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

Abstract

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.

Figure 1

Number of publications on brain age per year (2010-2020). The search was conducted on the PubMed database using the search term ‘brain age’. The number of publications per year was obtained using the ‘Results by Year’ function.

Figure 2

Overview on the machine learning method of a simplified brain age prediction study. a. Training and cross-validation (CV): A brain age study often uses k-fold CV during training, which means that k models are trained using (k-1)/k of the main sample, while 1/k of the sample (different for each fold) is used as a hold-out set to test how well the model predicts the subjects’ ages. CV may be used to tune hyperparameters of the machine learning model, where a different parameter is tested in each fold. This figure illustrates a 10-fold CV approach. b. Testing (optional): The trained model is applied to an independent dataset to test. Using an independent dataset allows a better estimation of model bias. c. Calculation of brain-age gap: Brain-age gap is calculated for each subject as predicted age – chronological age.

Figure 3

Potential clinical applications of brain age at different stages of the patient lifecycle. Brain age has a range of potential uses in health and disease of an individual person.