Home > Research > Publications & Outputs > Motor unit number estimation via sequential Mon...

Electronic data

  • 1804.03963v1

    Submitted manuscript, 686 KB, PDF document

  • MUNEpaper_401

    Rights statement: This is the author’s version of a work that was accepted for publication in Computational Statistics & Data Analysis. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Computational Statistics & Data Analysis, 144, 2019 DOI: 10.1016/j.csda.2019.106845

    Accepted author manuscript, 651 KB, PDF document

    Embargo ends: 14/10/20

    Available under license: CC BY-NC-ND: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License

Links

Text available via DOI:

Keywords

View graph of relations

Motor unit number estimation via sequential Monte Carlo

Research output: Contribution to journalJournal article

E-pub ahead of print
Article number106845
<mark>Journal publication date</mark>1/04/2020
<mark>Journal</mark>Computational Statistics and Data Analysis
Volume144
Number of pages16
Publication statusE-pub ahead of print
Early online date14/10/19
Original languageEnglish

Abstract

A change in the number of motor units that operate a particular muscle is an important indicator for the progress of a neuromuscular disease and the efficacy of a therapy. Inference for realistic statistical models of the typical data produced when testing muscle function is difficult, and estimating the number of motor units is an ongoing statistical challenge. We consider a set of models for the data, each with a different number of working motor units, and present a novel method for Bayesian inference based on sequential Monte Carlo. This provides estimates of the marginal likelihood and, hence, a posterior probability for each model. Implementing this approach in practice requires a sequential Monte Carlo method that has excellent computational and Monte Carlo properties. We achieve this by benefiting from the model's conditional independence structure, where, given knowledge of which motor units fired as a result of a particular stimulus, parameters that specify the size of each unit's response are independent of the parameters defining the probability that a unit will respond at all. The scalability of our methodology relies on the natural conjugacy structure that we create for the former and an enforced, approximate, conjugate structure for the latter. A simulation study demonstrates the accuracy of our method, and inferences are consistent across two different datasets arising from the same rat tibial muscle.

Bibliographic note

This is the author’s version of a work that was accepted for publication in Computational Statistics & Data Analysis. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Computational Statistics & Data Analysis, 144, 2019 DOI: 10.1016/j.csda.2019.106845