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Activity Number: 266 - Causal Inference in Neuroimaging and Radiology
Type: Topic Contributed
Date/Time: Tuesday, August 9, 2022 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistics in Imaging
Abstract #322606
Title: Multimodal Data Integration via Mediation Analysis with High-Dimensional Exposures and Mediators
Author(s): Yi Zhao* and Lexin Li
Companies: Indiana University and University of California, Berkeley
Keywords: Alzheimer's disease; Mediation analysis; Multimodal data integration; Neuroimaging; Principal component analysis

Motivated by an imaging proteomics study for Alzheimer’s disease (AD), in this article, we propose a mediation analysis approach with high-dimensional exposures and high-dimensional mediators to integrate data collected from multiple platforms. The proposed method combines principal component analysis with penalized least squares estimation for a set of linear structural equation models. The former reduces the dimensionality and produces uncorrelated linear combinations of the exposure variables, whereas the latter achieves simultaneous path selection and effect estimation while allowing the mediators to be correlated. Applying the method to the AD data identifies numerous interesting protein peptides, brain regions, and protein-structure-memory paths, which are in accordance with and also supplement existing findings of AD research. Additional simulations further demonstrate the effective empirical performance of the method.

Authors who are presenting talks have a * after their name.

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