Brain Age Prediction: A Comparison between Machine Learning Models Using Brain Morphometric Data.

ABSTRACT: Brain structural morphology varies over the aging trajectory, and the prediction of a person's age using brain morphological features can help the detection of an abnormal aging process. Neuroimaging-based brain age is widely used to quantify an individual's brain health as deviation from a normative brain aging trajectory. Machine learning approaches are expanding the potential for accurate brain age prediction but are challenging due to the great variety of machine learning algorithms. Here, we aimed to compare the performance of the machine learning models used to estimate brain age using brain morphological measures derived from structural magnetic resonance imaging scans. We evaluated 27 machine learning models, applied to three independent datasets from the Human Connectome Project (

SUBMITTER: Han J 

PROVIDER: S-EPMC9608785 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature