Hierarchical Bayesian modeling of multiregion brain cell count data.

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 ar

SUBMITTER: Dimmock S 

PROVIDER: S-EPMC12638045 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature