Causal Analysis of Electrically Connected Neural Networks

描述（由申请人提供）：在神经科学的许多子领域中，神经回路被认为主要由通过化学突触连接的神经元组成。例如，连接组学主要集中在定位神经元之间的化学突触连接;通过荧光工具进行神经通信的成像也主要集中在化学突触上，并且绝大多数体外和体内的电生理学研究都集中在化学突触传递上。然而，存在另一类神经通信机制，可以很好地管理神经元如何在完整的神经回路中一起工作以产生计算：电突触，由直接将细胞电耦合在一起的间隙连接介导。开创性的研究表明，间隙连接基因的慢性缺失可以改变海马振荡动力学，这可能对学习和癫痫很重要，改变大脑对电神经调节治疗的反应，改变神经迁移和大脑发育。在星形胶质细胞中，缝隙连接介导星形胶质细胞间的钙波和胶质细胞通讯，这一过程可能在精神病患者中受到损害。在脑外的细胞类型中，间隙连接的遗传缺失可导致内耳细胞损失，并且间隙连接也与视网膜功能有关。然而，仍然不可能以瞬时和可逆的方式来表征间隙连接功能，因此可以以时间分辨的方式来分析它们的作用，即在行为任务中的定义时间点或在特定的神经计算期间。因此，我们建议开发一个完全遗传编码的工具箱，用于控制和观察神经网络中定义的细胞类型中的间隙连接功能。

DESCRIPTION (provided by applicant): In many subfields in neuroscience, neural circuits are thought of as primarily composed of neurons connected by chemical synapses. For example, connectomics is largely focused on locating chemical synaptic connections between neurons; imaging of neural communication through fluorescent tools is also largely focused on chemical synapses, and the vast majority of electrophysiology studies in vitro and in vivo focus on chemical synaptic transmission. However, another class of neural communication mechanism exists that could well govern how neurons work together in intact neural circuits to generate computations: electrical synapses, mediated by gap junction connections that directly electrically couple cells together. Pioneering studies have shown that chronic deletion of gap junction genes can alter hippocampal oscillatory dynamics that may be important for learning and epilepsy, change response of the brain to electrical neuromodulation therapies, alter neural migration and brain development. In astrocytes, gap junctions mediate inter-astrocyte calcium waves and glial communication, a process that might be compromised in psychiatric patients. In cell types outside the brain, genetic deletion of gap junctions can result in inner ear cell loss, and gap junctions are also implicated in retinal function. However, it remains impossible to inactivate gap junction functionality in a transient and reversible fashion, so that their roles ca be analyzed in a time-resolved fashion, i.e. at defined time points in behavioral tasks or during specific neural computation. Accordingly, we propose to develop a fully genetically encoded toolbox for controlling and observing gap junction functionality in defined cell types in neural networks.