Plan Sample Size for Task fMRI Research using Bayesian Updating

Calculate the precision in mean differences (raw or Cohen's D) and correlation coefficients for different sample sizes. Uses permutations of the collected functional magnetic resonance imaging (fMRI) region of interest data. Method described in Klapwijk, Jongerling, Hoijtink and Crone (2024) .

neuroUp

Overview

The goal of neuroUp is to perform empirical sample size determination and Bayesian updating for region of interest based task-related fMRI studies. For a more elaborate introduction to the method, see our preprint:

Klapwijk, E. T., Jongerling, J., Hoijtink, H., & Crone, E. (2024, July 9). Sample size estimation for task-related functional MRI studies using Bayesian updating. https://doi.org/10.31234/osf.io/cz32t

Installation

Run the following in R:

install.packages("neuroUp")

Or you can install the latest development version of neuroUp from GitHub with:

install.packages("devtools")
devtools::install_github("eduardklap/neuroUp")

Then run the following to get started:

library(neuroUp)

Citation

To cite package neuroUp in publications use:

Klapwijk, E., Hoijtink, H., & Jongerling, J. (2024). neuroUp: Plan sample size for fMRI regions of interest research using Bayesian updating. https://doi.org/10.5281/zenodo.11526169