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

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层网络动力学，跨越两个不同的空间尺度，通过皮质电图（ECoG）记录，并证明这些动力学可以实时估计，并用于控制JHU应用物理实验室模块化假肢（MPL）在执行功能有用的复杂动作序列期间。我们的人类受试者将被指导完成日常生活活动的完整功能动作。我们将分析在视觉引导的伸手到掌握任务中已知的大规模皮层网络之间相互作用的强度和模式的任务相关的时间进化。使用多尺度硬脑膜下ECoG，结合常规临床宏观电极（直径2.3毫米，间距1厘米）记录神经网络广泛分布的元素/节点的活动，以及插入微电极阵列（直径75 μm，间距0.9毫米）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（Aim 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中感觉-运动加工需求的演变，而微观尺度的群体活动和运动皮层的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，并测试在宏观-微观尺度网络中是否存在空间异质性和分层结构。这些分析的结果对临床诊断目的的功能映射的最佳规模和神经义肢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记录和在线估计神经激活和大规模/局部网络相互作用的动态，以实现在功能有用任务期间对MPL的控制（目标2）。这种方法将超越传统的范例，这些范例已经发展出对个体自由度的神经控制。我们将通过在一个创新的框架中嵌入低级控制来实现这一点，在这个框架中，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MPL半自动ECoG控制系统的基础上，该系统采用机器视觉和路线规划算法，在与需要多个关节协调的物体进行复杂交互时进行控制。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力学的丰富复杂性，以实现高级神经假肢的功能有用控制。

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.