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Machine learning for brain age prediction: Introduction to methods and clinical applications
Open accessAbstract
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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Treder, M. S., Lee, S., & Tsvetanov, K. A. (2024) · Frontiers in Dementia · doi:10.3389/frdem.2024.1385303 · Cited by 63
Metadata
- Authors
- Baecker, L., Garcia-Dias, R., Vieira, S., Scarpazza, C., & Mechelli, A.
- Venue
- eBioMedicine
- Publisher
- Elsevier BV
- Published
- 2021-10
- Volume
- 72
- Pages
- 103600
- ISSN
- 2352-3964 (ISSN-L)
- OpenAlex
- W3202476286
- Cited by
- 267
- References
- 98
- FWCI
- 20.78
- Type
- journal article
- Language
- en
- Open access
- gold
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
