Compositional Brain Age Profiles Reveal Disease-Related Neuroimaging Patterns Beyond the Brain Age Gap

The brain age gap (BAG)—the difference between the predicted and chronological age—does not describe the imaging patterns underlying such deviation. To characterize these patterns, we developed a semantic-guided mixture-of-experts model using multimodal imaging-derived phenotypes and their metadata. Global and local expert outputs were combined to predict age and retained as an individualized composition profile. An evaluation in 37,894 UK Biobank participants used separate reference and clinical cohorts, with a test mean absolute error of 2.784–2.811 years. Participants with nearly identical standardized BAGs had different profiles, indicating imaging heterogeneity at similar levels of age deviation. Adding the profile to a baseline of age, standardized BAG, sex, and imaging center improved discrimination in four disease contrasts. The area under the receiver operating characteristic curve (AUC) increased by 0.018–0.060, with gains persisting after matching by age and standardized BAG. Higher AUC point estimates than equal-dimensional controls suggested that adding dimensions alone did not explain the gains. Combining the age-trained profile with unsupervised principal components improved discrimination further, suggesting complementary imaging information. These findings support composition profiles for examining disease-related imaging differences beyond overall brain age deviation.