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Brain age prediction from MRI has become a popular imaging biomarkerassociated with a wide range of neuropathologies. The datasets used fortraining, however, are often skewed and imbalanced regarding demographics,potentially making brain age prediction models susceptible to bias. We analysethe commonly used ResNet-34 model by conducting a comprehensive subgroupperformance analysis and feature inspection. The model is trained on 1,215T1-weighted MRI scans from Cam-CAN and IXI, and tested on UK Biobank(n=42,786), split into six racial and biological sex subgroups. With theobjective of comparing the performance between subgroups, measured by theabsolute prediction error, we use a Kruskal-Wallis test followed by twopost-hoc Conover-Iman tests to inspect bias across race and biological sex. Toexamine biases in the generated features, we use PCA for dimensionalityreduction and employ two-sample Kolmogorov-Smirnov tests to identifydistribution shifts among subgroups. Our results reveal statisticallysignificant differences in predictive performance between Black and White,Black and Asian, and male and female subjects. Seven out of twelve pairwisecomparisons show statistically significant differences in the featuredistributions. Our findings call for further analysis of brain age predictionmodels.

Analysing race and sex bias in brain age prediction