Department of Biomedical Engineering, Cullen College of Engineering, University of Houston, 3517 Cullen Blvd, SERC Room 2011, Houston, TX, 77204-5060, USA.
Department of Biomedical Engineering, Northwestern University, Evanston, IL, USA.
J Neuroeng Rehabil. 2022 Jul 1;19(1):67. doi: 10.1186/s12984-022-01045-z.
Abnormal patterns of muscle co-activation contribute to impaired movement after stroke. Previously, we developed a myoelectric computer interface (MyoCI) training paradigm to improve stroke-induced arm motor impairment by reducing the abnormal co-activation of arm muscle pairs. However, it is unclear to what extent the paradigm induced changes in the overall intermuscular coordination in the arm, as opposed to changing just the muscles trained with the MyoCI. This study examined the intermuscular coordination patterns of thirty-two stroke survivors who participated in 6 weeks of MyoCI training.
We used non-negative matrix factorization to identify the arm muscle synergies (coordinated patterns of muscle activity) during a reaching task before and after the training. We examined the extent to which synergies changed as the training reduced motor impairment. In addition, we introduced a new synergy analysis metric, disparity index (DI), to capture the changes in the individual muscle weights within a synergy.
There was no consistent pattern of change in the number of synergies across the subjects after the training. The composition of muscle synergies, calculated using a traditional synergy similarity metric, also did not change after the training. However, the disparity of muscle weights within synergies increased after the training in the participants who responded to MyoCI training-that is, the specific muscles that the MyoCI was targeting became less correlated within a synergy. This trend was not observed in participants who did not respond to the training.
These findings suggest that MyoCI training reduced arm impairment by decoupling only the muscles trained while leaving other muscles relatively unaffected. This suggests that, even after injury, the nervous system is capable of motor learning on a highly fractionated level. It also suggests that MyoCI training can do what it was designed to do-enable stroke survivors to reduce abnormal co-activation in targeted muscles. Trial registration This study was registered at ClinicalTrials.gov (NCT03579992, Registered 09 July 2018-Retrospectively registered, https://clinicaltrials.gov/ct2/show/NCT03579992?term=NCT03579992&draw=2&rank=1 ).
肌肉协同激活的异常模式导致中风后运动受损。之前,我们开发了一种肌电计算机接口(MyoCI)训练范式,通过减少手臂肌肉对的异常协同激活来改善中风引起的手臂运动障碍。然而,尚不清楚该范式在多大程度上改变了手臂的整体肌肉间协调性,而不仅仅是改变了使用 MyoCI 进行训练的肌肉。本研究检查了 32 名中风幸存者在接受 6 周 MyoCI 训练前后进行的伸展任务中的肌肉协同模式。
我们使用非负矩阵分解(NMF)来识别训练前后伸展任务中手臂肌肉协同作用(肌肉活动的协调模式)。我们检查了协同作用的变化程度,以了解训练如何减轻运动障碍。此外,我们引入了一个新的协同作用分析指标——离散度指数(DI),以捕捉协同作用中单个肌肉权重的变化。
训练后,受试者的协同作用数量没有一致的变化模式。使用传统协同作用相似性度量计算的肌肉协同作用的组成在训练后也没有改变。然而,在对 MyoCI 训练有反应的参与者中,协同作用内肌肉权重的离散度在训练后增加,即 MyoCI 针对的特定肌肉在协同作用内的相关性降低。在对训练没有反应的参与者中,没有观察到这种趋势。
这些发现表明,MyoCI 训练通过仅分离训练的肌肉来减轻手臂障碍,而使其他肌肉相对不受影响。这表明,即使在受伤后,神经系统仍然能够在高度分散的水平上进行运动学习。这也表明,MyoCI 训练可以完成其设计目的——使中风幸存者能够减少目标肌肉的异常协同激活。
本研究在 ClinicalTrials.gov 注册(NCT03579992,于 2018 年 7 月 9 日注册-回顾性注册,https://clinicaltrials.gov/ct2/show/NCT03579992?term=NCT03579992&draw=2&rank=1)。