Centre of Biomedical Engineering, Bulgarian Academy of Sciences, Acad. G. Bonchev Street, Bl.105, 1113 Sofia, Bulgaria.
J Biomech. 2010 May 28;43(8):1546-52. doi: 10.1016/j.jbiomech.2010.01.034. Epub 2010 Feb 24.
During normal daily activity, muscle motor units (MUs) develop unfused tetanic contractions evoked by trains of motoneuronal firings at variable interpulse intervals (IPIs). The mechanical responses of a MU to successive impulses are not identical. The aim of this study was to develop a mathematical approach for the prediction of each response within the tetanus as well as the tetanic force itself. Experimental unfused tetani of fast and slow rat MUs, evoked by trains of stimuli at variable IPIs, were decomposed into series of twitch-shaped responses to successive stimuli using a previously described algorithm. The relationships between the parameters of the modeled twitches and the tetanic force level at which the next response begins were examined and regression equations were derived. Using these equations, profiles of force for the same and different stimulation patterns were mathematically predicted by summating modeled twitches. For comparison, force predictions were made by the summation of twitches equal to the first one. The recorded and the predicted tetanic forces were compared. The results revealed that it is possible to predict tetanic force with high accuracy by using regression equations. The force predicted in this way was much closer to the experimental record than the force obtained by the summation of equal twitches, especially for slow MUs. These findings are likely to have an impact on the development of realistic muscle models composed of MUs, and will assist our understanding of the significance of the neuronal code in motor control and the role of biophysical processes during MU contractions.
在正常的日常活动中，肌肉运动单位（MUs）会在不同的脉冲间隔（IPIs）下通过运动神经元的 firing 产生未融合的强直收缩。MU 对连续脉冲的机械响应并不完全相同。本研究的目的是开发一种数学方法，用于预测强直收缩内的每个响应以及强直收缩本身的力。使用先前描述的算法，将快速和慢速大鼠 MU 的未融合强直收缩分解为对连续刺激的一系列 twitch 形状的响应。检查了模型化 twitch 的参数与下一个响应开始时的强直收缩力水平之间的关系，并推导出了回归方程。使用这些方程，可以通过叠加模型化 twitch 来数学预测相同和不同刺激模式的力曲线。为了比较，通过叠加等于第一个 twitch 的 twitch 来进行力预测。比较了记录的和预测的强直收缩力。结果表明，通过使用回归方程，可以非常准确地预测强直收缩力。这种方式预测的力比通过叠加相等的 twitch 获得的力更接近实验记录，尤其是对于慢速 MU。这些发现可能会对由 MU 组成的逼真肌肉模型的发展产生影响，并有助于我们理解神经元代码在运动控制中的意义以及在 MU 收缩过程中生物物理过程的作用。
