Noninvasive neural decoding of walking

描述（由申请人提供）：该项目的广泛、长期目标是开发新型非侵入性神经假体，用于脊髓损伤（SCI）、肌萎缩侧索硬化（ALS）、皮质下卒中或下肢截肢患者的双足运动恢复和/或康复。双足运动的控制是脑机接口（BMI）领域的一大兴趣，即利用神经活动来控制肢体假肢和步态康复的设备。由于运动缺陷通常与SCI和神经退行性疾病相关，因此还需要研究新的潜在疗法以恢复此类患者的步态控制。虽然在猴子和人类的研究中已经证明了上肢BMI的可行性，但人类双足运动的神经解码尚未得到证明。该项目建立在我们实验室非侵入性运动神经解码的研究结果的基础上，并遵循一种原则性的，逐步的，实验和计算方法，从头皮EEG和步态康复脑机接口的开发中对人类双足运动进行神经解码。该项目的具体目标是：1）研究从头皮EEG采集的脑活动中最好地预测哪些步态参数; 2）使用闭环BCI环境纵向检查在适应虚拟皮层损伤或步态运动学的虚拟扰动期间步态的皮层表示的变化。这将是第一次时间分辨检查皮层网络如何适应健康受试者步态神经表征的变化，并可能对研究脑损伤或身体残疾后的皮层可塑性以及步态恢复的BMI的发展产生影响。这项研究对步态功能受损的患者具有临床意义，例如中风患者，帕金森病，SCI和下肢截肢者，因为BMI有朝一日可能有助于恢复步态功能。

DESCRIPTION (provided by applicant): The broad, long-term goal of this project is to develop novel noninvasive neuroprosthetics for restoration and/or rehabilitation of bipedal locomotion in patients with spinal cord injury (SCI), amyotrophic lateral sclerosis (ALS), subcortical stroke or lower limb amputations. The control of bipedal locomotion is of great interest to the fields of brain machine interfaces (BMIs), i.e. devices that utilize neural activity to control limb prosthesis and gait rehabilitation. Since locomotion deficits are commonly associated with SCI and neurodegenerative diseases, there is also a need to investigate new potential therapies to restore gait control in such patients. While the feasibility of a BMI for upper limbs has been demonstrated in studies in monkeys and humans, neural decoding of bipedal locomotion in humans has not yet been demonstrated. This project builds upon findings from non-invasive neural decoding of movements in our laboratory, and follows a principled, step-by-step, experimental and computational approach to neural decoding of human bipedal locomotion from scalp EEG and the development of brain-computer interfaces for gait rehabilitation. The specific aims of this project are: 1) to investigate what gait parameters are best predicted from brain activity acquired with scalp EEG; 2) to examine longitudinally the changes in the cortical representation of gait during adaptation to virtual cortical lesions or virtual perturbations of gait kinematics using a closed-loop BCI environment. This will be the first time-resolved examination of how cortical networks may adapt to changes in the neural representation of gait in healthy subjects, and may have implications for studying cortical plasticity after brain injury or physical disability, and for the development of BMIs for gait restoration. This research is clinically significant to patients with impaired gait function, as in the case of stroke patients, Parkinson's disease, SCI and lower-limb amputees, as BMIs may one day help restore gait function.