Brain deviation scores derived from normative modelling identify dimensions of psychopathology in children and adolescents

Abstract

Psychiatric diagnoses lack neurobiological grounding, and brain based abnormalities correspond poorly to established diagnostic categories. While multivariate algorithms extract transdiagnostic dimensions from brain behavior covariation, most studies rely on conventional structural morphometric measures such as cortical thickness and surface area, which fail to capture atypical developmental trajectories. Normative modeling (NM) quantifies individual-level brain deviation scores by contrasting brain structural features with population derived percentiles, yielding continuous indices of atypical brain development. However, whether these scores can identify transdiagnostic dimensions remains unclear. We applied sparse canonical correlation analysis (sCCA) to investigate covariation between brain deviation scores and psychiatric symptoms in 1095 children and adolescents from the Healthy Brain Network. We identified an emotion-dysregulation dimension (r=0.33). When generalized to the independent cohort, this dimension showed nominal significance (r=0.18, PFDR = 0.05, uncorrectedP=0.01, n=198). By contrast, no significant dimension was detected using conventional structural brain features. Emotion dysregulation was primarily driven by deviations in cortical thickness and surface area, with distinct regional distributions; reductions in cortical surface area in the middle frontal gyrus, cingulate gyrus, superior temporal gyrus, and insula carried the highest weights. Latent brain scores for emotion dysregulation, computed using sCCA weights for brain deviation scores, showed specific correlations with out-of-model emotion dysregulation related phenotypes. In addition, females exhibited significantly higher latent brain scores than males. Collectively, NM based brain deviation scores were more sensitive than conventional structural metrics in identifying transdiagnostic psychiatric dimensions. Furthermore, latent brain scores derived from sCCA estimated weights may serve as a straightforward metric that captures the degree of dimension-specific atypical brain deviation.