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Contains fulltext : 291574.pdf (Publisher’s version ) (Open Access) Left-right asymmetry is an important organizing feature of the healthy brain that may be altered in schizophrenia, but most studies have used relatively small samples and heterogeneous approaches, resulting in equivocal findings. We carried out the largest case-control study of structural brain asymmetries in schizophrenia, with MRI data from 5,080 affected individuals and 6,015 controls across 46 datasets, using a single image analysis protocol. Asymmetry indexes were calculated for global and regional cortical thickness, surface area, and subcortical volume measures. Differences of asymmetry were calculated between affected individuals and controls per dataset, and effect sizes were meta-analyzed across datasets. Small average case-control differences were observed for thickness asymmetries of the rostral anterior cingulate and the middle temporal gyrus, both driven by thinner left-hemispheric cortices in schizophrenia. Analyses of these asymmetries with respect to the use of antipsychotic medication and other clinical variables did not show any significant associations. Assessment of age- and sex-specific effects revealed a stronger average leftward asymmetry of pallidum volume between older cases and controls. Case-control differences in a multivariate context were assessed in a subset of the data (N = 2,029), which revealed that 7% of the variance across all structural asymmetries was explained by case-control status. Subtle case-control differences of brain macrostructural asymmetry may reflect differences at the molecular, cytoarchitectonic, or circuit levels that have functional relevance for the disorder. Reduced left middle temporal cortical thickness is consistent with altered left-hemisphere language network organization in schizophrenia.

Background: Normative modeling is a statistical approach to quantify the degree to which a particular individual-level measure deviates from the pattern observed in a normative reference population. When applied to human brain morphometric measures it has the potential to inform about the significance of normative deviations for health and disease. Normative models can be implemented using a variety of algorithms that have not been systematically appraised. Methods: To address this gap, eight algorithms were compared in terms of performance and computational efficiency using brain regional morphometric data from 37,407 healthy individuals (53% female; aged 3-90 years) collated from 87 international MRI datasets. Performance was assessed with the mean absolute error (MAE) and computational efficiency was inferred from central processing unit (CPU) time. The algorithms evaluated were Ordinary Least Squares Regression (OLSR), Bayesian Linear Regression (BLR), Generalized Additive Models for Location, Scale, and Shape (GAMLSS), Parametric Lambda, Mu, Sigma (LMS), Gaussian Process Regression (GPR), Warped Bayesian Linear Regression (WBLG), Hierarchical Bayesian Regression (HBR), and Multivariable Fractional Polynomial Regression (MFPR). Model optimization involved testing nine covariate combinations pertaining to acquisition features, parcellation software versions, and global neuroimaging measures (i.e., total intracranial volume, mean cortical thickness, and mean cortical surface area). Findings: Statistical comparisons across models at PFDR

