Breaking the Norm: Population-Scale Normative Modeling of Brain Structure in Depression and Anxiety

Abstract We applied deep normative modeling to structural MRI data from two large cohorts (German National Cohort, N ≈ 29,000 and UK Biobank, N ≈ 25,000) to characterize individual-level brain deviations along symptom dimensions of depression, anxiety, and alcohol use. Each brain was embedded into a 256-dimensional latent space, allowing us to quantify both the magnitude and direction of deviation from a normative reference trained on the non/lowsymptomatic subpopulation. Deviation magnitude increased with symptom severity, and directional patterns separated mood-anxiety and alcohol-use tendencies. These deviation axes generalized across cohorts and supported individual-level classification of symptomatic group membership, especially at higher symptom levels. Combining deviations with polygenic risk scores improved classification performance, particularly for depressive and anxiety measures, indicating complementary contributions of imaging and genetics. Our findings demonstrate that structural brain deviations reflect meaningful, continuous variation in affective and behavioral symptoms.