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Brain aging is an inevitable process in adulthood, yet there remains a critical need for objective and accurate biomarkers to assess its progression. In this study, we develop a deep learning-based framework for brain age estimation using multiparameter MRI. Structural (T1 and T2 weighted) and diffusion-weighted images were acquired, from which we extracted cortical features, including gray matter volume, surface area, and thickness along with white matter integrity metrics such as fractional anisotropy, mean diffusivity, axial diffusivity, and radial diffusivity. To integrate these multimodal neuroimaging features, we propose MN-FNet (multimodal neurofeature fusion network), a dedicated regression architecture that effectively combines gray matter structure and white matter microstructure. Our model achieves accurate brain age prediction with low estimation error and identifies key neuroanatomical regions associated with aging, additionally providing evidence of hemispheric lateralization as a factor in brain aging. This approach offers a reliable and interpretable tool for brain age estimation, with potential applications in early detection of neurodegenerative conditions.

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Deep Learning Framework for Brain Age Prediction Integrating Gray Matter Structure and White Matter Microstructure

  • 会议论文

  • 发表日期：

    2026年

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  • Xinghao Wang,
  • Marco Maass,
  • Yuanheng Zhang,
  • Chen Li,
  • Xinyu Huang,
  • Hongzan Sun,
  • Marcin Grzegorzek

摘要

Brain aging is an inevitable process in adulthood, yet there remains a critical need for objective and accurate biomarkers to assess its progression. In this study, we develop a deep learning-based framework for brain age estimation using multiparameter MRI. Structural (T1 and T2 weighted) and diffusion-weighted images were acquired, from which we extracted cortical features, including gray matter volume, surface area, and thickness along with white matter integrity metrics such as fractional anisotropy, mean diffusivity, axial diffusivity, and radial diffusivity. To integrate these multimodal neuroimaging features, we propose MN-FNet (multimodal neurofeature fusion network), a dedicated regression architecture that effectively combines gray matter structure and white matter microstructure. Our model achieves accurate brain age prediction with low estimation error and identifies key neuroanatomical regions associated with aging, additionally providing evidence of hemispheric lateralization as a factor in brain aging. This approach offers a reliable and interpretable tool for brain age estimation, with potential applications in early detection of neurodegenerative conditions.