Clinical progression during psychosis has been closely associated with grey matter abnormalities resulting from atypical brain development. However, the complex interplay between psychopathology and heterogeneous maturational trajectories challenges the identification of neuroanatomical features that anticipate symptomatic decline.

To investigate cortical volume longitudinal deviations in first-episode psychosis (FEP) using normative modelling, exploring their relationship with long-term cognitive and symptomatic outcomes, as well as their cytoarchitectural and neurobiological underpinnings.

We collected magnetic resonance imaging (MRI), cognitive and symptomatic data from 195 healthy controls and 357 drug-naïve or minimally medicated FEP individuals that were followed up 1, 3, 5 and 10 years following the first episode (1209 MRI scans and assessments in total). Using normative modelling, we derived subject-specific centile scores for cortical volume to investigate atypical deviations in FEP and their relationship to long-term cognitive and symptomatic deterioration. The resulting centile association maps were further characterised by examining their cytoarchitectural and neurobiological attributes using normative atlases.

FEP centiles demonstrated a widespread reduction at treatment initiation, with longitudinal analysis showing an increase during treatment time, indicating convergence towards normal maturation trajectories. Interestingly, this effect was reduced in highly medicated individuals. Additionally, we found that cognitive impairments experienced during early FEP stages worsened under long-term medication. Positive symptomatology was negatively associated with regional centiles, and individuals with higher centiles benefited most from treatment. Cytoarchitectural and neurobiological analyses revealed that regional centiles related to FEP, as well as to symptomatology, were associated with specific molecular features, such as regional serotonin and dopamine receptor densities.

Collectively, these findings underscore the potential use of centile-based normative modelling for a better understanding of how atypical cortical development contributes to the long-term clinical progression of neurodevelopmental conditions.

The macro-social and environmental conditions in which people live, such as the level of a country’s development or inequality, are associated with brain-related disorders. However, the relationship between these systemic environmental factors and the brain remains unclear. We aimed to determine the association between the level of development and inequality of a country and the brain structure of healthy adults.

We conducted a cross-sectional study pooling brain imaging (T1-based) data from 145 magnetic resonance imaging (MRI) studies in 7,962 healthy adults (4,110 women) in 29 different countries. We used a meta-regression approach to relate the brain structure to the country’s level of development and inequality.

Higher human development was consistently associated with larger hippocampi and more expanded global cortical surface area, particularly in frontal areas. Increased inequality was most consistently associated with smaller hippocampal volume and thinner cortical thickness across the brain.

Our results suggest that the macro-economic conditions of a country are reflected in its inhabitants’ brains and may explain the different incidence of brain disorders across the world. The observed variability of brain structure in health across countries should be considered when developing tools in the field of personalized or precision medicine that are intended to be used across the world.

Predicting long-term outcome trajectories in psychosis remains a crucial and challenging goal in clinical practice. The identification of reliable neuroimaging markers has often been hindered by the clinical and biological heterogeneity of psychotic disorders and the limitations of traditional case-control methodologies, which often mask individual variability. Recently, normative brain charts derived from extensive magnetic resonance imaging (MRI) data-sets covering the human lifespan have emerged as a promising biologically driven solution, offering a more individualised approach.

To examine how deviations from normative cortical and subcortical grey matter volume (GMV) at first-episode psychosis (FEP) onset relate to symptom and functional trajectories.

We leveraged the largest available brain normative model (N > 100 000) to explore normative deviations in a sample of over 240 patients with schizophrenia spectrum disorders who underwent MRI scans at the onset of FEP and received clinical follow-up at 1, 3 and 10 years.

Our findings reveal that deviations in regional normative GMV at FEP onset are significantly linked to overall long-term clinical trajectories, modulating the effect of time on both symptom and functional outcome. Specifically, negative deviations in the left superior temporal gyrus and Broca’s area at FEP onset were notably associated with a more severe progression of positive and negative symptoms, as well as with functioning trajectories over time.

These results underscore the potential of brain developmental normative approaches for the early prediction of disorder progression, and provide valuable insights for the development of preventive and personalised therapeutic strategies.

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.