Permutation-based true discovery proportions for fMRI cluster analysis

arXiv (Cornell University)

Abstract

We develop a general permutation-based closed testing method to compute a simultaneous lower confidence bound for the true discovery proportions of all possible subsets of a hypothesis testing problem. It is particularly useful in functional Magnetic Resonance Imaging cluster analysis, where it is of interest to select a cluster of voxels and to provide a confidence statement on the percentage of truly activated voxels within that cluster, avoiding the well-known spatial specificity paradox. We offer a user-friendly tool to find the percentage of true discoveries for each cluster while controlling the familywise error rate for multiple testing and taking into account that the cluster was chosen in a data-driven way. Permutation theory adapts to the spatial correlation structure that characterizes functional Magnetic Resonance Imaging data and therefore gains power over parametric approaches.

Authors 5

1. Angela Andreella

   University of Padua

   Affiliation as printed

   >University of Padua

2. Jesse Hemerik

   Wageningen University & Research

   Affiliation as printed

   Wageningen University, and Research Centre

3. Wouter D. Weeda Aachen

   Leiden University

   Affiliation as printed

   Leiden University

4. Livio Finos

   University of Padua

   Affiliation as printed

   >University of Padua

5. Jelle J. Goeman Aachen

   Leiden University Medical Center

   Affiliation as printed

   LEIDEN UNIVERSITY (MEDICAL CENTER)