Influence of Task Complexity and Sensory Feedback on Cortical Control of Grasp Force

摘要 人类可以在像挥动网球拍这样复杂而动态的动作中巧妙地控制他们的抓握， 就像拿着一个公文包一样简单和静止。这两项任务都需要使用感官反馈来实现 并保持适当的抓握力。有证据表明运动和躯体感觉皮层 传达任务相关信息，以实现熟练的运动。我们的主要目标是揭露 运动皮层动力学基础的把握力控制，并确定在何种程度上，这些动态 通过行为背景和躯体感觉反馈的皮质皮层通信介导。 我们建议通过利用以下提供的独特实验范例来研究抓握的皮层控制： 一项双向人脑-计算机接口研究，其中四肢瘫痪的参与者在皮质内 植入运动和躯体感觉皮层的电极阵列。以前的工作，主要集中在达到 运动，已经证明，运动皮层表现出人口动态的限制在低， 维流形我们已经在人类运动皮层中发现了类似的动态反应， 关于抓取力的信息。然而，这些响应是任务相关的，并且可以随着任务的完成而改变。 近端臂运动变化的复杂性。在这里，我们将扩展这项工作，以研究背景- M1动力学在一系列静态和动态手部和手臂运动中的依赖性，包括 公开和隐蔽（即，想象）行为。 复杂的运动控制依赖于感觉信息来塑造从运动神经发出的神经控制信号。 皮层，但很少有人知道的信息流从体感到运动皮层的控制 的手。我们的目标是在一系列的任务环境中量化皮质皮层的通讯通路 通过分析运动和躯体感觉皮层的同步神经记录。然后我们将使用 皮质内微刺激以探测这些通信途径，同时提供任务相关的感觉 反馈以及任务无关的刺激作为对照。最后，我们将使用脑机接口， 测试是否有潜在的可塑性内皮质通信电路，或是否 通信受到区域间动态的限制。成功完成此提案将导致 关于M1在动态和静态抓握行为中的作用的新知识。我们将量化 躯体感觉输入与M1沟通，以及皮质皮质沟通途径是否可以 通过培训进行修改，这与理解技能学习和改善康复有关。

ABSTRACT Humans can skillfully control their grasp during actions as complex and dynamic as swinging a tennis racket, and as simple and static as holding a briefcase. Both tasks require the use of sensory feedback to achieve and maintain an appropriate grasp force. There is evidence that motor and somatosensory cortices communicate task-relevant information in order to enable skillful movement. Our primary goal is to uncover the motor cortical dynamics underlying grasp force control and determine the extent to which these dynamics are mediated by behavioral context and corticocortical communication of somatosensory feedback. We propose to study the cortical control of grasp by leveraging the unique experimental paradigms afforded by a bidirectional human brain-computer interface study in which participants with tetraplegia have intracortical electrode arrays implanted in motor and somatosensory cortex. Previous work, primarily focused on reaching movements, has demonstrated that motor cortex exhibits population dynamics that are constrained within low- dimensional manifold. We have identified similar dynamic responses within human motor cortex that contain information about grasp force. However, these responses are task-dependent and can change as the complexity of the proximal arm movement changes. Here we will extend that work to study the context- dependence of M1 dynamics across a range of static and dynamic hand and arm movements including both overt and covert (i.e., imagined) behaviors. Sophisticated motor control relies on sensory information to shape neural control signals emanating from motor cortex, yet very little is known about the flow of information from somatosensory to motor cortex for the control of the hand. We aim to quantify the corticocortical communication pathways across a range of task contexts through the analysis of simultaneous neural recordings in motor and somatosensory cortex. We will then use intracortical microstimulation to probe these communication pathways while providing task-relevant sensory feedback as well as task-irrelevant stimulation as a control. Finally, we will use a brain-computer interface to test whether there is the potential for plasticity within the corticocortical communication circuits, or whether communication is constrained by between-area dynamics. Successful completion of this proposal will lead to new knowledge about the role of M1 in dynamic and static grasp behaviors. We will quantify how somatosensory input is communicated with M1 and whether corticocortical communication pathways can be modified through training, which has relevance to understanding skill learning and improving rehabilitation.