Unveiling Alzheimer’s disease through brain age estimation using multi-kernel regression network and magnetic resonance imaging

• Published in: Computer methods and programs in biomedicine Vol. 261; p. 108617
• Main Authors: Pilli, Raveendra, Goel, Tripti, Murugan, R.
• Format: Journal Article
• Language: English
• Published: Ireland Elsevier B.V 01.04.2025
• Subjects: Aged; Aged, 80 and over; Aging; Alzheimer Disease - diagnostic imaging; Alzheimer’s disease; Brain - diagnostic imaging; Brain - pathology; Brain age estimation; Extreme learning machine; Female; Gray matter; Humans; Machine Learning; Magnetic Resonance Imaging; Male; Middle Aged; Regression Analysis; White matter; Brain age estimation; Alzheimer’s disease; Extreme learning machine; Magnetic resonance imaging; Gray matter; White matter
• ISSN: 0169-2607; 1872-7565; 1872-7565
• DOI: 10.1016/j.cmpb.2025.108617

Structural magnetic resonance imaging (MRI) studies have unveiled age-related anatomical changes across various brain regions. The disparity between actual age and estimated age, known as the Brain-Predicted Age Difference (Brain-PAD), serves as an indicator for predicting neurocognitive ailments or brain abnormalities resulting from diseases. This study aims to develop an accurate brain age prediction model that can assist in identifying potential neurocognitive impairments. The present study implemented a brain age prediction model using a ResNet-50 deep network and a multi-kernel extreme learning machine (MKELM) regression network, relying on MRI images. Kernel methods translate input information into higher-dimensional space by introducing nonlinearity and enabling the model to grasp complicated data patterns. A multi-kernel function combines the Gaussian and polynomial kernels and is incorporated into the brain age regression model. The model effectively utilizes the benefits of both kernel functions to estimate the ages accurately. MRI scans are segmented into gray matter (GM) and white matter (WM) maps preprocessed and extracted of significant features using the ResNet-50 deep network. Extracted features of the WM and GM datasets are fed into the MKELM regression model for brain age prediction. The proposed age estimation framework achieved 3.06 years of mean absolute error (MAE) and 4.12 years of root mean square error (RMSE) on healthy controls (HC) WM scans, and on GM scans, 2.73 years of MAE and 3.65 years of RMSE values. To further validate the importance of Brain-PAD as a biomarker for identifying brain health conditions, an independent testing dataset of mild cognitive impairment (MCI) and Alzheimer’s disease (AD) subjects age is predicted. The Brain-PAD values for AD subjects’ GM images are significantly higher compared to those of HC and MCI subjects, indicating distinct brain health conditions. Furthermore, variations in GM and WM tissue were identified in AD subjects, revealing that the parahippocampus and corpus callosum were notably affected. Our findings underscore the potential of Brain-PAD as a significant biomarker for assessing brain health, with implications for early detection of neurocognitive diseases. The developed framework effectively estimates brain age using MRI, contributing valuable insights into the relationship between brain structure and cognitive health. •The study emphasizes the Brain Age Gap as a crucial metric for early detection of Mild Cognitive Impairment (MCI) and Alzheimer’s disease.•Combines deep learning, machine learning, and Magnetic Resonance Imaging (MRI) to predict brain age, providing insights into abnormal aging.•Use ResNet-50 for extracting robust features from segmented gray matter (GM) and white matter (WM) regions.•Applies a multi-kernel extreme learning machine (ELM) regression model for accurate brain age prediction in both healthy individuals and Alzheimer’s patients.•Employs voxel-based morphometry (VBM) analysis to detect AD-affected regions in GM and WM, adding clinical value.•The methodology offers the potential for early detection and management of neurological disorders, providing key insights for improving patient care.