Motor unit number estimation using reversible jump Markov chain Monte Carlo methods
Motor unit number estimation using reversible jump Markov chain Monte Carlo methods
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DOI：
10.1111/j.1467-9876.2007.00576.x
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发表时间：
2007-01-01
影响因子：
1.6
通讯作者：
Henderson, R. D.
中科院分区：
文献类型：
--
作者：
Ridall, P. G.;Pettitt, A. N.;Henderson, R. D.
我们提出了一个应用程序的可逆跳马尔可夫链蒙特卡罗抽样从神经生理学领域，我们试图估计一个单一的肌肉内的运动单位的数量。这种估计是需要监测神经肌肉疾病，如肌萎缩侧索硬化症的进展。我们的数据包括从肌肉表面记录的动作电位，以响应施加到供应肌肉的神经的不同强度的刺激。在刺激强度从阈值逐渐增加到最大值的过程中，所有运动单位都逐渐兴奋。然而，在任何给定的次最大强度的刺激下，由于轴突兴奋性的随机波动，被兴奋的单位的数量是可变的。此外，个体运动单位动作电位表现出变异性。为了解释这些生物学特性，Ridall及其同事开发了一种运动单元激活模型，该模型能够描述运动单元数量N固定时的反应。本文的目的是扩展该模型，使可能的数量的电机单元，N，是一个随机变量。我们说明了我们的模型的元素，表明结果是可重复的，并表明我们的模型可以测量肌萎缩侧索硬化症的过程中运动单位数量的下降。我们的方法有望在神经源性疾病的研究中发挥作用。
We present an application of reversible jump Markov chain Monte Carlo sampling from the field of neurophysiology where we seek to estimate the number of motor units within a single muscle. Such an estimate is needed for monitoring the progression of neuromuscular diseases such as amyotrophic lateral sclerosis. Our data consist of action potentials that were recorded from the surface of a muscle in response to stimuli of different intensities applied to the nerve supplying the muscle. During the gradual increase in intensity of the stimulus from the threshold to supramaximal, all motor units are progressively excited. However, at any given submaximal intensity of stimulus, the number of units that are excited is variable, because of random fluctuations in axonal excitability. Furthermore, the individual motor unit action potentials exhibit variability. To account for these biological properties, Ridall and co-workers developed a model of motor unit activation that is capable of describing the response where the number of motor units, N, is fixed. The purpose of this paper is to extend that model so that the possible number of motor units, N, is a stochastic variable. We illustrate the elements of our model, show that the results are reproducible and show that our model can measure the decline in motor unit numbers during the course of amyotrophic lateral sclerosis. Our method holds promise of being useful in the study of neurogenic diseases.