The interplay between kinematic and force representations in motor and somatosensory cortices during reaching, grasping, and object transport

伸手、抓握和物体运输过程中运动和体感皮层运动学和力表征之间的相互作用

• 批准号: 10546486
• 负责人: Jennifer L. Collinger
• 金额: $ 62.83万
• 依托单位: UNIVERSITY OF CHICAGO
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-01-15 至 2026-12-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10546486
• 关键词: 3-Dimensional; Address; Algorithms; Animals; Area; Artificial Arm; Behavior; Behavioral; Biomimetics; Bionics; Brain; Clinical Trials; Code; Communication; Complex; Consensus; Cutaneous; Deafferentation procedure; Development; Digit structure; Elbow; Event; Exclusion; Feedback; Funding; Hand; Hand functions; Human; Individual; Limb Prosthesis; Limb structure; Manuals; Maps; Measures; Monitor; Monkeys; Motor; Motor Cortex; Movement; Neurons; Participant; Play; Population; Posture; Research; Robotics; Role; Sensory; Shapes; Shoulder; Signal Transduction; Site; Skin; Somatosensory Cortex; Stimulus; Systems Analysis; Tactile; Techniques; Test Result; Texture; Time; Touch sensation; United States National Institutes of Health; Wrist; active control; advanced analytics; arm; arm movement; brain computer interface; design; dexterity; dynamic system; experience; experimental study; grasp; improved; insight; kinematics; microstimulation; motor behavior; motor learning; neural; neuromechanism; neuronal patterning; neuroprosthesis; next generation; nonhuman primate; novel; response; restoration; success; transmission process

PROJECT SUMMARY Brain-Computer Interfaces (BCIs) have achieved remarkable progress over the last decade, including the direct control of sophisticated anthropomorphic robotic arms and the incorporation of tactile feedback. However, the dexterity of current brain-controlled prosthetic limbs is limited in two important ways. First, most neuroprosthetic control involves decoding kinematics from the responses of neurons in primary motor cortex (M1). While this approach has been successful for controlling the proximal arm (shoulder and elbow) to place and orient the hand, it is fundamentally inadequate for hand control and interactions with objects, which requires not only orienting the wrist and shaping the digits but also applying appropriate forces. This problem is complicated by the fact that force and kinematic signals as well as hand and arm signals are all intermingled in the neural population activity in M1. Furthermore, hand and arm representations of force and kinematics seem to depend on the task, as evidenced by the fact that decoders developed for one task fail to generalize to another. Second, tactile feedback is critical to manual behavior as evidenced by the severe deficits that result from deafferentation. To achieve dexterous control of a prosthetic arm thus also requires restoration of tactile feedback. One promising approach is intracortical microstimulation (ICMS) of somatosensory cortex (S1), which evokes vivid tactile percepts experienced on the (otherwise insensate) hand. There is a growing consensus that mimicking naturalistic patterns of neuronal activation will lead to more natural tactile percepts and more dexterous hand use. However, the neural basis of touch has been studied almost exclusively with stimuli passively presented to the unmoving hand, which precludes any understanding of how motor behavior shapes S1 responses and hinders the development of biomimetic encoding algorithms. To fill these gaps, we will have NHPs perform prehensile behaviors in which we systematically vary hand and arm kinematics and forces, and measure the time-varying postures of the entire limb and the forces exerted on objects, including contact forces at each digit. We seek to characterize (1) signals in M1 relating to kinematics and forces exerted by the arm and hand; (2) signals in S1 relating to active interactions with objects; and (3) signals transferred between M1 and S1. We propose to apply well-established encoding and decoding techniques to investigate the relationship between neural responses and movement parameters as well as a novel dynamical systems analysis. The resulting insights into the neural mechanisms of prehension will lead to (1) the development of decoders of intended limb state from M1 responses that include both kinematics and force control and generalize across behavioral tasks; (2) biomimetic sensory encoding algorithms informed by an understanding of active touch representations in S1. The research team is uniquely poised to test the resulting decoders and sensory encoding algorithms in human BCI participants as part an ongoing clinical trial at both sites through an ongoing NIH funded project.

项目摘要 在过去的十年中 控制精致的拟人机器人手臂和触觉反馈的结合。但是， 当前的脑控制假肢的敏捷性通过两种重要方式受到限制。首先，大多数神经假体 控制涉及从原发性运动皮层中神经元反应（M1）中解码运动学。同时 方法已成功控制近端手臂（肩膀和肘）以放置和定向 手从根本上来说，手工控制和与物体的互动不足，这不仅需要 定向手腕并塑造数字，但也施加适当的力。这个问题很复杂 力和运动信号以及手和手臂信号都混合在神经中的事实 M1中的人口活动。此外，力量和运动学的手和手臂表示似乎取决于 在任务上，为一个任务开发的解码器未能推广到另一个任务的事实证明。第二， 触觉反馈对手动行为至关重要，这是由脱落而导致的严重缺陷所证明的。 为了获得对假肢的灵活控制，还需要恢复触觉反馈。一个有希望的 方法是体感皮质（S1）的皮质内微刺激（ICMS），它唤起了生动的触觉 感知（否则无敏感）的手。越来越多的共识模仿 神经元激活的自然主义模式将导致更自然的触觉感知和更灵巧的手 使用。但是，触摸的神经基础几乎完全研究了被动刺激 不动的手，这排除了对运动行为如何塑造S1响应的任何理解和 阻碍了仿生编码算法的发展。 为了填补这些空白，我们将让NHP执行精神上的行为，在这些行为中，我们系统地改变了手，并且 手臂运动和力量，测量整个肢体的时变姿势以及施加的力 物体，包括每个数字的接触力。我们试图表征与运动学有关的M1中的（1）信号 和手臂和手施加的力； （2）S1中的信号与与对象的活动相互作用有关； （3） 信号在M1和S1之间传输。我们建议应用良好的编码和解码 研究神经反应与运动参数之间的关系以及 新型动力学系统分析。对预性神经机制的最终见解将导致 （1）从包括运动学和包括运动学和 强制控制和跨行为任务概括； （2）仿生的感觉编码算法由 对S1中主动触摸表示形式的理解。研究团队有独特的准备测试结果 在人类BCI参与者中，解码器和感觉编码算法是两者中持续的临床试验的一部分 通过正在进行的NIH资助项目的站点。