Decision-related information at single-neuron resolution in human motor cortex and its implications for neuroprosthetics

人类运动皮层单神经元分辨率的决策相关信息及其对神经假体的影响

• 批准号: 9977403
• 负责人: Sharlene Flesher
• 金额: $ 2.37万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-05-01 至 2020-09-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9977403
• 关键词: Affect; Animal Model; Animals; Area; Brain; Bypass; Cells; Clinical Trials; Cognitive; Data; Data Set; Decision Making; Devices; Dimensions; Discrimination; Disease; Enrollment; Environment; Feedback; Generations; Goals; Human; Injury; Intention; Knowledge; Laboratories; Limb structure; Link; Methods; Modeling; Motor; Motor Activity; Motor Cortex; Movement; Neurons; Outcome; Participant; Persons; Population; Process; Prosthesis; Reaction Time; Records; Research; Research Personnel; Resolution; Signal Transduction; Spinal cord injury; System; Techniques; Testing; Time; Training; Upper Extremity; User-Computer Interface; Work; base; brain computer interface; cognitive process; denoising; design; experimental study; flexibility; functional restoration; improved; insight; motor control; nervous system disorder; neural correlate; neural prosthesis; neurophysiology; neuroprosthesis; nonhuman primate; public health relevance; recurrent neural network; relating to nervous system; sensory feedback; tool

Project Summary / Abstract The long-term goal of my research is to gain a holistic understanding of how movement commands are generated- including their relationship to sensory feedback and movement context- in order to restore movement to those who have lost it. When the ability to move is lost, due to spinal cord injury or disease, the ability to generate movement commands is still intact, but the command cannot reach the end effectors. One means of restoring function after such an injury is brain-computer interface (BCI) which records the movement commands directly from the brain and bypasses the injury to move external end effectors. These neural prostheses rely on accurate decoding of movement intention to perform the user’s desired action. While good control of these devices has been demonstrated, the control is not as quick as movement of a native limb. This may be due to oversimplification of how movement is decoded- in isolation from factors such as feedback or movement context. Cognitive processes that may be occurring at the same time are not accounted for when determining the user’s movement intention. The specific objectives of this proposal are to identify and characterize activity related to decision making in human motor cortex, at the single-cell level, and how it relates to movement command generation. Using participants enrolled in a clinical trial, we will record neural activity intracortically from motor cortex during decision-making tasks to identify decision-related activity and examine its relationship to movement intention. This activity has not been characterized in human cortex at this resolution, and the data collected in the proposed work will allow us to compare to models of decision making generated from non-human primate work and expand on the uniquely human ability to complete a variety of tasks in a single day. The proposed experiments will produce a valuable data set that will enable me to (1) identify how neurons represent both movement and decision-related activity, (2) capitalize on the decades of research on non-human primate decision making to identify appropriate models for decision-related activity in human motor cortex, (3) generate a more inclusive model by incorporating data collected during decision-related and movement generation tasks, using multiple end effectors and (4) integrate decision-related activity into BCI decoders to improve our ability to decode a BCI user’s movement intention and enhance the user’s control of the device (R00). This will provide insight - supplementing knowledge from non-human primate studies - as to how the brain decides to move.

项目总结/摘要 我研究的长期目标是全面了解运动命令是如何 生成-包括它们与感觉反馈和运动背景的关系-以恢复运动 当由于脊髓损伤或疾病而失去移动能力时， 生成移动命令仍然完整，但命令无法到达末端效应器。的一种手段 脑-机接口（BCI）是脑损伤后恢复功能的一种方法， 直接从大脑和绕过损伤移动外部末端执行器。这些神经假体依赖于 准确解码运动意图以执行用户期望的动作。虽然很好地控制这些 设备已经被证明，控制不像原生肢体的运动那样快。这可能是由于 过度简化了运动如何被解码-与诸如反馈或运动上下文之类的因素隔离。 当确定用户的认知过程时，可能同时发生的认知过程不被考虑在内。 运动意图本提案的具体目标是确定和描述与以下方面相关的活动 人类运动皮层在单细胞水平上的决策，以及它如何与运动命令相关 一代使用参加临床试验的受试者，我们将记录皮质内的神经活动， 在决策任务中识别决策相关活动并检查其与运动的关系 意图在此分辨率下，这种活动尚未在人类皮层中进行表征，并且 这项工作将使我们能够与非人类灵长类动物的决策模型进行比较 工作和扩展人类在一天内完成各种任务的独特能力。拟议 实验将产生一个有价值的数据集，使我能够（1）识别神经元如何代表 运动和决策相关的活动，（2）利用几十年来对非人灵长类动物的研究 决策，以确定人类运动皮层中决策相关活动的适当模型，（3）生成 一个更具包容性的模型，通过整合在决策相关和运动生成任务期间收集的数据， 使用多个末端效应器和（4）将决策相关活动集成到BCI解码器中，以提高我们的能力 解码BCI用户的运动意图并增强用户对设备的控制（R 00）。这将提供 洞察力-补充非人类灵长类动物研究的知识-关于大脑如何决定移动。