Computational Approaches to Brain Age Prediction: From Feature Extraction to Clinical Use

Abstract

Brain age prediction has emerged as a powerful biomarker for understanding neurological health, early detection of brain disorders, and quantifying deviations from normal aging. This study explores computational approaches used to estimate brain age, focusing on the complete pipeline from feature extraction to clinical applications. Neuroimaging modalities—particularly MRI—are processed through advanced preprocessing techniques, followed by extraction of structural, functional, and connectivity-based features. Modern machine learning and deep learning models, including Support Vector Regression, Random Forests, Convolutional Neural Networks, and graph-based architectures, are evaluated for their predictive capabilities. Feature selection strategies and dimensionality reduction techniques further optimize model performance and computational efficiency. Clinical applications are highlighted, demonstrating how brain age prediction supports early diagnosis of neurodegenerative disorders, monitoring of cognitive decline, and assessment of lifestyle or environmental impacts on brain health. The study underscores the importance of computational methods in transforming neuroimaging data into meaningful clinical insights, paving the way for personalized neurological care and precision medicine.