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.

项目总结 脑-机接口(BCI)在过去的十年中取得了显著的进步，包括直接 控制复杂的拟人机械臂，并结合触觉反馈。然而， 目前脑控假肢的灵巧度受到两个重要方面的限制。首先，大多数神经假体 控制包括从初级运动皮质(M1)神经元的反应中解码运动学。虽然这件事 手术入路已成功控制近端手臂(肩部和肘部)以放置和定位 手，它从根本上不足以进行手控和与对象的交互，这不仅需要 调整手腕的方向，塑造脚趾，同时施加适当的力量。这个问题因以下原因而复杂化 力和运动学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，力和运动学的手和臂表示似乎依赖于 在任务上，为一个任务开发的解码器不能推广到另一个任务，这就是明证。第二, 触觉反馈对手动行为至关重要，这一点从去传入导致的严重缺陷中可见一斑。 因此，要实现对假肢的灵巧控制，还需要恢复触觉反馈。一件有希望的事 方法是皮质内微刺激(Icms)躯体感觉皮质(S1)，它唤起生动的触觉。 在(否则无知觉的)手上感受到的知觉。越来越多的人一致认为，模仿 自然主义的神经元激活模式将导致更自然的触觉感知和更灵巧的手 使用。然而，对触摸的神经基础的研究几乎完全是通过被动地呈现给 不动的手，这排除了任何对运动行为如何塑造S1反应和 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执行抓取行为，在这些行为中，我们系统地改变手和 手臂运动学和力，并测量整个肢体的随时间变化的姿势和施加在 物体，包括每个指头的接触力。我们试图描述(1)M1中与运动学有关的信号 以及手臂和手所施加的力；(2)S1中与与物体的主动交互有关的信号；以及(3) 信号在M1和S1之间传输。我们建议使用成熟的编码和解码 研究神经反应和运动参数之间的关系的技术以及 新的动力系统分析。由此产生的对恐惧的神经机制的洞察将导致 (1)从M1响应中开发预期肢体状态的解码器，包括运动学和 力控制和跨行为任务泛化；(2)仿生感觉编码算法 理解S1中的主动触摸表示法。研究团队正准备测试由此产生的 作为正在进行的临床试验的一部分，在人类脑机接口参与者中的解码器和感觉编码算法 通过美国国立卫生研究院正在进行的一个资助项目。