Neural Representation of Reach-to-Grasp for Cortical FES Neuroprostheses

描述 该提案研究了脑-机-接口(BCI)作为功能性电刺激(FES)神经假体系统的命令接口的使用。在没有传出皮质命令信号的情况下，FES对周围神经应用空间和时间上固定的电激活模式，以实现脊髓损伤(SCI)患者的运动恢复。上肢FES系统通常要求SCI患者保留一些意志运动，如肌肉收缩或损伤线以上的关节运动。因此，高位颈髓损伤导致完全性四肢瘫痪的人无法控制当前的FES系统。然而，BCI可能会为高颈性脊髓损伤患者提供一个自然的、自愿控制的FES命令接口，以重新获得对手臂和手的功能控制。大脑在控制手臂和手的运动中的作用在身体健全的非人类灵长类动物中得到了广泛的研究，但在长期和严重瘫痪的人类中研究较少。这一建议的中心假设是，运动受损的人的大脑皮质信号可靠地编码了“伸手抓住”手臂运动的特定参数，这些信号可以可靠地解码，以提供对FES神经假体系统中功能性“伸手抓住”运动的实时控制。该提案的目的将通过考察典型的伸手抓握动作的三个组成部分来研究这一假设。目的根据初级运动皮质(M1)的神经活动模式，建立区分想象握手和执行握手的模型。这些模型将辨别出哪些大脑皮层频段对手抓握的辨别最有用。当受试者想象并进行一系列握手时，将从M1的皮质表面记录下皮层脑电(ECoG)信号。离线判别分析(DA)模型将评估这些想象和执行的手势的神经活动可以被区分得多好。然后，受试者将尝试在一个闭环任务中使用这些DA模型来控制虚拟手的抓握姿势。选择正确姿势的数量以及选择正确姿势所需的时间将被用作评估受试者大脑皮质控制能力的指标。目的二研究握力控制是如何在大脑皮质信号中编码的。大脑皮质活动将被记录下来，来自先前植入ECoG记录阵列的人，当他们使用力量和横向握手进行力跟踪任务时。当抓取力在最大自愿抓握(MVG)力的10%到50%之间变化时，将记录皮质活动。连续抓取力将通过线性解码方法，如多输入单输出系统辨识(SID)或卡尔曼滤波(KF)，或必要时，非线性解码方法，如延时人工神经网络，与大脑皮层活动相关。然后，受试者将使用优化的解码模型，通过大脑信号直接控制虚拟抓取力，完成一项“力达到目标”的任务。控制的成功将通过成功收购目标的数量以及实现每个目标所需的时间来衡量。目的3作为BrainGate2临床试验的一部分，研究长期在M1植入微电极阵列的瘫痪患者对手臂和手的皮质控制的定位和抓取。在建立基于SID或KF技术的神经解码器的过程中，将表征局部场势频带中的单个单元活动和功率与观察到的手臂伸展运动的运动学的关系。参与者将使用这些解码器来控制虚拟手臂在内部(关节)和外部(全局)坐标系中的3D“中心-外-中心”任务中的伸展。衡量控制成功的指标将包括达到目标的时间、获得的目标数量和信息传输率。这三个目标的成功完成将使我们更好地理解伸手抓握的神经表征，以及如何最好地解码这些手臂运动组件以控制上肢FES神经假体。 公共卫生相关性 虽然大约11%的美国人是退伍军人，但他们几乎占所有脊髓损伤患者的20%。经历过高位颈髓损伤或其他严重瘫痪损伤的人，如肌萎缩侧索硬化症(ALS)，几乎没有什么选择来重新获得执行基本生活活动所需的独立性。这在很大程度上是因为目前的辅助设备要求它们能够自愿地生成用于控制的标准独立命令源。这些人没有这种能力。然而，他们通常会保留自己的认知能力和智力。因此，能够将这些保留的大脑信号用作辅助设备的控制输入，将对这一群体非常有利。这就是我这项提议所描述的研究的主要动机。拟议工作在实现这一目标方面取得的进展可以大大改善退伍军人的生活质量和护理，这是人们在经历了导致严重瘫痪的损害后所预期的。

DESCRIPTION This proposal investigates the use of Brain-Computer-Interfaces (BCIs) as a command interface for Functional Electrical Stimulation (FES) neuroprosthetic systems. In the absence of efferent cortical command signals, FES applies spatially and temporally stereotyped patterns of electrical activation to peripheral nerves to achieve movement restoration in spinal cord injured (SCI) persons. Upper-extremity FES systems typically require SCI persons to retain some volitional movement such as contraction of muscles or movement of joints above the line of injury. As a result, persons with high cervical spinal cord injury, resulting in complete tetraplega, are not able to control current FES systems. BCIs, however, may make it possible to give persons with high cervical SCI a natural and volitionally controlled FES command interface for regaining functional control of the arm and hand. The role of the brain in controlling arm and hand movements has been extensively studied in able-bodied non-human primates, but less so in humans with prolonged and severe paralysis. The central hypothesis of this proposal is that cortical signals in humans with impaired movement reliably encode specific parameters of "reaching-to-grasp" arm movements and these signals can be reliably decoded to provide real-time control of a functional "reaching-to-grasp" movement in an FES neuroprosthetic system. The aims of the proposal will investigate this hypothesis by examining three components of a typical reaching-to-grasp movement. Aim one develops models for discriminating imagined and performed functional hand grasps based upon the neural activity patterns of primary motor cortex (M1). These models will discern which cortical frequency bands are most useful for hand grasp discrimination. Electrocorticography (ECoG) signals will be recorded from the cortical surface of M1 while human subjects imagine and perform a series