Machine learning for brain age prediction: Introduction to methods and clinical applications

The rise of machine learning has unlocked new ways of analysing structural neuroimaging data, including brain age prediction. In this state-of-the-art review, we provide an introduction to the methods and potential clinical applications of brain age prediction. Studies on brain age typically involve the creation of a regression machine learning model of age-related neuroanatomical changes in healthy people. This model is then applied to new subjects to predict their brain age. The difference between predicted brain age and chronological age in a given individual is known as 'brain-age gap'. This value is thought to reflect neuroanatomical abnormalities and may be a marker of overall brain health. It may aid early detection of brain-based disorders and support differential diagnosis, prognosis, and treatment choices. These applications could lead to more timely and more targeted interventions in age-related disorders. (C) 2021 The Authors. Published by Elsevier B.V.

Automated summary

The rise of machine learning has enabled new ways of analyzing structural neuroimaging data, such as brain age prediction, which can help in early detection of brain-based disorders. The prediction model of brain age can reflect neuroanatomical abnormalities and support differential diagnosis and treatment choices.