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：体素级多重检验在神经影像数据分析中广泛应用。传统的错误发现率（FDR）控制方法往往忽略体素检验间的空间依赖性，导致检验效能显著下降。尽管近年来出现了空间FDR控制方法，但在处理大脑复杂的空间依赖性时，其有效性与最优性仍存疑。与此同时，深度学习技术彻底革新了图像分割领域，而该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。包含综合模拟实验与阿尔茨海默病FDG-PET图像分析在内的数值研究表明，DeepFDR在FDR控制、有效降低错误发现率方面均优于现有方法，且具有卓越的计算效率，非常适用于大规模神经影像数据。