Collaborative Research: NCS-FO: Intelligent Closed-Loop Neural Interface System for Studying Mechanisms of Somatosensory Feedback in Control of Functional and Stable Locomotion

合作研究：NCS-FO：智能闭环神经接口系统，用于研究体感反馈控制功能性和稳定运动的机制

• 批准号: 2024414
• 负责人: Boris Prilutsky
• 金额: $ 30.6万
• 依托单位: Georgia Tech Research Corporation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2024414&HistoricalAwards=false
• 关键词: Collaborative; Research; NCS; FO; Intelligent

Sensory feedback from moving legs is critical for functional and dynamically stable locomotion. Although it is clear that motion-related sensory feedback influences inter-leg coordination and selection of gaits (walking, trotting, galloping, etc.), it is not known which sensory modalities (e.g., muscle length- or force-related signals) and sources of feedback (e.g., hip or knee muscles) mediate these locomotor changes. Therefore, this project aims to understand how sensory neurons providing information about the length of hip muscles regulate interlimb coordination and gait selection. This goal will be accomplished by selectively and reversibly stimulating these sensory neurons in an intelligent, closed-loop, and well-controlled manner. This project will lead to the development of new neural implant tools and associated computational algorithms for an in-vivo manipulation of motion-related sensory signals in a large animal model, the cat. The new findings of this project and the developed methods will substantially enhance our understanding of the mechanisms of sensory locomotor control and contribute to developing novel therapeutic interventions. The proposed multidisciplinary research approaches will also significantly expand the utility and capabilities of the rapidly growing field of optogenetics, enabling transformative research and providing unprecedented new experimental tools for neuroscience. The most noticeable long-term benefits of this work to society will be an improvement in the quality of life for a sizable population of people affected by a wide range of movement deficits, from limb loss to sensory neuropathy. These individuals will benefit from the development of neural interfaces between the nervous and engineering systems controlled by machine learning algorithms. Throughout this project, efforts will be made to recruit and train graduate and undergraduate students from underrepresented groups. Outreach activities will also be organized to share resources, tools, and knowledge with teachers, students, and underrepresented groups. The results of the proposed research and educational activities will be shared with students, scientific communities, and the public through science fairs, publications, workshops, conferences, and the Internet.The overall goal of this proposal is to characterize the mechanisms of somatosensory control of interlimb coordination and gait selection by spindle afferents of hip muscles in the cat model by developing and utilizing in-vivo an intelligent and closed-loop optoelectronic neural interface system. In particular, in this proposal high-density, efficient, and wirelessly-powered implantable opto-electro (WIOE) neural interface devices will be developed. Each WIOE heterogeneously incorporates an optoelectronic array of 64 transparent microelectrodes and 16 microscale light-emitting-diodes (µLEDs), a system-on-a-chip (SoC), and a power receiver (Rx) coil in an mm3-size package, capable of optogenetic stimulation and electrical recording of neural activities. Wireless telemetry links will be implemented for efficient transcutaneous power and wideband data transmission between an external data-acquisition/control unit and the distributed array of WIOE implants. Multiple WIOE devices will be implanted in selected dorsal root ganglia (DRG) of the cat. Neural activities of DRG neurons, EMG activities of selected muscles of the four limbs, and full-body locomotor kinematics will be recorded, and spindle afferent activities will be manipulated via optogenetic stimulation in selected DRGs during unconstrained cat locomotion. Machine learning (ML) models leveraging the spatiotemporal structures in the signals and mapping afferent activities in DRGs to limb kinematics will be applied for achieving closed-loop control of the optogenetic neuromodulation. The proposed research activities will be conducted by a team of collaborators with complementary research expertise in the areas of bioMEMS, wireless microelectronics, machine learning, artificial intelligence, and behavioral neuroscience. The successful development of the proposed intelligent and closed-loop optoelectronic neural interface will yield a robust building block for a comprehensive set of minimally invasive neural interfaces to study somatosensory control of movement, as well as monitor or treat somatosensory pathological conditions.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

