Sensory and motor plasticity in learning

学习中的感觉和运动可塑性

• 批准号: RGPIN-2019-06684
• 负责人: Henriques, Denise
• 金额: $ 3.42万
• 依托单位: York University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2021
• 资助国家: 加拿大
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=739048
• 关键词: Sensory; motor; plasticity; learning

The motor-control systems in our brain are remarkably good at rapidly adapting to continuous changes in our environment and changes in our body. Yet, the mechanisms that underlie such sensorimotor learning remain largely unknown. The aims of this proposal are to 1) better understand the sources of information used to flexibly learn and adapt movements, 2) to explore processes in the brain that underlie sensorimotor learning, and 3) to investigate how this extends to interacting with tools and other objects. Being able to adapt to changes in our environment and body relies on our ability to estimate the location of our limbs, in particular, our hand. This is called state estimation and is a key element in all models of motor control. A state estimate combines sensory feedback with information related to outgoing motor commands (efferent copies) used by forward models in the brain to predict the sensory consequences of the outgoing motor command. Yet, most models of motor control assume that adaptation exclusively results from updates in prediction, but not updates in perception. Work from my lab shows that proprioceptive estimates of the hand do change during learning and partly account for changes in motor performance. To better understand how and to what extent both proprioception and prediction contribute to motor learning, we have developed a method for isolating their distinct contributions. In the first aim of the proposed research, we will test the sensitivity of each to multiple factors known to impact motor learning, as well as measure and model their separate time courses throughout learning. In the second aim, we will complement these behavioural and modeling studies with electroencephalography (EEG) to identify brain activity reflecting ongoing processing of sensory and prediction errors and the resulting changes in state estimation and motor planning throughout motor adaptation. Motor learning does not only involve forming and updating forward models of our effectors, but also building motor-related representations of objects and tools we interact with. Yet, this aspect of learning has been largely overlooked even though the diversity and complexity of human movements involves the ability to predict and skillfully manipulate objects. Thus, for our third aim we use 3D virtual reality (VR) technology to manipulate the normal physics associated with operating familiar tools and objects to characterize tool-skill acquisition. The proposed research will expand our understanding of human sensorimotor learning, including the underlying brain activity and skilled tool-use. This in turn could lead to improved rehabilitation, better human-machine interfaces, and better motor-skills training for all levels of performance.

我们大脑中的运动控制系统非常擅长快速适应环境和体内变化的持续变化。然而，这种感觉运动学习基础的机制在很大程度上尚不清楚。该提案的目的是对1）更好地了解用于灵活学习和适应运动的信息来源，2）探索大脑中的过程，这些过程是感官学习的基础，以及3）研究如何扩展到与工具和其他对象进行交互。 能够适应我们的环境和身体的变化取决于我们尤其是肢体位置的能力，尤其是我们的手。这称为状态估计，是所有电动机控制模型中的关键要素。州估计将感觉反馈与与外向模型使用的信息相关的信息与外向电机命令（传出副本）相关的信息，以预测即将发出的电动机命令的感觉后果。但是，大多数电动机控制模型都假定适应性仅来自预测的更新，而不是感知的更新。我实验室的工作表明，在学习过程中，手部的本体感受性估计确实发生了变化，并部分解释了运动性能的变化。为了更好地理解本体感受和预测在多大程度上有助于运动学习，我们开发了一种隔离其独特贡献的方法。在拟议的研究的第一个目的中，我们将测试每种研究的灵敏度对已知影响运动学习的多种因素，并在整个学习过程中测量和建模其单独的时间课程。在第二个目标中，我们将与脑电图（EEG）相互补充这些行为和建模研究，以鉴定大脑活动，以反映在整个运动适应过程中的感觉和预测误差正在进行的处理以及导致的状态估计和运动计划的变化。运动学习不仅涉及形成和更新效应子的前瞻性模型，而且还构建与我们与的对象和工具相关的运动相关表示形式。然而，即使人类运动的多样性和复杂性涉及预测和巧妙地操纵对象的能力，但学习的这一方面被大大忽视了。因此，对于我们的第三个目标，我们使用3D虚拟现实（VR）技术来操纵与操作熟悉的工具和对象相关的正常物理，以表征工具技能的获取。 拟议的研究将扩大我们对人类感觉运动学习的理解，包括潜在的大脑活动和熟练的工具使用。反过来，这可能会改善康复，更好的人机界面以及各种表现的更好的运动技能培训。