Sequential pathway inference for multimodal neuroimaging analysis

Motivated by a multimodal neuroimaging study for Alzheimer's disease, in this article, we study the inference problem, that is, hypothesis testing, of sequential mediation analysis. The existing sequential mediation solutions mostly focus on sparse estimation, while hypothesis testing is an utterly different and more challenging problem. Meanwhile, the few mediation testing solutions often ignore the potential dependency among the mediators or cannot be applied to the sequential problem directly. We propose a statistical inference procedure to test mediation pathways when there are sequentially ordered multiple data modalities and each modality involves multiple mediators. We allow the mediators to be conditionally dependent and the number of mediators within each modality to diverge with the sample size. We produce the explicit significance quantification and establish theoretical guarantees in terms of asymptotic size, power, and false discovery control. We demonstrate the efficacy of the method through both simulations and an application to a multimodal neuroimaging pathway analysis of Alzheimer's disease.

受一项针对阿尔茨海默病的多模态神经影像学研究的启发，在本文中，我们研究了序列中介分析的推断问题，即假设检验。现有的序列中介解决方案大多侧重于稀疏估计，而假设检验是一个完全不同且更具挑战性的问题。同时，少数中介检验解决方案往往忽略中介之间的潜在相关性，或者不能直接应用于序列问题。我们提出了一种统计推断程序，用于在存在顺序排列的多个数据模态且每个模态涉及多个中介时检验中介路径。我们允许中介有条件地相关，并且每个模态内中介的数量随样本量而变化。我们给出了明确的显著性量化，并在渐近大小、功效和错误发现控制方面建立了理论保证。我们通过模拟以及在阿尔茨海默病的多模态神经影像路径分析中的应用，证明了该方法的有效性。