Softmax-Derived Brain Age Mapping: An Interpretable Visualization Framework for MRI-Based Brain Age Prediction

Brain age has been widely recognized as an important biomarker for monitoring adolescent brain development and assessing dementia risk. However, existing model visualization methods primarily highlight brain regions associated with aging, making it difficult to comprehensively reveal broader brain changes. In this study, we developed a VGGNet-based brain age prediction model and proposed the Softmax-Derived Brain Age Mapping algorithm to simultaneously identify brain regions associated with both youthful and aging features. The resulting saliency maps provide explicit representations of developmental and degenerative processes across different brain regions. Brain Age Map analysis revealed that aging features in the healthy group were primarily confined to the frontal cortex, aligning with findings that the frontal lobe is the earliest region to undergo natural senescence. In contrast, the dementia group exhibited widespread aging across the frontal, temporal, parietal, and occipital lobes, as well as the ventricular regions. These results suggest that the spatial distribution of brain aging can serve as a critical biomarker for distinguishing normal aging trajectories from pathological degeneration. From an application perspective, we further explored the potential of the proposed framework in neurodegenerative diseases. The analysis reveals that dementia patients generally exhibit an advanced brain age, with cortical aging being markedly more pronounced than in age-matched healthy samples. Notably, although dementia cases were not included in the training set, the model was still able to localize abnormalities in relevant brain regions, underscoring its potential value as an assistive tool for early dementia diagnosis.