Dideriksen Jakob, Del Vecchio Alessandro
Department of Health Science and Technology, Aalborg University, Aalborg, Denmark.
Department Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander University Erlangen-Nürnberg, Erlangen, Germany.
J Neurophysiol. 2023 Jan 1;129(1):235-246. doi: 10.1152/jn.00222.2022. Epub 2022 Dec 14.
Changes in the discharge characteristics of motor units as well as in the maximum force-producing capacity of the muscle are observed following training, aging, and fatiguability. The ability to measure the adaptations in the neuromuscular properties underlying these changes experimentally, however, is limited. In this study we used a computational model to systematically investigate the effects of various neural and muscular adaptations on motor unit recruitment thresholds, average motor unit discharge rates in submaximal contractions, and maximum force. The primary focus was to identify candidate adaptations that can explain experimentally observed changes in motor unit discharge characteristics after 4 wk of strength training (Del Vecchio A, Casolo A, Negro F, Scorcelletti M, Bazzucchi I, Enoka R, Felici F, Farina D. 597: 1873-1887, 2019). The simulation results indicated that multiple combinations of adaptations, likely involving an increase in maximum discharge rate across motor units, may occur after such training. On a more general level, we found that the magnitude of the adaptations scales linearly with the change in recruitment thresholds, discharge rates, and maximum force. In addition, the combination of multiple adaptations can be predicted as the linear sum of their individual effects. Together, this implies that the outcomes of the simulations can be generalized to predict the effect of any combination of neural and muscular adaptations. In this way, the study provides a tool for estimating potential underlying adaptations in neural and muscular properties to explain any change in commonly used measures of rate coding, recruitment, and maximum force. Our ability to measure adaptations in neuromuscular properties in vivo is limited. Using a computational model, we quantify the effect of multiple neuromuscular adaptations on common measures of motor unit recruitment, rate coding, and force-producing capacity. Scaling and combining adaptations had a near-linear effect on these measures, indicating that the results can explain and predict neuromuscular adaptations in a wide range of conditions, including, but not limited to, strength training.
在训练、衰老和疲劳性改变的情况下，可以观察到运动单位放电特性以及肌肉最大力量产生能力的变化。然而，通过实验测量这些变化背后神经肌肉特性适应性的能力是有限的。在本研究中，我们使用了一个计算模型来系统地研究各种神经和肌肉适应性对运动单位募集阈值、次最大收缩时运动单位平均放电率以及最大力量的影响。主要重点是确定能够解释在进行4周力量训练后实验观察到的运动单位放电特性变化的候选适应性因素（德尔·韦基奥A、卡索洛A、内格罗F、斯科尔切莱蒂M、巴祖奇I、伊诺卡R、费利奇F、法里纳D. 597: 1873 - 1887, 2019）。模拟结果表明，经过此类训练后，可能会出现多种适应性组合，可能涉及运动单位最大放电率的增加。在更一般的层面上，我们发现适应性的程度与募集阈值、放电率和最大力量的变化呈线性比例关系。此外，多种适应性的组合可以预测为它们各自效应的线性总和。总之，这意味着模拟结果可以推广到预测任何神经和肌肉适应性组合的效果。通过这种方式，该研究提供了一种工具，用于估计神经和肌肉特性潜在的适应性，以解释速率编码、募集和最大力量等常用测量指标的任何变化。我们在体内测量神经肌肉特性适应性的能力是有限的。使用计算模型，我们量化了多种神经肌肉适应性对运动单位募集、速率编码和力量产生能力等常用测量指标的影响。适应性的缩放和组合对这些指标具有近乎线性的影响，表明结果可以解释和预测广泛条件下的神经肌肉适应性，包括但不限于力量训练。
