Psychiatric symptoms are typically highly inter-correlated at the group level. Collectively, these correlations define the architecture of psychopathology – informing taxonomic and mechanistic models in psychiatry. However, to date, it remains unclear if this architecture differs between etiologically distinct subgroups, despite the core relevance of this understanding for personalized medicine. Here, we introduce a new analytic pipeline to probe group differences in the psychopathology architecture – demonstrated through the comparison of two distinct neurogenetic disorders.

We use a large questionnaire battery in 300 individuals aged 5–25 years (n = 102 XXY/KS, n = 64 XYY, n = 134 age-matched XY) to characterize the structure of correlations among 53 diverse measures of psychopathology in XXY/KS and XYY syndrome – enabling us to compare the effects of X- versus Y-chromosome dosage on the architecture of psychopathology at multiple, distinctly informative levels.

Behavior correlation matrices describe the architecture of psychopathology in each syndrome. A comparison of matrix rows reveals that social problems and externalizing symptoms are most differentially coupled to other aspects of psychopathology in XXY/KS versus XYY. Clustering the difference between matrices captures coordinated group differences in pairwise coupling between measures of psychopathology: XXY/KS shows greater coherence among externalizing, internalizing, and autism-related features, while XYY syndrome shows greater coherence in dissociality and early neurodevelopmental impairment.

These methods offer new insights into X- and Y-chromosome dosage effects on behavior, and our shared code can now be applied to other clinical groups of interest – helping to hone mechanistic models and inform the tailoring of care.

A key step toward understanding psychiatric disorders that disproportionately impact female mental health is delineating the emergence of sex-specific patterns of brain organisation at the critical transition from childhood to adolescence. Prior work suggests that individual differences in the spatial organisation of functional brain networks across the cortex are associated with psychopathology and differ systematically by sex.

We aimed to evaluate the impact of sex on the spatial organisation of person-specific functional brain networks.

We leveraged person-specific atlases of functional brain networks, defined using non-negative matrix factorisation, in a sample of n = 6437 youths from the Adolescent Brain Cognitive Development Study. Across independent discovery and replication samples, we used generalised additive models to uncover associations between sex and the spatial layout (topography) of personalised functional networks (PFNs). We also trained support vector machines to classify participants’ sex from multivariate patterns of PFN topography.

Sex differences in PFN topography were greatest in association networks including the frontoparietal, ventral attention and default mode networks. Machine learning models trained on participants’ PFNs were able to classify participant sex with high accuracy.

Sex differences in PFN topography are robust, and replicate across large-scale samples of youth. These results suggest a potential contributor to the female-biased risk in depressive and anxiety disorders that emerge at the transition from childhood to adolescence.