Sample size estimation for task-related functional MRI studies using Bayesian updating

Eduard T. Klapwijk*, Joran Jongerling, Herbert Hoijtink, Eveline A. Crone

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

Task-related functional MRI (fMRI) studies need to be properly powered with an adequate sample size to reliably detect effects of interest. But for most fMRI studies, it is not straightforward to determine a proper sample size using power calculations based on published effect sizes. Here, we present an alternative approach of sample size estimation with empirical Bayesian updating. First, this method provides an estimate of the required sample size using existing data from a similar task and similar region of interest. Using this estimate researchers can plan their research project, and report empirically determined sample size estimations in their research proposal or pre-registration. Second, researchers can expand the sample size estimations with new data. We illustrate this approach using four existing fMRI data sets where Cohen's d is the effect size of interest for the hemodynamic response in the task condition of interest versus a control condition, and where a Pearson correlation between task effect and age is the covariate of interest. We show that sample sizes to reliably detect effects differ between various tasks and regions of interest. We provide an R package to allow researchers to use Bayesian updating with other task-related fMRI studies.

Original languageEnglish
Article number101489
Pages (from-to)1-12
Number of pages12
JournalDevelopmental Cognitive Neuroscience
Volume71
DOIs
Publication statusPublished - Jan 2025

Bibliographical note

Publisher Copyright:
© 2024 The Authors

Keywords

  • Bayesian updating
  • Effect size
  • Power analysis
  • R package
  • Region of interest
  • Sample sizes

Fingerprint

Dive into the research topics of 'Sample size estimation for task-related functional MRI studies using Bayesian updating'. Together they form a unique fingerprint.

Cite this