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The problem of poor reproducibility of scientific findings has received much attention over recent years, in a variety of fields including psychology and neuroscience. The problem has been partly attributed to publication bias and unwanted practices such as p‐hacking. Low statistical power in individual studies is also understood to be an important factor. In a recent multisite collaborative study, we mapped brain anatomical left–right asymmetries for regional measures of surface area and cortical thickness, in 99 MRI datasets from around the world, for a total of over 17,000 participants. In the present study, we revisited these hemispheric effects from the perspective of reproducibility. Within each dataset, we considered that an effect had been reproduced when it matched the meta‐analytic effect from the 98 other datasets, in terms of effect direction and significance threshold. In this sense, the results within each dataset were viewed as coming from separate studies in an “ideal publishing environment,” that is, free from selective reporting and p hacking. We found an average reproducibility rate of 63.2% (SD = 22.9%, min = 22.2%, max = 97.0%). As expected, reproducibility was higher for larger effects and in larger datasets. Reproducibility was not obviously related to the age of participants, scanner field strength, FreeSurfer software version, cortical regional measurement reliability, or regional size. These findings constitute an empirical illustration of reproducibility in the absence of publication bias or p hacking, when assessing realistic biological effects in heterogeneous neuroscience data, and given typically‐used sample sizes., Region‐wise effect sizes and reproducibility rates of hemispheric asymmetry effects. In general, effects with higher effect sizes showed higher reproducibility, given the same conditions (e.g., sample size and data heterogeneity).

The hippocampus is a heterogeneous structure, comprising histologically distinguishable subfields. These subfields are differentially involved in memory consolidation, spatial navigation and pattern separation, complex functions often impaired in individuals with brain disorders characterized by reduced hippocampal volume, including Alzheimer’s disease (AD) and schizophrenia. Given the structural and functional heterogeneity of the hippocampal formation, we sought to characterize the subfields’ genetic architecture. T1-weighted brain scans (n = 21,297, 16 cohorts) were processed with the hippocampal subfields algorithm in FreeSurfer v6.0. We ran a genome-wide association analysis on each subfield, co-varying for whole hippocampal volume. We further calculated the single-nucleotide polymorphism (SNP)-based heritability of 12 subfields, as well as their genetic correlation with each other, with other structural brain features and with AD and schizophrenia. All outcome measures were corrected for age, sex and intracranial volume. We found 15 unique genome-wide significant loci across six subfields, of which eight had not been previously linked to the hippocampus. Top SNPs were mapped to genes associated with neuronal differentiation, locomotor behaviour, schizophrenia and AD. The volumes of all the subfields were estimated to be heritable (h2 from 0.14 to 0.27, all p –16) and clustered together based on their genetic correlations compared with other structural brain features. There was also evidence of genetic overlap of subicular subfield volumes with schizophrenia. We conclude that hippocampal subfields have partly distinct genetic determinants associated with specific biological processes and traits. Taking into account this specificity may increase our understanding of hippocampal neurobiology and associated pathologies.

Background: The aortic valve is an important determinant of cardiovascular physiology and anatomic location of common human diseases. Methods: From a sample of 34 287 white British ancestry participants, we estimated functional aortic valve area by planimetry from prospectively obtained cardiac magnetic resonance imaging sequences of the aortic valve. Aortic valve area measurements were submitted to genome-wide association testing, followed by polygenic risk scoring and phenome-wide screening, to identify genetic comorbidities. Results: A genome-wide association study of aortic valve area in these UK Biobank participants showed 3 significant associations, indexed by rs71190365 (chr13:50764607, DLEU1 , P =1.8×10 −9 ), rs35991305 (chr12:94191968, CRADD , P =3.4×10 −8 ), and chr17:45013271:C:T ( GOSR2 , P =5.6×10 −8 ). Replication on an independent set of 8145 unrelated European ancestry participants showed consistent effect sizes in all 3 loci, although rs35991305 did not meet nominal significance. We constructed a polygenic risk score for aortic valve area, which in a separate cohort of 311 728 individuals without imaging demonstrated that smaller aortic valve area is predictive of increased risk for aortic valve disease (odds ratio, 1.14; P =2.3×10 −6 ). After excluding subjects with a medical diagnosis of aortic valve stenosis (remaining n=308 683 individuals), phenome-wide association of >10 000 traits showed multiple links between the polygenic score for aortic valve disease and key health-related comorbidities involving the cardiovascular system and autoimmune disease. Genetic correlation analysis supports a shared genetic etiology with between aortic valve area and birth weight along with other cardiovascular conditions. Conclusions: These results illustrate the use of automated phenotyping of cardiac imaging data from the general population to investigate the genetic etiology of aortic valve disease, perform clinical prediction, and uncover new clinical and genetic correlates of cardiac anatomy.

The aortic valve is an important determinant of cardiovascular physiology and anatomic location of common human diseases. From a sample of 26,142 European-ancestry participants, we estimated functional aortic valve area by planimetry from prospectively obtained cardiac MRI sequences of the aortic valve. A genome-wide association study of aortic valve area in these UK Biobank participants showed two significant associations indexed by rs71190365 (chr13:50764607,DLEU1, p=1.8×10−9) and rs35991305 (chr12:94191968,CRADD, p=3.4×10−8). From the GWAS findings we constructed a polygenic risk score for aortic valve area, which in a separate cohort of 311,728 individuals without imaging demonstrated that smaller aortic valve area is predictive of increased risk for aortic valve disease (Odds Ratio 0.88,p=2.3×10−6). After excluding subjects with a medical diagnosis of aortic valve stenosis (remaining n=310,546 individuals), phenome-wide association of >10,000 traits showed multiple links between the polygenic score for aortic valve disease and key health-related comorbidities involving the cardiovascular system and autoimmune disease. Genetic correlation analysis supports a shared genetic etiology with between aortic valve size and birthweight along with other cardiovascular conditions. These results illustrate the use of automated phenotyping of cardiac imaging data from the general population to investigate the genetic etiology of aortic valve disease, perform clinical prediction, and uncover new clinical and genetic correlates of cardiac anatomy.

