Decoding / encoding somatosensation from the hand area of the human primary somatosensory (S1) cortex for a closed-loop motor / sensory brain-machine interface (BMI)

项目总结/摘要 上肢的伸手和抓握动作需要复杂的皮质控制回路，包括运动神经和肌肉神经。 控制输出和实时体感反馈。神经系统疾病，如中风、脑部 创伤和脊髓损伤可能导致丧失执行这些任务的能力。许多团队，包括 我们自己的，正在努力恢复上肢功能，通过使用人类神经信号来控制， 具有多个自由度的机器人肢体的运动[1-3]。然而，如果没有体感 反馈，即使是最基本的肢体运动也很难以流畅和自然的方式进行[4，5]。 只有有限数量的人类研究探索如何产生体感反馈。 使用放置在人类初级体感（S1）手区的硬膜下皮层电图（ECoG）网格 在需要颅内监测的癫痫患者中，我们建议进行旨在了解 躯体感觉是如何被皮质编码的，以及我们如何通过 电刺激为了实现这一目标，我组建了一个多学科的指导团队，由Dr。 Gianluca Lazzi，在指导早期研究者方面有着成功的历史。从我的指导 团队，我计划学习神经建模，研究设计和生物统计学以及医疗设备 发展我的长期目标是成为一名独立的NIH资助的神经外科医生， 对我们理解感觉运动整合有着重要的贡献。在目标1中，我们将使用参与者 自己的ECoG对真实的触摸的响应，以引导刺激参数空间的系统映射， 不同的躯体感觉就像临床神经刺激器，如脑深部刺激器 (DBS)迷走神经刺激器（VNS）仅在特定刺激下具有治疗作用 设置，我们假设，我们会发现特定的刺激组合，导致不同类型的 躯体感觉在目标2中，我们将比较使用人工体感与使用自然体感的任务表现。 touch.在目标3中，我们将量化由ECoG刺激产生的真实的触摸和人工体感 真实的触摸/刺激和参与者感知之间的响应时间不同。这些结果和 通过这个K23计划提供的指导将是我过渡到独立的关键基础 感觉运动整合研究者。

PROJECT SUMMARY/ABSTRACT Upper limb reaching and grasping movements require complex cortical control circuits involving both motor- control outputs and real-time somatosensory feedback. Neurological disorders such as strokes, brain trauma, and spinal cord injury may result in a loss of the ability to perform these tasks. Many teams, including our own, are working to restore upper extremity function by using human neural signals to control the movements of a robotic limb with multiple degrees of freedom [1-3]. However, without somatosensory feedback, even the most basic limb movements are difficult to perform in a fluid and natural manner [4, 5]. There have only been a limited number of human studies exploring how to generate somatosensory feedback. Using subdural electrocorticography (ECoG) grids placed on the human primary somatosensory (S1) hand area in patients with epilepsy who require intracranial monitoring, we propose studies directed toward understanding how somatosensation is cortically encoded and how we can restore upper extremity somatosensation via electrical stimulation. To accomplish this, I have assembled a multidisciplinary mentoring team, led by Dr. Gianluca Lazzi, with an established history of success in mentoring early investigators. From my mentoring team, I plan on learning about neural modeling, study design and biostatistics, and medical device development. My long-term goal is to become an independent NIH-funded neurosurgeon-scientist who makes significant contributions to our understanding of sensorimotor integration. In Aim 1 we will use the participants own ECoG responses to real touch to guide a systematic mapping of stimulation parameter space to find distinct percepts of somatosensation. Much like how clinical neurostimulators such as deep brain stimulators (DBS) for movement disorders and vagus nerve stimulators (VNS) are therapeutic only at specific stimulation settings, we hypothesize that we will find specific stimulation combinations that result in different types of somatosensation. In Aim 2 we will compare task performance using artificial somatosensation versus native touch. In Aim 3 we will quantify how real touch and artificial somatosensation generated by ECoG stimulation differ in response time between real touch/stimulation and participant perception. These results and the mentoring provided through this K23 program will be a critical foundation for my transition to an independent investigator in sensorimotor integration.