Hierarchical Bayesian modeling of multi-region brain cell count data
Dimmock, S.; Exley, B. M. S.; Moore, G.; Menage, L.; Delogu, A.; Schultz, S. R.; Warburton, E. C.; Houghton, C. J.; O'Donnell, C.
Show 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 multi-region measurement, but because the imaging is done post-mortem, 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 under-sampled 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 multi-region 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 t-tests. Overall, inference for cell-count data is substantially improved by the ability of the Bayesian approach to capture nested data and by its rigorous handling of uncertainty in under-sampled data.
Matching journals
The top 5 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Hippocampal spike-time correlations and place-field overlaps during open field foraging 93%
- Linking minimal and detailed models of CA1 microcircuits reveals how theta rhythms emerge and how their frequencies are controlled 93%
- Conjunctive representation of what and when in monkey hippocampus and lateral prefrontal cortex during an associative memory task 92%
Similar papers in this journal
Similar papers in this journal
- Fighting or Embracing Multiplicity in Neuroimaging? Neighborhood Leverage versus Global Calibration 94%
- Neurochemistry-enriched dynamic causal models of magnetoencephalography, using magnetic resonance spectroscopy 93%
- Selective peak inference: Unbiased estimation of raw and standardized effect size at local maxima 92%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.