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，超过 每年新增病例12000例。超过一半的脊髓损伤发生在颈部水平，导致瘫痪。 颈部以下有不到1%的人完全恢复。瘫痪会导致丧失独立性， 以及需要护理员助理进行日常生活活动。这种独立性的丧失 增加了这种慢性疾病的情感、经济和家庭负担。 现在有可能恢复脊髓损伤后失去的手臂功能的硬件已经问世 受伤。具体地说，植入皮质内微电极的微小阵列能够检测到神经 负责产生伸展运动的大脑区域的放电模式。也是 植入周围神经上或周围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， 有可能恢复由自己的思想控制的手臂运动。然而，还需要做很多工作。 改进控制算法，将大脑信号转换为所需的刺激模式 做出想要的动作。 本研究将评估三种改进控制算法以恢复到达的方法 无论是人体模型还是动物模型。具体地说，我们正在探索各种选择，不仅控制 肢体的运动，以及肢体抵抗外力干扰的程度。渐增 通过激活相反或“对抗”的肌肉来使肢体“僵硬”，将有助于使肢体僵硬和稳定。 使其在受到碰撞时能抵抗意外移动。在第一种方法中，我们正在合并 基于上下文的自动刚度控制(即，根据预期的 速度和我们从大脑信号中解码的加/减速)。在第二种方法中，我们 将从大脑中提取一个单独的“僵硬”指令信号，并使用从大脑皮质获得的信号 实时调整肢体僵硬的命令。在第三种方法中，我们将单个记录的神经元 直接控制肌肉刺激器，使用简单的线性脑-肌肉刺激器映射。 这种方法让大脑负责学习如何在练习中优化肌肉激活 根据需要调整刚度。优化刺激传递方式以控制僵硬是很重要的 这项技术的实际使用，因为太少的僵硬会使手臂很容易被撞到 当然，太多的僵硬会浪费刺激器电池，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.