Network-level changes in the brain underlie fear memory strength

Haubrich, Josué; Nader, Karim

doi:10.7554/elife.88172

Cited by 2 publications

(2 citation statements)

References 73 publications

(88 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The zero-inflated Poisson model is intended to model a situation were there are zeros unrelated to the Poisson distribution, in this case this might, for example, be the result of an error in the automated registration process that identifies regions and counts their cells. It is a mixture model, if yi ∼ ZIPoisson(π, λi) [7] there is a probability π that yi = 0 and a 1−π probability that yi follows a Poisson distribution with rate λi. Importantly this means there are two ways in which yi can be zero, through the Bernoulli process parameterized by π, or through the Poisson distribution.…”

Section: Zero Inflationmentioning

confidence: 99%

See 1 more Smart Citation

Hierarchical Bayesian modeling of multi-region brain cell count data

Dimmock,

Exley,

Moore

et al. 2024

Preprint

View full text Add to dashboard Cite

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 demonstrate that hierarchical Bayesian methods are well suited to these data by presenting a ‘standard’ partially-pooled Bayesian model for multi-region cell-count data and applying it to two example datasets. For both datasets the Bayesian model outperformed standard parallel t-tests. Overall, the Bayesian approach’s ability to capture nested data and its rigorous handling of uncertainty in under-sampled data can substantially improve inference for cell-count data.Significance StatementCell-count data is important for studying neuronal activation and gene expression relating to the complex processes in the brain. However, the difficulty and expense of data collection means that such datasets often have small sample sizes. Many routine analyses are not well-suited, especially if there is high variability among animals and surprising outliers in the data. Here we describe a multilevel, mixed effects Bayesian model for these data and show that the Bayesian approach improves inferences compared to the usual approach for two different cell-count datasets with different data characteristics.

show abstract

Section: Zero Inflationmentioning

confidence: 99%

“…The resulting dataset consists of labeled cell counts across each of ∼10-100 brain regions. This technology is being deployed to address questions in a broad range of neuroscience subfields, for example: memory (6)(7)(8), neurodegenerative disorders (9), social behavior (10), and stress (11).…”

mentioning

confidence: 99%