Neckel Nathan D
Department of Neuroscience, Georgetown University, Washington, DC, USA.
Neurorehabil Neural Repair. 2025 Aug;39(8):602-611. doi: 10.1177/15459683251339809. Epub 2025 May 26.
. Conventional physical therapy following neurological injury 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 investigated the ability of RAGT to enhance the recovery of rats following a cervical spinal cord injury and found 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 unassisted overground locomotion. 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. . In the work presented here we regrouped and reanalyzed our existing data bi-directionally (does level of overground recovery predict RAGT force profile experienced? does force profile predict recovery?). . This method allowed us to uncover a training force profile that optimized overground recovery, specifically, low overall forces (<±6 N), positive and negative during swing, and minimal forces during stance (<±2 N). . This work provides new insights into the importance of the specific forces used in rehabilitation, a major shift in current clinical RAGT techniques, and could lead to improvements in patients' lives.
神经损伤后的传统物理治疗是一项艰巨的任务,收效甚微且恢复很快就会达到平台期。非传统疗法,如机器人辅助步态训练(RAGT),并未取得我们所期望的显著临床效果。啮齿动物的RAGT是一个新兴领域,但它的工作原理与临床应用相同。我们之前研究了RAGT对颈脊髓损伤大鼠恢复的促进能力,发现阻力场训练是有害的,而负粘度场训练比通过健康的步行动作模式主动引导肢体更好。不幸的是,这些治疗方法在恢复无辅助的地面行走方面都不是特别有效。之前我们根据动物接受的RAGT治疗对它们进行分组。经过进一步思考,这些分组不是基于动物实际经历的情况,而是基于机器人的编程方式。在本文所展示的工作中,我们对现有数据进行了双向重新分组和重新分析(地面恢复水平能否预测所经历的RAGT力分布?力分布能否预测恢复情况?)。这种方法使我们能够发现一种优化地面恢复的训练力分布,具体来说,是总体力较低(<±6 N),摆动期有正向和负向力,站立期力最小(<±2 N)。这项工作为康复中使用的特定力的重要性提供了新的见解,这是当前临床RAGT技术的一个重大转变,并可能改善患者的生活。