Optimizing oscillatory epidural electrical stimulation to selectively increase task-related population dynamics in motor areas

项目总结 在美国，中风是导致运动障碍的主要原因。同时通过脑刺激来增强运动能力 中风后的功能在小型研究中显示出希望，最近的两项大型中风试验没有发现证据表明 显著的好处。一个关键的不确定性是如何准确地量身定做大脑刺激以有效地调节神经 与运动准备和控制相关的动力学。我们最近对老鼠的研究(Ramanathan等人， 自然医学2018；Lemke等人，自然神经科学，2019年)证明了种群动态与 至低频振荡活动(0.5-4赫兹“LFO”)对于运动控制是必不可少的，并可作为目标 用于使用电刺激进行调制。更具体地说，大脑皮层刺激被发现既能增加LFO 为马达提供动力并增强其功能。我们现在在一个非人类灵长类动物模型中也有了大量的证据 这种方法可以在更复杂的大脑中有效。但是，有必要进一步优化 提供这样的刺激，以特别针对皮质动力学。因此，我们建议对参数进行优化 硬膜外刺激以选择性地调节完整运动网络中的群体动力学。我们的方法 需要同时记录非人灵长类运动网络中的单个神经元以及电 使用定制的硬膜外颅骨螺丝电极“环”进行刺激。此外，我们将使用计算 分析以确定触手抓握任务中与任务相关的神经动力学是如何被电调制的 刺激。具体地说，我们将优化和制定大规模电刺激的原则，以 有选择地增强分离到M1或PMD的“神经模式”，或跨越这两个区域的联合。这种方法是建立在 越来越多的共识认为，运动网络通过协调的整体活动来执行计算，或者 “神经模式”，即用降维方法测量的神经协变模式。激活 的神经模式(即，神经模型激活或NMA)似乎构成了计算的构件 基本的移动控制。我们的具体目标是：1)确定最佳的ACS参数 在到达抓取任务期间M1和PMD之间的局部和跨区域NMA；2)确定最优的ACS 在伸展抓取任务期间增加M1和S1之间的局部和跨区域NMA的参数；3) 为ACS确定参数，以便在任务离开期间增强任务NMA。完成 这些目标将为设计有选择地针对人群的治疗刺激提供关键信息 分布式电机网络中的动力学。所获得的信息也可能有助于改进非 侵入性脑刺激。

PROJECT SUMMARY Stroke is the leading cause of motor disability in the United States. While brain stimulation to enhance motor function after stroke has shown promise in small studies, two recent large stroke trials did not find evidence for significant benefits. A key uncertainty is about how to exactly tailor brain stimulation to effectively modulate neural dynamics associated with movement preparation and control. Our recent studies in rats (Ramanathan et al., Nature Medicine 2018; Lemke et al., Nature Neuroscience, 2019) demonstrated that population dynamics linked to low-frequency oscillatory activity (0.5-4Hz “LFO”) are essential for movement control and can serve as a target for modulation using electrical stimulation. More specifically, cortical stimulation was found to both boost LFO power and augment motor function. We now also have substantial evidence in a non-human primate model that such an approach can be effective in more complex brains. However, it is essential to further optimize the delivery of such stimulation to specifically target cortical dynamics. We thus propose to optimize parameters for epidural stimulation to selectively modulate population dynamics in the intact motor network. Our approach entails simultaneous recording of single neurons in the non-human primate motor network along with electrical stimulation using a customized “ring” of epidural cranial screw electrodes. Moreover, we will use computational analysis to determine how task-related neural dynamics in a reach-to-grasp task are modulated by electrical stimulation. More specifically, we will optimize and develop principles for large-scale electrical stimulation to selectively enhance “neural modes” isolated to M1 or PMd or joint across both areas. This approach is built on the growing consensus that motor networks perform computations through coordinated ensemble activity or “neural modes”, i.e. patterns of neural covariation measured with dimensionality reduction methods. Activation of neural modes (i.e. Neural Model Activation or NMA) appear to constitute building blocks for computations underlying movement control. Our specific aims are: 1) Determine optimal ACS parameters that increases both local and cross-area NMA between M1 and PMd during a reach-grasp task; 2) Determine optimal ACS parameters that increases both local and cross-area NMA between M1 and S1 during a reach-grasp task; 3) Determine parameters for ACS to enhance task NMA during time periods away from the task. Completion of these aims will provide critical information for designing therapeutic stimulation that selectively targets population dynamics in the distributed motor network. The information gained may also help improve methods for non- invasive brain stimulation.