Decoding of Force from Neural Signals in Motor Cortex

描述（由申请人提供）： 脑机接口（BMI）技术的出现使人们期望它将成为截肢或脊髓CRD损伤患者的现实和实用的治疗选择。然而，在实现这一期望之前，需要解决当前方法中导致假肢控制功能水平相对较低的显著限制。在当前的应用中，我们概述了三个具体的目标，解决了BMI效用的一些主要障碍，并提出了相关的假设，解决了运动神经科学中的基本问题。目标1：目的：研究在力场作用下，从初级运动皮层和背侧运动前皮层的神经信号中解码力输出的有效性和可靠性。我们的假设是，与手臂的力（动力学）轨迹相关的神经信号可以从运动和前运动皮层的神经信号中实时解码。目的2：测试不同力场和随时间推移的力解码性能的一般化。我们的假设是，多通道记录中的神经活动的时空模式对于行为状态（在这种情况下是力轨迹的方向）是特定的，并且这种模式随着时间的推移并且在不同的运动环境中是鲁棒的。目的3：研究运动皮层和前运动皮层中与自主运动输出（运动和力）相关的神经信号。我们的假设是，反映自我发起的自愿运动行为的神经信号在运动区是可检测的，并可用于实施“自由步调”的BMI控制。我们提出的具体目标将使用来自非人类灵长类动物实验的神经和行为数据来解决，这些灵长类动物被训练在模拟刚度，粘性和惯性的不同力场存在下对空间目标进行到达运动;我们每天处理的运动环境的特性。 基础本研究将通过长期植入初级运动皮层（M1）和背侧运动前区（PMd）的多电极阵列和微型脑电图（ECoG）阵列记录神经元的数据，研究几种不同的神经信号，包括单单位活动（SUA）、多单位活动（MUA）、局部场电位（LFP）和ECoG，这将为我们提供额外的优势，能够比较不同神经信号的解码质量

DESCRIPTION (provided by applicant): The advent of brain-machine interface (BMI) technology has brought with it the expectation that it will be a realistic and practical treatment option for patients with limb amputation or spinal crd injury. However, significant limitations in current approaches that result in a relatively low leve of functionality of prosthetic control need to be addressed before this expectation can be realized. In the current application, we outline three specific aims that tackle some of the major obstacles standing in the way of BMI utility, and propose related hypotheses that address fundamental issues in motor neuroscience. Aim 1: To examine the efficacy and reliability of decoding force output from neural signals in primary motor cortex and dorsal pre-motor cortex during reaching movements in a force field. Our hypothesis is that the neural signals related to the force (kinetic) trajectory of the arm can be decoded in real-time from neural signals in motor and premotor cortex. Aim 2: To test the generalization of force decoding performance across different force fields and over time. Our hypothesis, is that the spatial-temporal patterns of neural activity in multi- channel recordings are specific for the behavioral state (direction of foce trajectory in this case) and that such patterns are robust over time and across different motor environments. Aim 3: To characterize the neural signal associated with self-initiated motor output (movement and force) in motor and pre-motor cortex. Our hypothesis is that the neural signal reflecting self- initiated voluntary motor behavior is detectable in motor areas and can be used to implement 'free-paced' BMI control. The specific aims we propose will be addressed using neural and behavioral data from experiments in non-human primates trained to make reaching movements to spatial targets in the presence of distinct force fields that mimic stiffness, viscosity, and inertia; properties of the motor environment that we deal with on a daily basis. The neural data will be recorded from chronically implanted multi-electrode arrarys and mico-electroencephalography (ECoG) arrays in primary motor (M1) and dorsal premotor (PMd) cortex, We plan to study several different neural signals including single unit activity (SUA), multi-unit activity (MUA), local field potentials (LFP) and ECoG which will give us the added advantage of being able to compare the quality of decoding across the different neural signals