Predicting Brain Age of Healthy Adults Based on Morphological MRI Parcellation Using Radiomics

Motivation: A machine learning model capable of accurately estimating brain age could have a large clinical impact. Goal(s): To apply radiomics analysis to morphological MR images and train a machine learning model capable of accurately estimating subjects’ age from radiomics features. Approach: T1- and T2-weighted brain images of 725 healthy adults were used to extract 18324 radiomics features from bilateral caudate, putamen, and hippocampus, and used to train a stacking regressor machine learning model. Results: Our machine learning model accurately estimated the subjects’ age with a mean absolute error of 4.77±0.35 years using radiomics features from T1-(45%) and T2-weighted(55%). Impact: Investigating advanced machine learning methods to accurately estimate brain aging based on commonly used clinical MR images provides vital insights to further improve our understanding of brain changes in both healthy aging and neurodegeneration.