Modeling Alzheimers’ Disease Progression from Multi-task and Self-supervised Learning Perspective with Brain Networks

Abstract

Alzheimer’s disease (AD) is a common irreversible neurodegenerative disease among elderlies. Establishing relationships between brain networks and cognitive scores plays a vital role in identifying the progression of AD. However, most of the previous works focus on a single time point, without modeling the disease progression with longitudinal brain networks data. Besides, the longitudinal data is insufficient for sufficiently modeling the predictive models. To address these issues, we propose a $\pmb {\textrm{S}}$elf-supervised $\pmb {\textrm{M}}$ulti-Task learning $\pmb {\textrm{P}}$rogression model SMP-Net for modeling the relationship between longitudinal brain networks and cognitive scores. Specifically, the proposed model is trained in a self-supervised way by designing a masked graph auto-encoder and a temporal contrastive learning that simultaneously learn the structural and evolutional features from the longitudinal brain networks. Furthermore, we propose a temporal multi-task learning paradigm to model the relationship among multiple cognitive scores prediction tasks. Experiments on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset show the effectiveness of our method and achieve consistent improvements over state-of-the-art methods in terms of Mean Absolute Error (MAE), Pearson Correlation Coefficient (PCC) and Concordance Correlation Coefficient (CCC). Our code is available at https://github.com/IntelliDAL/Graph/tree/main/SMP-Net.