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

皮质 FES 神经假体的触手可及的神经表征

• 批准号: 8838222
• 负责人: ABIDEMI BOLU AJIBOYE
• 金额: --
• 依托单位: LOUIS STOKES CLEVELAND VA MEDICAL CENTER
• 依托单位国家: 美国
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-07-01 至 2016-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8838222
• 关键词: Activities of Daily Living; American; Amyotrophic Lateral Sclerosis; Biological Neural Networks; Brain; Caring; Cervical spinal cord injury; Cervical spinal cord structure; Clinical Trials; Cognitive; Discriminant Analysis; Discrimination; Electric Stimulation; Electrocorticogram; Faculty; Frequencies; Hand; Human; Impairment; Implant; Individual; Injury; Joints; Lateral; Lead; Measures; Methods; Metric; Microelectrodes; Modeling; Motivation; Motor Cortex; Movement; Muscle Contraction; Output; Paralysed; Participant; Pattern; Peripheral Nerves; Persons; Population; Positioning Attribute; Posture; Psyche structure; Quality of life; Research; Role; Self-Help Devices; Series; Signal Transduction; Source; Spinal cord injury; Stereotyping; Surface; System; Techniques; Time; Upper Extremity; Veterans; Work; arm; base; brain computer interface; experience; grasp; hand grasp; human subject; improved; joint mobilization; kinematics; neuroprosthesis; nonhuman primate; relating to nervous system; restoration; success; virtual

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.

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。在传出皮层命令信号的情况下，FES应用空间和时间定型模式的电激活周围神经，以实现运动恢复脊髓损伤（SCI）的人。上肢FES系统通常要求SCI患者保持一定的自主运动，例如损伤线上方的肌肉收缩或关节运动。因此，患有导致完全性四肢瘫痪的高位颈脊髓损伤的人不能控制当前的FES系统。然而，脑机接口可以为高位颈脊髓损伤患者提供一个自然的、意志控制的FES命令界面，以恢复手臂和手的功能控制。大脑在控制手臂和手部运动中的作用已经在身体健全的非人类灵长类动物中得到了广泛的研究，但在长期严重瘫痪的人类中却很少。该建议的中心假设是，在人类与受损的运动皮层信号可靠地编码特定参数的“伸手抓”手臂运动，这些信号可以可靠地解码，以提供实时控制的功能性“伸手抓”运动在FES神经假体系统。该提案的目的是通过检查一个典型的伸手抓握运动的三个组成部分来研究这一假设。目的一基于初级运动皮层（M1）的神经活动模式，建立区分想象和执行功能性手抓握的模型。这些模型将辨别哪些皮层频带对于手抓握辨别是最有用的。当人类受试者想象并执行一系列手部抓握时，将从M1的皮质表面记录皮质电图（ECoG）信号。离线判别分析（DA）模型将评估这些想象和执行的手部姿势的神经活动可以区分的程度。然后，受试者将尝试在闭环任务中使用这些DA模型来皮质控制虚拟手的抓取姿势。所选择的正确姿势的数量以及选择正确姿势所花费的时间将被用作评估受试者的皮质控制能力的指标。目的二研究抓握力的控制是如何在皮层信号中编码的。将记录先前植入ECoG记录阵列的人员在使用动力和侧向捏手抓握执行力跟踪任务时的皮质活动。当抓握力在最大随意抓握（MVG）力的10%和50%之间变化时，将记录皮质活动。连续抓握力将通过线性解码方法（诸如多输入单输出系统识别（SID）或卡尔曼滤波（KF））或（如果需要）非线性解码方法（诸如时间延迟人工神经网络）与皮层活动相关。然后，受试者将使用优化的解码模型，以控制虚拟抓力直接使用大脑信号在“力目标达成”的任务。控制的成功将通过成功获取目标的数量以及击中每个目标所需的时间来衡量。目的三：研究作为BrainGate 2临床试验的一部分，在M1中长期植入微电极阵列的瘫痪患者对手臂和手定位和抓握的皮层控制。局部场电位频带中的单个单元活动和功率与所观察到的手臂到达运动的运动学的关系将在基于SID或KF技术构建神经解码器的努力中被表征。参与者将使用这些解码器来控制虚拟手臂达到一个3D的“中心外”的任务，在内在（联合）和extrinsi（全球）坐标系。衡量控制成功的指标包括到达目标的时间、捕获目标的数量和信息传输速率。这三个目标的成功完成将导致更好地理解的神经表示达到把握，以及如何最好地解码这些手臂运动组件控制上肢FES神经假体。