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为患者做好准备 癫痫和他们的照顾者，并可能帮助治疗癫痫发作。 癫痫动物模型为探索大脑的本质提供了机会 癫痫发作前一段时间的活动。同时使用小鼠和大鼠模型 全面性失神癫痫，我们发现丘脑有一种特殊的积聚 在每次癫痫发作前几秒钟的神经尖峰活动。这部小说 电生理特征出现在没有任何明显的癫痫样症状时。 脑电活动。我们建议确定神经回路负责 高密度多道硅探头记录癫痫发作前活动 广泛存在于小鼠的癫痫发作网络中。我们还将衡量 神经元胞体内钙离子水平及其与神经活动的关系 用荧光钙指示剂(GCaMPs)和多光子标记的输出轴突 用显微镜捕捉癫痫发作前活动的高度互补成分 具有高空间分辨率。神经活动数据将与 脑电、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癫痫发作前活动流。这 信息将被输入到无偏见的机器学习方法中来开发 预测算法。我们将直接测试内部的耦合强度 丘脑皮质癫痫发作前网络进行网络级和靶向性研究 急性脑片中的单细胞记录。要确定PRE的特定角色，请执行以下操作 癫痫网络在产生癫痫的过程中，我们将测试化学发生或 癫痫发作前关键网络元件的光发生沉默可减少癫痫发作 发病率或严重性。最后，我们将测试我们是否可以使用癫痫预测 实时干预和防止癫痫发作的信号。 总之，这些实验将为一种新的治疗方法提供概念证明 方法：针对癫痫发作前的状态，改善癫痫发作控制。

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.