Multi-scale network dynamics of human upper limb movements: characterization and

人类上肢运动的多尺度网络动力学：表征和

• 批准号: 9096272
• 负责人: NATHAN E CRONE
• 金额: $ 35.44万
• 依托单位: JOHNS HOPKINS UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-06-01 至 2019-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9096272
• 关键词: Activities of Daily Living; Algorithms; Area; Artificial Arm; Attention; Biological Neural Networks; Brain; Caliber; Characteristics; Clinical; Cognitive; Communication; Complex; Computer Vision Systems; Cues; Diagnostic; Eating; Electrocorticogram; Electrodes; Elements; Epilepsy; Evolution; Freedom; Future Generations; Goals; Hand; Health; Human; Individual; Joints; Knowledge; Limb Prosthesis; Limb structure; Location; Maps; Measures; Mediating; Methods; Microelectrodes; Modeling; Modification; Motor; Motor Cortex; Movement; Neurosciences; Operative Surgical Procedures; Patients; Pattern; Performance; Physics; Population; Population Dynamics; Positioning Attribute; Process; Prosthesis; Recruitment Activity; Resolution; Robotics; Route; Sampling; Sensory; Signal Transduction; Site; Staging; Structure; Surface; System; Testing; Time; Training; Translations; Upper Extremity; Upper limb movement; Vision; arm; drinking; grasp; human subject; implantation; innovation; joint mobilization; kinematics; motor control; neuroprosthesis; neuroregulation; relating to nervous system; success; temporal measurement; time use

DESCRIPTION (provided by applicant): The overall project goals are to study the cortical network dynamics of human upper limb motor control spanning two distinct spatial scales recorded with electrocorticography (ECoG), and to demonstrate that these dynamics can be estimated in real-time and used to control the JHU Applied Physics Lab Modular Prosthetic Limb (MPL) during execution of functionally useful complex action sequences. Our human subjects will be instructed to perform complete functional movements characteristic of activities of daily living. We will analyze the task-related temporal evolution in the strength and pattern o interactions among large-scale cortical networks known to be recruited in visually-guided reach-to-grasp tasks. Using multi-scale subdural ECoG with combinations of routine clinical macro-electrodes (2.3 mm diameter, 1 cm spacing) recording activity of broadly spread elements/nodes of neural networks, and inset arrays of microelectrodes (75 �m diameter, 0.9 mm spacing) recording the activity of local sub-networks, we will test our overall hypothesis that there is a functional hierarchy between the two scales (Aim 1). More specifically, we hypothesize that large-scale network dynamics involving premotor/motor cortex reflect the evolution of sensory-motor processing demands during complex action sequences, while micro-scale population activity and network dynamics in motor cortex reflect the low-level kinematics of these tasks. We will utilize methods of estimating dynamic effective connectivity developed by our team to study interactions between these scales and test whether there exists a spatially heterogeneous and hierarchical structure within the macro-micro scale networks. The results of these analyses have wide-ranging clinical implications for both the optimal scale of functional mapping for clinical diagnostic purposes and the extent of implantations for neuroprosthetic control. We will exploit multi-scale ECoG recordings and online estimates of the dynamics of neural activation and large-scale/local network interactions to achieve control of the MPL during functionally useful tasks (Aim 2). This approach will go beyond traditional paradigms that have developed neural control over individual degrees of freedom. We will do this by embedding low-level control within an innovative framework whereby knowledge of task goals supplement direct kinematic decoding. This project will build on our team's previous successes in implementing a system for semi-autonomous ECoG control of the MPL, employing machine vision and route-planning algorithms, during complex interactions with objects requiring the coordination of multiple joints. This system will be able to leverage for the first time the rich complexity of temporally and spatially resolved network dynamics correlated with high-level goals to achieve functionally useful control of an advanced neuroprosthetic limb.

描述(由申请人提供)：总体项目目标是研究人类上肢运动控制的皮质网络动力学，跨越两个不同的空间尺度，由皮层脑电图仪(ECoG)记录，并证明这些动力学可以被实时估计并用于在执行功能有用的复杂动作序列期间控制JHU应用物理实验室的模块化假肢(MPL)。我们的人类受试者将被指示执行日常生活活动所特有的完整功能动作。我们将分析在视觉引导的触达抓取任务中招募的大规模皮质网络之间相互作用的强度和模式中与任务相关的时间演变。使用多尺度硬膜下皮层脑电图，结合常规临床大电极(直径2.3 mm，间距1 cm)记录神经网络广泛分布的元素/节点的活动，以及嵌入微电极阵列(直径75�m，间距0.9 mm)记录局部子网络的活动，我们将检验我们的总体假设，即两个尺度之间存在功能层次(目标1)。更具体地说，我们假设，涉及运动前/运动皮质的大规模网络动力学反映了复杂动作序列中感觉-运动加工需求的演变，而运动皮质中的微观群体活动和网络动力学反映了这些任务的低水平运动学。我们将利用我们团队开发的动态有效连通性估计方法来研究这些尺度之间的相互作用，并测试宏观-微观尺度网络中是否存在空间异质和等级结构。这些分析的结果对临床诊断功能图的最佳范围和神经假体控制的植入范围都有广泛的临床意义。我们将利用多尺度ECoG记录和对神经激活动力学和大规模/局部网络相互作用的在线估计，在功能有用的任务期间实现对MPL的控制(目标2)。这种方法将超越神经控制个体自由度的传统范式。我们将通过在一个创新的框架中嵌入低级别控制来实现这一点，通过该框架，任务目标的知识补充了直接的运动学解码。该项目将建立在我们团队之前成功实施的MPL半自动ECoG控制系统的基础上，该系统使用机器视觉和路径规划算法，在与需要多个关节协调的对象进行复杂交互时。该系统将首次能够利用与高级目标相关的时间和空间分辨网络动力学的丰富复杂性，实现对先进神经假肢的功能有用的控制。