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

DeepFDR: A Deep Learning-based False Discovery Rate Control Method for Neuroimaging Data