Bayesian meta-analysis of fMRI image data.

We composed an R-based script for Image-based Bayesian random-effect meta-analysis of previous fMRI studies. It meta-analyzes second-level test results of the studies and calculates Bayes Factors indicating whether the effect in each voxel is significantly different from zero. We compared results from Bayesian and classical meta-analyses by examining the overlap between the result from each method and that created by NeuroSynth as the target. As an example, we analyzed previous fMRI studies focusing on working memory extracted from NeuroSynth. The result from our Bayesian method showed a greater overlap than the classical method. In addition, Bayes Factors proved a better way to examine whether the evidence supported hypotheses than p-values. Given these, Bayesian meta-analysis provides neuroscientists with an alternative meta-analysis method for fMRI studies given the improved overlap with the NeuroSynth result and the practical and epistemological value of Bayes Factors that can directly test the presence of an effect.