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Motivated by an imaging proteomics study for Alzheimer's disease (AD), inthis article, we propose a mediation analysis approach with high-dimensionalexposures and high-dimensional mediators to integrate data collected frommultiple platforms. The proposed method combines principal component analysiswith penalized least squares estimation for a set of linear structural equationmodels. The former reduces the dimensionality and produces uncorrelated linearcombinations of the exposure variables, whereas the latter achievessimultaneous path selection and effect estimation while allowing the mediatorsto be correlated. Applying the method to the AD data identifies numerousinteresting protein peptides, brain regions, and protein-structure-memorypaths, which are in accordance with and also supplement existing findings of ADresearch. Additional simulations further demonstrate the effective empiricalperformance of the method.

Multimodal Data Integration via Mediation Analysis with High-Dimensional Exposures and Mediators