School of Biomedical Sciences Faculty of Biological Sciences, University of Leeds, Leeds, United Kingdom.
Institute for Artificial Intelligence and Biological Computation School of Computing, University of Leeds, Leeds, United Kingdom.
J Neurophysiol. 2022 Jan 1;127(1):173-187. doi: 10.1152/jn.00208.2021. Epub 2021 Dec 8.
The influence of proprioceptive feedback on muscle activity during isometric tasks is the subject of conflicting studies. We performed an isometric knee extension task experiment based on two common clinical tests for mobility and flexibility. The task was carried out at four preset angles of the knee, and we recorded from five muscles for two different hip positions. We applied muscle synergy analysis using nonnegative matrix factorization on surface electromyograph recordings to identify patterns in the data that changed with internal knee angle, suggesting a link between proprioception and muscle activity. We hypothesized that such patterns arise from the way proprioceptive and cortical signals are integrated in neural circuits of the spinal cord. Using the MIIND neural simulation platform, we developed a computational model based on current understanding of spinal circuits with an adjustable afferent input. The model produces the same synergy trends as observed in the data, driven by changes in the afferent input. To match the activation patterns from each knee angle and position of the experiment, the model predicts the need for three distinct inputs: two to control a nonlinear bias toward the extensors and against the flexors, and a further input to control additional inhibition of rectus femoris. The results show that proprioception may be involved in modulating muscle synergies encoded in cortical or spinal neural circuits. The role of sensory feedback in motor control when limbs are held in a fixed position is disputed. We performed a novel experiment involving fixed position tasks based on two common clinical tests. We identified patterns of muscle activity during the tasks that changed with different leg positions and then inferred how sensory feedback might influence the observations. We developed a computational model that required three distinct inputs to reproduce the activity patterns observed experimentally. The model provides a neural explanation for how the activity patterns can be changed by sensory feedback.
本体感受反馈对等长任务中肌肉活动的影响是相互矛盾的研究主题。我们基于两种常见的移动性和灵活性临床测试进行了等长膝关节伸展任务实验。任务在膝关节的四个预设角度进行,我们记录了两种不同髋关节位置的五个肌肉的活动。我们使用非负矩阵分解对表面肌电图记录进行肌肉协同分析,以识别随内部膝关节角度变化的数据模式,这些模式表明本体感受和肌肉活动之间存在联系。我们假设这些模式源自本体感受和皮质信号在脊髓神经回路中整合的方式。我们使用 MIIND 神经模拟平台,根据对脊髓回路的现有理解,为具有可调节传入输入的计算模型开发了一个模型。该模型产生与数据中观察到的相同的协同趋势,这是由传入输入的变化驱动的。为了匹配实验中每个膝关节角度和位置的激活模式,模型预测需要三个不同的输入:两个输入用于控制对伸肌的非线性偏向和对屈肌的偏向,以及进一步的输入用于控制对股直肌的额外抑制。结果表明,本体感受可能参与调节皮质或脊髓神经回路中编码的肌肉协同作用。在四肢固定位置时,感觉反馈在运动控制中的作用存在争议。我们进行了一项涉及基于两种常见临床测试的固定位置任务的新型实验。我们确定了任务期间随不同腿部位置而变化的肌肉活动模式,然后推断感觉反馈可能如何影响观察结果。我们开发了一个计算模型,该模型需要三个不同的输入来重现实验中观察到的活动模式。该模型提供了一个神经解释,说明活动模式如何通过感觉反馈进行改变。