Tracking pre-seizure dynamics to predict and control seizures

跟踪癫痫发作前动态以预测和控制癫痫发作

基本信息

批准号：
10269920
负责人：
Surya Ganguli
金额：
$ 42.46万
依托单位：
STANFORD UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-09-30 至 2025-05-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10269920
关键词：
Absence Epilepsy Acute Address Animal Model Animals Axon Brain Calcium ion Caliber Caregivers Cells Characteristics Clozapine Coupled Coupling Data Detection Dissection Electrodes Electroencephalography Electrophysiology (science)Elements Epilepsy Event Image In Vitro Incidence Interruption Lead Light Locomotion Machine Learning Measures Methods Modeling Motor Activity Mus Nature Neocortex Neurons Noise Output Oxides Patients Pattern Persons Pharmacology Population Preparation Prevention Process Pupil Quality of life Rattus Resolution Rodent Model Role SCN8A gene Seizures Sensory Severities Signal Transduction Silicon Slice Specificity Stimulus Stream Synapses Testing Thalamic Nuclei Thalamic structure Therapeutic Intervention Time Virus awake base calcium indicator density efficacy testing experimental study improved in vivo in vivo two-photon imaging mortality multimodal data multimodality multiphoton microscopy neural circuit neuronal cell body novel novel strategies novel therapeutic intervention optogenetics prediction algorithm predictive modeling prevent relating to nervous system response sensory stimulus unsupervised learning

项目摘要

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.

癫痫发作是不可预测的事件，会显着降低生活质量。预测下一次癫痫发作何时发生可以让人们做好准备癫痫及其护理人员，并可能有助于治疗癫痫发作。癫痫动物模型提供了探索大脑本质的机会癫痫发作前一段时间的活动。使用小鼠和大鼠模型全身性失神性癫痫，我们发现丘脑的特定结构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在没有任何明显癫痫样的情况下发生脑电图活动。我们建议确定负责的神经回路使用高密度多通道硅探针记录癫痫发作前的活动广泛分布于小鼠的癫痫发作网络中。我们也会测量钙离子水平，神经元细胞体及其神经活动的读数使用荧光钙指示剂 (GCaMP) 和多光子输出轴突显微镜捕获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图、运动信号、感觉诱发反应和瞳孔直径创建全面的多式联运预检活动流程。这信息将被输入公正的机器学习方法中以开发预测算法。我们将直接测试内的耦合强度丘脑皮质癫痫发作前网络，通过进行网络级和有针对性的急性脑切片中的单细胞记录。确定预定义的具体角色癫痫发作网络在产生癫痫发作时，我们将测试化学遗传学或癫痫发作前关键网络元件的光遗传学沉默可减少癫痫发作发生率或严重程度。最后，我们将测试是否可以使用癫痫发作预测实时干预并在癫痫发作之前预防癫痫发作的信号。总之，这些实验将为新型治疗方法提供概念证明方法：针对癫痫发作前状态以改善癫痫发作控制。