Generalized Liquid Association Analysis for Multimodal Data Integration

• Activity Number: 494 - Advanced Developments in Methods and Algorithms for Modern Complex Imaging Data
• Type: Invited
• Date/Time: Thursday, August 11, 2022 : 8:30 AM to 10:20 AM
• Sponsor: Section on Statistical Computing
• Author(s): Lexin Li and Jing Zeng and Xin Zhang*
• Companies: University of California, Berkeley and Florida State University and Florida State University
• Keywords: Liquid association; Multimodal neuroimaging; Sufficient dimension reduction; Tensor Analysis; Tucker tensor decomposition

Abstract

One of the central questions in the multimodal integrative analysis is to understand how two data modalities associate and interact with each other given another modality or demographic variables. The problem can be formulated as studying the associations among three sets of random variables, a question that has received relatively less attention in the literature. In this article, we propose a novel generalized liquid association analysis method, which offers a new and unique angle to this important class of problem of studying three-way associations. We extend the notion of liquid association from the univariate setting to the sparse, multivariate, and high-dimensional setting. We establish a population dimension reduction model, transform the problem to sparse Tucker decomposition of a three-way tensor, and develop a higher-order orthogonal iteration algorithm for parameter estimation. We demonstrate the efficacy of the method through both simulations and a multimodal neuroimaging application for Alzheimer’s disease research.