CRCNS: MOVE!-MOdeling of fast Movement for Enhancement via neuroprosthetics

CRCNS：MOVE！-通过神经修复术增强快速运动建模

基本信息

批准号：
10385747
负责人：
Sridevi V. Sarma
金额：
$ 33.26万
依托单位：
JOHNS HOPKINS UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2018
资助国家：
美国
起止时间：
2018-07-01 至 2023-03-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10385747
关键词：
Affect Agonist Amyotrophic Lateral Sclerosis Anatomy Animals Architecture Area Behavioral Biological Models Brain Brain region Cerebellum Cheetahs Data Dependence Derivation procedure Device Designs Devices Disease Feedback Financial compensation Frequencies Generations Goals Injections Injury Joints Lead Lidocaine Modeling Modernization Monkeys Motor Motor Cortex Motor Neurons Movement Movement Disorders Multiple Sclerosis Muscimol Muscle Neurons Outcome Paresis Parkinson Disease Patients Performance Physiologic pulse Play Primates Process Research Role Running Self-Help Devices Signal Transduction Speed Spinal Cord Spinal cord injury Stroke System Testing Theoretical model Torque Training Translations antagonist arm control theory density design exoskeleton experimental study neural prosthesis neurophysiology neuroprosthesis nonhuman primate performance tests programs receptor relating to nervous system skills theories transmission process visual feedback

项目摘要

Tracking fast unpredictable movements is a valuable skill, applicable in many situations. In the animal kingdom, the context includes the action of a predator chasing its prey that is running and dodging at high speeds, like a cheetah chasing a gazelle. The sensorimotor control system (SCS) is responsible for such actions and its performance clearly depends on the computing power of neurons, delays between brain and muscles, and the dynamics of muscles involved. Despite these obvious factors that set the limits on how fast an animal can track a moving object, tracking performance of the SCS and its dependence on neural computing, delays, and muscle dynamics have not been explicitly quantified. In this program, we will build upon new theory developed using feedback control principles and an appropriately simplified model of the SCS to identify how neural computing, delays, and muscles interact during the generation of fast movements. Therefore if one component is compromised, we can take advantage of the other components to restore motor performance with assistive neuroprosthetic devices. The program objectives are to first parameterize the major factors (brain and body) limiting fast movements and to derive how these parameters must interact to achieve tracking of fast movements in the SCS. Then, the parameterization and quantified interactions will be tested experimentally in subjects through manipulation of (i) neural computing power, (ii) transmission delays, and (iii) muscle dynamics. If discrepancies emerge between experiments and theory, the SCS model and theory will be modified to explain observation data. Finally, the theoretical model of interactions required to achieve tracking of fast movements will be exploited to apply compensation to account for degradation of some parameters by "boosting" others. More specifically, we will design assistive neuroprosthetic devices for subjects having compromised neural real estate to restore performance of fast movements. For example, if primary motor cortex is compromised due to disease or damage, we can manipulate muscle dynamics by adding the necessary compensatory forces to restore motor performance, and more importantly restore fast and agile movements. Just how one should compensate will be informed by our SCS model and theory.

跟踪快速不可预测的运动是一项宝贵的技能，适用于许多情况。在动物中王国，背景包括捕食者追逐在高处奔跑和躲避的猎物的动作速度就像猎豹追逐瞪羚一样。感觉运动控制系统（SCS）负责这种动作及其性能显然取决于神经元的计算能力、大脑和大脑之间的延迟肌肉，以及所涉及肌肉的动力学。尽管有这些明显的因素限制了速度动物可以跟踪移动的物体，跟踪 SCS 的性能及其对神经元的依赖计算、延迟和肌肉动力学尚未明确量化。在这个计划中，我们将构建基于使用反馈控制原理和适当简化的模型开发的新理论 SCS 用于识别神经计算、延迟和肌肉在快速运动生成过程中如何相互作用。因此，如果一个组件受到损害，我们可以利用其他组件来恢复辅助神经假体装置的运动表现。该计划的目标是首先参数化限制快速运动的主要因素（大脑和身体）并推导出这些参数必须如何相互作用才能实现对 SCS 中快速运动的跟踪。然后，参数化和量化的相互作用将通过受试者进行实验测试操纵（i）神经计算能力，（ii）传输延迟，以及（iii）肌肉动力学。如果实验和理论之间出现差异，SCS模型和理论将被修改以解释观察数据。最后，实现快速运动跟踪所需的交互理论模型将被用来通过“增强”其他参数来应用补偿来解决某些参数的退化。更具体地说，我们将为神经受损的受试者设计辅助神经假体装置房地产恢复业绩快速走势。例如，如果初级运动皮层受到损害由于疾病或损伤，我们可以通过添加必要的代偿来控制肌肉动力学力恢复运动性能，更重要的是恢复快速敏捷的运动。到底有多一应该补偿将由我们的 SCS 模型和理论告知。