mouse and rat models of generalized absence epilepsy, we have found a specific build up of thalamic neural spiking activity for several seconds before each seizure. This novel electrophysiological signature occurs in the absence of any overt epileptiform EEG activity. We propose to identify the neural circuits that are responsible for pre-seizure activity using high-density multi-channel silicon probes to record broadly across seizure-generating networks in the mouse. We will also measure calcium ion levels, a readout of neural activity, in neuronal cell bodies and their output axons using fluorescent calcium indicators (GCaMPs) and multiphoton microscopy to capture a highly complementary component of pre-seizure activity with high spatial resolution. Neural activity data will be collected together with EEG, locomotion signals, sensory-evoked responses, and pupil diameter to create a comprehensive multimodal stream of pre-seizure activity. This information will be fed into unbiased machine learning approaches to develop predictive algorithms. We will directly test coupling strength within thalamocortical pre-seizure networks by conducting network-level and targeted single-cell recordings in acute brain slices. To determine a specific role of pre- seizure networks in generating seizures, we will test whether chemogenetic or optogenetic silencing of key pre-seizure network elements reduces seizure incidence or severity. Finally, we will test whether we can use seizure-predictive signals to intervene in real-time and prevent seizures before they take hold. Together, these experiments will provide proof of concept for a novel therapeutic approach: targeting the pre-seizure state to improve seizure control.