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.

癫痫发作是无法预测的事件，可显着降低生活质量。预测下一次癫痫发作何时发生癫痫病及其护理人员，并有助于治疗癫痫发作。癫痫的动物模型为探索大脑的本质提供了机会在癫痫发作的期间的活动。使用鼠标和大鼠模型普遍的缺席癫痫，我们发现了丘脑的特定积累每次癫痫发作之前，神经尖峰活动几秒钟。这本小说电生理特征发生在没有任何明显的癫痫样的情况下脑电图活动。我们建议确定负责的神经回路使用高密度的多通道硅探针记录的塞氏菌活性鼠标中的癫痫发作网络广泛。我们还将衡量钙离子水平，神经活动的读数，神经元细胞及其其使用荧光钙指示器（GCAMP）和多光子的输出轴突显微镜以捕获塞氏前活性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，感官诱发的反应和瞳孔直径创建综合的塞氏赛前活动的多模式流。这信息将被馈入公正的机器学习方法以开发预测算法。我们将直接测试耦合强度通过进行网络级别并针对性急性脑切片中的单细胞记录。确定前的特定作用癫痫发作网络在产生癫痫发作时，我们将测试化学遗传或关键塞氏疗前网络元素的光遗传沉默减少了癫痫发作发病率或严重程度。最后，我们将测试是否可以使用癫痫发作预测性信号实时干预并防止癫痫发作。这些实验将共同为新型治疗提供概念证明方法：针对塞氏菌状态以改善癫痫发作的控制。