Permutation-based true discovery proportions for functional Magnetic Resonance Imaging cluster analysis

TL;DR

This paper introduces a permutation-based statistical method to estimate the lower bounds of true discoveries in fMRI cluster analysis, providing confidence statements on activated voxels while controlling error rates.

Contribution

It presents a novel permutation-based approach that offers valid confidence bounds for true discoveries in fMRI data, improving power over traditional parametric methods.

Findings

Provides high-confidence lower bounds for true discoveries in fMRI clusters.

Controls family-wise error rate in a data-driven, spatially correlated setting.

Outperforms parametric approaches in power and validity.

Abstract

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.