Regional Neuroanatomic Effects on Brain Age Inferred Using Magnetic Resonance Imaging and Ridge Regression

The biological age of the brain differs from its chronological age (CA) and can be used as biomarker of neural/cognitive disease processes and as predictor of mortality. Brain age (BA) is often estimated from magnetic resonance images (MRIs) using machine learning (ML) that rarely indicates how regional brain features contribute to BA. Leveraging an aggregate training sample of 3 418 healthy controls (HCs), we describe a ridge regression model that quantifies each region’s contribution to BA. After model testing on an independent sample of 651 HCs, we compute the coefficient of partial determination R̄² _p for each regional brain volume to quantify its contribution to BA. Model performance is also evaluated using the correlation r between chronological and biological ages, the mean absolute error (MAE ) and mean squared error (MSE) of BA estimates. On training data, r = 0.92, MSE = 70.94 years, MAE = 6.57 years, and R̄² = 0.81; on test data, r = 0.90, MSE = 81.96 years, MAE = 7.00 years, and R̄² = 0.79. The regions whose volumes contribute most to BA are the nucleus accumbens (R̄² _p = 7.27%), inferior temporal gyrus (R̄² _p = 4.03%), thalamus (R̄² _p = 3.61%), brainstem (R̄² _p = 3.29%), posterior lateral sulcus (R̄² _p = 3.22%), caudate nucleus (R̄² _p = 3.05%), orbital gyrus (R̄² _p = 2.96%), and precentral gyrus (R̄² _p = 2.80%). Our ridge regression, although outperformed by the most sophisticated ML approaches, identifies the importance and relative contribution of each brain structure to overall BA. Aside from its interpretability and quasi-mechanistic insights, our model can be used to validate future ML approaches for BA estimation.