ArticleeLife2025
Hierarchical Bayesian modeling of multiregion brain cell count data.
Article in eLife, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed.
- Seizures drive tau propagation in a tauopathy mouse model.bioRxiv : the preprint server for biology · 2026Article
- Hyperactive neuronal networks enhance tau spread in an Alzheimer's disease mouse model.bioRxiv : the preprint server for biology · 2025Article
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Authors and funding
9 authors.
Funding
Abstract
We can now collect cell-count data across whole animal brains quantifying recent neuronal activity, gene expression, or anatomical connectivity. This is a powerful approach since it is a multiregion measurement, but because the imaging is done postmortem, each animal only provides one set of counts. Experiments are expensive, and since cells are counted by imaging and aligning a large number of brain sections, they are time-intensive. The resulting datasets tend to be undersampled with fewer animals than brain regions. As a consequence, these data are a challenge for traditional statistical approaches. We present a 'standard' partially pooled Bayesian model for multiregion cell-count data and apply it to two example datasets. These examples demonstrate that hierarchical Bayesian methods are well suited to these data. In both cases, the Bayesian model outperformed standard parallel
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.