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Updated: Jun 30, 2025

Automated Sholl Analysis of Digitized Neuronal Morphology at Multiple Scales
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A model-based hierarchical Bayesian approach to Sholl analysis.

Erik VonKaenel1, Alexis Feidler2, Rebecca Lowery2

  • 1Department of Biostatistics and Computational Biology, University of Rochester, Rochester, NY 14642, United States.

Bioinformatics (Oxford, England)
|March 21, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a novel hierarchical Bayesian model for analyzing microglia morphology data. This method preserves rich data structures, enabling more robust inference without data reduction.

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Area of Science:

  • Neuroscience
  • Immunology
  • Computational Biology

Background:

  • Microglial morphology reflects central nervous system immune status and brain homeostasis.
  • Sholl analysis is a common method for quantifying microglia morphology from imaging data.
  • Existing Sholl analysis methods often require data hierarchy reduction, losing valuable information.

Purpose of the Study:

  • To develop a statistical approach for analyzing hierarchical Sholl data without information loss.
  • To enable robust inference from complex microglia morphology datasets.
  • To provide a method that respects the natural structure of imaging data.

Main Methods:

  • A parametric hierarchical Bayesian model was developed for Sholl data analysis.
  • The model was applied to real-world microglia imaging data.
  • Simulation studies were conducted to compare the proposed method with existing alternatives.

Main Results:

  • The proposed hierarchical Bayesian model allows inference on rich, hierarchical Sholl data.
  • The method avoids aggressive data reduction, preserving analytical power.
  • Simulation studies demonstrated the effectiveness of the new approach compared to alternatives.

Conclusions:

  • Hierarchical Bayesian modeling offers a powerful solution for analyzing complex microglia morphology data.
  • This approach enhances the understanding of brain homeostasis and immune responses.
  • The developed methods and software facilitate advanced morphological analysis in neuroscience research.