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，跨越用皮质电图（ECoG）记录的两个不同空间尺度，并证明这些动力学可以实时估计用于在执行功能上有用的复杂动作序列期间控制 JHU 应用物理实验室模块化假肢 (MPL)。我们将指导人类受试者执行日常生活活动的完整功能动作。使用多尺度硬膜下 ECoG 与常规临床大电极（2.3 毫米）的组合，已知在视觉引导的抓取任务中招募的大规模皮层网络之间的相互作用的强度和模式与任务相关的时间演变。直径，1 厘米间距）记录广泛分布的神经网络元件/节点的活动，以及微电极插入阵列（75 微米直径，0.9 毫米间距）记录局部神经网络的活动子网络中，我们将测试我们的总体假设，即两个尺度之间存在功能层次结构（目标 1）。更具体地说，我们捕捉到涉及前运动/运动皮层的大规模网络动态反映了感觉运动处理需求的演变。在复杂的动作序列中，而运动皮层中的微观群体活动和网络动态反映了这些任务的低级运动学，我们将利用我们团队开发的估计动态有效连接的方法来研究这些尺度之间的相互作用并测试是否存在。空间上存在一个这些分析的结果对于临床诊断目的的功能映射的最佳尺度和神经假体控制的植入范围具有广泛的临床意义。 ECoG 记录和神经激活动态和大规模/局部网络交互的在线估计，以在功能有用的任务期间实现对 MPL 的控制（目标 2）。这种方法将超越已经开发出对个体程度的神经控制的传统范例。自由。我们将通过在创新框架中嵌入低级控制来实现这一目标，其中任务目标知识补充直接运动学解码。该项目将建立在我们团队之前利用机器视觉实现 MPL 半自主 ECoG 控制系统的成功基础上。和路线规划算法，在与需要多个关节协调的对象的复杂交互过程中，该系统将能够首次利用与高级目标相关的时间和空间解析网络动态的丰富复杂性来实现。对先进的神经假肢进行功能上有用的控制。