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Hierarchical Bayesian modeling of multiregion brain cell count data.

Sydney Dimmock¹, Benjamin M S Exley², Gerald Moore³

¹School of Engineering Mathematics and Technology, University of Bristol, Michael Ventris Building, Bristol, United Kingdom.

|November 21, 2025

Summary

Bayesian models improve analysis of undersampled brain cell-count data. This hierarchical approach better handles uncertainty and nested data compared to traditional statistical methods.

Keywords:

Bayesian analysis cell-count data hierarchical modeling mouse neuroscience rat

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Topographical Estimation of Visual Population Receptive Fields by fMRI

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Published on: February 3, 2015

Area of Science:

Neuroscience
Computational Biology
Biostatistics

Background:

Multiregion cell-count data from whole animal brains quantify neuronal activity, gene expression, or connectivity.
Postmortem imaging limits experiments to single data points per animal, leading to expensive and time-intensive data collection.
Resulting datasets are often undersampled, posing challenges for traditional statistical analysis.

Purpose of the Study:

To present a partially pooled Bayesian model for analyzing multiregion cell-count data.
To demonstrate the suitability of hierarchical Bayesian methods for such datasets.
To improve statistical inference for undersampled neurobiological data.

Main Methods:

Development and application of a standard partially pooled Bayesian model.
Analysis of two example datasets with multiregion cell counts.
Comparison of Bayesian model performance against standard parallel t-tests.

Main Results:

The Bayesian model effectively captured nested data structures.
Hierarchical Bayesian methods demonstrated rigorous handling of uncertainty in undersampled data.
The Bayesian model outperformed standard parallel t-tests in both example datasets.

Conclusions:

Hierarchical Bayesian methods are well-suited for analyzing undersampled multiregion cell-count data.
The proposed Bayesian approach substantially improves statistical inference for this type of neurobiological data.
Bayesian modeling offers a robust framework for complex, high-dimensional biological datasets.