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

arXiv (Cornell University)

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.

Authors 5

1. Angela Andreella

   Ca' Foscari University of Venice

   Affiliation as printed

   Department of Economics , University Ca' Foscari Venezia , Venezia , Italy

2. Jesse Hemerik

   Wageningen University & Research

   Affiliation as printed

   Biometris , Wageningen University and Research , Wageningen , The Netherlands

3. Wouter D. Weeda Aachen Department of Psychology, Leiden University

   Leiden University

   Affiliation as printed

   Department of Psychology , Leiden University , Leiden , The Netherlands

4. Livio Finos

   University of Padua

   Affiliation as printed

   Department of Developmental Psychology and Socialization , University of Padova , Padova , Italy

5. Jelle J. Goeman Aachen Department of Biomedical Data Sciences

   Leiden University Medical Center

   Affiliation as printed

   Department of Biomedical Data Sciences , Leiden University Medical Center , Leiden , The Netherlands