Take-down policy If you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim. Heritability and polygenic predictionIn the EUR sample, the SNP-based heritability (h 2 SNP ) (that is, the proportion of variance in liability attributable to all measured SNPs)

12The cerebral cortex underlies our complex cognitive capabilities, yet we know little about the specific genetic loci influencing human cortical structure. To identify genetic variants, including structural variants, impacting cortical structure, we conducted a genome-wide association meta-analysis of brain MRI data from 51,662 individuals. We analysed the surface area and average thickness of the whole cortex and 34 regions with known functional specialisations. We identified 255 nominally significant loci (P ≤ 5 x 10 -8 ); 199 survived multiple testing correction (P ≤ 8.3 x 10 -10 ; 187 surface area; 12 thickness). We found significant enrichment for loci influencing total surface area within regulatory elements active during prenatal cortical development, supporting the radial unit hypothesis. Loci impacting regional surface area cluster near genes in Wnt signalling pathways, known to influence progenitor expansion and areal identity. Variation in cortical structure is genetically correlated with cognitive function, Parkinson's disease, insomnia, depression and ADHD.One Sentence Summary: Common genetic variation is associated with inter-individual variation in the structure of the human cortex, both globally and within specific regions, and is shared with genetic risk factors for some neuropsychiatric disorders.The human cerebral cortex is the outer grey matter layer of the brain, which is implicated in multiple aspects of higher cognitive function. Its distinct folding pattern is characterised by convex (gyral) and concave (sulcal) regions. Computational brain mapping approaches use the consistent folding patterns across individual cortices to label brain regions(1). During fetal development excitatory neurons, the predominant neuronal cell-type in the cortex, are generated from neural progenitor cells in the developing germinal zone(2). The radial unit hypothesis(3) posits that the expansion of cortical surface area (SA) is driven by the proliferation of these neural progenitor cells, whereas thickness (TH) is determined by the number of neurogenic divisions. Variation in global and regional measures of cortical SA and TH are associated with neuropsychiatric disorders and psychological traits(4) ( Table S1). Twin and family-based brain imaging studies show that SA and TH measurements are highly heritable and are largely influenced by independent genetic factors(5). Despite extensive studies of genes impacting cortical structure in model organisms (6), our current understanding of genetic variation impacting human cortical size and patterning is limited to rare, highly penetrant variants (7,8). These variants often disrupt cortical development, leading to altered post-natal structure. However, little is known about how common genetic variants impact human cortical SA and TH.To address this, we conducted genome-wide association meta-analyses of cortical SA and TH measures in 51,662 individuals from 60 cohorts from around the world (Tables S2-S4). Cortical measures were extracted from structural brain MRI scan...

Delineating the association of age and cortical thickness in healthy individuals is critical given the association of cortical thickness with cognition and behavior. Previous research has shown that robust estimates of the association between age and brain morphometry require large-scale studies. In response, we used cross-sectional data

Background. Our understanding of the complex relationship between schizophrenia symptomatology and etiological factors can be improved by studying brain-based correlates of schizophrenia. Research showed that impairments in value processing and executive functioning, which have been associated with prefrontal brain areas [particularly the medial orbitofrontal cortex (MOFC)], are linked to negative symptoms. Here we tested the hypothesis that MOFC thickness is associated with negative symptom severity. Results. Meta-analytical results showed that left, but not right, MOFC thickness was significantly associated with negative symptom severity (β std = −0.075; p = 0.019) after accounting for age, gender, and site. This effect remained significant (p = 0.036) in a model including overall illness severity. Covarying for duration of illness, age of onset, antipsychotic medication or handedness weakened the association of negative symptoms with left MOFC thickness. As part of a secondary analysis including 10 other prefrontal regions further associations in the left lateral orbitofrontal gyrus and pars opercularis emerged.Conclusions. Using an unusually large cohort and a meta-analytical approach, our findings point towards a link between prefrontal thinning and negative symptom severity in schizophrenia. This finding provides further insight into the relationship between structural brain abnormalities and negative symptoms in schizophrenia.

