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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 …