A Two-Stage Multi-Modal MRI Framework for Lifespan Brain Age Prediction
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arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| _version_ | 1866917417750364160 |
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| author | Zhang, Dingyi Liu, Ruiying Wang, Yun |
| author_facet | Zhang, Dingyi Liu, Ruiying Wang, Yun |
| contents | The accurate quantification of brain age from MRI has emerged as an important biomarker of brain health. However, existing approaches are often restricted to narrow age ranges and single-modality MRI data, limiting their capacity to capture the coordinated macro- and microstructural changes that unfold across the human lifespan. To address these limitations, we developed a multi-modal brain age framework to characterize the integrated evolution of brain morphology and white matter organization. Our model adopts a two-stage architecture, where modalities are processed independently and integrated via late fusion in both stages: first to classify each subject into one of six developmental stages, and then to estimate age within the predicted stage. This design enables a unified and lifespan-spanning assessment of brain maturity across diverse developmental periods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_16655 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | A Two-Stage Multi-Modal MRI Framework for Lifespan Brain Age Prediction Zhang, Dingyi Liu, Ruiying Wang, Yun Image and Video Processing Artificial Intelligence Computer Vision and Pattern Recognition The accurate quantification of brain age from MRI has emerged as an important biomarker of brain health. However, existing approaches are often restricted to narrow age ranges and single-modality MRI data, limiting their capacity to capture the coordinated macro- and microstructural changes that unfold across the human lifespan. To address these limitations, we developed a multi-modal brain age framework to characterize the integrated evolution of brain morphology and white matter organization. Our model adopts a two-stage architecture, where modalities are processed independently and integrated via late fusion in both stages: first to classify each subject into one of six developmental stages, and then to estimate age within the predicted stage. This design enables a unified and lifespan-spanning assessment of brain maturity across diverse developmental periods. |
| title | A Two-Stage Multi-Modal MRI Framework for Lifespan Brain Age Prediction |
| topic | Image and Video Processing Artificial Intelligence Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2604.16655 |