SET-GFRN: Hybrid Architecture Fusing Structural and Functional MRI for Brain Age Estimation

Brain age has emerged as a critical biomarker for assessing neurodevelopmental health and aging trajectories, demonstrating significant potential in early detection and monitoring of neurological and psychiatric disorders. While existing brain age prediction models predominantly rely on structural MRI (sMRI) due to their rich anatomical detail and predictive accuracy, resting-state functional MRI (rs-fMRI)—which captures dynamic brain connectivity—remains underutilized despite offering complementary insights. This study proposes a highly accurate and generalizable brain age prediction framework that effectively fuses sMRI and rs-fMRI modalities. Specifically, we integrate a Squeeze-and-Excitation Transformer for structural feature extraction with a Graph Frequency Recurrent Network for modeling functional dynamics. Our hybrid model achieves a MAE of 1.31 years and Pearson's R of 0.976 on the ABIDE I dataset while generalizing effectively across independent datasets, thus demonstrating the utility of multimodal fusion for robust brain age estimation.