Distribution Bias in Brain Age Research: Toward Age-Specific Interpretation of Brain Age Gaps.

BACKGROUND: The brain age biomarker estimates biological age from brain structure and is discussed as a potential screening tool for clinically relevant brain aging patterns in individuals. For brain age estimates to be of clinical utility, they must be meaningful for individual patients and free from systematic bias. Here, we investigate how biases from training data age skewness, termed distribution bias, impact the reliability and biological interpretability of this promising biomarker.

METHODS: Using Monte Carlo simulations with data from 9305 individuals and external validation in neuropsychiatric cohorts (1345 individuals), we trained 100 brain age models for each of the 4 differently age-skewed training distributions, respectively. For each model, we evaluated predictive performance, conducted standard group-level analyses for different neurodegenerative and psychiatric diseases, and evaluated the clinical utility of the prediction as an individual risk marker.

RESULTS: Training data age distribution significantly influenced model predictions, causing substantial fluctuations in predicted brain ages across the aging continuum. Statistical analyses revealed that these fluctuations impacted effect sizes and statistical significance across all diseases. Moreover, we found limited effectiveness of the brain age gap (BAG) as an individual risk marker and different levels of disease-associated brain age across the aging continuum.

CONCLUSIONS: Skewed training data age distributions significantly impact brain age model predictions and may compromise scientific results. Based on our findings, we want to raise awareness about distribution bias and propose agewise interpretation of BAGs as a practical solution for robust research and meaningful clinical application.