Special Issue: The ENIGMA Consortium: the first 10 years., 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 from 17,075 individuals aged 3–90 years from the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) Consortium to infer age-related changes in cortical thickness. We used fractional polynomial (FP) regression to quantify the association between age and cortical thickness, and we computed normalized growth centiles using the parametric Lambda, Mu, and Sigma method. Interindividual variability was estimated using meta-analysis and one-way analysis of variance. For most regions, their highest cortical thickness value was observed in childhood. Age and cortical thickness showed a negative association; the slope was steeper up to the third decade of life and more gradual thereafter; notable exceptions to this general pattern were entorhinal, temporopolar, and anterior cingulate cortices. Interindividual variability was largest in temporal and frontal regions across the lifespan. Age and its FP combinations explained up to 59% variance in cortical thickness. These results may form the basis of further investigation on normative deviation in cortical thickness and its significance for behavioral and cognitive outcomes., European Community's Seventh Framework Programme, Grant/Award Numbers: 278948, 602450, 603016, 602805; US National Institute of Child Health and Human Development, Grant/Award Numbers: RO1HD050735, 1009064, 496682; QIMR Berghofer Medical Research Institute and the Centre for Advanced Imaging, University of Queensland; ICTSI NIH/NCRR, Grant/Award Number: RR025761; European Community's Horizon 2020 Programme, Grant/Award Numbers: 667302, 643051; Vici Innovation Program, Grant/Award Numbers: #91619115, 016-130-669; NWO Brain & Cognition Excellence Program, Grant/Award Number: 433-09-229; Biobanking and Biomolecular Resources Research Infrastructure (Netherlands) (BBMRI-NL); Spinozapremie, Grant/Award Number: NWO-56-464-14192; Biobanking and Biomolecular Resources Research Infrastructure, Grant/Award Numbers: 184.033.111, 184.021.007; Netherlands Organization for Health Research and Development (ZonMW), Grant/Award Numbers: 480-15-001/674, 024.001.003, 911-09-032, 056-32-010, 481-08-011, 016-115-035, 31160008, 400-07-080, 400-05-717, 451-04-034, 463-06-001, 480-04-004, 904-61-193, 912-10-020, 985-10-002, 904-61-090; NIMH, Grant/Award Number: R01 MH090553; Geestkracht programme of the Dutch Health Research Council, Grant/Award Number: 10-000-1001; FP7 Ideas: European Research Council; Nederlandse Organisatie voor Wetenschappelijk Onderzoek, Grant/Award Numbers: NWO/SPI 56-464-14192, NWO-MagW 480-04-004, 433-09-220, NWO 51.02.062, NWO 51.02.061; National Center for Advancing Translational Sciences, National Institutes of Health, Grant/Award Number: UL1 TR000153; National Center for Research Resources; National Center for Research Resources at the National Institutes of Health, Grant/Award Numbers: NIH 1U24 RR025736-01, NIH 1U24 RR021992; NIH Institutes contributing to the Big Data to Knowledge; U.S. National Institutes of Health, Grant/Award Numbers: R01 CA101318, P30 AG10133, R01 AG19771; Medical Research Council, Grant/Award Numbers: U54EB020403, G0500092; National Institute of Mental Health, Grant/Award Numbers: R01MH117014, R01MH042191; Fundación Instituto de Investigación Marqués de Valdecilla, Grant/Award Numbers: API07/011, NCT02534363, NCT0235832; Instituto de Salud Carlos III, Grant/Award Numbers: PI14/00918, PI14/00639, PI060507, PI050427, PI020499; Swedish Research Council, Grant/Award Numbers: 523-2014-3467, 2017-00949, 521-2014-3487; South-Eastern Norway Health Authority; the Research Council of Norway, Grant/Award Number: 223273; South Eastern Norway Regional Health Authority, Grant/Award Numbers: 2017-112, 2019107; Icahn School of Medicine at Mount Sinai; Seventh Framework Programme (FP7/2007-2013), Grant/Award Number: 602450; National Institutes of Health, Grant/Award Numbers: R01 MH116147, R01 MH113619, R01 MH104284; South London and Maudsley NHS Foundation Trust; the National Institute for Health Research (NIHR)

Contains fulltext : 248376.pdf (Publisher’s version ) (Open Access) For many traits, males show greater variability than females, with possible implications for understanding sex differences in health and disease. Here, the ENIGMA (Enhancing Neuro Imaging Genetics through Meta-Analysis) Consortium presents the largest-ever mega-analysis of sex differences in variability of brain structure, based on international data spanning nine decades of life. Subcortical volumes, cortical surface area and cortical thickness were assessed in MRI data of 16,683 healthy individuals 1-90 years old (47% females). We observed significant patterns of greater male than female between-subject variance for all subcortical volumetric measures, all cortical surface area measures, and 60% of cortical thickness measures. This pattern was stable across the lifespan for 50% of the subcortical structures, 70% of the regional area measures, and nearly all regions for thickness. Our findings that these sex differences are present in childhood implicate early life genetic or gene-environment interaction mechanisms. The findings highlight the importance of individual differences within the sexes, that may underpin sex-specific vulnerability to disorders.