移动腿的感觉反馈对于功能和动态稳定的运动至关重要。尽管很明显，与运动相关的感觉反馈会影响步态间的配位和选择步态（步行，小跑，疾驰等），但尚不清楚哪些感觉方式（例如，肌肉长度或力相关的信号）和反馈来源（例如，臀部或膝盖或膝盖肌肉）使这些locomotor变化。因此，该项目旨在了解感官神经元如何提供有关髋部肌肉长度的信息，以调节跨越的配位和步态选择。该目标将通过以智能，闭环和控制良好的方式选择性和可逆地刺激这些感觉神经元来实现。该项目将导致开发新的神经元和相关的计算算法，以在大型动物模型猫中对运动相关的感觉信号进行体内操纵。该项目和开发方法的新发现将大大增强我们对感觉运动控制机制的理解，并有助于开发新的治疗干预措施。拟议的多学科研究方法还将显着扩大光遗传学快速增长领域的效用和能力，从而实现变革性研究并为神经科学提供前所未有的新实验工具。这项工作对社会的最明显的长期好处是，从肢体损失到感觉神经病，受广泛运动定义影响的大量人群的生活质量改善。这些人将受益于由机器学习算法控制的神经和工程系统之间的神经表面的发展。通过这个项目，将努力从代表性不足的团体中招募和培训毕业生和本科生。外展活动还将组织以与教师，学生和代表性不足的群体共享资源，工具和知识。拟议的研究和教育活动的结果将通过科学博览会，出版物，讲习班，研讨会，会议和互联网与学生，科学社区和公众共享。该提案的总体目标是表征跨越型群体和uip肌肉的智能群体的跨性别和步态选择的机制来表征对跨性别和步态选择的机制。光电神经元接口系统。特别是，将开发此提案中的高密度，高效和无线植入的光电（WIOE）神经元界面设备。每个Wioe异质均包含一个光电阵列，该阵列有64个透明的微电极和16个微观发光 - 发光 - 二极管（µleds），一个芯片上的系统（SOC）和一个功率接收器（RX），以MM3 size包装，可在MM3 size包装中，具有光学上的仿制和电子仿真记录。将实现无线遥测链接，以在外部数据收购/控制单元和Wioe Imprans的分布式阵列之间进行有效的经皮和宽带数据传输。猫的选定背根神经节（DRG）将实现多个Wioe设备。将记录DRG神经元的神经活性，四肢选定肌肉的EMG活性以及全身运动运动学的神经活性，并将通过在无约束的CAT机车期间通过选定的DRG进行光遗传学模拟来操纵主轴传入活动。机器学习（ML）模型利用信号中的时空结构并将DRG中的传入活动映射到肢体运动学中，将用于实现对光遗传学神经调节的闭环控制。拟议的研究活动将由一个合作者团队进行，在生物元素，无线微电学，机器学习，人工智能和行为神经科学领域具有完整的研究专业知识。拟议的智能和闭环光电神经元界面的成功开发将为一组全面的构建块，用于一组全面的最小入侵性神经元界面，以研究对运动的体感控制，以及监测或治疗体验性的病理条件，以构建良好的良好影响，以表达NSF的稳定性和稳定性，以表现出稳定的依据。 标准。

项目成果

Neural Control Principles: Bernstein’s Insights from Biomechanics of Human Movement

神经控制原理：伯恩斯坦对人体运动生物力学的见解

• DOI: 10.4324/9780367816797
• 发表时间: 2021
• 期刊: Bernstein's Construction of Movements: The Original Text and Commentaries
• 作者: Prilutsky, Boris I;Zatsiorsky, Vladimir M.
• 通讯作者: Zatsiorsky, Vladimir M.

Control of Mammalian Locomotion by Somatosensory Feedback.

• DOI: 10.1002/cphy.c210020
• 发表时间: 2021-12-29
• 期刊: COMPREHENSIVE PHYSIOLOGY
• 影响因子: 5.8
• 作者: Frigon, Alain;Akay, Turgay;Prilutsky, Boris I.
• 通讯作者: Prilutsky, Boris I.

Transformation from arm joint coordinates to hand external coordinates explains non-uniform precision of hand position sense in horizontal workspace

从手臂关节坐标到手外部坐标的转换解释了水平工作空间中手位置感精度的不均匀

• DOI: 10.1016/j.humov.2022.103020
• 发表时间: 2022
• 期刊: Human Movement Science
• 影响因子: 2.1
• 作者: Oh, Kyunggeune;Prilutsky, Boris I.
• 通讯作者: Prilutsky, Boris I.

How to distinguish between referent configuration and internal models hypotheses of motor control?

如何区分运动控制的参考配置和内部模型假设？

• DOI: 10.1016/j.plrev.2021.02.004
• 发表时间: 2021
• 期刊: Physics of Life Reviews
• 影响因子: 11.7
• 作者: Prilutsky, Boris I.