Motor unit number estimation using reversible jump Markov chain Monte Carlo methods

Ridall, P. G., Pettitt, A. N., Friel, N., McCombe, P. A. and Henderson, R. D. (2007) Motor unit number estimation using reversible jump Markov chain Monte Carlo methods. Journal of the Royal Statistical Society, 56 3: 235-269. doi:10.1111/j.1467-9876.2007.00576.x


Author Ridall, P. G.
Pettitt, A. N.
Friel, N.
McCombe, P. A.
Henderson, R. D.
Title Motor unit number estimation using reversible jump Markov chain Monte Carlo methods
Journal name Journal of the Royal Statistical Society   Check publisher's open access policy
ISSN 0035-9254
1467-9876
Publication date 2007-05
Sub-type Article (original research)
DOI 10.1111/j.1467-9876.2007.00576.x
Open Access Status
Volume 56
Issue 3
Start page 235
End page 269
Total pages 35
Editor Glasbey, C. A.
Gilmour, S. G.
Place of publication Oxford United Kingdom
Publisher Wiley-Blackwell
Collection year 2008
Language eng
Subject C1
730104 Nervous system and disorders
320700 Neurosciences
Abstract 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.
Keyword Statistics & Probability
alternation
amyotrophic lateral sclerosis
Markov chain Monte Carlo methods
motor neurone disease
motor unit number estimation
reversible jump
Amyotrophic-lateral-sclerosis
Muscle
Nerve
Model
Q-Index Code C1
Q-Index Status Confirmed Code

Document type: Journal Article
Sub-type: Article (original research)
Collections: Excellence in Research Australia (ERA) - Collection
2008 Higher Education Research Data Collection
School of Medicine Publications
 
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Citation counts: TR Web of Science Citation Count  Cited 10 times in Thomson Reuters Web of Science Article | Citations
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Created: Mon, 03 Mar 2008, 11:29:54 EST