Learning Semi-Supervised Representation Enrichment Using Longitudinal Imaging-Genetic Data

Alzheimer's Disease (AD) is a progressive memory disorder that causes irreversible cognitive decline. Recently, many statistical learning methods have been presented to predict cognitive declines by using longitudinal imaging data. However, missing records that broadly exist in the longitudinal neuroimaging data have posed a critical challenge for effectively using these data in machine learning models. To tackle this difficulty, in this paper we propose a novel approach to integrate longitudinal (dynamic) phenotypic data and static genetic data to learn a fixed-length biomarker representation using the enrichment learned from the temporal data in multiple imaging modalities. Armed with this enriched biomarker representation, as a fixed-length vector per participant, conventional machine learning models can be used to predict clinical outcomes associated with AD. We have applied our new method on the Alzheimer's Disease Neruoimaging Initiative (ADNI) cohort and achieved promising experimental results that validate its effectiveness.