DeepFDR: A Deep Learning-based False Discovery Rate Control Method for Neuroimaging Data
This addresses the need for more powerful and efficient false discovery rate control methods for researchers analyzing large-scale neuroimaging data, representing an incremental improvement over existing spatial methods.
The paper tackled the problem of false discovery rate control in neuroimaging data, where traditional methods lose power by ignoring spatial dependencies, and proposed DeepFDR, a method that uses unsupervised deep learning for image segmentation to improve testing power, demonstrating superiority in simulations and Alzheimer's disease image analysis with enhanced computational efficiency.
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.