Among the major psychiatric disorders, anxious-depressive disorders stand out as one of the more prevalent and more frequently associated with hypothalamic-pituitary-adrenal (HPA) axis abnormalities. Methylation at the exon 1F of the glucocorticoid receptor gene NR3C1 has been associated with both early stress exposure and risk for developing a psychiatric disorder; however, other NR3C1 promoter regions have been underexplored. Exon 1D emerges as a suggestive new target in stress-related disorders epigenetically sensitive to early adversity. After assessment of 48 monozygotic twin pairs (n=96 subjects) informative for lifetime history of anxious-depressive disorders, they were classified as concordant, discordant or healthy in function of whether both, one or neither twin in each pair had a lifetime diagnosis of anxious-depressive disorders. DNA for epigenetic analysis was extracted from peripheral blood. Exon 1F and exon 1D CpG-specific methylation was analysed by means of pyrosequencing technology. Functional magnetic resonance imaging was available for 54 subjects (n=27 twin pairs). Exon 1D CpG-specific methylation within a glucocorticoid responsive element (GRE) was correlated with familial burden of anxious-depressive disorders (r=0.35, z=2.26, p=0.02). Right hippocampal connectivity was significantly associated with CpG-specific GRE methylation (β=-2.33, t=-2.85, p=0.01). Exon 1F was uniformly hypomethylated across all subgroups of the present sample. GRE hypermethylation at exon 1D of the NR3C1 gene in monozygotic twins concordant for anxious-depressive disorders suggests this region plays a role in increasing vulnerability to psychosocial stress, partly mediated by altered hippocampal connectivity.

Alzheimer's disease (AD) is a debilitating age-related neurodegenerative disorder. Accurate identification of individuals at risk is complicated as AD shares cognitive and brain features with aging. We applied linked independent component analysis (LICA) on three complementary measures of gray matter structure: cortical thickness, area and gray matter density of 137 AD, 78 mild (MCI) and 38 subjective cognitive impairment patients, and 355 healthy adults aged 18-78 years to identify dissociable multivariate morphological patterns sensitive to age and diagnosis. Using the lasso classifier, we performed group classification and prediction of cognition and age at different age ranges to assess the sensitivity and diagnostic accuracy of the LICA patterns in relation to AD, as well as early and late healthy aging. Three components showed high sensitivity to the diagnosis and cognitive status of AD, with different relationships with age: one reflected an anterior-posterior gradient in thickness and gray matter density and was uniquely related to diagnosis, whereas the other two, reflecting widespread cortical thickness and medial temporal lobe volume, respectively, also correlated significantly with age. Repeating the LICA decomposition and between-subject analysis on ADNI data, including 186 AD, 395 MCI and 220 age-matched healthy controls, revealed largely consistent brain patterns and clinical associations across samples. Classification results showed that multivariate LICA-derived brain characteristics could be used to predict AD and age with high accuracy (area under ROC curve up to 0.93 for classification of AD from controls). Comparison between classifiers based on feature ranking and feature selection suggests both common and unique feature sets implicated in AD and aging, and provides evidence of distinct age-related differences in early compared to late aging. The work was supported by the European Commission’s 7th Framework Programme (#602450, IMAGEMEND), Research Council of Norway (213837, 223273, 204966/F20), the South-Eastern Norway Regional Health Authority (2013123, 2014097, 2015073, 2016083), The Norwegian Health Association's Dementia Research Program, and KG Jebsen Foundation. We acknowledge the contribution of patient data from the Norwegian registry for persons being evaluated for cognitive symptoms in specialized care (NorCog) by the Norwegian National Advisory Unit on Ageing and Health. Data collection and sharing for this project was funded by the Alzheimer's Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: AbbVie, Alzheimer's Association; Alzheimer's Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.; Cogstate; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Lumosity; Lundbeck; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer's Therapeutic Research Institute at the University of Southern California. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California.

Although cerebellar involvement across a wide range of cognitive and neuropsychiatric phenotypes is increasingly being recognized, previous large-scale studies in schizophrenia (SZ) have primarily focused on supratentorial structures. Hence, the across-sample reproducibility, regional distribution, associations with cerebrocortical morphology and effect sizes of cerebellar relative to cerebral morphological differences in SZ are unknown. We addressed these questions in 983 patients with SZ spectrum disorders and 1349 healthy controls (HCs) from 14 international samples, using state-of-the-art image analysis pipelines optimized for both the cerebellum and the cerebrum. Results showed that total cerebellar grey matter volume was robustly reduced in SZ relative to HCs (Cohens's d= - 0.35), with the strongest effects in cerebellar regions showing functional connectivity with frontoparietal cortices (d=-0.40). Effect sizes for cerebellar volumes were similar to the most consistently reported cerebral structural changes in SZ (e.g., hippocampus volume and frontotemporal cortical thickness), and were highly consistent across samples. Within groups, we further observed positive correlations between cerebellar volume and cerebral cortical thickness in frontotemporal regions (i.e., overlapping with areas that also showed reductions in SZ). This cerebellocerebral structural covariance was strongest in SZ, suggesting common underlying disease processes jointly affecting the cerebellum and the cerebrum. Finally, cerebellar volume reduction in SZ was highly consistent across the included age span (16-66 years) and present already in the youngest patients, a finding that is more consistent with neurodevelopmental than neurodegenerative etiology. Taken together, these novel findings establish the cerebellum as a key node in the distributed brain networks underlying SZ.

