Correlation-Aware Sparse and Low-Rank Constrained Multi-Task Learning for Longitudinal Analysis of Alzheimer's Disease

Alzheimer's disease (AD), as a severe neurodegenerative disease, is now attracting more and more researchers' attention in the healthcare. With the development of magnetic resonance imaging (MRI), the neuroimaging-based longitudinal analysis is gradually becoming an important research direction to understand and trace the process of the AD. In addition, regression analysis has been commonly adopted in the AD pattern analysis and progression prediction. However, most existing methods assume that all input features are equally related to the output variables, which ignore the difference in terms of the correlation. In this paper, we proposed a novel multi-task learning formulation, which considers a correlation-aware sparse and low-rank constrained regularization, for accurately predicting the cognitive scores of the patients at different time points and identifying the most predictive biomarkers. In addition, an efficient iterative algorithm is developed to optimize the proposed non-smooth convex objective formulation. We also have performed experiments using data from the AD neuroimaging initiative dataset to evaluate the proposed optimization formulation. Especially, we will predict cognitive scores of multiple time points through the baseline MRI features. The results not only indicate the rationality and correctness of the proposed method for predicting disease progression but also identify some stable and important MRI features that are consistent with the previous research.