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

EBioMedicine. 2021 Oct:72:103600. doi: 10.1016/j.ebiom.2021.103600. Epub 2021 Oct 4.


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.

Keywords: ageing; brain age; brain-age gap; machine learning.

Publication types

  • Review

MeSH terms

  • Aging / pathology*
  • Brain / pathology*
  • Brain Diseases / diagnosis*
  • Brain Diseases / pathology*
  • Humans
  • Machine Learning
  • Magnetic Resonance Imaging / methods*
  • Neuroimaging / methods*