Permutation-based true discovery proportions for functional magnetic resonance imaging cluster analysis

We propose a permutation-based method for testing a large collection of hypotheses simultaneously. Our method provides lower bounds for the number of true discoveries in any selected subset of hypotheses. These bounds are simultaneously valid with high confidence. The methodology is particularly useful in functional Magnetic Resonance Imaging cluster analysis, where it provides a confidence statement on the percentage of truly activated voxels within clusters of voxels, avoiding the well-known spatial specificity paradox. We offer a user-friendly tool to estimate the percentage of true discoveries for each cluster while controlling the family-wise error rate for multiple testing and taking into account that the cluster was chosen in a data-driven way. The method adapts to the spatial correlation structure that characterizes functional Magnetic Resonance Imaging data, gaining power over parametric approaches.

Automated summary

We propose a permutation-based method for testing a large collection of hypotheses simultaneously. The method provides lower bounds for the number of true discoveries in any selected subset of hypotheses, while maintaining high confidence levels. This method is particularly useful in functional Magnetic Resonance Imaging cluster analysis, addressing the spatial specificity paradox and accounting for cluster selection in a data-driven manner. The method is advantageous over parametric approaches due to its ability to adapt to the spatial correlation structure in fMRI data.