Multimodal Neuroimaging Data Integration and Pathway Analysis

• Activity Number: 29 - Biometrics Section Byar Award Student Paper Session I
• Type: Topic Contributed
• Date/Time: Monday, August 3, 2020 : 10:00 AM to 11:50 AM
• Sponsor: Biometrics Section
• Author(s): Yi Zhao* and Lexin Li and Brian Caffo
• Companies: Indiana University and University of California, Berkeley and Johns Hopkins University
• Keywords: Brain connectivity analysis; Linear structural equation model; Mediation analysis; Multimodal data integration; Regularization

Abstract

With fast advancements in technologies, the collection of multiple types of measurements on a common set of subjects is becoming routine in science. Integrative analysis of multimodal data allows scientists to interrogate new mechanistic questions. However, the data collection and generation of integrative hypotheses is outpacing available methodology for joint analysis of multimodal measurements. In this article, we study high-dimensional multimodal data integration in the context of mediation analysis. We aim to understand the roles different data modalities play as possible mediators in the pathway between an exposure variable and an outcome. We propose a mediation model framework with two data types serving as separate sets of mediators, and develop a penalized optimization approach for parameter estimation. We study both the theoretical properties of the estimator through an asymptotic analysis, and its finite-sample performance through simulations. We illustrate our method with a multimodal brain pathway analysis having both structural and functional connectivities as mediators in the association between sex and language processing.