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Spatial models for muscle fibre data

Project Description

In humans and other mammals, individual muscle fibres can be broadly classified into two distinct categories — "slow contracting" (type I) and "fast contracting" (type II). Given a cross-sectional image of a suitably processed muscle, we observe both fibre types distributed spatially across the sample [1-3]. It is acknowledged that the spatial configuration of type I and II fibres is reflective of both natural ageing and certain neuromuscular diseases [4-6]. However, with statistical tools for the analysis of such data largely restricted to test-based methods and simple models (see e.g. an overview of statistical techniques in [7]), our ability to quantify and hence understand the complex biological drivers of such changes in muscle tissue is limited.

This project will focus on the design, methodological development, fitting and interpretation of statistical models geared to capture the complex spatial and temporal behaviours present in muscle fibre data. Hierarchical models fitted using Bayesian techniques will be of primary focus. The project will build on recent work in applied statistics by the interdisciplinary supervisory team [7-9] on these problems.

The successful candidate will work in an established interdisciplinary team, primarily supervised by Dr Tilman Davies ( based in the Statistics group at the University of Otago. Other members of the team include Dr Matthew Schofield (, also a member of the Department of Mathematics and Statistics; A/Prof Philip Sheard ( at the Department of Physiology and Dr Jon Cornwall ( at the Otago Medical School. A strong background in statistics and statistical modelling (i.e. a graduate degree or equivalent in statistics), as well as computing skills (e.g. familiarity with the R language), are essential. An interest in spatial statistics, Bayesian statistics, and biological applications will also serve the candidate well.

Interested candidates should get in touch using the email link below and include a CV. Transcripts of undergraduate and Honours/Masters studies, as well as the names and contact details of two referees, will also be requested.

Funding Notes

The candidate is expected to secure funding through a competitive University of Otago PhD scholarship. These are open to both domestic and international applicants and include a 3-year stipend of NZ$27,000 per year (tax free and includes a fee waiver), research costs, and travel support to national and international conferences. For more information on studying for a PhD at the University of Otago see (link). The supervisory team will assist in the application process for this funding.


[1] Lexell, J., Taylor, C.C. and Sjöström, M. (1988). What is the cause of the ageing atrophy? Total number, size and proportion of different fiber types studied in whole vastus lateralis muscle from 15- to 83-year-old men. J. Neurol. Sci. 84 275-294.
[2] Miller, A., Woodley, S. and Cornwall, J. (2016). Fibre types of the longus capitis and longus colli muscles in elderly females. Anat. Sci. Int. 91 163-168.
[3] Cornwall, J. and Kennedy, E. (2015). Fibre types of the anterior and lateral cervical muscles. Eur. Spine J. 24 1986-1991.
[4] Aare, S., Spendiff, S., Vuda, M., Elkrief, D., Perez, A., Wu, Q., Mayaki, D., Hussain, S.N.A., Hettwer, S. and Hepple, R.T. (2016). Failed reinnervation in ageing skeletal muscle. Skeletal Muscle 6 29.
[5] Cornwall, J. and Sheard, P. W. (2012). Spatial aggregation of type II fibres in human cervical muscles. Clin. Anat. 25 533.
[6] Webster, C., Silverstein, L., Hays, A.P. and Blau, H.M. (1988). Fast muscle fibers are preferentially affected in Duchenne muscular dystrophy. Cell 52 503–513.
[7] Davies, T.M., Cornwall, J. and Sheard, P.W. (2013). Modelling dichotomously marked muscle fibre configurations. Stat. Med. 32 4240-4258.
[8] Davies, T.M., Sheard, P.W. and Cornwall, J. (2016). Comment on Makino et al. and observations on spatial modeling. Anat. Sci. Int. 91 423-424.
[9] Davies, T.M., Schofield, M.R., Cornwall, J. and Sheard, P.W. (2019). Modelling multilevel spatial behaviour in binary-mark muscle fibre configurations. Ann. Appl. Stat. {to appear}.

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