Annals of Applied Statistics

Modelling multilevel spatial behaviour in binary-mark muscle fibre configurations

Tilman M. Davies, Matthew R. Schofield, Jon Cornwall, and Philip W. Sheard

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The functional properties of skeletal muscles depend on the spatial arrangements of fast and slow muscle fibre types. Qualitative assessment of muscle configurations suggest that muscle disease and normal ageing are associated with visible changes in the spatial pattern, though a lack of statistical modelling hinders our ability to formally assess such trends. We design a nested Gaussian conditional autoregressive (CAR) model to quantify spatial features of dichotomously marked muscle fibre networks and implement it within a Bayesian framework. Our model is applied to data from a human skeletal muscle and results reveal spatial variation at multiple levels across the muscle. The model provides the foundation for future research in describing the extent of change to normal muscle fibre type parameters under experimental or pathological conditions.

Article information

Ann. Appl. Stat., Volume 13, Number 3 (2019), 1329-1347.

Received: March 2018
Revised: September 2018
First available in Project Euclid: 17 October 2019

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Zentralblatt MATH identifier

Gaussian process hierarchical model Bayesian inference physiology


Davies, Tilman M.; Schofield, Matthew R.; Cornwall, Jon; Sheard, Philip W. Modelling multilevel spatial behaviour in binary-mark muscle fibre configurations. Ann. Appl. Stat. 13 (2019), no. 3, 1329--1347. doi:10.1214/18-AOAS1214.

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Supplemental materials

  • Geometry, Diagnostics, and $\boldsymbol{\beta}$ Posterior Plots. A PDF document providing additional examples of the geometrical treatment of the muscle data, various diagnostic plots of the MCMC, and additional graphics related to the sampled posteriors of $\boldsymbol{\beta}$.
  • R Code and Data Files. A .zip archive containing R code and data necessary to repeat the analysis. Readers should open muscle.R and follow the instructions therein.