Monozygotic (MZ) twin studies constitute a key resource for the dissection of environmental and biological risk factors for human complex disorders. Given that epigenetic differences accumulate throughout the lifespan, the assessment of MZ twin pairs discordant for depression offers a genetically informative design to explore DNA methylation while accounting for the typical confounders of the field, shared by co-twins of a pair. In this review, we systematically evaluate all twin studies published to date assessing DNA methylation in association with depressive phenotypes. However, difficulty to recruit large numbers of MZ twin pairs fails to provide enough sample size to develop genome-wide approaches. Alternatively, region and pathway analysis revealed an enrichment for nervous system related functions; likewise, evidence supports an accumulation of methylation variability in affected subjects when compared to their co-twins. Nevertheless, longitudinal studies incorporating known risk factors for depression such as childhood trauma are required for understanding the role that DNA methylation plays in the etiology of depression.

Congenital heart disease is the most common birth defect in newborns and the leading cause of death in infancy, affecting nearly 1% of live births. A locus in chromosome 4p16, adjacent to MSX1 and STX18, has been associated with atrial septal defects (ASD) in multiple European and Chinese cohorts. Here, genotyping data from the UK Biobank was used to test for associations between this locus and congenital heart disease in adult survivors of left ventricular outflow tract obstruction (n=164) and ASD (n=223), with a control sample of 332,788 individuals, and a meta-analysis of the new and existing ASD data was performed.The results show an association between the previously reported markers at 4p16 and risk for either ASD or left ventricular outflow tract obstruction, with effect sizes similar to the published data (OR between 1.27-1.45; all pp−4) for the association with ASD.The findings show that the genetic associations with ASD can be generalized to adult survivors of both ASD and left ventricular lesions. Although the 4p16 associations are statistically compelling, the mentioned alleles confer only a small risk for disease and their frequencies in this adult sample are the same as in children, likely limiting their clinical significance. Further epidemiological and functional studies may elicit factors triggering disease in interaction with the risk alleles.

Sensitivity to external demands is essential for adaptation to dynamic environments, but comes at the cost of increased risk of adverse outcomes when facing poor environmental conditions. Here, we apply a novel methodology to perform genome-wide association analysis of mean and variance in ten key brain features (accumbens, amygdala, caudate, hippocampus, pallidum, putamen, thalamus, intracranial volume, cortical surface area, and cortical thickness), integrating genetic and neuroanatomical data from a large lifespan sample (n = 25,575 individuals; 8–89 years, mean age 51.9 years). We identify genetic loci associated with phenotypic variability in thalamus volume and cortical thickness. The variance-controlling loci involved genes with a documented role in brain and mental health and were not associated with the mean anatomical volumes. This proof-of-principle of the hypothesis of a genetic regulation of brain volume variability contributes to establishing the genetic basis of phenotypic variance (i.e., heritability), allows identifying different degrees of brain robustness across individuals, and opens new research avenues in the search for mechanisms controlling brain and mental health. This is an Accepted Manuscript of an article published by Nature Publishing Group, available online: http://dx.doi.org/10.1038/s41380-020-0664-1

BACKGROUND: Accumulating evidence supports cerebellar involvement in mental disorders, such as schizophrenia, bipolar disorder, depression, anxiety disorders, and attention-deficit/hyperactivity disorder. However, little is known about the cerebellum in developmental stages of these disorders. In particular, whether cerebellar morphology is associated with early expression of specific symptom domains remains unclear.METHODS: We used machine learning to test whether cerebellar morphometric features could robustly predict general cognitive function and psychiatric symptoms in a large and well-characterized developmental community sample centered on adolescence (Philadelphia Neurodevelopmental Cohort, n = 1401, age 8-23 years).RESULTS: Cerebellar morphology was associated with both general cognitive function and general psychopathology (mean correlations between predicted and observed values: r = .20 and r = .13; p CONCLUSIONS: The robust associations with psychiatric symptoms in the age range when these typically emerge highlight the cerebellum as a key brain structure in the development of severe mental disorders.

