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

Research output: Contribution to journalReview articlepeer-review

Original languageEnglish
Article number103600
Pages (from-to)103600
Early online date4 Oct 2021
Accepted/In press14 Sep 2021
E-pub ahead of print4 Oct 2021
PublishedOct 2021

Bibliographical note

Funding Information: This research has been supported by a Wellcome Trust's Psychosis Flagship Innovations (220402/Z/20/Z) to AM. The present work was carried out within the scope of the research programme Dipartimenti di Eccellenza (art.1, commi 314-337 legge 232/2016), which was supported by a grant from MIUR to the Department of General Psychology, University of Padua. Funders had no role in data collection, analysis, interpretation, writing, or the decision to submit this manuscript for publication. Publisher Copyright: © 2021 The Authors

King's Authors


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.

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