MJNMF-GAT: Multi-Task Joint Non-Negative Matrix Factorization Graph Attention Network for Understanding Adolescent Neurodevelopment.

Integration of multi-task brain imaging data is crucial for furthering our understanding of neurodevelopment. By utilizing functional magnetic resonance imaging (fMRI) data and machine learning methods such as graph neural networks to measure brain activity, one can efficiently capture complex brain network interactions. In this work, we introduce the Multi-Task Joint Non-Negative Matrix Factorization Graph Attention Network (MJNMF-GAT) to investigate fMRI data of multiple tasks with the goal of determining age-related differences in adolescence. Our framework integrates joint non-negative matrix factorization for extracting shared latent features across tasks with graph attention networks to model brain connectivity patterns. The model is capable of integrating data from different fMRI tasks to enhance both predictive performance and network interpretation. We applied GNNExplainer for model interpretability and identified key subnetworks that contribute to prediction tasks. The proposed MJNMF-GAT model demonstrates superior results over existing methods, achieving a root mean square error (RMSE) of 1.9212 ± 0.1742, mean absolute error (MAE) of 1.5368 ± 0.1469, and correlation of 0.8032 ± 0.0469 in an age prediction task on the Philadelphia Neurodevelomental Cohort (PNC). The model also identifies the critical functional connections that change with the different stages of adolescent neurodevelopment. Our approach improves the predictive accuracy and, more importantly, interprets informative brain connectivity patterns, especially for detecting those important sub-networks responsible in age prediction. The MJNMF-GAT framework opens a new avenue for analyzing multi-task neuroimaging studies, which helps identify significant brain networks and develop a deeper understanding of functional connectivity changes in adolescence.