ImportanceBetween-subject variability in brain structure is determined by gene-environment interactions, possibly reflecting differential sensitivity to environmental and genetic perturbations. Magnetic resonance imaging (MRI) studies have revealed thinner cortices and smaller subcortical volumes in patients. However, such group-level comparisons may mask considerable within-group heterogeneity, which has largely remained unnoticed in the literatureObjectiveTo compare brain structural variability between individuals with SZ and healthy controls (HC) and to test if respective variability reflects the polygenic risk for SZ (PRS) in HC.Design, Setting, and ParticipantsWe compared MRI derived cortical thickness and subcortical volumes between 2,010 healthy controls and 1,151 patients with SZ across 16 cohorts. Secondly, we tested for associations between PRS and MRI features in 12,490 participants from UK Biobank.Main Outcomes and MeasuresWe modeled mean and dispersion effects of SZ and PRS using double generalized linear models. We performed vertex-wise analyses for thickness, and region-of-interest analysis for cortical, subcortical and hippocampal subfield volumes. Follow-up analyses included within-sample analysis, controlling for intracranial volume and population covariates, test of robustness of PRS threshold, and outlier removal.ResultsCompared to controls, patients with SZ showed higher heterogeneity in cortical thickness, cortical and ventricle volumes, and hippocampal subfields. Higher PRS was associated with thinner frontal and temporal cortices, as well as smaller left CA2/3, but was not significantly associated with dispersion.Conclusion and relevanceSZ is associated with substantial brain structural heterogeneity beyond the mean differences. These findings possibly reflect higher differential sensitivity to environmental and genetic perturbations in patients, supporting the heterogeneous nature of SZ. Higher PRS for SZ was associated with thinner fronto-temporal cortices and smaller subcortical volumes, but there were no significant associations with the heterogeneity in these measures, i.e. the variability among individuals with high PRS were comparable to the variability among individuals with low PRS. This suggests that brain variability in SZ results from interactions between environmental and genetic factors that are not captured by the PGR. Factors contributing to heterogeneity in fronto-temporal cortices and hippocampus are thus key to further our understanding of how genetic and environmental factors shape brain biology in SZ.Key PointsQuestion:Is schizophrenia and its polygenic risk associated with brain structural heterogeneity in addition to mean changes?Findings:In a sample of 1151 patients and 2010 controls, schizophrenia was associated with increased heterogeneity in fronto-temporal thickness, cortical, ventricle, and hippocampal volumes, besides robust reductions in mean estimates. In an independent sample of 12,490 controls, polygenic risk for schizophrenia was associated with thinner fronto-temporal cortices and smaller CA2/3 of the left hippocampus, but not with heterogeneity.Meaning:Schizophrenia is associated with increased inter-individual differences in brainstructure, possibly reflecting clinical heterogeneity, gene-environment interactions, or secondary disease factors.

Supported by histological and genetic evidence implicating myelin, neuroinflammation and oligodendrocyte dysfunction in schizophrenia spectrum disorders (SZ), diffusion tensor imaging (DTI) studies have consistently shown white matter (WM) abnormalities when compared to healthy controls (HC). The diagnostic specificity remains unclear, with bipolar disorders (BD) frequently conceptualized as a less severe clinical manifestation along a psychotic spectrum. Further, the age-related dynamics and possible sex differences of WM abnormalities in SZ and BD are currently understudied.Using tract-based spatial statistics (TBSS) we compared DTI-based microstructural indices between SZ (n=128), BD (n=61), and HC (n=293). We tested for age-by-group and sex-by-group interactions, computed effect sizes within different age-bins and within genders.TBSS revealed global reductions in fractional anisotropy (FA) and increases in radial (RD) diffusivity in SZ compared to HC, with strongest effects in the body and splenium of the corpus callosum, and lower FA in SZ compared to BD in right inferior longitudinal fasciculus and right inferior fronto-occipital fasciculus, and no significant differences between BD and HC. The results were not strongly dependent on age or sex. Despite lack of significant group-by-age interactions, a sliding-window approach supported widespread WM involvement in SZ with most profound differences in FA from the late 20s.

Numerous genetic and environmental factors contribute to psychiatric disorders and other brain disorders. Common risk factors likely converge on biological pathways regulating the optimization of brain structure and function across the lifespan. Here, using structural magnetic resonance imaging and machine learning, we estimated the gap between brain age and chronological age in 36,891 individuals aged 3 to 96 years, including individuals with different brain disorders. We show that several disorders are associated with accentuated brain aging, with strongest effects in schizophrenia, multiple sclerosis and dementia, and document differential regional patterns of brain age gaps between disorders. In 16,269 healthy adult individuals, we show that brain age gap is heritable with a polygenic architecture overlapping those observed in common brain disorders. Our results identify brain age gap as a genetically modulated trait that offers a window into shared and distinct mechanisms in different brain disorders.

The hippocampus is a heterogeneous structure, comprising histologically distinguishable subfields. These subfields are differentially involved in memory consolidation, spatial navigation and pattern separation, complex functions often impaired in individuals with brain disorders characterized by reduced hippocampal volume, including Alzheimer’s disease (AD) and schizophrenia. Given the structural and functional heterogeneity of the hippocampal formation, we sought to characterize the subfields’ genetic architecture. T1-weighted brain scans (n=21297, 16 cohorts) were processed with the hippocampal subfields algorithm in FreeSurfer v6.0. We ran a genome-wide association analysis on each subfield, covarying for total hippocampal volume. We further calculated the single nucleotide polymorphism (SNP)-based heritability of twelve subfields, as well as their genetic correlation with each other, with other structural brain features, and with AD and schizophrenia. All outcome measures were corrected for age, sex, and intracranial volume. We found 15 unique genome-wide significant loci across six subfields, of which eight had not been previously linked to the hippocampus. Top SNPs were mapped to genes associated with neuronal differentiation, locomotor behaviour, schizophrenia and AD. The volumes of all the subfields were estimated to be heritable (h2 from .14 to .27, all p< 1×10-16) and clustered together based on their genetic correlations compared to other structural brain features. There was also evidence of genetic overlap of subicular subfield volumes with schizophrenia. We conclude that hippocampal subfields have partly distinct genetic determinants associated with specific biological processes and traits. Taking into account this specificity may increase our understanding of hippocampal neurobiology and associated pathologies.

