  Voxel-based multiple testing is widely used in neuroimaging data analysis.
Traditional false discovery rate (FDR) control methods often ignore the spatial
dependence among the voxel-based tests and thus suffer from substantial loss of
testing power. While recent spatial FDR control methods have emerged, their
validity and optimality remain questionable when handling the complex spatial
dependencies of the brain. Concurrently, deep learning methods have
revolutionized image segmentation, a task closely related to voxel-based
multiple testing. In this paper, we propose DeepFDR, a novel spatial FDR
control method that leverages unsupervised deep learning-based image
segmentation to address the voxel-based multiple testing problem. Numerical
studies, including comprehensive simulations and Alzheimer's disease FDG-PET
image analysis, demonstrate DeepFDR's superiority over existing methods.
DeepFDR not only excels in FDR control and effectively diminishes the false
nondiscovery rate, but also boasts exceptional computational efficiency highly
suited for tackling large-scale neuroimaging data.
