Mapping the neural circuitry underlying walking

绘制行走背后的神经回路

• 批准号: 10388902
• 负责人: Sumaira Zamurrad
• 金额: $ 0.25万
• 依托单位: COLUMBIA UNIVERSITY HEALTH SCIENCES
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-06-01 至 2023-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10388902
• 关键词: ARNT gene; Address; Affect; Afferent Neurons; Anatomy; Animal Model; Animals; Anions; Artificial Intelligence; Behavior; Behavioral; Biological Assay; Brain; Chest; Cloning; Color; Communities; Complex; Data; Disease; Drosophila genus; Environment; Functional disorder; Gait; Generations; Genetic; Genetic Epistasis; Individual; Knowledge; Label; Lead; Leg; Limb structure; Link; Maps; Methods; Morphology; Motor; Motor output; Movement; Nerve; Neural Pathways; Neurodegenerative Disorders; Neurons; Neuropil; Output; Pattern; Play; Resolution; Resources; Rhodopsin; Role; Running; Sensory; Synapses; Techniques; Testing; Walking; design; experimental study; flexibility; fly; interest; kinematics; mind control; motor behavior; motor control; motor disorder; neural circuit; neural network; optogenetics; relating to nervous system; tool

Project Summary Walking is an essential and conserved behavior across the animal kingdom. The ability to move in a coordinated, robust yet flexible manner is a crucial for an animal’s survival in the ever changing environment. Motor behaviors are controlled by sensory neuropils in the brain which talk to motor centers such as the ventral nerve cord (VNC) in the thorax to execute precise motor outputs. Descending neurons are the primary conduits of information that accomplish this connection between the brain and the VNC. However the downstream target neurons and complex circuits that precisely govern the execution of a specific kind of limbed motor output such as running, turning, slowing down, switching gait, climbing, stepping etc remain individually undefined. This project aims to map these individual circuits that control different walking kinematics. In aim 1, we will map the post-synaptic targets of descending neurons using a translabeling technique called Trans Tango. We already know from previous studies that have characterized the morphology of all the descending neurons in Drosophila, which DNs target the leg neuropils in the ventral nerve chord. These descending neurons are very likely to be directly implicated in controlling walking behavior. Additionally, we also have broad behavioral information for a subset of the DNs. Using this existing morphological and behavioral data, we can start to map out the motor and premotor circuits that are downstream of these DNs and reveal the distinct circuit components that control specific walking kinematics. Some of the DNs we plan to screen, affect similar behaviors hence it will be interesting to reveal whether they converge onto similar or different targets. In the second aim, we aim to use a computational approach search for generation Gal4 lines whose neuronal expression patterns match the downstream target neurons we want to study. Existing split Gal4 lines will be identified or new ones will be generated in order to gain genetic access to the downstream circuit neurons of interest. In the third aim, we will use three behavior setups: Flywalker, the Arena and the fly-on-ball setup to study a range of walking parameters in high resolution using optogenetics intersectional strategies. We will use optogenetic activation, silencing and also epistasis experiments to map out which components of the circuit are sufficient or necessary for a particular walking kinematic.

项目概要 步行是动物王国中一种重要且保守的行为。移动的能力 协调、稳健而灵活的方式对于动物在不断变化的环境中生存至关重要。 运动行为由大脑中的感觉神经元控制，这些神经元与运动中枢（例如腹侧）对话 胸部神经索（VNC）执行精确的运动输出。下降神经元是主要管道 完成大脑和 VNC 之间这种连接的信息。然而下游 目标神经元和复杂电路精确控制特定类型四肢运动的执行 跑步、转弯、减速、切换步态、攀爬、迈步等输出均保持单独状态 不明确的。该项目旨在绘制控制不同步行运动学的这些单独电路的图。在目标 1 中， 我们将使用称为 Trans 的转标记技术来绘制下行神经元的突触后目标 探戈。我们已经从之前的研究中了解到，这些研究已经描述了所有下降的形态特征 果蝇中的神经元，其 DN 靶向腹神经索中的腿部神经细胞。这些下降的 神经元很可能直接参与控制步行行为。此外，我们还有 DN 子集的广泛行为信息。使用现有的形态和行为数据， 我们可以开始绘制这些 DN 下游的电机和前电机电路，并揭示 控制特定行走运动学的不同电路组件。我们计划筛选的一些 DN 会影响 相似的行为，因此揭示它们是否收敛到相似或不同的目标将会很有趣。在 第二个目标，我们的目标是使用计算方法搜索神经元的 Gal4 代系 表达模式与我们想要研究的下游目标神经元相匹配。现有的 Gal4 分割线将 将产生已识别的或新的神经元，以便获得对下游回路神经元的遗传访问 兴趣。在第三个目标中，我们将使用三种行为设置：Flywalker、Arena 和 Fly-on-ball 设置来 使用光遗传学交叉策略以高分辨率研究一系列步行参数。我们将使用 光遗传学激活、沉默和上位实验，以确定电路的哪些组件 对于特定的行走运动学来说是足够的或必要的。