Machine learning for brain age prediction: Introduction to methods and clinical applications.
Machine learning for brain age prediction: Introduction to methods and clinical applications.
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DOI:
10.1016/j.ebiom.2021.103600
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发表时间:
2021-10
期刊:
影响因子:
11.1
通讯作者:
Mechelli A
中科院分区:
文献类型:
--
作者:
Baecker L;Garcia-Dias R;Vieira S;Scarpazza C;Mechelli A
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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DOI:
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发表时间:
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