Construction of brain age models based on structural and white matter information

Brain aging is an inevitable process in adulthood, yet there is a lack of objective measures to accurately assess its extent. This study aims to develop brain age prediction model using magnetic resonance imaging (MRI), which includes structural information of gray matter and integrity information of white matter microstructure. Multiparameter MRI was performed on two population cohorts. We collected structural MRI data from T1- and T2-sequences, including gray matter volume, surface area, and thickness in different areas. For diffusion tensor imaging (DTI), we derived four white matter parameters: fractional anisotropy, mean diffusivity, axial diffusivity, and radial diffusivity. To achieve reliable brain age prediction based on structure and white matter integrity, we employed LASSO regression. We successfully constructed a brain age prediction model based on multiparameter brain MRI (Mean absolute error of 3.87). Using structural and diffusion metrics, we identified and visualized which brain areas were notably involved in brain aging. Simultaneously, we discovered that lateralization during brain aging is a significant factor in brain aging models. We have successfully developed a brain age estimation model utilizing white matter and gray matter metrics, which exhibits minimal errors and is suitable for adults.