Improving intracortical control of reaching after paralysis

摘要 脊髓损伤导致瘫痪影响全国超过25万人， 每年新增12,000例。超过一半的脊髓损伤发生在颈部，导致瘫痪 不到1%的患者完全康复。瘫痪会导致失去独立性， 以及需要护理助理来执行日常生活活动。失去独立性 增加了这种慢性疾病的情感、经济和家庭负担。 现在有硬件可以恢复脊髓损伤后失去的手臂功能 损伤具体来说，植入大脑皮层内的微电极的微小阵列能够检测到神经元的活动。 负责产生伸展运动的大脑区域的放电模式。也 植入在周围神经上或附近的植入刺激电极能够复活 瘫痪后的肌肉结合这些植入的记录和刺激技术， 恢复由自己的思想控制的手臂运动的潜力。然而，还需要做很多工作 改进控制算法，将大脑信号转化为所需的刺激模式， 做出想要的动作。 本研究将评估三种方法，改善控制算法恢复达到在 人类和动物模型。具体而言，我们正在探索各种选择，不仅控制 肢体的运动，以及肢体抵抗外力扰动的能力。增加 通过激活对侧或拮抗肌，使肢体“僵硬”，有助于使肢体稳定 使其在受到碰撞时能够抵抗非预期的移动。在第一种方法中，我们将 基于情境的自动刚度控制（即，基于预期的 我们从大脑信号解码的速度和加速度/减速度）。在第二种方法中，我们 将从大脑中提取一个单独的“刚度”命令信号，并使用皮质衍生的 命令以真实的时间调整肢体刚度。在第三种方法中，我们将单个记录的神经元 使用简单的线性脑-肌肉-刺激器映射直接控制肌肉刺激器。 这种方法让大脑负责学习如何优化肌肉激活， 根据需要调节刚度。优化刺激输送方式以控制刚度非常重要 因为太小的刚度将使臂容易被碰撞 然而，超过所需的刚度将浪费刺激器电池，并可能导致 肌肉疲劳

Abstract Spinal cord injury resulting in paralysis affects more than 250,000 people nationwide with over 12,000 new cases each year. More than half of all SCIs occur at cervical levels resulting in paralysis below the neck with less than 1% achieving full recovery. Paralysis results in a loss of independence, and the need for caregiver assistants to perform activities of daily living. This loss of independence adds to the emotional, financial, and family burden of this chronic condition. Hardware is now available that holds the potential to restore lost arm function after spinal cord injury. Specifically, tiny arrays of implanted intracortical microelectrode are able to detect the neural firing patterns in the areas of the brain responsible for producing reaching movements. Also implanted stimulating electrodes implanted on or near the peripheral nerves are able to reanimate muscles after paralysis. Combining these implanted recording and stimulation technologies holds the potential to restore arm movement controlled by one's own thoughts. However, much work is needed to refine the control algorithms that translate the brain signals into the stimulation patterns needed to make the desired movements. This study will evaluate three methods of improving control algorithms for restoring reaching in both human and animal models. Specifically we are exploring options for controlling not only the motion of the limb, but also how well the limb resists perturbations from external forces. Increasing limb `stiffness' by activating opposing or `antagonist' muscles will help to stiffen and stabilize the limb making it resistant to unintended movement if bumped. In the first method, we are incorporating automated stiffness control based on context (i.e. limb stiffness is modulated based on the intended speed and acceleration/deceleration we decode from the brain signals). In the second method, we will extract a separate `stiffness' command signal from the brain and use that cortically-derived command to adjust limb stiffness in real time. In the third method, we put individual recorded neurons in direct control of the muscle stimulators using a simple linear brain-to-muscle-stimulator mapping. This method puts the brain in charge of learning with practice how to optimize muscle activation to modulate stiffness as needed. Optimizing how stimulation is delivered to control stiffness is important for practical use of this technology because too little stiffness will allow the arm to be easily bumped off course whereas more stiffness than is needed will waste stimulator batteries and can cause muscle fatigue.