Sensorized Neural Prosthetic

描述（由申请人提供）：本申请解决了广泛的挑战领域（06）使能技术和特定的挑战主题，06-HD-101改进假体接口以改善康复效果。在过去的十年里，在开发BMI方面取得了相当大的进展--BMI是一种将大脑活动转化为人工致动器（如假肢）命令的设备。基于BMI技术的神经修复术旨在恢复脑损伤、神经系统疾病和肢体丧失患者的肢体活动能力。BMI研究中一个重要的未知数是神经假体向用户提供感官反馈的问题。该领域知识的这一差距阻碍了临床相关神经假体的发展，因为神经假体系统努力再现的正常运动行为严重依赖于来自皮肤、肌肉和肌腱受体的感觉反馈。为了解决这个问题，我们建议开发一种传感器神经假体，其中来自假肢手的触摸和压力传感器的信号被传递到大脑作为初级体感皮层的ICMS的时空模式。我们有能力进行这些实验并取得成功，因为我们在BMI的猴子模型和以前的工作中有丰富的经验，我们使用时空皮层刺激来指导动物的运动表现。这些实验将在恒河猴中进行，恒河猴首先被训练用虚拟环境中的手臂抓握物体，然后用机器人外骨骼包裹猴子自己的手臂，这些猴子将在皮层运动和体感区域长期植入多电极阵列。BMI解码算法将从运动皮层植入物记录的神经活动中提取运动命令。这些命令将被用来制定由人工致动器执行的到达和抓取。触觉，压力和纹理的感觉将通过体感皮层植入物传递的ICMS的时空模式来模仿。 公共卫生相关性：这项研究预计将对临床神经修复术的发展产生重大影响，因为它将产生有效的技术，使假肢感觉到身体的一部分，并提高手动操作的准确性。

DESCRIPTION (provided by applicant): This application addresses broad Challenge Area (06) Enabling Technologies and specific Challenge Topic, 06-HD-101 Improved interfaces for prostheses to improve rehabilitation outcomes. Considerable progress has been made during the last decade in the development of BMIs -- devices that translate brain activity into the commands to artificial actuators, such as prosthetic limbs. Neuroprosthetics based on BMI technology aim to restore limb mobility in people suffering from brain injury, neurological diseases and limb loss. One significant unknown in BMI research is the issue of providing sensory feedback from neuroprosthetics to their users. This gap in the field's knowledge impedes the development of clinically relevant neuroprosthetics because normal motor behaviors, which neuroprosthetic systems strive to reproduce, critically depend on the sensory feedback from the skin, muscle and tendon receptors. To address this problem, we propose developing a sensorized neuroprosthesis in which the signals from touch and pressure sensors of a prosthetic hand are delivered to the brain as spatiotemporal patterns of ICMS of the primary somatosensory cortex. We are well positioned to conduct these experiments and achieve success because of our extensive experience with monkey models of BMIs and previous work in which we used spatiotemporal cortical stimulation to guide animal motor performance. The experiments will be conducted in rhesus macaques trained to grip objects initially using a virtual-environment arm and then a robotic exoskeleton encasing the monkey's own arm. These monkeys will be chronically implanted with multielectrode arrays in cortical motor and somatosensory areas. BMI decoding algorithms will extract motor commands from the neural activity recorded by motor cortex implants. These commands will be used to enact reaching and gripping performed by artificial actuators. Sensations of touch, pressure and texture will be mimicked by spatiotemporal patterns of ICMS delivered through the somatosensory cortex implants. PUBLIC HEALTH RELEVANCE: This study is expected to have a significant impact on the development of clinical neuroprosthetics because it will produce effective techniques for sensorizing prosthetic limbs, making them feel as parts of the body and improving the accuracy of manual operations.