bioRxiv2025
BackgroundNormative models have gained popularity in computational psychiatry for studying individual-level differences relative to population norms in biological data such as brain imaging, where measures like cortical thickness are typically predicted from variables such as age and sex. Nearly all published models to date are based on cross-sectional data, limiting their ability to predict longitudinal change. MethodsHere, we used longitudinal brain data from the Adolescent Brain Cognitive Development (ABCD) study, comprising cortical thickness measures from 180 regions per hemisphere in youths at baseline (N=6179; 47% females), 2-year (N=6179; 47% females), and 4-year (N=805; 45% females) follow-up. A training set was established from baseline and 2-year follow-up data (N=5374; 47% females), while data from individuals with all three time points available served as an independent test set (N=805; 45% females). We developed sex-specific Baseline-Conditioned Norms (B-Norms) that predict brain region thickness at follow-up based on baseline thickness, baseline age, and follow-up age, and compared them to sex-specific Cross-Sectional Norms (C-Norms) based on age alone. ResultsOut-of-sample testing in 2-year and 4-year follow-up data showed that B-Norms consistently provided better fits than C-Norms for nearly all cortical regions. Explained variance was higher in B-Norms than in C-Norms. No significant differences between time points (p = 0.45) were detected. Repeated measures ANOVA revealed differences in higher-order moments (e.g., skewness and kurtosis) for both models; for example, skewness varied by model, sex, time point, and their interactions. ConclusionWhile improved fit alone does not necessarily indicate a superior normative model, since normative models aim to capture population variance rather than simply optimize fit, we demonstrated that four regions were associated with pubertal changes in B-Norms but not in C-Norms, suggesting enhanced sensitivity of B-Norms to developmental processes. Together, our findings highlight the potential of B-Norms for capturing normative variation in longitudinal structural brain change. HighlightsO_LIBaseline-Conditioned (B-Norm) models consistently outperform Cross-Sectional (C-Norm) models across all cortical regions. C_LIO_LIB-Norm deviation scores show stronger associations with pubertal progression in females than C-Norm deviations, indicating higher developmental sensitivity. C_LIO_LIThis contrasts with brain-age models, where improved fit typically reduces the association between residuals and downstream clinical or developmental variation. C_LI SummaryNormative modeling has been applied to study how brain measures, such as gray matter thickness or volume, change across development. These models help identify how an individuals brain may differ from what is typical for their age or sex, which could eventually support more personalized treatments. However, most existing models use only one-time (cross-sectional) data, meaning they cannot capture how the brain changes over time. Longitudinal data, tracking the same individuals across multiple time points, is more informative but harder and more expensive to collect. We analyzed brain scans from over 6000 young people in the Adolescent Brain Cognitive Development (ABCD) study, about half of whom were girls. Each participant had brain scans at the start of the study, two and four years later. We showed that Baseline-Conditioned Norms (B-Norms), used each persons first scan and their ages at baseline and follow-up timepoint to predict later brain changes. We compared this to Cross-Sectional Norms (C-Norm), which only used age. B-Norms predicted brain thickness more accurately and importantly were better at detecting brain differences linked to puberty, especially in girls. Although better fit alone does not prove superiority, our findings suggest that using our proposed B-Norms, we potentially also capture more developmental variance suggesting that our B-Norms are possibly more sensitive to sex-specific brain development over time.