Multimodal MRI radiomics and deep learning for brain age prediction: age-corrected brain age gap analysis in patients with insomnia

This study aimed to develop and validate a high-precision brain age prediction model by integrating multimodal MRI radiomics features from T1- and T2-weighted images with deep learning. The model was trained on healthy individuals for chronological age estimation and applied to patients with insomnia to calculate the Brain Age Gap (BAG), evaluating whether chronic insomnia is associated with accelerated brain aging. A total of 1,200 participants were retrospectively included, comprising 942 healthy controls and 258 patients with insomnia. Healthy data were obtained from the IXI public dataset and Shenzhen Hospital (Futian), Guangzhou University of Chinese Medicine. All insomnia patients were recruited from the same hospital. T1- and T2-weighted MRI underwent standardized preprocessing, including resampling, gray-level discretization, and automated segmentation for radiomics feature extraction. After variance-based feature selection, multimodal features were combined to construct a deep learning regression model trained on healthy subjects and evaluated using mean absolute error (MAE), root mean square error (RMSE), and R 2 . The model was then applied to the insomnia cohort to estimate BAG, followed by age-bias correction and group comparisons. Three models were constructed: T1-based, T2-based, and multimodal fusion. In validation, the T1 model achieved MAE of 7.58 years (R 2 = 0.57), the T2 model 7.90 years (R 2 = 0.51), and the fusion model 6.42 years (R 2 = 0.68; all p < 0.001). The insomnia group showed significantly higher BAG than controls both before (8.10 ± 8.57 vs. 1.26 ± 8.30 years, p < 0.001) and after age correction (1.60 ± 6.49 vs. − 2.18 ± 7.75 years, p < 0.001). The multimodal MRI radiomics–deep learning fusion model enables accurate brain age prediction and reveals evidence of accelerated brain aging in patients with insomnia.