First-degree relatives of patients diagnosed with schizophrenia (SZ-FDRs) show similar patterns of brain abnormalities and cognitive alterations to patients, albeit with smaller effect sizes. First-degree relatives of patients diagnosed with bipolar disorder (BD-FDRs) show divergent patterns; on average, intracranial volume is larger compared to controls, and findings on cognitive alterations in BD-FDRs are inconsistent. Here, we performed a meta-analysis of global and regional brain measures (cortical and subcortical), current IQ, and educational attainment in 5,795 individuals (1,103 SZ-FDRs, 867 BD-FDRs, 2,190 controls, 942 schizophrenia patients, 693 bipolar patients) from 36 schizophrenia and/or bipolar disorder family cohorts, with standardized methods. Compared to controls, SZ-FDRs showed a pattern of widespread thinner cortex, while BD-FDRs had widespread larger cortical surface area. IQ was lower in SZ-FDRs (d= -0.42,p= 3 x 10(-5)), with weak evidence of IQ reductions among BD-FDRs (d= -0.23,p= .045). Both relative groups had similar educational attainment compared to controls. When adjusting for IQ or educational attainment, the group-effects on brain measures changed, albeit modestly. Changes were in the expected direction, with less pronounced brain abnormalities in SZ-FDRs and more pronounced effects in BD-FDRs. To conclude, SZ-FDRs and BD-FDRs show a differential pattern of structural brain abnormalities. in contrast, both had lower IQ scores and similar school achievements compared to controls. Given that brain differences between SZ-FDRs and BD-FDRs remain after adjusting for IQ or educational attainment, we suggest that differential brain developmental processes underlying predisposition for schizophrenia or bipolar disorder are likely independent of general cognitive impairment., Australian National Health and Medical Research CouncilNational Health and Medical Research Council of Australia [1037196, 1063960, 1066177, 510135, 1176716]; Canadian Institutes of Health ResearchCanadian Institutes of Health Research (CIHR) [103703, 106469, 142255]; Departament de Salut de la Generalitat de CatalunyaGeneralitat de Catalunya [SLT002/16/00331]; Deutsche ForschungsgemeinschaftGerman Research Foundation (DFG) [1617]; Development Service Merit Review Award [I01CX000227]; Dokuz Eylul University Department of Scientific Research [2012.KB.SAG.062]; e:Med program [O1ZX1314B, O1ZX1314G]; Ege University School of Medicine Research Foundation [2009-D-00017]; Fundacio Marato TV3 [091630]; Netherlands Organisation for Health Research and DevelopmentNetherlands Organization for Health Research and Development [10-000-1002]; Generalitat de CatalunyaGeneralitat de Catalunya [2017SGR01271]; German Federal Ministry for Education and ResearchFederal Ministry of Education & Research (BMBF); Medical Research CouncilMedical Research Council UK (MRC) [G0901310]; Ministerstvo Zdravotnictvi Ceske Republiky [NR8786, NT13891]; National Alliance for Research on Schizophrenia and DepressionNARSAD [17319, 20244, 26731]; Swiss National Centre of Competence in Research Robotics [51NF40-185897]; National Institute of Mental HealthUnited States Department of Health & Human ServicesNational Institutes of Health (NIH) - USANIH National Institute of Mental Health (NIMH) [1S10OD017974-01, P30 NS076408, R01 MH052857, R01 MH080912, R01 MH113619, U01 MH108150, R01 MH085667]; National Institute on AgingUnited States Department of Health & Human ServicesNational Institutes of Health (NIH) - USANIH National Institute on Aging (NIA) [T32AG058507]; National Institutes of HealthUnited States Department of Health & Human ServicesNational Institutes of Health (NIH) - USA [P41 EB015922, R01 MH111671, R01 MH116147, R01 MH117601, R01MH121246, R03 MH105808, U54EB020403]; Research Council of NorwayResearch Council of Norway [223273]; Spanish Ministry of Economy and Competitiveness/Instituto de Salud Carlos III [CPII19/00009, PI070066, PI1100683, PI1500467, PI18/00976]; Stanley Medical Research Institute; Swedish Research CouncilSwedish Research Council [K2007-62X-15077-04-1, K2008-62P-20597-01-3, K2010-62X-15078-07-2, K2012-61X-15078-09-3]; Swiss National Science FoundationSwiss National Science Foundation (SNSF) [32003B_156914]; VIDINetherlands Organization for Scientific Research (NWO) [452-11-014, 917-46-370]; Wellcome TrustWellcome Trust [085475/B/08/Z, 085475/Z/08/Z, 064971]; ZonMwNetherlands Organization for Health Research and Development [908-02-123], Australian National Health and Medical Research Council Grants, Grant/Award Numbers: 1037196, 1063960, 1066177, 510135, 1176716; Canadian Institutes of Health Research, Grant/Award Numbers: 103703, 106469, 142255; Departament de Salut de la Generalitat de Catalunya, Grant/Award Number: SLT002/16/00331; Deutsche Forschungsgemeinschaft, Grant/Award Number: 1617; Development Service Merit Review Award, Grant/Award Number: I01CX000227; Dokuz Eylul University Department of Scientific Research Projects Funding, Grant/Award Number: 2012.KB.SAG.062; e:Med program, Grant/Award Numbers: O1ZX1314B, O1ZX1314G; Ege University School of Medicine Research Foundation, Grant/Award Number: 2009-D-00017; Fundacio Marato TV3, Grant/Award Number: 091630; Geestkracht program of the Netherlands Organisation for Health Research and Development, Grant/Award Number: 10-000-1002; Generalitat de Catalunya, Grant/Award Number: 2017SGR01271; German Federal Ministry for Education and Research; Medical Research Council, Grant/Award Number: G0901310; Ministerstvo Zdravotnictvi Ceske Republiky, Grant/Award Numbers: NR8786, NT13891; National Alliance for Research on Schizophrenia and Depression, Grant/Award Numbers: 17319, 20244, 26731; Swiss National Centre of Competence in Research Robotics, Grant/Award Number: 51NF40-185897; National Institute of Mental Health, Grant/Award Numbers: 1S10OD017974-01, P30 NS076408, R01 MH052857, R01 MH080912, R01 MH113619, U01 MH108150, R01 MH085667; National Institute on Aging, Grant/Award Number: T32AG058507; National Institutes of Health, Grant/Award Numbers: P41 EB015922, R01 MH111671, R01 MH116147, R01 MH117601, R01MH121246, R03 MH105808, U54EB020403; Research Council of Norway, Grant/Award Number: 223273; Spanish Ministry of Economy and Competitiveness/Instituto de Salud Carlos III, Grant/Award Numbers: CPII19/00009, PI070066, PI1100683, PI1500467, PI18/00976; Stanley Medical Research Institute; Swedish Research Council, Grant/Award Numbers: K2007-62X-15077-04-1, K2008-62P-20597-01-3, K2010-62X-15078-07-2, K2012-61X-15078-09-3; Swiss National Science Foundation, Grant/Award Number: 32003B_156914; VIDI, Grant/Award Numbers: 452-11-014, 917-46-370; Wellcome Trust, Grant/Award Numbers: 085475/B/08/Z, 085475/Z/08/Z; Wellcome Trust Research Training Fellowship, Grant/Award Number: 064971; ZonMw, Grant/Award Number: 908-02-123

