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.

翻译：体素级多重检验广泛应用于神经影像数据分析。传统的错误发现率（FDR）控制方法往往忽略体素间检验的空间依赖性，导致检验效能显著降低。尽管近年来涌现出一些空间FDR控制方法，但在处理大脑复杂空间依赖性时，其有效性和最优性仍存疑问。与此同时，深度学习技术革新了图像分割领域——这一任务与体素级多重检验密切相关。本文提出DeepFDR，一种利用无监督深度学习图像分割解决体素级多重检验问题的新型空间FDR控制方法。数值实验（包括全面仿真分析与阿尔茨海默病FDG-PET影像分析）表明，DeepFDR相较现有方法具有显著优越性。该方法不仅能精准控制FDR并有效降低假阴性率，更具备卓越的计算效率，特别适用于大规模神经影像数据分析。