Over the past 25 years, neuroimaging has become a ubiquitous tool in basic research and clinical studies of the human brain. However, there are no reference standards against which to anchor measures of individual differences in brain morphology, in contrast to growth charts for traits such as height and weight. Here, we built an interactive online resource (www.brainchart.io) to quantify individual differences in brain structure from any current or future magnetic resonance imaging (MRI) study, against models of expected age-related trends. With the goal of basing these on the largest and most inclusive dataset, we aggregated MRI data spanning 115 days post-conception through 100 postnatal years, totaling 122,123 scans from 100,071 individuals in over 100 studies across 6 continents. When quantified as centile scores relative to the reference models, individual differences show high validity with non-MRI brain growth estimates and high stability across longitudinal assessment. Centile scores helped identify previously unreported brain developmental milestones and demonstrated increased genetic heritability compared to non-centiled MRI phenotypes. Crucially for the study of brain disorders, centile scores provide a standardised and interpretable measure of deviation that reveals new patterns of neuroanatomical differences across neurological and psychiatric disorders emerging during development and ageing. In sum, brain charts for the human lifespan are an essential first step towards robust, standardised quantification of individual variation and for characterizing deviation from age-related trends. Our global collaborative study provides such an anchorpoint for basic neuroimaging research and will facilitate implementation of research-based standards in clinical studies.

Background Recent discovery of hundreds of common gene variants associated with schizophrenia has enabled polygenic risk scores (PRS) to be measured in the population. It is hypothesized that normal variation in genetic risk of schizophrenia should be associated with MRI changes in brain morphometry and tissue composition. Methods We used the largest extant genome-wide association dataset (N = 69,369 cases and N = 236,642 healthy controls) to measure PRS for schizophrenia in a large sample of adults from the UK Biobank (Nmax = 29,878) who had multiple micro- and macro-structural MRI metrics measured at each of 180 cortical areas and seven subcortical structures. Linear mixed effect models were used to investigate associations between schizophrenia PRS and brain structure at global and regional scales, controlled for multiple comparisons. Results Micro-structural phenotypes were more robustly associated with schizophrenia PRS than macro-structural phenotypes. Polygenic risk was significantly associated with reduced neurite density index (NDI) at global brain scale, at 149 cortical regions, and five subcortical structures. Other micro-structural parameters, e.g., fractional anisotropy, that were correlated with NDI were also significantly associated with schizophrenia PRS. Genetic effects on multiple MRI phenotypes were co-located in temporal, cingulate and prefrontal cortical areas, insula, and hippocampus. Conclusions We show widespread cortical and subcortical grey matter micro-structure associations with schizophrenia PRS. Across all investigated phenotypes NDI, a measure of the density of myelinated axons and dendrites, showed the most robust associations with schizophrenia PRS. We interpret these results as indicative of reduced density of myelinated axons and dendritic arborization in large-scale cortico-subcortical networks mediating the genetic risk for schizophrenia.

Question: How do genes for educational attainment interact with risk genes for autism and schizophrenia? Findings: We show that genes for educational attainment (edu genes) are significantly likely to be mutated in autism and intellectual disability. We further show that edu genes also interact with co-expression modules that are associated with autism or schizophrenia and are enriched for differentially expressed genes in autism or schizophrenia. Finally, we identify that the enrichment between risk genes for autism and schizophrenia and human accelerated regions are driven, in part, by their overlap with edu genes.Meaning: Edu genes interact with schizophrenia and autism risk genes in specific pathways, contributing to both cognitive deficits and talents. AbstractImportance: The genetic relationship between cognition, autism, and schizophrenia is complex. It is unclear how genes that contribute to cognition also contribute to risk for autism and schizophrenia.Objective: To investigate the interaction between genes related to cognition (measured via proxy through educational attainment, which we call 'edu genes') and genes/biological pathways that are atypical in autism and schizophrenia.Design: Genetic correlation and enrichment analysis were conducted to identify the interaction between edu genes and risk genes and biological pathways for autism or schizophrenia. Results:First, edu genes are enriched in a specific developmental co-expression module that is also enriched for high confidence autism risk genes. Second, modules enriched for genes that are dysregulated in autism and schizophrenia are also enriched for edu genes. Finally, genes that overlap between the two above modules and educational attainment are significantly enriched for genes that flank human accelerated regions, suggesting increased positive selection for the overlapping gene sets.3 Conclusion: Our results identify distinct co-expression modules where risk genes for the two psychiatric conditions interact with edu genes. This suggests specific pathways that contribute to both cognitive deficits and cognitive talents, in individuals with schizophrenia or autism.

Previous research indicates a link between autism and transgender and gender-diverse identities, though the association is not yet fully understood. The current study examined autistic traits (Autism Spectrum Quotient [AQ]), empathizing (Empathizing Quotient-Short [EQ-S]), and systemizing (Systemizing Quotient-Short [SQ-S]) in a sample of 89 adults and aimed to test whether gender-diverse individuals exhibit cognitive profiles consistent with predictions derived from the Extreme Male Brain (EMB) theory. As most research has considered only cisgender people, we recruited a more diverse sample by contacting > 200 UK LGBTQ+ organizations and posting on social media. A range of non-cisgender identities (e.g., transgender male, transgender female, non-binary, genderqueer, transmasculine) and non-heterosexual orientations (e.g., bisexual) were represented, and participants were categorized into one of four groups: (1) assigned female at birth but does not identify as female (transgender AFAB) (n = 32), (2) cisgender female (n = 21), (3) assigned male at birth but does not identify as male (transgender AMAB) (n = 18), and (4) cisgender male (n = 18). After controlling for age and autism diagnostic status, transgender AFAB participants had marginally higher AQ scores, and significantly higher SQ-S and systemizing-relative-to-empathizing (D) scores, compared with the cisgender female group. No such differences were detected between the transgender AMAB and cisgender male groups. Our findings are broadly in line with predictions derived from the EMB theory, though as no transgender AFAB participants reported being heterosexual, it was not possible to determine whether these effects relate specifically to gender identity, to sexual orientation, or to both.

Previous research indicates a link between autism and gender variance, though the basis for this association is not fully understood. The current study examined autistic traits (as measured by the Autism Spectrum Quotient [AQ]) and empathizing and systemizing (as measured by the Empathizing Quotient-Short [EQ-S] and Systemizing Quotient-Short [SQ-S]) in a sample of n=89 UK adults representing a broad range of gender identities and sexual orientations. Compared with cisgender individuals (i.e. those who identify as the same gender as that assigned at birth), gender variant participants had significantly higher AQ and SQ-S scores, and stronger systemizing relative to empathizing (D-score). Further analysis revealed that there were significant differences between cisgender females and those assigned female at birth who do not identify as female (transgender AFAB), but not between cisgender males and those assigned male at birth who do not identify as male (transgender AMAB). These findings are broadly in line with the extreme male brain theory of autism, and may be relevant for developing effective support for gender variant and/or autistic individuals.