The human brain undergoes major developmental changes during pregnancy. Three-dimensional (3D) ultrasound images allow for the opportunity to investigate typical prenatal brain development on a large scale. Here, we developed a convolutional neural network (CNN) model for automated segmentation of fetal intracranial volume (ICV) from 3D ultrasound, and we applied the trained model in a large independent sample (N = 9795 ultrasounds; N=1763 participants) from the YOUth Baby and Child cohort measured at 20- and 30-week of gestational age to investigate sex differences in fetal ICV and ICV growth. 168 3D ultrasound images with ICV annotations were included to develop a 3D CNN model for automated fetal ICV segmentation. A data augmentation strategy provided physical variation and additional data to train the network. K-fold cross-validation and Bayesian optimization were used for network selection and the ensemble-based system combined multiple networks to form the final ensemble network. The final ensemble network produced consistent and high-quality segmentations of ICV. Our trained model successfully predicted ICV and ICV growth in a large independent longitudinal data set. Boys had significantly larger ICV at 20- (B=2.83;p=1.4e-13) and 30-weeks of pregnancy (B=12.01;p=2.0e-28), and more pronounced ICV growth than girls (t=-4.395;p=1.3e-5). Our approach provides us with the opportunity to investigate fetal brain development on a much larger scale and to answer fundamental questions related to prenatal brain development.

Surface rendering of MRI brain scans may lead to identification of the participant through facial characteristics. In this study, we evaluate three methods that overwrite voxels containing privacy‐sensitive information: Face Masking, FreeSurfer defacing, and FSL defacing. We included structural T1‐weighted MRI scans of children, young adults and older adults. For the young adults, test–retest data were included with a 1‐week interval. The effects of the de‐identification methods were quantified using different statistics to capture random variation and systematic noise in measures obtained through the FreeSurfer processing pipeline. Face Masking and FSL defacing impacted brain voxels in some scans especially in younger participants. FreeSurfer defacing left brain tissue intact in all cases. FSL defacing and FreeSurfer defacing preserved identifiable characteristics around the eyes or mouth in some scans. For all de‐identification methods regional brain measures of subcortical volume, cortical volume, cortical surface area, and cortical thickness were on average highly replicable when derived from original versus de‐identified scans with average regional correlations >.90 for children, young adults, and older adults. Small systematic biases were found that incidentally resulted in significantly different brain measures after de‐identification, depending on the studied subsample, de‐identification method, and brain metric. In young adults, test–retest intraclass correlation coefficients (ICCs) were comparable for original scans and de‐identified scans with average regional ICCs >.90 for (sub)cortical volume and cortical surface area and ICCs >.80 for cortical thickness. We conclude that apparent visual differences between de‐identification methods minimally impact reliability of brain measures, although small systematic biases can occur., De‐identification methods remove or blur facial characteristics on anatomical MRI scans, but also have a small effect on FreeSurfer brain measures. Furthermore, some methods are too invasive in younger participants or do not always succeed to remove all facial characteristics.

Schizophrenia patients show signs of accelerated aging in cognitive and physiological domains. Both schizophrenia and accelerated aging, as measured by MRI brain images and epigenetic clocks, are correlated with increased mortality. However, the association between these aging measures have not yet been studied in schizophrenia patients. In schizophrenia patients and healthy subjects, accelerated aging was assessed in brain tissue using a longitudinal MRI (N = 715 scans; mean scan interval 3.4 year) and in blood using two epigenetic age clocks (N = 172). Differences (‘gaps’) between estimated ages and chronological ages were calculated, as well as the acceleration rate of brain aging. The correlations between these aging measures as well as with polygenic risk scores for schizophrenia (PRS; N = 394) were investigated. Brain aging and epigenetic aging were not significantly correlated. Polygenic risk for schizophrenia was significantly correlated with brain age gap, brain age acceleration rate, and negatively correlated with DNAmAge gap, but not with PhenoAge gap. However, after controlling for disease status and multiple comparisons correction, these effects were no longer significant. Our results imply that the (accelerated) aging observed in the brain and blood reflect distinct biological processes. Our findings will require replication in a larger cohort.

