ENT Clinic, Münster University Hospital, Kardinal-von-Galen-Ring 10, 48129 Münster, Germany.
J Theor Biol. 2011 Oct 7;286(1):41-9. doi: 10.1016/j.jtbi.2011.07.002. Epub 2011 Jul 14.
The vestibular evoked myogenic potential (VEMP) can be modeled (scaling factors aside) as a convolution of the motor unit action potential (MUAP) of a representative motor unit, h(t), with the temporal modulation of the MUAP rate of all contributing motor units, r(t). Accordingly, the variance modulation associated with the VEMP can be modeled as a convolution of r(t) with the square of h(t). To get a deeper theoretical understanding of the VEMP phenomenon, a specific realization of this general model is investigated here. Both r(t) and h(t) were derived from a Gaussian probability density function (in the latter case taking the first derivative). The resulting model turned out to be simple enough to be evaluated analytically in the time and in the frequency domain, while still being realistic enough to account for the basic aspects of the VEMP generation. Perhaps the most significant conclusion of this study is that, in the case of noisy data, it may be difficult to falsify the hypothesis of a rate modulation of infinitesimal duration. Thus, certain aspects of the data (particularly the peak amplitudes) can be interpreted using a short-modulation approximation rather than the general model. The importance of this realization arises from the fact that the approximation offers an exceptionally simple and convenient way for a model-based interpretation of experimental data, whereas any attempt to use the general model for that purpose would result in an ill-posed inverse problem that is far from easy to solve.
前庭诱发肌源性电位(VEMP)可被建模为(除比例因子外)一个代表性运动单位的动作电位(MUAP)的卷积,h(t),与所有参与运动单位的 MUAP 速率的时变调制,r(t)。因此,与 VEMP 相关的方差调制可以被建模为 r(t)与 h(t)的平方的卷积。为了更深入地理解 VEMP 现象,本文研究了这种通用模型的一个具体实现。r(t)和 h(t)均由高斯概率密度函数导出(在后一种情况下取一阶导数)。结果表明,该模型足够简单,可以在时间和频率域中进行解析评估,同时仍然足够真实,可以解释 VEMP 产生的基本方面。也许这项研究最重要的结论是,在存在噪声数据的情况下,很难否定微小持续时间的速率调制的假设。因此,可以使用短调制近似来解释数据的某些方面(特别是峰值幅度),而不是使用通用模型。这种实现的重要性在于,该近似提供了一种基于模型的解释实验数据的非常简单和方便的方法,而任何试图为此目的使用通用模型的尝试都会导致病态的逆问题,远非易于解决。