Motor learning and memory in health and disease

描述（由申请人提供）：当一个人将他们的手从一个点移动到另一个点时，大脑通过依赖于神经结构来引导手臂，该神经结构估计任务的物理动力学并将期望的运动转换为运动命令。如果我们的手拿着一个物体，手臂动力学的微妙变化会被这些神经结构考虑在内，这反映在改变的运动指令中。这些观察表明，在产生运动指令时，大脑强烈依赖于预测外部世界物理动力学的内部模型。内部模型是通过实践学习的，似乎是自主运动控制的基本组成部分。然而，我们对大脑中哪些神经结构参与形成运动控制的内部模型以及它们如何学习表示这些模型知之甚少。我们的目标是联合收割机结合行为和数学工具来推断人类是如何学习内部模型的，以及当大脑中特定的运动结构受损时，这一过程是如何受到影响的。我们通过考虑一项任务来解决这个问题，其中到达运动的物理动力学被改变。当人们练习这个任务时，我们会问，在一个给定的动作中经历的错误是如何影响随后的动作的？我们得到了一个泛化函数，它在数学上描述了大脑如何改变内部模型来响应错误。泛化函数的形状预测了参与表示相对于运动学的内部模型的元素的感受野。我们研究了这些泛化功能的位置，速度和加速度空间的arm.Preliminary结果表明，这些行为推断的基础和典型的调谐性能的细胞在初级运动皮层之间的显着相似性。这表明，这些细胞的调节特性可能反映在人类行为中，就像我们学习和概括力的模式一样。我们将研究扩展到包括从一只手臂到另一只手臂的泛化。我们进一步扩展的研究，动态不仅依赖于手臂运动学的运动，但也对其他线索：外部线索，动态链接到一个任意的空间或颜色线索，内部线索，动态取决于位置的运动与序列。我们比较了亨廷顿病和小脑疾病对大脑损伤的影响。然而，运动记忆不是静态的。它们的功能特性在学习一项任务后的几个小时内发生变化。我们问这种变化如何影响泛化功能。在记忆巩固后，大脑对错误的反应方式与学习阶段的早期不同吗？

DESCRIPTION (provided by applicant): When one moves their hand from one point to another, the brain guides the arm by relying on neural structures that estimate physical dynamics of the task and transform the desired motion into motor commands. If our hand is holding an object, the subtle changes in the dynamics of the arm are taken into account by these neural structures and this is reflected in the altered motor commands. These observations have suggested that in generating motor commands, the brain strongly relies on internal models that predict physical dynamics of the external world. The internal models are learned with practice, and appear to be a fundamental part of voluntary motor control. However, we know very little about which neural structures in the brain are involved in formation of internal models for motor control and how they learn to represent these models. Our aim here is to combine behavioral and mathematical tools to infer how humans learn internal models, and how the process is affected when there is damage to specific motor structures in the brain. We approach the problem by considering a task where physical dynamics of reaching movements are altered. As people practice the task, we ask how did an error that was experienced in a given movement affect subsequent movements? We arrive at a generalization function that mathematically describes how the brain changes the internal model in response to an error. The shape of the generalization function predicts the receptive field of the elements that took part in representing the internal model with respect to movement kinematics. We study these generalization functions in position, velocity, and acceleration space of the arm. Preliminary results demonstrate a remarkable similarity between these behaviorally inferred bases and typical tuning properties of cells in the primary motor cortex. This suggests that tuning properties of these cells might be reflected in human behavior in the way that we learn and generalize patterns of force. We extend the studies to include generalization from one arm to the other. We further extend the studies to movements where dynamics are not dependent only on arm kinematics, but also on other cues: external cues where dynamics are linked to an arbitrary spatial or color cue, internal cues where dynamics depends on position of a movement with in a sequence. We compare how damage to the brain in Huntington's disease vs. cerebellar disease affects this learning. However, motor memories are not static. Their functional properties change within hours after a task is learned. We ask how this change affects the generalization function. Is the brain different in the way it responds to an error after a memory has consolidated vs. early in the learning phase?