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: 10357463
• 负责人: SLIMAN BENSMAIA
• 金额: $ 64.94万
• 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/10357463
• 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; 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; neuromechanism; neuronal patterning; neuroprosthesis; next generation; nonhuman primate; novel; relating to nervous system; response; restoration; success

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 脑机接口（BCI）在过去十年中取得了显着的进展，包括直接 先进的拟人机器人手臂的控制和触觉反馈的结合。但 当前脑控假肢的灵活性在两个重要方面受到限制。首先，大多数神经假体 控制涉及从初级运动皮层（M1）中的神经元的反应解码运动学。虽然这 该方法已经成功地控制近端臂（肩和肘）以放置和定向 手，它是根本不足的手控制和与对象的互动，这不仅需要 定向手腕和塑造手指，但也施加适当的力。这一问题因以下因素而变得复杂： 力和运动学信号以及手和手臂信号都混合在神经系统中， 在M1的人口活动。此外，力和运动学的手和手臂表示似乎取决于 在任务上，正如为一个任务开发的解码器不能推广到另一个任务的事实所证明的那样。第二、 触觉反馈对于手动行为是至关重要的，如由传入神经阻滞导致的严重缺陷所证明的。 因此，为了实现对假肢的灵巧控制，还需要恢复触觉反馈。一个有希望 方法是体感皮层（S1）的皮质内微刺激（ICMS），它唤起生动的触觉 在（其他无感觉的）手上体验到的感知。越来越多的人认为模仿 神经元激活的自然模式将导致更自然的触觉感知和更灵巧的手 使用.然而，触摸的神经基础几乎完全是用被动呈现给 不动的手，这使得我们无法理解运动行为如何塑造S1反应， 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执行类似的行为，在这种行为中，我们系统地改变手和脚的位置。 手臂运动学和力量，并测量整个肢体的时变姿势和施加在 对象，包括每个手指的接触力。我们试图表征（1）与运动学相关的M1中的信号 以及手臂和手施加的力;（2）S1中与物体的主动交互有关的信号;以及（3） 在M1和S1之间传输信号。我们建议应用完善的编码和解码 研究神经反应和运动参数之间关系的技术，以及 新动力系统分析由此产生的对脑下垂体神经机制的深入了解将导致 (1)从包括运动学和运动学的M1反应中开发预期肢体状态的解码器， 力控制和概括行为任务;（2）仿生感觉编码算法， 理解S1中的主动触摸表示。研究小组正准备测试 解码器和感觉编码算法在人类BCI参与者中作为正在进行的临床试验的一部分， 通过一个正在进行的NIH资助的项目。