BackgroundDepression is a complex disorder with large inter-individual variability in symptom profiles that often occur alongside symptoms of other psychiatric domains such as anxiety. A dimensional and symptom-based approach may help refine the characterization and classification of depressive and anxiety disorders and thus aid in establishing robust biomarkers. We assess the brain functional connectivity correlates of a symptom-based clustering of individuals using functional brain imaging data.MethodsWe assessed symptoms of depression and anxiety using Beck’s Depression and Beck’s Anxiety inventories in individuals with or without a history of depression, and high dimensional data clustering to form subgroups based on symptom profiles. To assess the biological relevance of this subtyping, we compared functional magnetic resonance imaging-based dynamic and static functional connectivity between subgroups in a subset of the total sample.ResultsWe identified five subgroups with distinct symptom profiles, cutting across diagnostic boundaries and differing in terms of total severity, symptom patterns and centrality. For instance, inability to relax, fear of the worst, and feelings of guilt were among the most severe symptoms in subgroup 1, 2 and 3, respectively. These subgroups showed evidence of differential static brain connectivity patterns, in particular comprising a fronto-temporal network. In contrast, we found no significant associations with clinical sum scores, dynamic functional connectivity or global connectivity measures.ConclusionAdding to the ongoing pursuit of individual-based treatment, the results show subtyping based on a dimensional conceptualization and unique constellations of anxiety and depression symptoms is supported by distinct brain static functional connectivity patterns.

ImportanceAccumulating evidence supports cerebellar involvement in mental disorders such as schizophrenia, bipolar disorder, depression, anxiety disorders and attention-deficit hyperactivity disorder. However, little is known about cerebellar involvement in the developmental stages of these disorders. In particular, whether cerebellar morphology is associated with early expression of specific symptom domains remains unclear.ObjectiveTo determine the robustness and specificity of associations between cerebellar morphology, general cognitive function, general psychopathology and sub-clinical psychiatric symptom domains in adolescence.Design, setting and participantsAssessment of parametric structure-function associations between MR-based brain morphometric features and data-driven cognitive and clinical phenotypes in the Philadelphia Neurodevelopmental Cohort (N=1401, age-range: 8 - 23).Main outcomes and measuresRobust prediction of cognitive and clinical symptom domain scores from cerebellar, subcortical and cerebro-cortical brain features using machine learning with 10-fold internal cross-validation and permutation-based statistical inference.ResultsCerebellar morphology predicted both general cognitive function and general psychopathology (mean Pearson correlation coefficients between predicted and observed values: r = .20 and r = .13, respectively; corrected p-values < .0009). Analyses of specific sub-clinical symptom domains revealed significant associations with rates of norm-violating behavior (r = .17; p < .0009), prodromal psychotic symptoms (r = .12; p < .0009) and anxiety symptoms (r = .09; p =.0117). In contrast, we observed no significant associations between cerebellar features and the severity of attention deficits, depressive, manic or obsessive-compulsive symptoms (all rs =< .03, all ps => .1). Associations with norm-violating behavior and prodromal psychotic symptoms were stronger for the cerebellum than for subcortical and cerebro-cortical regions, while anxiety and general cognitive function were related to more global brain morphology patterns. The association between cerebellar volume and prodromal psychotic symptoms, and to a lesser extent norm violating behavior, remained significant when adjusting for potentially confounding factors such as general cognitive function, general psychopathology, parental education level and use of psychoactive substances.Conclusions and relevanceThe robust associations with sub-clinical psychiatric symptoms in the age range when these typically emerge highlight the cerebellum as a key brain structure in the development of severe mental disorders.Key pointsQuestionsIs cerebellar morphology associated with sub-clinical psychiatric symptoms in adolescence? Do such associations show symptom domain specificity or do they rather constitute a marker of general psychopathology?FindingsMachine learning utilizing cerebellar morphology features significantly predicted the severity of prodromal psychotic symptoms, norm-violating behavior and anxiety, but not attention deficits, depressive, manic or obsessive-compulsive sub-clinical symptoms. Associations with prodromal psychotic symptoms were stronger for the cerebellum than for cerebral subcortical and cerebro-cortical regions, and remained significant when adjusting for several potentially confounding factors.MeaningThe cerebellum appears to play a key role in the development of severe mental illness.

Hemispheric asymmetry is a cardinal feature of human brain organization. Altered brain asymmetry has also been linked to some cognitive and neuropsychiatric disorders. Here the ENIGMA consortium presents the largest ever analysis of cerebral cortical asymmetry and its variability across individuals. Cortical thickness and surface area were assessed in MRI scans of 17,141 healthy individuals from 99 datasets worldwide. Results revealed widespread asymmetries at both hemispheric and regional levels, with a generally thicker cortex but smaller surface area in the left hemisphere relative to the right. Regionally, asymmetries of cortical thickness and/or surface area were found in the inferior frontal gyrus, transverse temporal gyrus, parahippocampal gyrus, and entorhinal cortex. These regions are involved in lateralized functions, including language and visuospatial processing. In addition to population-level asymmetries, variability in brain asymmetry was related to sex, age, and brain size (indexed by intracranial volume). Interestingly, we did not find significant associations between asymmetries and handedness. Finally, with two independent pedigree datasets (N = 1,443 and 1,113, respectively), we found several asymmetries showing modest but highly reliable heritability. The structural asymmetries identified, and their variabilities and heritability provide a reference resource for future studies on the genetic basis of brain asymmetry and altered laterality in cognitive, neurological, and psychiatric disorders.Significance StatementLeft-right asymmetry is a key feature of the human brain's structure and function. It remains unclear which cortical regions are asymmetrical on average in the population, and how biological factors such as age, sex and genetic variation affect these asymmetries. Here we describe by far the largest ever study of cerebral cortical brain asymmetry, based on data from 17,141 participants. We found a global anterior-posterior 'torque' pattern in cortical thickness, together with various regional asymmetries at the population level, which have not been previously described, as well as effects of age, sex, and heritability estimates. From these data, we have created an on-line resource that will serve future studies of human brain anatomy in health and disease.

