Attention-Guided Multimodal Neuroimaging Fusion Network for Modeling Brain Aging Pattern

Abstract

Brain age prediction from neuroimaging data provides critical insights into neurodevelopmental trajectories and neurodegenerative processes. However, effectively leveraging complementary structural and functional brain information for accurate prediction remains a major challenge. In this study, we propose an Attention-guided Multimodal brain Age prediction Network (AMAge-Net), a novel framework that integrates resting-state functional MRI (fMRI) and structural MRI (sMRI) to enhance brain age estimation. In AMAge-Net, functional features are captured from fMRI through a hierarchical Graph Attention Network, while structural features are learned from sMRI via a 3D DenseNet architecture. To enable effective cross-modal integration, AMAge-Net incorporates a Multi-Head Cross-Attention mechanism followed by a Gated Fusion Module, allowing the model to dynamically prioritize the most informative features from each modality, thereby improving interpretability and predictive accuracy. Evaluation on the Cam-CAN dataset (652 participants, aged 18-89) demonstrates that AMAge-Net outperforms state-of-the-art unimodal and multimodal baselines, achieving a mean absolute error (MAE) of 5.09, root mean square error (RMSE) of 6.52, R2 of 0.87, and Pearson correlation (PCC) of 0.94. The proposed model further demonstrates robust generalization, achieving an MAE of 4.29, RMSE of 5.59, R2 of 0.58, and PCC of 0.77 on the independent OASIS-3 dataset. Comparative and ablation studies further confirm the effectiveness of the proposed fusion strategy and modality-specific encoders. Beyond predictive performance, AMAge-Net highlights interpretable brain regions that provide insights into the mechanisms of functional and structural brain aging, while gender-specific analyses reveal distinct aging trajectories between males and females. These findings establish AMAge-Net as a powerful and interpretable approach to brain age estimation, advancing efforts to characterize healthy aging and detect early deviations associated with neurological and psychiatric disorders. Author summaryEstimating the biological age of the brain from imaging data offers a window into normal development, healthy aging, and the early stages of disease. A major challenge is how to combine information from structural scans, which show brain anatomy, and functional scans, which capture brain activity. Here, we present a new computational framework that integrates both types of data to improve the accuracy and interpretability of brain age prediction. Applied to two independent, large-scale lifespan magnetic resonance imaging datasets of individuals spanning early adulthood to late life, our framework produced highly accurate predictions and consistently outperformed existing methods. Beyond predictive performance, the model highlighted brain regions that appear especially important for age-related changes, and it revealed distinct aging patterns between men and women. These findings provide a powerful and interpretable tool for studying how the brain changes across the lifespan, with potential applications in detecting early deviations linked to neurological and psychiatric disorders.