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质网络动态，跨越两个不同的空间尺度，用皮层电图（ECoG）记录，并证明这些动态可以实时估计，并用于在执行功能有用的复杂动作序列期间控制JHU应用物理实验室模块化假肢（MPL）。 我们的人类受试者将被指示进行日常生活活动的完整功能性运动。 我们将分析任务相关的时间演变的强度和模式之间的相互作用的大规模皮质网络被招募在视觉引导的伸手抓任务。 使用多尺度硬膜下ECoG，结合常规临床宏电极（直径2.3 mm，间距1 cm）记录广泛分布的神经网络元素/节点的活动，以及微电极（直径75 mm，间距0.9 mm）的插入阵列记录局部子网络的活动，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（目标1）。 更具体地说，我们假设，涉及运动前区/运动皮层的大规模网络动态反映了复杂动作序列中感觉运动处理需求的演变，而运动皮层中的微规模群体活动和网络动态反映了这些任务的低水平运动学。 我们将利用我们的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。 这些分析的结果具有广泛的临床意义的最佳规模的功能映射的临床诊断目的和神经假体控制的程度precatations。 我们将利用多尺度ECoG记录和在线估计的神经激活和大规模/本地网络的相互作用的动态，以实现控制MPL在功能上有用的任务（目标2）。 这种方法将超越传统的范式，已经开发了神经控制个人自由度。 我们将通过在一个创新的框架内嵌入低级别控制来实现这一点，从而使任务目标的知识补充直接的运动学解码。 该项目将建立在我们团队之前成功实施MPL半自主ECoG控制系统的基础上，采用机器视觉和路线规划算法，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，以实现对先进神经假肢的功能性有用控制。

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 �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.