Reverse Engineering the Brain Stem Circuits that Govern Exploratory Behavior

概述-摘要 脑干功能对于维持生命的功能如呼吸和生存功能是必要的， 比如觅食个体的运动动作是由特定的脑干颅运动核团激活的。的 单个运动动作的特异性反映了运动核团在闭环回路中的参与 在传感器和肌肉执行器之间。然而，这些循环也是嵌套的，并连接到反馈， 前馈通路，其是口面运动动作之间协调的基础。一个关键的问题是 建议是如何协调不同的行动，以形成丰富的行为剧目，如节奏， 与呼吸相关的运动，以及头、鼻、舌和触须的协调位移 在探索中。我们假设口面运动协调的最佳候选接口是运动前区 脑干网状结构中的前2运动神经元群：这些神经元投射到颅运动 神经核，接收来自脑干外部的下行输入，并相互连接。 我们的方法利用并扩展了广泛的创新实验工具。这些包括 最先进的行为方法来研究运动动作及其协调行为。从 从实验的角度来看，每个口面运动动作的潜在神经回路可以被访问， 通过从肌肉激活物或外周相关感受器开始的跨突触转运。这些 研究将利用分子，遗传和功能标记方法，使细胞表型和 电路跟踪这些数据将建立"组件"，即，所有研究的脑干核团连接 项目这些研究通过体内电生理学和光遗传学进行补充，以便测量和 干扰信号流在勘探和决策：这些研究将建立orofacial "布线 图表"。这些技术的总和将使我们能够阐明内在脑干回路的功能 以及它们通过下行通路的调节。 我们的数据将以两种方式整合。首先，我们将开始发展的计算模型， 动态的主动感知口面电机厂和脑干电路。这些活动最初将侧重于 触须系统，从机械和机械神经元转换的特征开始， 触须运动和延伸到探索脑干电路，驱动触须设定点和节奏 搅拌最后，触须前馈通路将被计算建模，以探索感觉输入如何 影响触须的动力学其次，为了记录我们实验追踪研究中产生的连接数据， 我们将构建一个可训练的基于纹理的数字地图集， 脑干核团的解剖学注释。Atlas旨在实现标签的精确3D对齐 神经元，即使标记的神经元位于明确的脑干核之外的小的子区域， 根据阿特拉斯地标建筑的三角测量此外，连续切片的大脑数据集的数字化 允许对小的脑干子区域进行3D重建和对齐，以及整理来自 把不同的大脑放进同一个地图册里 我们提出的关于脑干回路和动力学的计划将产生关于 神经元计算将提供为这些研究开发的分析和解剖工具 通过我们的数据科学核心，向更大的神经科学社区传播。

Overview - Abstract Brainstem function is necessary for life-sustaining functions such as breathing and for survival functions, such as foraging for food. Individual motor actions are activated by specific brainstem cranial motor nuclei. The specificity of individual motor actions reflects the participation of motor nuclei in circuits within closed loops between sensors and muscle actuators. However, these loops are also nested and connect to feedback and feedforward pathways, which underlie coordination between orofacial motor actions. A key question for this proposal is how different actions are coordinated to form a rich repertoire of behaviors, such as rhythmic motions linked to breathing, and the orchestrated displacements of the head, nose, tongue, and vibrissae during exploration. We postulate that the best candidate interface for orofacial motor coordination are premotor and pre2motor neuron populations in the brainstem reticular formation: these neurons project to cranial motor nuclei, receive descending inputs from outside of the brainstem, and interconnected to each other. Our approach exploits and expands upon a broad spectrum of innovative experimental tools. These include state-of-the-art behavioral methods to study motor actions and their coordination into behaviors. From an experimental perspective, the underlying neuronal circuitry for each orofacial motor action may be accessed via transsynaptic transport starting at the muscle activators or associated sensors in the periphery. These studies will make use of molecular, genetic, and functional labeling methods to enable cell phenotyping and circuit tracing. These data will establish the "Components", i.e., brainstem nuclei connectivity for all Research Projects. These studies are complemented by in vivo electrophysiology and optogenetics in order measure and perturb the signal flow during exploration and decision-making: these studies will establish orofacial “Wiring Diagrams”. The sum of these techniques will permit us to elucidate the functions of intrinsic brainstem circuits and their modulation by descending pathways. Our data will be integrated in two ways. First we will begin development of computational models of the dynamics of active sensing by the orofacial motor plant and brainstem circuits. These will initially focus on the vibrissa system, starting with characterizations of mechanics and mechano-neuronal transformations of vibrissa movement and extending to exploration of brainstem circuits that drive vibrissa set-point and rhythmic whisking. Finally, vibrissa feedforward pathways will be computationally modeled to explore how sensory input affects vibrissa dynamics. Second, to record connectivity data that arises from our experimental tracing studies, we will construct an Trainable Texture-based Digital Atlas that utilizes machine learning to automate anatomical annotation of brainstem nuclei. The Atlas is designed to allow accurate 3D alignment of labeled neurons, even when labeled neurons reside in small sub-regions outside of well-defined brainstem nuclei, based on triangulation to Atlas landmark structures. Further, digitization of serially sectioned brain data sets allows 3D reconstruction and alignment of small brainstem subregions as well as the collation of this data from different brains into the same Atlas. Our proposed program on brainstem circuitry and dynamics will yield general lessons about the nature of neuronal computation. The analytic and anatomical tools developed for these studies will be made available through our data science core to the larger neuroscience community.