Hybrid feature fusion and randomized regression network for brain age prediction using magnetic resonance imaging: Analyzing age-related cortical structure variations in healthy adults

• Published in: Biomedical signal processing and control Vol. 110; p. 108255
• Main Authors: Pilli, Raveendra, Goel, Tripti, Murugan, R.
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.12.2025
• Subjects: Alzheimer’s disease; Brain age prediction; Magnetic resonance imaging; Random vector functional link network; Surface-based morphometry; Vision transformer; Brain age prediction; Vision transformer; Surface-based morphometry; Alzheimer’s disease; Magnetic resonance imaging; Random vector functional link network
• ISSN: 1746-8094
• DOI: 10.1016/j.bspc.2025.108255

The disparity between real age and predicted age computed using machine learning/deep learning and magnetic resonance imaging (MRI), known as the brain age gap (BAG), serves as an indicator for predicting neurocognitive disorders. While effective at extracting local features, Convolutional neural networks (CNNs) often struggle to capture global dependencies essential for accurate brain age estimation. In contrast, the Vision Transformer (ViT) model captures global features via self-attention, modeling relationships across the entire image. In this study, a brain age estimation framework is developed using T1-weighted structural MRI data from three publicly available neuroimaging databases. 1,070 healthy control (HC) subjects are used for the age prediction model training and testing. Brain age prediction is performed using deep features extracted directly from whole-brain MRI scans via ViT and ResNet-50, without relying on handcrafted morphometry features. These features are concatenated and input into an l1-regularized random vector functional link (RVFL) regression network, encouraging model sparsity and reducing overfitting by minimizing irrelevant feature contributions. The proposed model achieved state-of-the-art performance, with a mean absolute error (MAE) of 2.34 years and 3.21 years of root mean square error (RMSE). To evaluate BAG as a potential biomarker for assessing brain health, independent testing is conducted on 240 subjects from the ADNI database, including 100 with mild cognitive impairment (MCI), and 140 with Alzheimer’s disease (AD). The findings revealed that individuals affected by Alzheimer’s exhibit a higher rate of error outcomes, suggesting accelerated brain aging. Additionally, age-related cortical changes such as increased surface variability and right-hemisphere cortical thinning are observed in older individuals compared to younger subjects.