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Machine learning for brain age prediction: Introduction to methods and clinical applications
Lea Baecker1, Rafael Garcia-Dias1, Sandra Vieira1
1Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK.
Ebiomedicine
|October 6, 2021
Summary
Machine learning models predict brain age from neuroimaging data. The brain-age gap may indicate health issues, aiding early diagnosis and treatment for brain disorders.
Area of Science:
- Neuroimaging
- Machine Learning
- Biomedical Data Analysis
Background:
- Machine learning advances enable novel analysis of structural neuroimaging data.
- Brain age prediction is a key application, modeling age-related neuroanatomical changes.
Purpose of the Study:
- To review methods and clinical applications of brain age prediction.
- To highlight the potential of brain-age gap as a biomarker for brain health.
Main Methods:
- Developing regression machine learning models using neuroimaging data from healthy individuals.
- Applying these models to predict brain age in new subjects.
Main Results:
- The brain-age gap, the difference between predicted and chronological age, may reflect neuroanatomical abnormalities.
- This gap is a potential indicator of overall brain health.
Conclusions:
- Brain age prediction can aid in the early detection of brain-based disorders.
- It supports differential diagnosis, prognosis, and treatment decisions for age-related conditions.
- Applications may lead to timely, targeted interventions for neurological disorders.