Scaling between subcomponents of folding and total brain volume (TBV) in healthy individuals (HIs) is allometric. It is unclear whether this is true in schizophrenia (SZ) or first-episode psychosis (FEP). This study confirmed normative allometric scaling norms in HIs using discovery and replication samples. Cross-sectional and longitudinal diagnostic differences in folding subcomponents were then assessed using an allometric framework. Structural imaging from a longitudinal (Sample 1: HI and SZ, nHI Baseline= 298, nSZ Baseline= 169, nHI Follow-up= 293, nSZ Follow-up= 168, totaling 1087 images, all individuals ≥ 2 images, age 16-69 years) and a cross-sectional sample (Sample 2: nHI= 61 and nFEP= 89, age 10-30 years), all human males and females, is leveraged to calculate global folding and its nested subcomponents: sulcation index (SI, total sulcal/cortical hull area) and determinants of sulcal area: sulcal length and sulcal depth. Scaling of SI, sulcal area, and sulcal length with TBV in SZ and FEP was allometric and did not differ from HIs. Longitudinal age trajectories demonstrated steeper loss of SI and sulcal area through adulthood in SZ. Longitudinal allometric analysis revealed that both annual change in SI and sulcal area was significantly stronger related to change in TBV in SZ compared with HIs. Our results detail the first evidence of the disproportionate contribution of changes in SI and sulcal area to TBV changes in SZ. Longitudinal allometric analysis of sulcal morphology provides deeper insight into lifespan trajectories of cortical folding in SZ.SIGNIFICANCE STATEMENTPsychotic disorders are associated with deficits in cortical folding and brain size, but we lack knowledge of how these two morphometric features are related. We leverage cross-sectional and longitudinal samples in which we decompose folding into a set of nested subcomponents: sulcal and hull area, and sulcal depth and length. We reveal that, in both schizophrenia and first-episode psychosis, (1) scaling of subcomponents with brain size is different from expected scaling laws and (2) caution is warranted when interpreting results from traditional methods for brain size correction. Longitudinal allometric scaling points to loss of sulcal area as a principal contributor to loss of brain size in schizophrenia. These findings advance the understanding of cortical folding atypicalities in psychotic disorders.

Schizophrenia is a biologically complex disorder with multiple regional deficits in cortical brain morphology. In addition, interindividual heterogeneity of cortical morphological metrics is larger in patients with schizophrenia when compared to healthy controls. Exploiting interindividual differences in the severity of cortical morphological deficits in patients instead of focusing on group averages may aid in detecting biologically informed homogeneous subgroups. The person-based similarity index (PBSI) of brain morphology indexes an individual’s morphometric similarity across numerous cortical regions amongst a sample of healthy subjects. We extended the PBSI such that it indexes the morphometric similarity of an independent individual (eg, a patient) with respect to healthy control subjects. By employing a normative modeling approach on longitudinal data, we determined an individual’s degree of morphometric dissimilarity to the norm. We calculated the PBSI for sulcal width (PBSI-SW) in patients with schizophrenia and healthy control subjects (164 patients and 164 healthy controls; 656 magnetic resonance imaging scans) and associated it with cognitive performance and cortical sulcation index. A subgroup of patients with markedly deviant PBSI-SW showed extreme deficits in cognitive performance and cortical sulcation. Progressive reduction of PBSI-SW in the schizophrenia group relative to healthy controls was driven by these deviating individuals. By explicitly leveraging interindividual differences in the severity of PBSI-SW deficits, neuroimaging-driven subgrouping of patients is feasible. As such, our results pave the way for future applications of morphometric similarity indices for subtyping of clinical populations.

Mechanisms underpinning age-related variations in cortical thickness in the human brain remain poorly understood. We investigated whether inter-regional age-related variations in cortical thinning (in a multicohort neuroimaging dataset from the ENIGMA Lifespan Working Group totalling 14,248 individuals, aged 4-89 years) depended on cell-specific marker gene expression levels. We found differences amidst early-life (60 years) in the patterns of association between inter-regional profiles of cortical thickness and expression profiles of marker genes for CA1 and S1 pyramidal cells, astrocytes, and microglia. Gene ontology and enrichment analyses indicated that each of the three life-stages was associated with different biological processes and cellular components: synaptic modeling in early life, neurotransmission in mid-life, and neurodegeneration in late-life. These findings provide mechanistic insights into age-related cortical thinning during typical development and aging.

