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Mapping the Genetic-Imaging-Clinical Pathway with Applications to Alzheimer’s Disease

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  • Dengdeng Yu
  • Linbo Wang
  • Dehan Kong
  • Hongtu Zhu

Abstract

Alzheimer’s disease is a progressive form of dementia that results in problems with memory, thinking, and behavior. It often starts with abnormal aggregation and deposition of β amyloid and tau, followed by neuronal damage such as atrophy of the hippocampi, leading to Alzheimer’s disease (AD). The aim of this article is to map the genetic-imaging-clinical pathway for AD in order to delineate the genetically-regulated brain changes that drive disease progression based on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset. We develop a novel two-step approach to delineate the association between high-dimensional 2D hippocampal surface exposures and the Alzheimer’s Disease Assessment Scale (ADAS) cognitive score, while taking into account the ultra-high dimensional clinical and genetic covariates at baseline. Analysis results suggest that the radial distance of each pixel of both hippocampi is negatively associated with the severity of behavioral deficits conditional on observed clinical and genetic covariates. These associations are stronger in Cornu Ammonis region 1 (CA1) and subiculum subregions compared to Cornu Ammonis region 2 (CA2) and Cornu Ammonis region 3 (CA3) subregions. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.

Suggested Citation

  • Dengdeng Yu & Linbo Wang & Dehan Kong & Hongtu Zhu, 2022. "Mapping the Genetic-Imaging-Clinical Pathway with Applications to Alzheimer’s Disease," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 117(540), pages 1656-1668, October.
  • Handle: RePEc:taf:jnlasa:v:117:y:2022:i:540:p:1656-1668
    DOI: 10.1080/01621459.2022.2087658
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    Cited by:

    1. Shu Jiang & Graham A. Colditz, 2023. "Causal mediation analysis using high‐dimensional image mediator bounded in irregular domain with an application to breast cancer," Biometrics, The International Biometric Society, vol. 79(4), pages 3728-3738, December.
    2. Wang, Chuchu & Song, Xinyuan, 2024. "Nonparametric quantile scalar-on-image regression," Computational Statistics & Data Analysis, Elsevier, vol. 191(C).

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