Background:Though cognitive abilities in adulthood are largely influenced by individual genetic background, they have also been shown to be importantly influenced by environmental factors. Some of these influences are mediated by epigenetic mechanisms. Accordingly, polymorphic variants in the epigenetic gene DNMT3B have been linked to neurocognitive performance. Since monozygotic (MZ) twins may show larger or smaller intrapair phenotypic differences depending on whether their genetic background is more or less sensitive to environmental factors, a twin design was implemented to determine if particular polymorphisms in the DNMT3B gene may be linked to a better (worse) response to enriched (deprived) environmental factors.Methods:Applying the variability gene methodology in a sample of 54 healthy MZ twin pairs (108 individuals) with no lifetime history of psychopathology, two DNMT3B polymorphisms were analyzed in relation to their intrapair differences for either intellectual quotient (IQ) or working memory performance.Results:MZ twin pairs with the CC genotype for rs406193 SNP showed statistically significant larger intrapair differences in IQ than CT pairs.Conclusions:Results suggest that DNMT3B polymorphisms may explain variability in the IQ response to either enriched or impoverished environmental conditions. Accordingly, the applied methodology is shown as a potentially valuable tool for determining genetic markers of cognitive plasticity. Further research is needed to confirm this specific result and to expand on other putative genetic markers of environmental sensitivity.

Loss of function variants in NOTCH1 cause left ventricular outflow tract obstructive defects (LVOTO) in a small percentage of families. Clinical surveys report an increased prevalence of missense variants in NOTCH1 in family members of individuals with LVOTO and other types of congenital heart disease (CHD). However, the risk conferred by rare variants in NOTCH1 for LVOTO remains largely uncharacterized. In a cohort of 49 families affected by hypoplastic left heart syndrome, a severe form of LVOTO, we discovered predicted loss of function NOTCH1 variants in 6% of individuals. Rare missense variants were found in an additional 16% of families. To make a quantitative estimate of the genetic risk posed by variants in NOTCH1 for LVOTO, we studied associations of 400 coding and non-coding variants in NOTCH1 in 271 adult cases and 333,571 controls from the UK Biobank. Two rare intronic variants in strong linkage disequilibrium displayed significant association with risk for LVOTO (g.chr9:139427582C>T, Odds Ratio 16.9, p=3.12e-6; g.chr9:139435649C>T, Odds Ratio 19.6, p = 2.44e-6) amongst European-ancestry British individuals. This result was replicated in an independent analysis of 51 cases and 68,901 controls of non-European and mixed ancestry. We conclude that carrying rare predicted loss of function variants or either of two intronic variants in NOTCH1 confer significant risk for LVOTO. Our approach demonstrates the utility of population-based datasets in quantifying the specific risk of individual variants for disease related phenotypes.Author summaryCongenital heart defects are the most common class of birth defect and are present in 1% of live births. Although CHD cases are often clustered in families, and thus the causal variant(s) are seemingly inherited, finding genetic variants causing these defects has been challenging. With the knowledge that variation in the NOTCH1 gene previously has been associated with CHDs affecting the left side of the heart, our aim was to further investigate the role of different types of NOTCH1 variants in left sided CHDs in two cohorts – a cohort of Finnish families with severe lesions affecting the left side of the heart, and the UK Biobank population including individuals with less severe left-sided lesions such as bicuspid aortic valve, congenital aortic stenosis, and coarctation of the aorta. We found a causal loss-of-function NOTCH1 variant in 6% of the families in the Finnish cohort and in the UK Biobank cohort, we identified two rare variants in the non-coding region of NOTCH1, associated with severe left-sided CHDs. These findings support screening of NOTCH1 loss-of-function variants in patients with severe left sided congenital heart defects and suggests that non-coding region variants in NOTCH1 play a role in CHDs.

A healthy transition from adolescence to adulthood relies on a continuous individual adaptation to a dynamic environment. Here, we employed data driven multivariate approaches to derive both general cognitive and psychopathology factors as well as brain phenotypes in children and adolescents in the publicly available PNC sample. We identified a distinct brain white matter pattern which proved central for prediction of heritable cognition and psychopathology scores, highlighting the importance of fronto-temporal connections for intellectual and mental development.

Cognitive and brain development are determined by dynamic interactions between genes and environment across the lifespan. Aside from marker-by-marker analyses of polymorphisms, biologically meaningful features of the whole-genome (derived from the combined effect of individual markers) have been postulated to inform on human phenotypes including cognitive traits and their underlying biological substrate.Here, estimates of inbreeding and genetic susceptibility for schizophrenia calculated from genome-wide data –runs of homozygosity (ROH) and schizophrenia polygenic risk score (PGRS)– are analyzed in relation to cognitive abilities (n=4183) and brain structure (n=516) in a general-population sample of European-ancestry participants aged 8-22, from the Philadelphia Neurodevelopmental Cohort.The findings suggest that a higher ROH burden and higher schizophrenia PGRS are associated with higher intelligence. Cognition~ROH and cognition~PGRS associations obtained in this cohort may respectively evidence that assortative mating influences intelligence, and that individuals with high schizophrenia genetic risk who do not transition to disease status are cognitively resilient.Neuroanatomical data showed that the effects of schizophrenia PGRS on cognition could be modulated by brain structure, although larger imaging datasets are needed to accurately disentangle the underlying neural mechanisms linking IQ with both inbreeding and the genetic burden for schizophrenia.

Purpose:To test whether firstly, different parental rearing components were associated with different dimensions of psychiatric symptoms in adulthood, secondlyBDNF-Val66Met polymorphism moderated this association and thirdly, this association was due to genetic confounding.Method:Perceived parental rearing according to Parental Bonding Instrument (PBI), psychiatric symptoms evaluated with the Brief Symptom Inventory (BSI) and theBDNF-Val66Met polymorphism were analyzed in a sample of 232 adult twins from the general population.Results:In the whole sample, paternal care was negatively associated with depression. Maternal overprotection was positively associated with paranoid ideation, obsession-compulsion and somatization. Gene-environment interaction effects were detected between theBDNF-Val66Met polymorphism and maternal care on phobic anxiety, paternal care on hostility, maternal overprotection on somatization and paternal overprotection also in somatization. In the subsample of MZ twins, intrapair differences in maternal care were associated with anxiety, paranoid ideation and somatization.Conclusions:Met carriers were, in general, more sensitive to the effects of parental rearing compared to Val/Val carriers in relation to anxiety and somatization. Contra-intuitively, our findings suggest that high rates of maternal care might be of risk for Met carriers regarding anxiety. Results from analyses controlling for genetic confounding were in line with this finding.

The hippocampus is a key modulator of stress responses underlying depressive behavior. While FKBP5 has been found associated with a large number of stress-related outcomes and hippocampal features, its potential role in modifying the hippocampal communication transfer mechanisms with other brain regions remains largely unexplored. The putative genetic or environmental roots of the association between depression and structural connectivity alterations of the hippocampus were evaluated combining diffusion weighted imaging with both a quantitative genetics approach and molecular information on the rs1360780 single nucleotide polymorphism, in a sample of 54 informative monozygotic twins (27 pairs). Three main results were derived from the present analyses. First, graph-theoretical measures of hippocampal connectivity were altered in depression. Specifically, decreased connectivity strength and increased network centrality of the right hippocampus were found in depressed individuals. Second, these hippocampal alterations are potentially driven by familial factors (genes plus shared environment). Third, there is an additive interaction effect between FKBP5’s rs1360780 variant and the graph-theoretical metrics of hippocampal connectivity to influence depression risk. Our data reveals alterations of the communication patterns between the hippocampus and the rest of the brain in depression, effects potentially driven by overall familial factors (genes plus shared twin environment) and modified by the FKBP5 gene.

As findings on the neuropathological and behavioral components of Alzheimer’s disease (AD) continue to accrue, converging evidence suggests that macroscale brain functional disruptions may mediate their association. Recent developments on theoretical neuroscience indicate that instantaneous patterns of brain connectivity and metastability may be a key mechanism in neural communication underlying cognitive performance. However, the potential significance of these patterns across the AD spectrum remains virtually unexplored. We assessed the clinical sensitivity of static and dynamic functional brain disruptions across the AD spectrum using resting-state fMRI in a sample consisting of AD patients (n = 80) and subjects with either mild (n = 44) or subjective (n = 26) cognitive impairment (MCI, SCI). Spatial maps constituting the nodes in the functional brain network and their associated time-series were estimated using spatial group independent component analysis and dual regression, and whole-brain oscillatory activity was analyzed both globally (metastability) and locally (static and dynamic connectivity). Instantaneous phase metrics showed functional coupling alterations in AD compared to MCI and SCI, both static (putamen, dorsal and default-mode) and dynamic (temporal, frontal-superior and default-mode), along with decreased global metastability. The results suggest that brains of AD patients display altered oscillatory patterns, in agreement with theoretical premises on cognitive dynamics.

Recent efforts using diffusion tensor imaging (DTI) have documented white matter (WM) alterations in Alzheimer’s disease (AD). The full potential of whole-brain DTI, however, has not been fully exploited as studies have focused on individual microstructural indices independently. In patients with AD (n = 79), mild (MCI, n = 55) and subjective (SCI, n = 30) cognitive impairment, we applied linked independent component analysis (LICA) to model inter-subject variability across five complementary DTI measures (fractional anisotropy (FA), axial/radial/mean diffusivity, diffusion tensor mode), two crossing fiber measures estimated using a multi-compartment crossing-fiber model reflecting the volume fraction of the dominant (f1) and non-dominant (f2) diffusion orientation, and finally, connectivity density obtained from full-brain probabilistic tractography. The LICA component explaining the largest data variance was highly sensitive to disease severity (AD

Schizophrenia (SZ) is a severe mental illness with high heritability and complex etiology. Mounting evidence from neuroimaging has implicated disrupted brain network connectivity in the pathophysiology. However, previous findings are inconsistent, likely due to a combination of methodological and clinical variability and relatively small sample sizes. Few studies have used a data-driven approach for characterizing pathological interactions between regions in the whole brain and evaluated the generalizability across independent samples. To overcome this issue, we collected resting-state functional magnetic resonance imaging data from 3 independent samples (1 from Norway and 2 from Sweden) consisting of 182 persons with a SZ spectrum diagnosis and 348 healthy controls. We used a whole-brain data-driven definition of network nodes and regularized partial correlations to evaluate and compare putatively direct brain network node interactions between groups. The clinical utility of the functional connectivity features and the generalizability of effects across samples were evaluated by training and testing multivariate classifiers in the independent samples using machine learning. Univariate analyses revealed 14 network edges with consistent reductions in functional connectivity encompassing frontal, somatomotor, visual, auditory, and subcortical brain nodes in patients with SZ. We found a high overall accuracy in classifying patients and controls (up to 80%) using independent training and test samples, strongly supporting the generalizability of connectivity alterations across different scanners and heterogeneous samples. Overall, our findings demonstrate robust reductions in functional connectivity in SZ spectrum disorders, indicating disrupted information flow in sensory, subcortical, and frontal brain regions.

