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A Bayesian hierarchical point process model for epidermal nerve fiber patterns.

C Andersson1, T Rajala2, A Särkkä1

  • 1Department of Mathematical Sciences, Chalmers University of Technology and the University of Gothenburg, Göteborg SE-412 96, Sweden.

Mathematical Biosciences
|May 4, 2019
PubMed
Summary
This summary is machine-generated.

We developed a Bayesian hierarchical Thomas process model for nested point pattern data. This model reveals that nerve disorder severity correlates with a decreasing number of nerve fiber clusters.

Keywords:
Bayesian estimationCluster point processEpidermal nerve fibersHierarchical structureNerve entry pointsThomas process

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Area of Science:

  • Statistics
  • Computational Biology
  • Neuroscience

Background:

  • Point pattern data with nested structures are common in biological and medical research.
  • Existing models may not adequately capture the hierarchical nature of such data, particularly in clinical settings.
  • Understanding nerve fiber organization is crucial for diagnosing and managing nerve disorders.

Purpose of the Study:

  • To introduce and apply a Bayesian hierarchical Thomas process model for analyzing nested point pattern data.
  • To investigate the relationship between nerve disorder severity and nerve fiber point patterns.
  • To evaluate the performance of the proposed model and its associated Markov Chain Monte Carlo (MCMC) algorithm.

Main Methods:

  • Development of a Bayesian hierarchical model using the Thomas process for point pattern analysis.
  • Application of the model to a nerve fiber dataset comprising 47 subjects across three diagnosed severity groups.
  • Implementation and evaluation of a custom MCMC algorithm for model parameter estimation.

Main Results:

  • The simulation study demonstrated reliable estimation of group-level parameter means, though between-subject variance estimation proved challenging.
  • Analysis of the nerve fiber data indicated that the intrinsic structure of nerve fiber clusters is consistent across all disorder severity groups.
  • A significant finding was the inverse relationship between nerve disorder progression and the number of observed nerve fiber clusters.

Conclusions:

  • The Bayesian hierarchical Thomas process offers a robust framework for modeling nested point patterns in biomedical data.
  • The model successfully identified that nerve disorder severity is associated with a reduction in the number of nerve fiber clusters.
  • Further research could refine the estimation of between-subject variance and explore other applications of this modeling approach.