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

TL;DR

DeepFDR introduces a deep learning-based spatial FDR control method that improves power and efficiency in neuroimaging data analysis by leveraging unsupervised image segmentation to handle complex spatial dependencies.

Contribution

The paper presents DeepFDR, a novel method combining deep learning and spatial FDR control, addressing limitations of existing methods in neuroimaging data analysis.

Findings

DeepFDR outperforms existing methods in FDR control.

It reduces false nondiscoveries effectively.

It is computationally efficient for large-scale data.

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.