No abstract

Age has a major effect on brain volume. However, the normative studies available are constrained by small sample sizes, restricted age coverage and significant methodological variability. These limitations introduce inconsistencies and may obscure or distort the lifespan trajectories of brain morphometry. In response, we capitalized on the resources of the Enhancing Neuroimaging Genetics through Meta‐Analysis (ENIGMA) Consortium to examine age‐related trajectories inferred from cross‐sectional measures of the ventricles, the basal ganglia (caudate, putamen, pallidum, and nucleus accumbens), the thalamus, hippocampus and amygdala using magnetic resonance imaging data obtained from 18,605 individuals aged 3–90 years. All subcortical structure volumes were at their maximum value early in life. The volume of the basal ganglia showed a monotonic negative association with age thereafter; there was no significant association between age and the volumes of the thalamus, amygdala and the hippocampus (with some degree of decline in thalamus) until the sixth decade of life after which they also showed a steep negative association with age. The lateral ventricles showed continuous enlargement throughout the lifespan. Age was positively associated with inter‐individual variability in the hippocampus and amygdala and the lateral ventricles. These results were robust to potential confounders and could be used to examine the functional significance of deviations from typical age‐related morphometric patterns.

Objective Based on the role of the superior temporal gyrus (STG) in auditory processing, language comprehension and self-monitoring, this study aimed to investigate the relationship between STG cortical thickness and positive symptom severity in schizophrenia. Method This prospective meta-analysis includes data from 1,987 individuals with schizophrenia collected at seventeen centers around the world that contribute to the ENIGMA Schizophrenia Working Group. STG thickness measures were extracted from T1-weighted brain scans using FreeSurfer. The study performed a meta-analysis of effect sizes across sites generated by a model predicting left or right STG thickness with a positive symptom severity score (harmonized SAPS or PANSS positive scores), while controlling for age, sex, and site. Secondary models investigated relationships between antipsychotic medication, duration of illness, overall illness severity, handedness and STG thickness. Results Positive symptom severity was negatively related to STG thickness in both hemispheres (left: βstd=−0.052; p=0.021; right: βstd=−0.073; p=0.001) when statistically controlling for age, sex and site. This effect remained stable in models including duration of illness, antipsychotic medication or handedness. Conclusion Our findings further underline the important role of the STG in hallmark symptoms in schizophrenia. These findings can assist in advancing insight into symptom-relevant pathophysiological mechanisms in schizophrenia.

Under the influence of genes and a varying environment, human brain structure changes throughout the lifespan. Even in adulthood, when the brain seems relatively stable, individuals differ in the profile and rate of brain changes 1 . Longitudinal studies are crucial to identify genetic and environmental factors that influence the rate of these brain changes throughout development 2 and aging 3 . Inter-individual differences in brain development are associated with general cognitive function 4,5 and risk for psychiatric disorders 6,7 and neurological diseases 8,9 . Genetic factors involved in brain development and aging overlap with those for cognition 10 and risk for neuropsychiatric disorders 11 . A recent cross-sectional study showed brain age to be advanced in several brain disorders. Brain age is an estimate of biological age based on brain structure, which can deviate from chronological age. Several shared loci were found between the genome-wide association study (GWAS) summary statistics for advanced brain age and psychiatric disorders 12 . However, information is still lacking on which genetic variants influence an individual's brain changes throughout life, because this requires longitudinal data. Discovering genetic factors that explain variation between individuals in brain structural changes may reveal key biological pathways that drive normal development and aging and may contribute to identifying disease risk and resilience-a crucial goal given the urgent need for new treatments for aberrant brain development and aging worldwide.As part of the Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA) consortium 13 , the ENIGMA Plasticity Working Group quantified the overall genetic contribution to longitudinal brain changes by combining evidence from multiple twin cohorts across the world 14 . Most global and subcortical brain measures showed genetic influences on change over time, with a higher genetic contribution in the elderly (heritability, 16-42%). Genetic factors that influence longitudinal changes were partially independent of those that influence baseline volumes of brain structures, suggesting that there might be genetic variants that specifically affect the rate of development or aging. However, the genes involved in these processes are still not known, with only a single, small-scale GWAS performed for longitudinal volume change in gray and white matter of the cerebrum, basal ganglia and cerebellum 15 . In this study, we set out to find genetic variants that may influence rates of brain changes over time, using genome-wide analysis in individuals scanned with magnetic resonance imaging (MRI) on more than one occasion. We also aimed to identify references