Sequential pathway inference for multimodal neuroimaging analysis

Li, Lexin, Shi, Chengchun ORCID: 0000-0001-7773-2099, Guo, Tengfei and Jagust, William J. (2022) Sequential pathway inference for multimodal neuroimaging analysis. Stat, 11 (1). ISSN 2049-1573

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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.

Item Type:	Article
Official URL:	https://onlinelibrary.wiley.com/journal/20491573
Additional Information:	© 2021 John Wiley & Sons Ltd
Divisions:	Statistics
Subjects:	H Social Sciences > HA Statistics
Date Deposited:	13 Sep 2021 11:27
Last Modified:	20 Dec 2024 00:41
URI:	http://eprints.lse.ac.uk/id/eprint/111904

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