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

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.

Keywords:

• 2D Surface
• Behavioral deficits
• Confounders
• Hippocampus
• Variable selection

Supplementary Materials

Supplementary material available online contains detailed derivations and explanations of the main algorithm, ADNI data usage acknowledgement, image and genetic data preprocessing steps, screening results and sensitivity analyses of the ADNI data application with a subgroup analysis including only MCI and AD patients, detailed procedure and results for the SNP-imaging-outcome mediation analyses, additional simulation results, theoretical properties of the proposed procedure including the main theorems, assumptions needed for our main theorems, and proofs of auxiliary lemmas and main theorems.

National Institutes of Health;Natural Sciences and Engineering Research Council of Canada;Natural Sciences and Engineering Research Council of Canada;Natural Sciences and Engineering Research Council of Canada;Natural Sciences and Engineering Research Council of Canada;Natural Sciences and Engineering Research Council of Canada;

Acknowledgments

The authors thank the editor, associate editor and referees for their constructive comments, which have substantially improved the paper. Yu was partially supported by the Canadian Statistical Sciences Institute (CANSSI) postdoctoral fellowship and the startup fund of University of Texas at Arlington.

Additional information

Funding

Wang and Kong were partially supported by the Natural Science and Engineering Research Council of Canada and the CANSSI Collaborative Research Team Grant. Zhu was partially supported by the NIH-R01-MH116527. Yu was partially supported by the Canadian Statistical Sciences Institute (CANSSI) postdoctoral fellowship and the startup fund of University of Texas at Arlington.