ECoG Correlates of Visuomotor Transformation and Application to a Force-Based BCI

描述（由申请人提供）：本提案的长期目标是开发一种新型脑机接口（BCI）算法，用于瘫痪肢体的功能性神经肌肉刺激等应用。为了实现这一总体目标，本提案的第一个目标是确定运动皮层区域的特性，这些特性可能有助于我们的BCI研究。具体地说，我们的第一个目标是，当末端效应器为一项任务而运动时，先前的研究表明，高伽马带ECoG和LFP活动（>60 Hz）运动前皮质区与伸手动作的手速有很好的相关性，而单单位研究表明，运动前皮质区可能比身体运动本身更参与伸手动作的规划和/或感知。然而，这些研究通常采用线性变换的物理手臂运动的视觉反馈的末端执行器的任务。为了进一步研究大脑皮层区域运动表征的差异，我们将训练三只雄性恒河猴，（猕猴）在2-D中心外操纵杆任务中，操纵杆的位置（用作手位置的代理）被重新映射到面向目标的计算机光标的位置、速度或加速度，从而消除了用于速度和加速度重映射方案的操纵杆和光标之间的任何线性相关性。我们将首先检查植入M1、PMd和顶叶区域5的阵列的硬膜外ECoG频谱，以确定与操纵杆或光标的运动参数的相关性，并比较这些相关性，以确定特定区域是否与运动的规划/感知方面或其物理执行更相关。我们的第二个目标是利用这些ECoG表示的属性，并确定猴子是否可以学习调制基于力的控制信号在脑机接口（BCI）任务。迄今为止，大多数BCI实验都使用基于运动学的控制信号（即大脑信号被转换为对象的位置或速度）。然而，这些控制信号可能不适合于诸如瘫痪肢体的功能性神经肌肉刺激（FNS）的应用，其中个体手臂的准确模型是未知的或不完整的。为了解决这个问题，并评估可能更适合FNS的基于动力学的控制算法的可行性，我们将在基于力的BCI任务上训练猴子。在这些实验中，高伽马（75-105 Hz）ECoG活动将被映射到中心向外类任务中的计算机光标上的虚拟力，其中计算机光标将用于“抓取”并移动具有质量的外围目标。虚拟环境还将结合简单的现实世界物理学，如重力和可变质量，以评估受试者在现实生活中使用感兴趣的控制算法适应这些扰动的能力。 公共卫生相关性：这项提议调查了大脑如何代表自愿运动，无论是计划，执行还是感知。了解这个系统的复杂性和灵活性对于开发影响运动系统的神经损伤和疾病的治疗方法非常重要。有了这些知识，我们寻求开发新的脑机接口（BCI）技术，可以恢复脊髓损伤患者的肌肉控制。

DESCRIPTION (provided by applicant): The long-term objective of this proposal is to develop a novel brain computer interface (BCI) algorithm targeted for use in applications such as functional neuromuscular stimulation of paralyzed limbs. Toward this overall goal, the first aim of this proposal seeks to identify properties of motor cortical areas that may aid in our BCI pursuits. Specifically, our first objective will seek to identify differences and flexibility in electrocorticography (ECoG) representations of movement parameters across motor-associated cortical areas when movement of the end-effector for a task (i.e. a computer cursor used to complete the task) is dissociated from the physical movement leading up to it. Previous studies have shown that high gamma band ECoG and LFP activity (>60 Hz) over motor cortex is well correlated to hand speed during reaching tasks, while single unit studies have suggested that premotor cortical areas may be more involved in the planning and/or perception of reaching movements than the physical movement itself. However, these studies usually employed a linear transformation of the physical arm movements to visual feedback of the end-effector for the task. To further investigate the differences in representation of movement across cortical areas, we will train three male rhesus monkeys (Macaca mulatta) on a 2-D center-out joystick task in which position of the joystick (used as a proxy for hand position) is remapped to the position, velocity, or acceleration of a goal-oriented computer cursor, thus removing any linear correlations between joystick and cursor for velocity and acceleration remapping schemes. We will first examine the epidural ECoG spectra of arrays implanted over M1, PMd, and parietal area 5 for correlations with movement parameters of the joystick or cursor, and compare these correlations across remapping schemes to identify whether a particular area is more correlated with planning/perceptual aspects of a movement or with its physical execution. Our second objective is to utilize properties of these ECoG representations and determine whether monkeys can learn to modulate a force-based control signal in a brain- computer interface (BCI) task. The majority of BCI experiments to date have utilized kinematic-based control signals (i.e. a brain signal is translated to the position or velocity of an object). However, these control signals may not be suitable for applications such as functional neuromuscular stimulation (FNS) of a paralyzed limb where an accurate model of an individual's arm is unknown or incomplete. To address this problem and assess the feasibility of a dynamics-based control algorithm that may be more appropriate for FNS, we will train monkeys on a force-based BCI task. In these experiments, high gamma (75-105 Hz) ECoG activity will be mapped to a virtual force on a computer cursor in a center-out like task in which the computer cursor will be used to "grab" and move peripheral targets with mass. The virtual environment will also incorporate simple real-world physics such as gravity and variable masses to assess how well a subject might be able to adapt to these perturbations in real-life with the control algorithm of interest. PUBLIC HEALTH RELEVANCE: This proposal investigates how the brain represents voluntary movements, whether it be their planning, execution, or perception. Understanding the intricacies and flexibility of this system is important for developing treatments for neurological damage and disorders that impact the motor system. With this knowledge we seek to develop new Brain Computer Interface (BCI) technologies that could restore muscular control to patients such as those with spinal cord injuries.