Cerebellar and basal ganglia contributions to motor learning

小脑和基底神经节对运动学习的贡献

• 批准号: 2673866
• 金额: --
• 依托单位: University of Edinburgh
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2673866
• 关键词: Cerebellar; basal; ganglia; contributions; motor

Learning and refinement of new movements is an essential component of our everyday lives. We use observation of previous movement outcomes and utilise our capacity for adaptable motor control to successfully interact with objects in our environment. This process is thought to involve brain areas concerned with action selection/vigour (basal ganglia) and motor control/learning (cerebellum), which converge at the level of the motor thalamus to select the correct action type and execute movements. Although we have an in-depth understanding of the brain areas involved, the importance of the timing of these signals remains unresolved. Furthermore, the consistency of these signals between different movements and learning periods is also unknown. In this programme of work, we will test three main hypotheses:1. Timing of cerebellar and basal ganglia input to motor thalamus becomes tightly correlated as a task becomes learned2. Across several different behaviours, basal ganglia provide a consistent signal to motor thalamus, correlating with the vigour of each behaviour3. Functional convergence of these inputs at the level of motor thalamus is important for the learning of a motor taskTo determine the population-level representations of motor learning in subcortical motor regions, we will use wide-field 2-photon population calcium imaging and electrophysiological recordings of neuronal activity in subcortical motor regions (basal ganglia / motor thalamus) during execution of a Go/NoGo task. Viral-based opto-/chemogenetic manipulation strategies will be used to investigate causality between activity and learning.We will also use dimensionality reduction methods and Bayesian decoders to explore causal links between neuronal activity patterns in motor regions and learning. By applying advanced computational methods and computer vision algorithms we will develop an in-depth understanding of how single cell and network dynamics in subcortical motor regions combine to learning complex tasks and generation of accurate movements.

学习和改进新的动作是我们日常生活的重要组成部分。我们通过观察先前的运动结果，并利用我们的适应性运动控制能力，成功地与环境中的物体进行互动。这个过程被认为涉及与动作选择/活力（基底神经节）和运动控制/学习（小脑）有关的大脑区域，这些区域在运动丘脑水平会聚以选择正确的动作类型并执行动作。虽然我们对所涉及的大脑区域有了深入的了解，但这些信号的时间的重要性仍然没有得到解决。此外，这些信号在不同运动和学习周期之间的一致性也是未知的。在本工作方案中，我们将检验三个主要假设：1。随着任务的学习，小脑和基底神经节向运动丘脑输入的时间变得密切相关2。在几种不同的行为中，基底神经节向运动丘脑提供一致的信号，与每种行为的活力相关。这些输入在运动丘脑水平上的功能会聚对于运动任务的学习是重要的。为了确定皮质下运动区域中运动学习的群体水平表征，我们将使用宽场双光子群体钙成像和在执行Go/NoGo任务期间皮质下运动区域（基底神经节/运动丘脑）中神经元活动的电生理记录。我们将使用基于病毒的光学/化学遗传操作策略来研究活动和学习之间的因果关系。我们还将使用降维方法和贝叶斯解码器来探索运动区神经元活动模式和学习之间的因果关系。通过应用先进的计算方法和计算机视觉算法，我们将深入了解皮层下运动区域的单细胞和网络动力学如何结合联合收割机来学习复杂的任务和生成准确的动作。