Multiple timescales of motor planning and execution in mouse cortex

项目概要/摘要： 对于动物执行复杂的行为，成功的运动规划和执行是 具有本质意义此外，导致成功的基于目标的行为的事件顺序需要 在很大的时间范围内发生。例如，从家步行上班时，必须 首先做一个抽象的，长期的决定去工作，然后转化为 一系列较短时间尺度的左右转弯决定，这些决定被转化为精细的 控制肌肉的波动电流模式运动规划和执行是如何发生的 同时在运动皮层神经元群体中进行多个时间尺度的研究， 明白对人类和非人类灵长类动物的大量研究表明， 刺激在多个时间尺度上积分。这项工作表明，早期的感觉区域，如 初级视觉皮层对环境的快速波动做出反应。该信息 整合到次级皮层区域的更长时间尺度的信息，最长的- 额叶和联合区的时间尺度信息。因此，我们假设， 运动皮层（M2）神经元在比初级运动皮层更长的时间尺度上控制行为 (M1)神经元为了研究这种现象，我建立了一个装置， 在虚拟现实中到达一个有奖励的地方在这个设置中，我可以从动物的各个方面录制视频 用于运动行为的高时空分辨率测量。我开发了机器 学习算法从这些视频中提取3D姿态数据。在目标1中，我将使用钙成像 为了记录小鼠M1和M2中大量的神经元， 神经元和种群对动物正在进行的姿势运动学的影响。我们将补充 有针对性的硅探针记录，以捕捉快速的神经反应。在目标2中，我将比较 训练小鼠执行虚拟运动的M1和M2神经元群体中的钙动力学 计划任务与未训练的小鼠的比较。我们假设训练运动规划 动作增加了M1/M2神经活动的时间尺度。在目标3中，我们将使用光遗传学 大脑皮层特定区域的沉默干扰动物的运动行为。我们假设 当M2受到扰动时，扰动运动的持续时间将长于M1。在这 通过这种方式，我们将研究不同的皮层区域如何在许多时间尺度上与行为相关。这 这项提议将拓宽我们对大脑皮层处理的一般知识，以及运动规划和 尤其是执行。患有精神疾病的患者，如多动症，自闭症和自闭症 障碍显示计划即将到来的运动的能力受损。成功治疗的第一步 这些疾病是为了更好地了解运动规划一般是如何发生的。

Project Summary/Abstract: For animals to execute complicated behaviors, successful motor planning and execution is essential. Moreover, the sequence of events leading to successful goal-based behavior takes place over a wide range of timescales. For example, when walking from home to work, one must first make an abstract, long-timescale decision to go to work, which much then be translated into a sequence of shorter-timescale right-left turning decisions, which are translated into the finely fluctuating electrical patterns that control the muscles. How motor planning and execution occur simultaneously over many timescales in populations of motor cortex neurons is not well understood. Much work in humans and nonhuman primates have shown that visual and auditory stimuli integrate over multiple timescales. This work has shown that early sensory regions, like primary visual cortex, respond to fast fluctuations in the environment. This information is integrated to longer-timescale information in secondary cortical regions, with the longest- timescale information in frontal and association areas. We therefore hypothesize that secondary motor cortex (M2) neurons control behavior over longer timescales than primary motor cortex (M1) neurons. To study this phenomenon, I have built a setup in which head-fixed mice navigate in virtual reality to a rewarded location. In this setup, I can record video from all sides of the animal for high spatiotemporal resolution measurement of motor behaviors. I have developed machine learning algorithms to extract 3D pose data from these videos. In Aim 1, I will use calcium imaging to record large numbers of neurons in mouse M1 and M2 to correlate the activity of individual neurons and populations to the animal’s ongoing pose kinematics. We will supplement with targeted silicon probe recordings to capture fast neural responses. In Aim 2, I will compare the calcium dynamics in populations of M1 and M2 neurons in mice trained to perform a virtual motor planning task versus mice that have not been trained. We hypothesize that training to plan motor actions increases the timescale of M1/M2 neural activity. In Aim 3, we will use optogenetic silencing in specific regions of cortex to perturb the animal’s motor behavior. We hypothesize that the duration of the perturbed movements will be longer when M2 is perturbed than M1. In this way, we will study how different cortical regions relate to behavior over many timescales. This proposal will broaden our knowledge of cortical processing in general, and motor planning and execution in particular. Patients with mental illness, such as ADHD, autism, and Asperger’s disorder show impaired ability to plan upcoming movements. The first step to successfully treating these illnesses is to better understand how motor planning occurs in general.