Interpretable brain-age modeling reveals network-level reorganization in functional brain aging

Abstract

Brain-age models based on functional connectivity have shown promise for characterizing large-scale age-related network changes and advancing understanding of neural aging mechanisms. However, existing approaches still face challenges in achieving both robust predictive performance and stable identification of connectivity signatures. To address this, we developed a brain-age modeling framework that integrates SHAP-guided PCA back-projection to link model predictions to stable, interpretable connectivity signatures and aging trajectories. Resting-state fMRI data from 599 adults (age range 36–95 years) were used to compute whole-brain functional connectivity. An Orthogonal Auxiliary Guidance Convolutional Autoencoder (OAG-CAE) was developed to learn age-relevant latent representations while separating age-residual variation, followed by a regressor for brain-age prediction. To achieve stable connection-level interpretability, we designed a SHAP-guided PCA back-projection module that applies SHAP to PCA-reduced FC features, propagates attribution through the latent representation and predictive pathway, and projects PCA-level attributions back to connectivity space to localize age-relevant circuits. The brain-age model achieved robust accuracy across mid- to late adulthood (MAE = 6.308 ± 0.566 years; \({R}^{2}\hspace{0.17em}\) = 0.702 ± 0.043). SHAP analysis revealed systematic patterns of functional reorganization with aging. Negative SHAP-identified connectivity was concentrated within and between the somatomotor, ventral attention, and default mode networks, as well as in basal ganglia–thalamus–cortical pathways. Notably, a substantial proportion of these connections exhibited an inverted U trajectory peaking around age 70, including within-network effects in the somatomotor and default mode networks and cross-network thalamo-cortical and somatomotor–ventral attention links. In contrast, positive SHAP-identified connectivity emerged mainly in cerebellar links with the default mode, frontoparietal, and dorsal attention networks, as well as default mode–visual and dorsal attention–thalamic pathways. By integrating OAG-CAE-based latent representation learning with SHAP-guided PCA back-projection, this study establishes an interpretable brain-age modeling framework that links prediction to specific connectivity circuits. The identified signatures reveal stage-dependent reorganization across large-scale networks, including non-linear transitions during mid- to late adulthood. These findings offer new insight into large-scale functional brain aging and provide a structured basis for future investigations in pathological aging and independent cohorts.