Beyond single scores: A multi-cognitive objective learning for AD progression prediction

Amidst a rising global incidence of Alzheimer’s Disease (AD) and an aging population, the search for predictive structural brain biomarkers and effective treatments is critical. Traditional multi-task learning (MTL) methods for predicting AD progression often focus on single cognitive scores across multiple time points. In contrast, our innovative approach, the Adaptive Multi-Cognitive Objective Temporal (AMCOT) mechanism, predicts AD progression by leveraging multiple cognitive assessments. This method not only integrates diverse cognitive data but also captures temporal correlations between different cognitive tasks, enhancing the model’s ability to forecast disease progression. Unlike conventional MTL approaches that focus on isolated cognitive scores or single-time series predictions, AMCOT dynamically identifies temporal correlations across various cognitive-objective scores at different time points. We developed a novel MTL methodology using sparse group lasso techniques to pinpoint biomarkers linked to cognitive assessments accurately. A robust algorithm designed for large datasets significantly outperforms existing models in both overall and task-specific performance. Moreover, we applied stability selection to determine stable MRI biomarkers, analyzing their temporal patterns to deepen our understanding of AD progression. This comprehensive approach offers significant advancements in predictive accuracy and biomarker identification for AD. The implementation source can be found at https://github.com/XuanhanFan/AMCOT