Externalizing behavior in its more extreme form is often considered a problem to the individual, their families, teachers and society as a whole. Several brain structures have been linked to externalizing behavior and such associations may arise if the (co)development of externalizing behavior and brain structures share the same genetic and/or environmental factor(s). We assessed externalizing behavior with the Child Behavior Checklist and Youth Self Report, and brain volumes and white matter integrity (FA and MD) with magnetic resonance imaging in the BrainSCALE cohort, consisting of twins and their older siblings from 112 families measured longitudinally at ages 10, 13, and 18 years of the twins. Genes influenced externalizing behavior and changes therein (h2 up to 88%). More pronounced externalizing behavior was associated with higher FA (observed correlation rph up to +0.20) and lower MD (rph up to –0.20); with sizeable genetic correlations (FA ra up to +0.42; MD ra up to –0.33). Cortical gray matter (CGM; rph up to –0.20) and cerebral white matter (CWM; rph up to +0.20) volume were phenotypically but not genotypically associated with externalizing behavior. These results suggest a potential mediating role for global brain structures in the display of externalizing behavior during adolescence that are both partially explained by the influence from the same genetic factor.

Objective Sex differences in disease susceptibility might be explained by sexual dimorphism in hypothalamic-pituitary-adrenal axis activity, which has been postulated to emerge during puberty. However, studies conducted thus far lacked an assessment of Tanner pubertal stage. This study aimed to assess the contribution of pubertal development to sexual dimorphism in cortisol production and metabolism. Methods Participants (n = 218) were enrolled from a population-based Netherlands Twin Register. At the ages of 9, 12 and 17 years, Tanner pubertal stage was assessed and early morning urine samples were collected. Cortisol metabolites were measured with GC-MS/MS and ratios were calculated, representing cortisol metabolism enzyme activities, such as A-ring reductases, 11β-HSDs and CYP3A4. Cortisol production and metabolism parameters were compared between sexes for pre-pubertal (Tanner stage 1), early pubertal (Tanner stage 2–3) and late-pubertal (Tanner stage 4–5) stages. Results Cortisol metabolite excretion rate decreased with pubertal maturation in both sexes, but did not significantly differ between sexes at any pubertal stage, although in girls a considerable decrease was observed between early and late-pubertal stage (P < 0.001). A-ring reductase activity was similar between sexes at pre- and early pubertal stages and was lower in girls than in boys at late-pubertal stage. Activities of 11β-HSDs were similar between sexes at pre-pubertal stage and favored cortisone in girls at early and late-pubertal stages. Cytochrome P450 3A4 activity did not differ between sexes. Conclusions Prepubertally, sexes were similar in cortisol parameters. During puberty, as compared to boys, in girls the activities of A-ring reductases declined and the balance between 11β-HSDs progressively favored cortisone. In addition, girls showed a considerable decrease in cortisol metabolite excretion rate between early and late-pubertal stages. Our findings suggest that the sexual dimorphism in cortisol may either be explained by rising concentrations of sex steroids or by puberty-induced changes in body composition.

