Using Drosophila Olfactory Navigation to Study Principles of Motor Encoding

利用果蝇嗅觉导航研究运动编码原理

• 批准号: 10669719
• 负责人: Hannah Gattuso
• 金额: $ 3.94万
• 依托单位: NEW YORK UNIVERSITY SCHOOL OF MEDICINE
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-08-10 至 2024-08-09
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10669719
• 关键词: Anatomy; Animals; Behavior; Behavioral; Behavioral Paradigm; Brain; Complex; Computer Analysis; Courtship; Desire for food; Drosophila genus; Drosophila melanogaster; Elements; Environment; Evolution; Exhibits; Food; Foundations; Frequencies; Generations; Genetic; Genetic Models; Goals; Hand; Image; Injury; Insecta; Invertebrates; Locomotion; Maps; Measures; Mediating; Modality; Modeling; Motor; Movement; Nerve; Nervous System; Neurons; Odors; Pathway interactions; Pattern; Phase; Phenotype; Physiologic pulse; Population; Probability; Role; Running; Searching Behavior; Sensory; Series; Shapes; Signal Transduction; Spinal Cord; Stimulus; Structure; System; Translating; Walking; Withdrawal; analytical tool; computational basis; experimental study; fly; genetic analysis; goal oriented behavior; insight; large datasets; model organism; motor behavior; motor control; neural; optogenetics; programs; response; sensory input; sensory stimulus; statistics; treadmill; two-photon

Project Summary/Abstract Goal directed actions are often composed of shorter stochastic motor elements. How motor circuits are organized to translate a sensory-determined goal into a set of stochastic motor actions is unclear. Here I propose to use olfactory navigation behavior in the genetic model organism Drosophila melanogaster to identify the circuitry and computational basis of motor control in a complex, goal-oriented task. Fly olfactory navigation is a highly robust behavior composed of shorter, stochastic motifs. Olfactory navigation involves three stages. At baseline, flies explore their environment in a stochastic fashion. When presented with an appetitive odor, flies orient and run upwind. At odor offset, flies complete a search-like behavior, consisting of high angular velocity movements. Each phase has both reliable components (upwind orientation, increased angular velocity) and stochastic components (the precise timing of turns and runs). Our lab has developed a high-throughput paradigm in which these three phases can be elicited repeatedly either though presentation of an attractive odor, or through presentation of a fictive optogenetic odor. The large datasets I can obtain with this paradigm are amenable to both computational and genetic analysis. In my first Aim, I will perform a computational analysis of olfactory navigation behavior, identifying the timescales at which behavior is modulated following odor presentation or withdrawal, and decomposing fly trajectories into a series of behavioral motifs. Based on my motif analysis I will construct a Markovian model that seeks to reproduce the complex statistics of navigation behavior, and to understand how the stochastic elements of navigation are concatenated to produce reliable goal-finding. In the second Aim, I will use genetic silencing and activation to identify descending neurons (DNs) that contribute to the behavior motifs and temporal structure identified in the first Aim. DNs carry motor information from the brain to the ventral nerve cord, similar to neurons in the vertebrate that carry information from the brain to the spinal cord. This analysis will allow me to obtain a fairly complete circuit map of the motor circuitry the contributes to olfactory navigation. Finally, in my third Aim, I will determine what features of sensory and motor information are encoded in the activity of particular DNs. Currently, two views of motor encoding exist in the fruit fly. Some studies support the notion that DNs relay motor information depending on behavioral context, while others suggest they encode for specific movements, regardless of sensory driver. Olfactory navigation, composed of epochs of varying stimulus and behavioral goal, is poised to determine how movements of different sensory origin or behavioral context are encoded in motor circuitry. Using a closed loop behavioral apparatus, I will image from select DNs during olfactory navigation and correlate activity with both behavioral motifs and navigational phase. Together, these experiments will help to uncover principles of motor encoding, which could aid in understanding how the brain is able to regain motor control after injury.

项目总结/摘要 目标导向的动作通常由较短的随机运动元素组成。电机电路是如何 将一个由感官决定的目标转化为一组随机的运动动作的组织尚不清楚。这里我 建议在遗传模式生物黑腹果蝇中使用嗅觉导航行为， 在一个复杂的，以目标为导向的任务中识别运动控制的电路和计算基础。 飞行嗅觉导航是一种高度鲁棒的行为，由较短的随机基序组成。嗅觉导航 包括三个阶段。在基线上，苍蝇以随机的方式探索它们的环境。呈现时 有一种诱人的气味，苍蝇会定向并逆风奔跑。在气味抵消时，苍蝇完成了类似搜索的行为， 由高角速度运动组成。每一阶段都有两个可靠的组成部分（逆风方向， 增加的角速度）和随机分量（转弯和跑动的精确定时）。我们的实验室 开发了一个高通量的范例，其中这三个阶段可以反复引发， 通过呈现吸引人的气味，或通过呈现虚构的光遗传学气味。大型数据集I 用这种范式可以获得的结果既适合计算分析又适合遗传分析。 在我的第一个目标中，我将对嗅觉导航行为进行计算分析， 气味呈现或消失后行为调节的时间尺度，以及分解苍蝇 一系列的行为模式。基于我的模体分析，我将构建一个马尔可夫模型， 它试图重现导航行为的复杂统计数据，并了解随机性是如何影响导航行为的。 导航元素被连接以产生可靠的目标发现。在第二个目标中，我将使用遗传 沉默和激活，以识别有助于行为基序的下行神经元（DN）， 在第一个目标中确定的时间结构。DNs将运动信息从大脑传递到腹神经 脊髓，类似于脊椎动物中将信息从大脑传递到脊髓的神经元。该分析 将使我获得一个相当完整的电路图的运动电路，有助于嗅觉导航。 最后，在我的第三个目标中，我将确定感觉和运动信息的哪些特征被编码在大脑中。 特别是DNs。目前，在果蝇中存在两种运动编码的观点。一些研究支持 这种观点认为，DN根据行为背景中继运动信息，而其他人则认为它们编码 特定的动作，不管感觉驱动器。嗅觉导航，由不同的时代组成 刺激和行为目标，准备确定如何运动的不同感官来源或行为 上下文被编码在运动回路中。使用闭环行为装置，我将从选定的DN中成像 在嗅觉导航和相关活动与行为图案和导航阶段。在一起， 这些实验将有助于揭示运动编码的原理，这有助于理解运动编码是如何产生的。 大脑能够在受伤后恢复运动控制。