A multi-target multi-task approach based on correlated multiple cognitive scores for AD progression prediction

Alzheimer's disease (AD) is the most common dementia in today's aging society. Accurately predicting its progress remains a major challenge. Multi-task learning methods are widely used in AD research to help understand the progression of AD by predicting cognitive performance and identifying key imaging biomarkers. Previous work has selected representative feature subsets from magnetic resonance imaging (MRI) features. The design of these models is based on the assumption that correlations are consistent across different tasks. Specifically, the model only focuses on a single cognitive score in each prediction and ignores the correlation between different cognitive scores. However, clinicians often use a combination of assessment scores and other tests to more comprehensively assess cognitive status and make a diagnosis. Combining scores from multiple cognitive assessments helps improve accurate predictions of disease progression. Previous research models have primarily focused on predicting a single cognitive score longitudinally. In this paper, we propose a multi-target, multi-task learning method that comprehensively considers the correlation between different cognitive scores and the relationship between longitudinal tasks to simultaneously predict multiple cognitive scores to more comprehensively capture the disease characteristics of development, thereby effectively predicting disease progression. We also adopt a structure matrix to explicitly represent the correlation between tasks, further improving the accuracy and interpretability of the model. Results from extensive experiments using the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset show that our method exhibits balanced multi-target performance when dealing with three cognitive scores. Compared to models focusing on a single cognitive target score, our method performs better in the early prediction of cognitive scores.