Decoding of Force from Neural Signals in Motor Cortex

运动皮层神经信号中力的解码

基本信息

批准号：
8548964
负责人：
JAMES ASHE
金额：
--
依托单位：
MINNEAPOLIS VA MEDICAL CENTER
依托单位国家：
美国
项目类别：
财政年份：
2012
资助国家：
美国
起止时间：
2012-08-01 至 2016-07-31
项目状态：
已结题

项目摘要

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

描述（由申请人提供）：脑机接口（BMI）技术的出现，人们期望它将成为肢体截肢或脊髓损伤患者的一种现实且实用的治疗选择。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相对较低水平的重大限制。在当前的申请中，我们概述了三个具体目标，以解决 BMI 实用性方面的一些主要障碍，并提出解决运动神经科学中基本问题的相关假设。目标 1：检查在力场中到达运动过程中，初级运动皮层和背侧前运动皮层的神经信号解码力输出的有效性和可靠性。我们的假设是，与手臂的力（运动）轨迹相关的神经信号可以从运动皮层和前运动皮层的神经信号中实时解码。目标 2：测试力解码性能在不同力场和时间上的泛化性。我们的假设是，多通道记录中神经活动的时空模式特定于行为状态（在本例中为焦点轨迹的方向），并且这种模式随着时间的推移和在不同的运动环境中是稳健的。目标 3：表征运动皮层和前运动皮层中与自启动运动输出（运动和力）相关的神经信号。我们的假设是，反映自发自愿运动行为的神经信号在运动区域是可检测到的，并且可用于实施“自由节奏”的 BMI 控制。我们提出的具体目标将使用非人类灵长类动物实验中的神经和行为数据来解决，这些动物经过训练，可以在模拟刚度、粘度和惯性的不同力场的情况下做出到达空间目标的运动；我们日常处理的运动环境的特性基础。神经数据将从初级运动 (M1) 和背侧前运动 (PMd) 皮质中长期植入的多电极阵列和微脑电图 (ECoG) 阵列中记录，我们计划研究几种不同的神经信号，包括单单位活动 (SUA) 、多单元活动 (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