Sequential pathway inference for multimodal neuroimaging analysis. Issue 1 (1st April 2022)

• Record Type: Journal Article
• Authors: Li, Lexin; Shi, Chengchun; Guo, Tengfei; Jagust, William J.
• Is Part Of: Stat. Volume 11:Issue 1(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-04-01
• Subjects: Alzheimer's disease -- boolean matrix -- directed acyclic graph -- high‐dimensional inference -- mediation analysis -- multimodal neuroimaging analysis; Statistics -- Periodicals; 519.2
• DOI: 10.1002/sta4.433
• Languages: English
• ISSNs: 2049-1573

Abstract

Abstract : 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.