ENIGMA-CNV Working Group: van der Meer, Dennis; Sonderby, Ida E; Kaufmann, Tobias; Walters, G Bragi; Abdellaoui, Abdel; Ames, David; Amunts, Katrin; Andersson, Micael; Armstrong, Nicola J; Bernard, Manon; Blackburn, Nicholas B; Blangero, John; Boomsma, Dorret I; Brodaty, Henry; Brouwer, Rachel M; Bulow, Robin; Cahn, Wiepke; Calhoun, Vince D; Caspers, Svenja; Cavalleri, Gianpiero L; Ching, Christopher R K; Cichon, Sven; Ciufolini, Simone; Corvin, Aiden; Crespo-Facorro, Benedicto; Curran, Joanne E; Dalvie, Shareefa; Dazzan, Paola; de Geus, Eco J C; de Zubicaray, Greig I; de Zwarte, Sonja M C; Delanty, Norman; den Braber, Anouk; Desrivieres, Sylvane; Di Forti, Marta; Doherty, Joanne L; Donohoe, Gary; Ehrlich, Stefan; Eising, Else; Espeseth, Thomas; Fisher, Simon E; Fladby, Tormod; Frei, Oleksandr; Frouin, Vincent; Fukunaga, Masaki; Gareau, Thomas; Glahn, David C; Grabe, Hans J; Groenewold, Nynke A; Gustafsson, Omar; Haavik, Jan; Haberg, Asta K; Hashimoto, Ryota; Hehir-Kwa, Jayne Y; Hibar, Derrek P; Hillegers, Manon H J; Hoffmann, Per; Holleran, Laurena; Hottenga, Jouke-Jan; Hulshoff Pol, Hilleke E; Ikeda, Masashi; Jacquemont, Sebastien; Jahanshad, Neda; Jockwitz, Christiane; Johansson, Stefan; Jonsson, Erik G; Kikuchi, Masataka; Knowles, Emma E M; Kwok, John B; Le Hellard, Stephanie; Linden, David E J; Liu, Jingyu; Lundervold, Arvid; Lundervold, Astri J; Martin, Nicholas G; Mather, Karen A; Mathias, Samuel R; McMahon, Katie L; McRae, Allan F; Medland, Sarah E; Moberget, Torgeir; Moreau, Clara; Morris, Derek W; Muhleisen, Thomas W; Murray, Robin M; Nordvik, Jan E; Nyberg, Lars; Olde Loohuis, Loes M; Ophoff, Roel A; Owen, Michael J; Paus, Tomas; Pausova, Zdenka; Peralta, Juan M; Pike, Bruce; Prieto, Carlos; Quinlan, Erin Burke; Reinbold, Celine S; Reis Marques, Tiago; Rucker, James J H; Sachdev, Perminder S; Sando, Sigrid B; Schofield, Peter R; Schork, Andrew J; Schumann, Gunter; Shin, Jean; Shumskaya, Elena; Silva, Ana I; Sisodiya, Sanjay M; Steen, Vidar M; Stein, Dan J; Strike, Lachlan T; Tamnes, Christian K; Teumer, Alexander; Thalamuthu, Anbupalam; Tordesillas-Gutierrez, Diana; Uhlmann, Anne; Ulfarsson, Magnus O; van 't Ent, Dennis; van den Bree, Marianne B M; Vassos, Evangelos; Wen, Wei; Wittfeld, Katharina; Wright, Margaret J; Zayats, Tetyana; Dale, Anders M; Djurovic, Srdjan; Agartz, Ingrid; Westlye, Lars T; Stefansson, Hreinn; Stefansson, Kari; Thompson, Paul M; Andreassen, Ole A., [Importance] Recurrent microdeletions and duplications in the genomic region 15q11.2 between breakpoints 1 (BP1) and 2 (BP2) are associated with neurodevelopmental disorders. These structural variants are present in 0.5% to 1.0% of the population, making 15q11.2 BP1-BP2 the site of the most prevalent known pathogenic copy number variation (CNV). It is unknown to what extent this CNV influences brain structure and affects cognitive abilities., [Objective] To determine the association of the 15q11.2 BP1-BP2 deletion and duplication CNVs with cortical and subcortical brain morphology and cognitive task performance., [Design, Setting, and Participants] In this genetic association study, T1-weighted brain magnetic resonance imaging were combined with genetic data from the ENIGMA-CNV consortium and the UK Biobank, with a replication cohort from Iceland. In total, 203 deletion carriers, 45 247 noncarriers, and 306 duplication carriers were included. Data were collected from August 2015 to April 2019, and data were analyzed from September 2018 to September 2019., [Main Outcomes and Measures] The associations of the CNV with global and regional measures of surface area and cortical thickness as well as subcortical volumes were investigated, correcting for age, age2, sex, scanner, and intracranial volume. Additionally, measures of cognitive ability were analyzed in the full UK Biobank cohort., [Results] Of 45 756 included individuals, the mean (SD) age was 55.8 (18.3) years, and 23 754 (51.9%) were female. Compared with noncarriers, deletion carriers had a lower surface area (Cohen d = −0.41; SE, 0.08; P = 4.9 × 10−8), thicker cortex (Cohen d = 0.36; SE, 0.07; P = 1.3 × 10−7), and a smaller nucleus accumbens (Cohen d = −0.27; SE, 0.07; P = 7.3 × 10−5). There was also a significant negative dose response on cortical thickness (β = −0.24; SE, 0.05; P = 6.8 × 10−7). Regional cortical analyses showed a localization of the effects to the frontal, cingulate, and parietal lobes. Further, cognitive ability was lower for deletion carriers compared with noncarriers on 5 of 7 tasks., [Conclusions and Relevance] These findings, from the largest CNV neuroimaging study to date, provide evidence that 15q11.2 BP1-BP2 structural variation is associated with brain morphology and cognition, with deletion carriers being particularly affected. The pattern of results fits with known molecular functions of genes in the 15q11.2 BP1-BP2 region and suggests involvement of these genes in neuronal plasticity. These neurobiological effects likely contribute to the association of this CNV with neurodevelopmental disorders., This study is supported in part by grants U54 EB20403, R01MH116147, and R56AG058854 from the National Institutes of Health, grant 609020 from the European Union Seventh Framework Programme, and grants 223273 and 276082 from the Research Council Norway. Part of this work was performed using the Service for Sensitive Data (TSD), which is developed and operated by the TSD Service Groupand owned by the University of Oslo.

