Alzheimer's Disease (AD) is the most prevalent neurodegenerative disorder in aging populations, posing a significant and escalating burden on global healthcare systems. While Multi-Tusk Learning (MTL) has emerged as a powerful computational paradigm for modeling longitudinal AD data, existing frameworks do not account for the time-varying nature of feature correlations. To address this limitation, we propose a novel MTL framework, named Feature Similarity Laplacian graph Multi-Task Learning (MTL-FSL). Our framework introduces a novel Feature Similarity Laplacian (FSL) penalty that explicitly models the time-varying relationships between features. By simultaneously considering temporal smoothness among tasks and the dynamic correlations among features, our model enhances both predictive accuracy and biological interpretability. To solve the non-smooth optimization problem arising from our proposed penalty terms, we adopt the Alternating Direction Method of Multipliers (ADMM) algorithm. Experiments conducted on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset demonstrate that our proposed MTLFSL framework achieves state-of-the-art performance, outperforming various baseline methods. The implementation source can be found at https://github.com/huatxxx/MTL-FSL.

As the population rapidly ages, Alzheimer’s disease (AD), the most common form of dementia, urgently requires the identification of reliable structural brain biomarkers and the development of effective therapeutic strategies. Multiple multi-task learning (MTL) paradigms have been developed to enhance model generalization by sharing information between tasks to predict AD progression and accurately identify MRI-associated biomarkers. Unlike previous MTL approaches that consider only a single kind of cognitive score to predict the complicated AD progression over time, we have developed an innovative MTL method to deal with various cognitive scores simultaneously, with each focusing on different aspects of patient cognition. To effectively capture the intricate associations among different cognitive scores at multiple time points, we first propose an Adaptive Multiple Cognitive Objective Temporal (AMCOT) task-relationship binding penalty mechanism. This mechanism adaptively reveals temporal correlations between various cognitive scores at different time points and uses these relationships to predict cumulative disease progression accurately. To select the most informative MRI features in AD progression, we consider integrating the sparse group Lasso into our model. Our algorithms are designed to handle large datasets efficiently. Empirical evaluation on the Alzheimer’s disease dataset shows that our approach significantly outperforms existing state-of-the-art algorithms in both overall and individual task performance. Additionally, we applied stability selection techniques to identify stable MRI biomarkers and analyzed their temporal patterns to gain insights into AD progression. The implementation source can be found at https://github.com/XuanhanFan/MTL-AMCOT-BB.

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.