of hand grasps. Offline discriminant analysis (DA) models will assess how well the neural activities of these imagined and performed hand postures can be differentiated. Subjects will then attempt to use these DA models in a closed-loop task to cortically control the grasp postures of a virtual hand. The number of correct postures selected, as well as the time taken to select the correct posture will be used as metrics to assess subjects' cortical control capabilities. Aim two examines how control of grasp force is encoded in cortical signals. Cortical activity will be recorded from persons with previously implanted ECoG recording arrays while they perform a force tracking task using power and lateral pinch hand grasps. Cortical activity will be recorded as the grasp force is varied between 10% and 50% of maximal voluntary grasp (MVG) force. The continuous grasp force will be related to the cortical activity through linear decoding methods such as multiple-input-single- output System Identification (SID) or Kalman Filtering (KF), or if necessary, nonlinear decoding methods such as time-delayed artificial neural networks. Subjects will then use the optimized decoding models to control virtual grasp force directly using brain signals in a "force target reaching" task. Success of control will be measured by the number of successful target acquisitions, as well as the time necessary to hit each target. Aim three investigates cortical control of arm and hand positioning and grasping by paralyzed persons chronically implanted with a microelectrode array in M1 as part of the BrainGate2 Clinical Trial. The relationship of single unit activity and power in local field potential frequeny bands to kinematics of observed arm reaching movements will be characterized in the effort to build neural decoders, based upon SID or KF techniques. Participants will use these decoders to control virtual arm reaching in a 3D "center-out-center" task, in intrinsic (joint) and extrinsi (global) coordinate frames. Metrics to measure success of control will include time to target, number of targets acquired, and information transfer rate. Successful completion of these three aims will lead to a greater understanding of the neural representation of reaching-to-grasp, and how to best decode these arm movement components for controlling an upper-extremity FES neuroprosthesis. PUBLIC HEALTH RELEVANCE Although about 11% of Americans are Veterans, they comprise almost 20% of all individuals with spinal cord injuries. Individuals who have experienced high cervical spinal cord injury, or other severely paralyzing impairments, such as amyotrophic lateral sclerosis (ALS), have few options for regaining the independence needed to perform basic activities of daily living. This is in large part because current assistive devices require them to be able to volitionally generate standard independent command sources for control. These individuals do not have that ability. However, they usually retain their cognitive ability and mental faculties. Hence, it would be highly beneficial to this population to be able to use these retained brain signals as control inputs for their assistive devices. This is the overarching motivation for the research described i this proposal. Advancements towards this end resulting from the proposed work could significantly improve the quality of life and care of Veterans that is expected after one experiences an impairment resulting in severe paralysis.