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

Publication Type	Academic Article
Authors	Kim T, Shu H, Jia Q, de Leon M
Journal	Proc Mach Learn Res
Volume	238
Pagination	946-954
Date Published	05/01/2024
ISSN	2640-3498
Abstract	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.
PubMed ID	38741695
PubMed Central ID	PMC11090200