Experimentally valid predictions of muscle force and EMG in models of motor-unit function are most sensitive to neural properties
Experimentally valid predictions of muscle force and EMG in models of motor-unit function are most sensitive to neural properties
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DOI：
10.1152/jn.00577.2007
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发表时间：
2007-09-01
影响因子：
2.5
通讯作者：
Valero-Cuevas, Francisco J.
中科院分区：
文献类型：
--
作者：
Keenan, Kevin G.;Valero-Cuevas, Francisco J.
运动单位群体的计算模型是神经和肌肉特性引起肌电图（EMG）和力的假设机制的客观实现。然而，这些模型中使用的参数的可变性/不确定性以及它们如何影响预测-混淆了对这些假设机制的评估。我们进行了大规模的计算灵敏度分析的国家的最先进的计算模型的表面肌电图，力，力的变化相结合的全面审查已发表的实验数据与蒙特卡洛模拟。为了彻底探索模型的性能和鲁棒性，我们运行了许多迭代模拟，每个模拟都使用了九个常用运动神经元和肌肉参数的随机值集。在其报告的实验范围内对参数值进行采样。439次模拟后的收敛发现，只有3次模拟符合我们的两个适应性标准：近似的肌电图振幅和力的变异性与平均力的缩放的良好建立的实验关系。另外424个模拟优先对这3个有效模拟的邻域进行采样，收敛以揭示65个另外的参数值集，对于这些参数值集，模型预测近似于实验已知的关系。我们发现该模型对肌肉特性不敏感，但对几个运动神经元特性非常敏感-特别是峰值放电率和募集范围。因此，为了促进我们对EMG和肌肉力量的理解，评估在当今最先进的运动单元功能模型中实现的假设神经机制是至关重要的。我们讨论了实验和分析的途径，这样做，以及新的功能，可能会被添加到未来的实现电机单元模型，以提高其实验的有效性。
Computational models of motor-unit populations are the objective implementations of the hypothesized mechanisms by which neural and muscle properties give rise to electromyograms (EMGs) and force. However, the variability/uncertainty of the parameters used in these models and how they affect predictions - confounds assessing these hypothesized mechanisms. We perform a large-scale computational sensitivity analysis on the state-of-the-art computational model of surface EMG, force, and force variability by combining a comprehensive review of published experimental data with Monte Carlo simulations. To exhaustively explore model performance and robustness, we ran numerous iterative simulations each using a random set of values for nine commonly measured motor neuron and muscle parameters. Parameter values were sampled across their reported experimental ranges. Convergence after 439 simulations found that only 3 simulations met our two fitness criteria: approximating the well-established experimental relations for the scaling of EMG amplitude and force variability with mean force. An additional 424 simulations preferentially sampling the neighborhood of those 3 valid simulations converged to reveal 65 additional sets of parameter values for which the model predictions approximate the experimentally known relations. We find the model is not sensitive to muscle properties but very sensitive to several motor neuron properties - especially peak discharge rates and recruitment ranges. Therefore to advance our understanding of EMG and muscle force, it is critical to evaluate the hypothesized neural mechanisms as implemented in today's state-of the art models of motor unit function. We discuss experimental and analytical avenues to do so as well as new features that may be added in future implementations of motor-unit models to improve their experimental validity.