Physiology and function of basal ganglia subcircuits in sequence learning

项目总结： 对于生物体来说，将一系列动作分块成序列并获得大量的 生存和繁殖的动作剧目。将行为组织成动作序列，以及它是如何 是在神经系统中实现的，一直是神经科学的中心问题。皮质功能障碍- 基底节环路与许多神经和精神疾病患者的顺序行为受损有关 这些疾病包括帕金森氏症、亨廷顿氏症和强迫症。 纹状体是基底神经节的主要输入核团，接受感觉、运动和认知。 大脑皮层的信息。目前的基底节模型认为，有两种主要的神经 亚回路，称为“直接”和“间接”通路，分别用于选择和抑制动作。 然而，这种过于简单化的对手观点受到了最近的研究的挑战。此外，除了 直接和间接的通路，人们很早就知道，在 纹状体，称为斑块(纹状体)和基质，可由免疫球蛋白的表达来定义。 像Mu阿片受体这样的组织化学标记物。已经提出了重要的功能差异 根据对人类基底节疾病的观察，斑块和基质之间的间隔。 然而，对补片和基质隔室的功能理解及其在控制中的作用 目前很大程度上还没有采取行动。传统的解剖学和电生理学方法是病态的 适合于解决这些问题，因为这些隔室形状不规则，细胞类型不同 分布参差不齐，使得精确的损伤或生理研究相当困难，如果不是不可能的话。 该项目将利用一系列尖端的神经技术，包括活体细胞记录 类型鉴定、光遗传学、快速扫描循环伏安法、病毒追踪和微型望远镜成像相结合 通过定量行为和计算模型，剖析特定纹状体隔区在 动作序列的学习和执行，与纹状体通路的功能相比较。此外，它还 目的是系统地研究不同类型纹状体细胞的生理和功能及其相互作用 在行为过程中有特定的皮质输入。首先，提出了一种新的小鼠动作序列任务 行为学将被开发来确定纹状体在分子和细胞中的作用序列 级别。然后进行体内的电化学、电生理和光遗传学实验 确定各种纹状体细胞类型对序列执行的活动和贡献。最终修改狂犬病 病毒将被用来定义特定细胞类型的皮质-纹状体通路，并解剖生理和 这些通路在行为中的功能与先进的成像和光遗传学。这个项目将结合在一起 加深对两种动作序列特定皮质纹状体回路的功能和逻辑的理解 学习和执行。

Project Summary: It is a fundamental challenge for organisms to chunk a series of actions into sequence and acquire a large action repertoire for survival and reproduction. The organization of behavior into action sequences, and how it is realized in the nervous system has been a central question in neuroscience. Dysfunctions of the cortico- basal ganglia circuits are associated with impaired sequential behavior in many neurological and psychiatric diseases including Parkinson's disease, Huntington's disease and Obsessive-Compulsive Disorder (OCD). The striatum is the major input nuclei of the basal ganglia, which receive sensory, motor and cognitive information across cerebral cortex. Current model of basal ganglia suggested that there are two major neural subcircuits, called the “direct” and “indirect” pathways, for selecting and inhibiting actions respectively. Nevertheless, this over-simplified opponent view has been challenged by recent work. In addition, besides the direct and indirect pathways, it has been known for a long time that there are two compartments in the striatum, termed the patch (striosome) and matrix, which can be defined by the expression of immune- histochemical markers like mu-opioid receptors. Important functional differences have been suggested between the patch and matrix compartments based on the observations in human basal ganglia disorders. However, the functional understanding of the patch vs. matrix compartment and their roles in controlling actions are largely missing at this moment. Conventional anatomical and electrophysiological methods are ill- suited to address these questions because these compartments are irregular in shape and different cell types are mixed in distribution, making the precise lesion or physiological studies rather difficult if not impossible. This project will take advantage of a series of cutting-edge neurotechniques including in vivo recording with cell type identification, optogenetics, fast-scan cyclic voltammetry, viral tracing and miniscope imaging, combined with quantitative behavior and computational modeling, to dissect the role of specific striatal compartments in action sequence learning and execution, in comparison with the function of striatal pathways. Furthermore, it aims to systemically investigate the physiology and function of different striatal cell types and their interaction with specific cortical inputs during behavior. Firstly, a novel action sequence task in mice with quantitative behavior will be developed to determine the striatal involvement in action sequences at molecular and cellular levels. It is then followed by in vivo electro-chemical, electrophysiological and optogenetic experiments to define the activity and contribution of various striatal cell types to sequence execution. Finally modified rabies virus will be utilized to define cell-type-specific cortico-striatal pathways, and dissect the physiology and function of these pathways during behavior with advanced imaging and optogenetics. Together this project will advance the understanding of the function and logic of specific corticostriatal circuitry for both action sequence learning and execution.