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: 8764874
• 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/8764874
• 关键词: Activities of Daily Living; Algorithms; Area; 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 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; 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 μm 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，跨越用皮质电图记录仪（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并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）。我们的人类受试者将被指导执行日常生活活动特有的完整功能性动作。我们将分析已知在视觉引导的触及任务中招募的大规模皮层网络之间相互作用的强度和模式的与任务相关的时间演化。使用多尺度硬膜下 ECoG 结合常规临床大电极（直径 2.3 毫米，间距 1 厘米）记录广泛分布的神经网络元件/节点的活动，以及记录局部子网络活动的微电极插入阵列（直径 75 微米，间距 0.9 毫米），我们将检验我们的总体假设，即存在 两个量表之间的功能层次结构（目标 1）。更具体地说，我们假设涉及前运动/运动皮层的大规模网络动力学反映了复杂动作序列期间感觉运动处理需求的演变，而运动皮层中的微观群体活动和网络动力学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，并测试宏微观尺度网络中是否存在空间异构和层次结构。这些分析的结果对于临床诊断目的的功能映射的最佳范围和神经假体控制的植入范围具有广泛的临床意义。我们将利用多尺度 ECoG 记录和神经激活动态的在线估计以及大规模/本地网络交互，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自由度的神经控制的传统范式。我们将通过在创新框架中嵌入低级控制来实现这一目标，其中任务目标的知识补充了直接运动学解码。该项目将建立在我们团队之前成功实施 MPL 半自主 ECoG 控制系统的基础上，在与需要多个关节协调的物体进行复杂交互时，采用机器视觉和路线规划算法。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，以实现对先进神经假肢的功能上有用的控制。