Fig. 2.

Different Brain-age regressors were fit to data of an aging population, using as features a simple set of approximately 150 ROI volumes parcelating the brain. All models gave relatively good cross-validated accuracy on cross-sectional datasets, with correlation coefficients varying from 0.8 to 0.84 (r = 0.84 was achieved via the linear SVR and the multi-layer perceptron artificial neural network using 5 hidden layers). However, when these models were applied prospectively to longitudinal data, only the linear SVR gave a distribution of rate of change of the brain-age scores centered around 1. Although ground truth is not available for these datasets, it is reasonable to assume that these brains age by approximately 1 year per year, plus/minus some range that defines accelerated/resilient brain aging. Of all these models, the simpler linear SVR regressor is therefore the best one, for this specific problem.