Department of Movement, Human and Health Sciences, University of Rome "Foro Italico", Rome, Italy.
Department of Bioengineering, Imperial College London, London, United Kingdom.
J Appl Physiol (1985). 2017 Oct 1;123(4):835-843. doi: 10.1152/japplphysiol.00482.2017. Epub 2017 Jul 27.
The surface interference EMG signal provides some information on the neural drive to muscles. However, the association between neural drive to muscle and muscle activation has long been debated with controversial indications due to the unavailability of motor unit population data. In this study, we clarify the potential and limitations of interference EMG analysis to infer motor unit recruitment strategies with an experimental investigation of several concurrently active motor units and of the associated features of the surface EMG. For this purpose, we recorded high-density surface EMG signals during linearly increasing force contractions of the tibialis anterior muscle, up to 70% of maximal force. The recruitment threshold (RT), conduction velocity (MUCV), median frequency (MDF), and amplitude (RMS) of action potentials of 587 motor units from 13 individuals were assessed and associated with features of the interference EMG. MUCV was positively associated with RT ( = 0.64 ± 0.14), whereas MDF and RMS showed a weaker relation with RT ( = 0.11 ± 0.11 and 0.39 ± 0.24, respectively). Moreover, the changes in average conduction velocity estimated from the interference EMG predicted well the changes in MUCV ( = 0.71), with a strong association to ankle dorsiflexion force ( = 0.81 ± 0.12). Conversely, both the average EMG MDF and RMS were poorly associated with motor unit recruitment. These results clarify the limitations of EMG spectral and amplitude analysis in inferring the neural strategies of muscle control and indicate that, conversely, the average conduction velocity could provide relevant information on these strategies. The surface EMG provides information on the neural drive to muscles. However, the associations between EMG features and neural drive have been long debated due to unavailability of motor unit population data. Here, by using novel highly accurate decomposition of the EMG, we related motor unit population behavior to a wide range of voluntary forces. The results fully clarify the potential and limitation of the surface EMG to provide estimates of the neural drive to muscles.
表面干扰肌电图信号提供了一些关于肌肉神经驱动的信息。然而，由于缺乏运动单位群体数据，神经驱动与肌肉激活之间的关联一直存在争议。在这项研究中，我们通过对几个同时活动的运动单位以及相关表面肌电图特征进行实验研究，阐明了干扰肌电图分析推断运动单位募集策略的潜力和局限性。为此，我们在 13 名个体的胫骨前肌线性递增力收缩过程中记录了高密度表面肌电图信号，最大力达到 70%。评估了 587 个运动单位的募集阈值 (RT)、运动神经传导速度 (MUCV)、中值频率 (MDF) 和动作电位幅度 (RMS)，并将其与干扰肌电图的特征相关联。MUCV 与 RT 呈正相关 ( = 0.64 ± 0.14)，而 MDF 和 RMS 与 RT 的相关性较弱 ( = 0.11 ± 0.11 和 0.39 ± 0.24)。此外，从干扰肌电图中估计的平均传导速度的变化很好地预测了 MUCV 的变化 ( = 0.71)，与踝关节背屈力的相关性很强 ( = 0.81 ± 0.12)。相反，平均肌电图 MDF 和 RMS 与运动单位募集的相关性都很差。这些结果阐明了肌电图频谱和幅度分析在推断肌肉控制的神经策略方面的局限性，并表明，相反，平均传导速度可以提供有关这些策略的相关信息。表面肌电图提供了关于肌肉神经驱动的信息。然而，由于缺乏运动单位群体数据，肌电图特征与神经驱动之间的关联一直存在争议。在这里，我们通过使用新型高度精确的肌电图分解，将运动单位群体行为与广泛的自愿力相关联。研究结果充分阐明了表面肌电图提供肌肉神经驱动估计的潜力和局限性。
