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...

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Veröffentlicht in:EBioMedicine Jg. 72; S. 103600
Hauptverfasser: Baecker, Lea, Garcia-Dias, Rafael, Vieira, Sandra, Scarpazza, Cristina, Mechelli, Andrea
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Netherlands Elsevier B.V 01.10.2021
Elsevier
Schlagworte:
Advanced Basic Science
ageing
Aging - pathology
Brain - pathology
brain age
Brain Diseases - diagnosis
Brain Diseases - pathology
brain-age gap
Humans
Internal Medicine
Machine Learning
Magnetic Resonance Imaging - methods
Neuroimaging - methods
Review
brain-age gap
ageing
brain age
machine learning
ISSN:2352-3964, 2352-3964
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Zusammenfassung: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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ISSN:2352-3964
2352-3964
DOI:10.1016/j.ebiom.2021.103600