Identifying Robotic Training Forces Which Lead To Optimal Recovery of Overground Locomotion

传统的物理治疗脊髓损伤（SCI）是一项艰巨的任务，回报率很低 并迅速恢复平稳。非常规疗法，如机器人辅助步态训练（RAGT）， 并没有产生我们所希望的强大的临床收益。啮齿动物RAGT是一个新兴的领域，但它的工作 与临床上的相同原则。我们之前已经量化了功能的丧失， 大鼠脊髓损伤后自发性运动恢复。我们还研究了RAGT的能力， 加强这种复苏。在研究了100多只老鼠后，我们了解到，在阻力场中训练是一种非常有效的方法。 在负粘度场中训练比积极引导四肢通过健康的 步进模式不幸的是，这些治疗方法都不能很好地恢复运动能力。我们 我们相信，对现有数据的重新分析将揭示最佳的RAGT技术。之前我们分组 动物基于其接受的RAGT治疗。经过进一步思考，这些群体并不是基于 动物们实际上经历了什么，以及机器人是如何被编程的。可能会发现 在训练过程中施加的力量，一个力量的轮廓，是什么导致更大的恢复。通过这项提议，我们计划 通过双向重新分析我们现有的数据，揭示最佳的RAGT力分布（力分布是否 预测复苏？恢复预测力分布吗？）。这将提供新的见解的重要性 在康复中使用的具体力量，从而优化RAGT。目标1是使用聚类分析创建 新的治疗组基于训练过程中相似的力量曲线，看看是否有差异的水平， 恢复运动能力目的2是进行离群值分析，以确定大鼠是否表现出更大的恢复， 运动在训练期间具有类似的力分布。通过使用两种不同的技术，我们希望揭示一个 优化RAGT的单一（或非常相似）力分布。在这种部队情况下进行训练将是一种 目前RAGT技术的重大转变，并导致患者生活的改善。

Conventional physical therapy following spinal cord injury (SCI) is an arduous task met with minimal returns and quickly plateauing recovery. Unconventional therapies, such as robotic assisted gait training (RAGT) have not produced the robust clinical gains that we all had hoped. Rodent RAGT is a nascent field, but it works on the same principles as the clinical counterpart. We have previously quantified the loss of function and spontaneous recovery of locomotion following SCI in rats. We have also investigated the ability of RAGT to enhance this recovery. After studying over 100 rats we have learned that training in a resistive field is detrimental, and training in a negative viscosity field is better than actively guiding the limbs through a healthy stepping pattern. Unfortunately, none of these treatments are particularly good at restoring locomotion. We believe that reanalysis of our existing data will uncover the optimal RAGT technique. Previously we grouped animals based on the RAGT treatment they received. Upon further reflection, these groups are not based on what the animals actually experienced, but how the robot was programmed. It may come to light that the actual forces applied during training, a force profile, is what leads to greater recovery. With this proposal we plan to uncover the optimal RAGT force profile by reanalyzing our existing data bi-directionally (does force profile predict recovery?, does recovery predict force profile?). This will provide new insights into the importance of the specific forces used in rehabilitation, and thus optimize RAGT. Aim 1 is to use cluster analysis to create new treatment groups based on similar force profiles during training, and see if there is a difference in the level of locomotor recovery. Aim 2 is to conduct outlier analysis to determine if rats that showed greater recovery of locomotion had similar force profiles during training. By using two separate techniques we hope to uncover a single (or very similar) force profile that optimizes RAGT. Training with such a force profile would represent a major shift in current RAGT techniques, and lead to improvements in patients’ lives.