Many mental disorders emerge during adolescence, which may reflect a cost of the potential for brain plasticity offered during this period. Brain dysconnectivity has been proposed as a common factor across diagnostic categories.To investigate the hypothesis that brain dysconnectivity is a transdiagnostic phenotype in adolescence with increased susceptibility and symptoms of psychiatric disease.We investigated clinical symptoms as well as cognitive function in 6487 individuals aged 8 to 21 years from November 1, 2009, to November 30, 2011, in the Philadelphia Neurodevelopmental Cohort and analyzed diffusion magnetic resonance imaging brain scans for 748 of the participants.Independent component analysis was used to derive dimensional psychopathology scores, and genome-wide complex trait analysis was used to estimate its heritability. Multimodal fusion simultaneously modeled contributions of the diffusion magnetic resonance imaging metrics fractional anisotropy, mean diffusivity, radial diffusivity, L1 (the principal diffusion tensor imaging eigen value), mode of anisotropy, as well as dominant and secondary fiber orientations, and structural connectivity density, and their association with general psychopathology and cognition.Machine learning with 10-fold cross-validation and permutation testing in 729 individuals (aged 8 to 22 years; mean [SD] age, 15.1 [3.3] years; 343 females [46%]) revealed significant association with general psychopathology levels (r = 0.24, P .001) and cognition (r = 0.39, P .001). A brain white matter pattern reflecting frontotemporal connectivity and crossing fibers in the uncinate fasciculus was the most associated feature for both traits. Univariate analysis across a range of clinical domains and cognitive test scores confirmed its transdiagnostic importance. Both the general psychopathology (16%; SE, 0.095; P = .05) and cognitive (18%; SE, 0.09; P = .01) factor were heritable and showed a negative genetic correlation.Dimensional and heritable general cognitive and psychopathology factors are associated with specific patterns of white matter properties, suggesting that dysconnectivity is a transdiagnostic brain-based phenotype in individuals with increased susceptibility and symptoms of psychiatric disorders.

Hosting nearly eighty percent of all human neurons, the cerebellum is functionally connected to large regions of the brain. Accumulating data suggest that some cerebellar resting-state alterations may constitute a key candidate mechanism for depressive psychopathology. While there is some evidence linking cerebellar function and depression, two topics remain largely unexplored. First, the genetic or environmental roots of this putative association have not been elicited. Secondly, while different mathematical representations of resting-state fMRI patterns can embed diverse information of relevance for health and disease, many of them have not been studied in detail regarding the cerebellum and depression. Here, high-resolution fMRI scans were examined to estimate functional connectivity patterns across twenty-six cerebellar regions in a sample of 48 identical twins (24 pairs) informative for depression liability. A network-based statistic approach was employed to analyze cerebellar functional networks built using three methods: the conventional approach of filtered BOLD fMRI time-series, and two analytic components of this oscillatory activity (amplitude envelope and instantaneous phase). The findings indicate that some environmental factors may lead to depression vulnerability through alterations of the neural oscillatory activity of the cerebellum during resting-state. These effects may be observed particularly when exploring the amplitude envelope of fMRI oscillations. Acknowledgements: We are indebted to the Medical Image core facility of the Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS) for the technical help. L.F. was supported by the Spanish SAF2008-05674-C03-01, the European Twins Study Network on Schizophrenia Research Training Network (grant number EUTwinsS, MRTN-CT-2006-035987), the Catalan 2014SGR1636 and the Ministry of Science and Innovation (PIM2010ERN-00642) in frame of ERA-NET NEURON. L.F., N.B., P.B., B. C.-F. and A.C.-P. were supported by the Spanish Ministry of Science and Innovation (ES-EUEpiBrain, Grant SAF 2015-71526-REDT). G.D. was supported by the ERC Advanced Grant DYSTRUCTURE (n. 295129), by the FET Flagship Human Brain Project (n. 604102), by the Spanish Research Project PSI2013-42091, by the FP7-ICT BrainScaleS (n. 269921) and CORONET (n. 269459) and by EraNet Neuron SEMAINE (PCIN-2013-026). P.B. was partially supported by The Bial Foundation (Grant 262/2012). We are also grateful to all the participants, to Ximena Goldberg and Silvia Alemany for their contribution to sample collection, and to the MRI technicians César Garrido and Santi Sotés for their technical assistance.

IntroductionDespite the fact that depression has an estimated heritability around 40%, there is no definitive candidate gene contributing to its etiology. The lack of an identified genetic component for high-heritability disorders, like depression, gave rise to the concept of missing heritability. The epigenetics’ field has pushed forward new hypotheses to fill this gap since transgenerational inheritance of epigenetic patterns has been described both in animal models and, more recently, in humans. Depression is usually associated with an abnormal stress response and an altered hypothalamic-pituitary-adrenal axis, regulated by the glucocorticoid receptor (coded by NR3C1 gene). Therefore, NR3C1 has been widely investigated as a functional candidate gene involved in anxious-depressive spectrum disorders (ADSD) although a more comprehensive study of its methylation is further required (Palma-Gudiel et al., 2015).AimsTo analyze NR3C1 promoter's methylation and to study its association with anxious-depressive spectrum disorders.MethodsThe sample consisted of 48 pairs of monozygotic twins, from the UB twin register, grouped as concordant, discordant and healthy pairs depending on whether both, one or none of the co-twins of each pair were affected by a lifetime ADSD, according to DSM-IV criteria (SCID). DNA methylation was assessed by bisulfite conversion and subsequent pyrosequencing.ResultsHypermethylation at specific CpG sites, not previously reported, was detected in concordant twin pairs as compared with discordant and healthy groups (P = 0.03).ConclusionsThe epigenetic pattern newly described in NR3C1 gene may be contributing to the familial component of depression and thus could be putatively explained by transgenerational inheritance of epigenetic phenomena.Disclosure of interestThe authors have not supplied their declaration of competing interest.