Abstract
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.
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