EFRI-M3C: Distributed Brain Dynamics in Human Motor Control

EFRI-M3C：人类运动控制中的分布式大脑动力学

• 批准号: 1137279
• 负责人: Gert Cauwenberghs
• 金额: $ 188.97万
• 依托单位: University of California-San Diego
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-09-15 至 2015-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1137279&HistoricalAwards=false
• 关键词: EFRI; M3C; Distributed; Brain; Dynamics

Intellectual Merit: This project aims at combining cognitive and computational neuroscience, neuroengineering and system identification towards a transformative understanding of the way distributed brain dynamics interact with motor activity in humans. 3-D body and limbs movement kinematics, eye movements and electroencephalographic (EEG) spatiotemporal brain data will be recorded simultaneously during motor control and adaptation in healthy and Parkinson?s disease patients. In particular, altered and real world motor tasks will be simulated in 3-D immersive virtual reality technology with force feedback robots providing proprioceptive interaction and feedback. Cognitive, behavioral and kinematics data will constrain the design of large-scale computational models of motor control and adaptation based on known anatomy and physiology of the basal ganglia. Neuromorphic engineering will guide the design of mobile embedded computational systems for real-time emulation of the brain-body models and closed-loop sensory-motor control for Parkinson?s patients. We expect that the development of new machines for neuro-rehabilitation will result in a threefold synergetic interaction between engineering and neuroscience: human-machine interactions will transform the notion of movement control and provide new contexts to study embodied cognition that will benefit neuroscience; in turn, new knowledge in neuroscience and motor control will accelerate the development of adaptive machines for rehabilitation and/or enhancement. Finally, comprehensive and predictive mathematical models of motor control implemented in neuromorphic hardware are expected to lead to new intelligent neuroprosthetic tools.Broader Impact: Outcomes of this research will contribute to the system-level understanding of humanmachine interactions and motor learning and control in real world environments for humans, and will lead to the development of a new generation of wireless brain and body activity sensors and adaptive prosthetics devices. This will advance our knowledge of human-machine interactions, stimulate the engineering of new brain/body sensors and actuators, and have a direct influence in diverse areas where humans are coupled with machines, such as brain-machine interfaces, prosthetics and telemanipulation. We anticipate that the confluence of cognitive and computational neuroscience, control theory and wearable, unobtrusive bioinstrumentation will provide novel non-invasive approaches or the treatment and neuro-rehabilitation of Parkinson?s disease and will potentially transform our understanding of brain/body interactions. The project draws graduate and undergraduate students across divisions and in the NSF Temporal Dynamics of Learning Center (TDLC) and Institute of Neural Computation (INC) at UCSD participating in interdisciplinary engineering and neuroscience aspects of the design, optimization, and training of largescale neuromorphic systems and their human interfaces. Through outreach channels on campus supported by the TDLC and the NSF Research Experience for Undergraduates (REU), the program will actively pursue increased participation in research and education of the next generation of scientists and engineers.

智力价值：这个项目旨在结合认知和计算神经科学、神经工程学和系统识别，以变革性的方式理解分布式大脑动力学与人类运动活动相互作用的方式。在正常人和帕金森-S病患者的运动控制和适应过程中，将同时记录三维身体和肢体运动运动学、眼球运动和脑电时空脑数据。特别是，变化的和现实世界的运动任务将在3-D沉浸式虚拟现实技术中模拟，力反馈机器人提供本体感知交互和反馈。认知、行为和运动学数据将限制基于已知的基底节解剖学和生理学的运动控制和适应的大规模计算模型的设计。神经形态工程学将指导移动嵌入式计算系统的设计，用于实时仿真帕金森-S患者的脑体模型和闭环感觉-运动控制。我们预计，神经康复新机器的发展将导致工程学和神经科学之间的三个方面的协同作用：人机交互将改变运动控制的概念，并为研究体现认知提供新的背景，这将有利于神经科学；反过来，神经科学和运动控制的新知识将加速康复和/或增强的适应性机器的发展。最后，在神经形态硬件中实现的运动控制的综合和预测数学模型有望带来新的智能神经假肢工具。广泛影响：本研究的结果将有助于从系统层面理解人类在真实世界环境中的人机交互和运动学习与控制，并将导致新一代无线脑体活动传感器和自适应假肢设备的开发。这将促进我们对人机交互的了解，刺激新的大脑/身体传感器和执行器的工程，并对人与机器耦合的不同领域产生直接影响，如脑机接口、假肢和远程操纵。我们预计，认知和计算神经科学、控制理论和可穿戴的、不显眼的生物仪器的融合将为帕金森？S病的治疗和神经康复提供新的非侵入性方法，并可能改变我们对脑/身体相互作用的理解。该项目吸引了不同部门的研究生和本科生，以及加州大学圣迭戈分校的NSF时间动力学学习中心(TDLC)和神经计算研究所(INC)，参与大型神经形态系统及其人机接口的设计、优化和培训的跨学科工程和神经科学方面。通过TDLC和NSF本科生研究体验(REU)支持的校园外展渠道，该计划将积极推动更多地参与下一代科学家和工程师的研究和教育。