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November 21, 2025eLifeOpen Access

Hierarchical Bayesian modeling of multiregion brain cell count data

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Authors

SDSydney DimmockBEBenjamin MS ExleyGMGerald Moore

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Overview

Bayesian modeling improves accuracy of cell count evaluation in brain regions, suggesting advantages over classical methods.

Key Points

  • To develop and apply a Bayesian model for analyzing cell count data across multiple brain regions.
  • Implemented a partially pooled Bayesian model for cell count data across brain sections.
  • Applied the model to two example datasets from postmortem brain imaging.
  • Compared the Bayesian approach with traditional parallel t-tests.
  • Bayesian model outperformed standard t-tests in both datasets.
  • Demonstrated improved handling of uncertainty and undersampling in cell count data.
  • Captured nested data structures effectively, enhancing inference for neuronal activity.

Cite This Study

Dimmock et al. (2025) studied this question.

synapsesocial.com/papers/6924e3f2c0ce034ddc34f17chttps://doi.org/10.7554/elife.102391.3
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