OTFPF: Optimal transport based feature pyramid fusion network for brain age estimation

Deep neural networks have shown promise in predicting the chronological age of a healthy brain using T1-weighted magnetic resonance images (T1 MRIs). This predicted brain age has the potential to serve as a valuable biomarker for identifying development-related and aging-related disorders. In this study, we propose the Optimal Transport based Feature Pyramid Fusion (OTFPF) network for estimating brain age using T1 MRIs. The OTFPF network comprises three key modules: the Optimal Transport based Feature Pyramid Fusion (OTFPF) module, the 3D overlapped ConvNeXt (3D OL-ConvNeXt) module, and the fusion module. These modules enhance the OTFPF network's ability to comprehend the semi-multimodal and multi-level feature pyramid information of each brain, thereby improving its understanding of brain development and aging. Compared to recent state-of-the-art models, the proposed OTFPF network demonstrates faster convergence, superior performance, and enhanced interpretability. Experimental results utilizing a dataset of 12,909 MRIs from individuals aged 3–97 years demonstrate the accurate estimation of brain age by the OTFPF network, achieving a mean absolute error (MAE) of 1.846, Pearson's correlation coefficient (PCC) of 0.9941, and Spearman's rank correlation coefficient (SRCC) of 0.9802. Thorough parameter evaluations, quantitative comparison experiments, dataset-scale evaluations, cross-validations, and ablation studies convincingly demonstrate the stability, interpretability, and superiority of the OTFPF network. According to the OTFPF network, the age-related heatmaps of the brain explain the biological mechanisms underlying brain aging. Furthermore, the OTFPF network is applied to analyze datasets associated with brain disorders, effectively demonstrating its practical utility.