Life-course experiences have been postulated to program hypothalamus–pituitary–adrenal (HPA) axis activity, suggesting that HPA axis activity is, at least partially, stable over time. Yet, there is paucity of data on the long-term stability of cortisol production and metabolism. We performed a prospective follow-up study in twins recruited from a nationwide register to estimate the stability of cortisol production and metabolism over time, and the contribution of genetic and environmental factors to this stability. In total, 218 healthy mono- and dizygotic twins were included. At the ages of 9, 12 and 17 years, morning urine samples were collected for assessment (by gas chromatography-tandem mass spectrometry) of cortisol metabolites, enabling the calculation of cortisol metabolite excretion rate and cortisol metabolism activity. Our results showed a low stability for both cortisol metabolite excretion rate (with correlations

Context Inter-individual differences in cortisol production and metabolism emerge with age and may be explained by genetic factors. Objective To estimate the relative contributions of genetic and environmental factors to inter-individual differences in cortisol production and metabolism throughout adolescence. Design Prospective follow-up study of twins. Setting Nationwide register. Participants 218 mono- and dizygotic twins (N = 109 pairs) born between 1995 amd 1996, recruited from the Netherlands Twin Register. Cortisol metabolites were determined in 213, 169, and 160 urine samples at the ages of 9, 12, and 17, respectively. Main outcome measures The total contribution of genetic factors (broad-sense heritability) and shared and unshared environmental influences to inter-individual differences in cortisol production and activities of 5α-reductase, 5β-reductase, and 11β-hydroxysteroid dehydrogenases and cytochrome P450 3A4. Results For cortisol production rate at the ages of 9, 12, and 17, broad-sense heritability was estimated as 42%, 30%, and 0%, respectively, and the remainder of the variance was explained by unshared environmental factors. For cortisol metabolism indices, the following heritability was observed: for the A-ring reductases (5α-and 5β-reductases), broad-sense heritability increased with age (to >50%), while for the other indices (renal 11β-HSD2, global 11β-HSD, and CYP3A4), the contribution of genetic factors was highest (68%, 18%, and 67%, respectively) at age 12. Conclusions The contribution of genetic factors to inter-individual differences in cortisol production decreased between 12 and 17y, indicative of a predominant role of individual circumstances. For cortisol metabolism, distinct patterns of genetic and environmental influences were observed, with heritability that either increased with age or peaked at age 12y.

Sex differences in development and aging of human sulcal morphology have been understudied. We charted sex differences in trajectories and inter-individual variability of global sulcal depth, width, and length, pial surface area, exposed (hull) gyral surface area, unexposed sulcal surface area, cortical thickness, and cortex volume across the lifespan in a longitudinal sample (700 scans, 194 participants two scans, 104 three scans, age range: 16-70 years) of neurotypical males and females. After adjusting for brain volume, females had thicker cortex and steeper thickness decline until age 40 years; trajectories converged thereafter. Across sexes, sulcal shortening was faster before age 40, while sulcal shallowing and widening were faster thereafter. While hull area remained stable, sulcal surface area declined and was more strongly associated with sulcal shortening than with sulcal shallowing and widening. Males showed greater variability for cortex volume and thickness and lower variability for sulcal width. Across sexes, variability decreased with age for all measures except for cortical volume and thickness. Our findings highlight the association between loss of sulcal area, notably through sulcal shortening, with cortex volume loss. Studying sex differences in lifespan trajectories may improve knowledge of individual differences in brain development and the pathophysiology of neuropsychiatric conditions.

Self-regulation refers to the ability to monitor and modulate emotions, behavior, and cognition, which in turn allows us to achieve goals and adapt to ever changing circumstances. This trait develops from early infancy well into adulthood, and features both low-level executive functions such as reactive inhibition, as well as higher level executive functions such as proactive inhibition. Development of self-regulation is linked to brain maturation in adolescence and adulthood. However, how self-regulation in daily life relates to brain functioning in pre-adolescent children is not known. To this aim, we have analyzed data from 640 children aged 8–11, who performed a stop-signal anticipation task combined with functional magnetic resonance imaging, in addition to questionnaire data on self-regulation. We find that pre-adolescent boys and girls who display higher levels of self-regulation, are better able to employ proactive inhibitory control strategies, exhibit stronger frontal activation and more functional coupling between cortical and subcortical areas of the brain. Furthermore, we demonstrate that pre-adolescent children show significant activation in areas of the brain that were previously only associated with reactive and proactive inhibition in adults and adolescents. Thus, already in pre-adolescent children, frontal-striatal brain areas are active during self-regulatory behavior., Highlights • Children with higher levels of self-regulation employ more proactive inhibition. • During proactive inhibition, children aged 8–11 show activation in frontal-cortical areas. • Children higher in self-regulation exhibit more cortical-subcortical coupling. • Children aged 8–11 show similar brain activation as adults during inhibition.