A Systematic Evaluation of the Performance of Multiple Brain Age Algorithms in Two Cohorts of Youth

The brain matures rapidly during childhood and adolescence. The environment may calibrate the pace of this process to shape cognition and mental health. Extending its utility as a risk marker from older to younger populations, brain age has been proposed to capture relative brain maturity in youth. Multiple algorithms have been developed to estimate brain age in predominantly White advantaged adults. Whether these models are useful in youth, particularly in more representative cohorts, remains unclear. Here, we systematically compare five influential algorithms (Drobinin, Whitmore, Pyment, Kaufmann, Centile) in two population-based youth cohorts as a benchmark for future applied research. We examined (a) prediction accuracy (correlation with chronological age, mean absolute error), (b) sensitivity to scanning parameters (acquisition sequence, image quality), demographics (sex, puberty), and genetic similarity (intraclass correlations in pairs of monozygotic twins), and (c) strength of convergence between algorithms. In our primary sample of twins recruited from birth records to represent families in disadvantaged neighborhoods (N = 593; 9-19 years), three algorithms (Drobinin, Pyment, Centile) exhibited strong predictions from structural MRI data (correlations with chronological age = 0.51-0.68, mean absolute error = 1.60-3.02). These algorithms also generated correlated brain age values and gaps, and the expected pattern of strong but not identical intraclass correlations in monozygotic twins. Pyment exhibited the strongest correlation with age and was not sensitive to acquisition sequence, image quality, sex, and puberty. In a second sample of predominantly Black, low-income youth with a narrow age range (N = 198; 15-17 years), these five algorithms exhibited weak predictions. This study raises critical questions about what "brain age" means, how it can best be estimated depending on the research question and study population, and whether it can be universally applied across samples with heterogeneous backgrounds and age ranges that are narrow or misaligned with the training data.