UK Biobank-centric advances in brain age prediction: a comprehensive review

Abstract

With the accelerating global population aging, establishing effective brain health assessment systems has emerged as a critical challenge in public health. Neuroimaging-based brain age prediction, serving as a potential biomarker for evaluating individual brain aging, has achieved remarkable breakthroughs in recent years. However, the accuracy of current brain age prediction models remains substantially dependent on the quality and representativeness of their training datasets. Consequently, constructing larger-scale, population-representative, and high-quality datasets is essential for enhancing the reliability of brain age prediction. This systematic review synthesizes findings from 70 peer-reviewed studies (2014-2024) that utilized the UK Biobank (UKB) for brain age prediction, focusing on paradigm-shifting advancements in machine learning and deep learning algorithms. We comprehensively analyze influential factors associated with brain age and their clinical implications, while critically evaluating the unique advantages and inherent limitations of the UKB dataset in this research domain. Furthermore, this work proposes future research directions to address existing methodological gaps and enhance clinical applicability. This study systematically elucidates the advancements in brain age prediction research based on the UKB dataset, aiming to promote deeper exploration in this field and provide theoretical foundations and practical guidance for the precise diagnosis and treatment of neurodegenerative diseases, as well as the formulation of individualized intervention strategies.

Keywords: UK Biobank; aging-related factors; brain age prediction; deep learning; machine learning.