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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)

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

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

BackgroundAberrant brain connectivity during emotional processing, especially within the fronto-limbic pathway, is one of the hallmarks of major depressive disorder (MDD). However, the methodological heterogeneity of previous studies made it difficult to determine the functional and etiological implications of specific alterations in brain connectivity. We previously reported alterations in psychophysiological interaction measures during emotional face processing, distinguishing depressive pathology from at-risk/resilient and healthy states. Here, we extended these findings by effective connectivity analyses in the same sample to establish a refined neural model of emotion processing in depression.MethodsThirty-seven patients with MDD, 45 first-degree relatives of patients with MDD and 97 healthy controls performed a face-matching task during functional magnetic resonance imaging. We used dynamic causal modeling to estimate task-dependent effective connectivity at the subject level. Parametric empirical Bayes was performed to quantify group differences in effective connectivity.ResultsMDD patients showed decreased effective connectivity from the left amygdala and left lateral prefrontal cortex to the fusiform gyrus compared to relatives and controls, whereas patients and relatives showed decreased connectivity from the right orbitofrontal cortex to the left insula and from the left orbitofrontal cortex to the right fusiform gyrus compared to controls. Relatives showed increased connectivity from the anterior cingulate cortex to the left dorsolateral prefrontal cortex compared to patients and controls.ConclusionsOur results suggest that the depressive state alters top-down control of higher visual regions during face processing. Alterations in connectivity within the cognitive control network present potential risk or resilience mechanisms.

The reproducibility crisis in neuroimaging has led to an increased demand for standardized data processing workflows. Within the ENIGMA consortium, we developed HALFpipe (Harmonized Analysis of Functional MRI pipeline), an open-source, containerized, user-friendly tool that facilitates reproducible analysis of task-based and resting-state fMRI data through uniform application of preprocessing, quality assessment, single-subject feature extraction, and group-level statistics. It provides state-of-the-art preprocessing using fMRIPrep without the requirement for input data in Brain Imaging Data Structure (BIDS) format. HALFpipe extends the functionality of fMRIPrep with additional preprocessing steps, which include spatial smoothing, grand mean scaling, temporal filtering, and confound regression. HALFpipe generates an interactive quality assessment (QA) webpage to rate the quality of key preprocessing outputs and raw data in general. HALFpipe features myriad post-processing functions at the individual subject level, including calculation of task-based activation, seed-based connectivity, network-template (or dual) regression, atlas-based functional connectivity matrices, regional homogeneity (ReHo), and fractional amplitude of low-frequency fluctuations (fALFF), offering support to evaluate a combinatorial number of features or preprocessing settings in one run. Finally, flexible factorial models can be defined for mixed-effects regression analysis at the group level, including multiple comparison correction. Here, we introduce the theoretical framework in which HALFpipe was developed, and present an overview of the main functions of the pipeline. HALFpipe offers the scientific community a major advance toward addressing the reproducibility crisis in neuroimaging, providing a workflow that encompasses preprocessing, post-processing, and QA of fMRI data, while broadening core principles of data analysis for producing reproducible results. Instructions and code can be found at https://github.com/HALFpipe/HALFpipe.

Common variation in the gene encoding the neuron-specific RNA splicing factor RNA Binding Fox-1 Homolog 1 (RBFOX1) has been identified as a risk factor for several psychiatric conditions, and rare genetic variants have been found causal for autism spectrum disorder (ASD). Here, we explored the genetic landscape of RBFOX1 more deeply, integrating evidence from existing and new human studies as well as studies in Rbfox1 knockout mice. Mining existing data from large-scale studies of human common genetic variants, we confirmed gene-based and genome-wide association of RBFOX1 with risk tolerance, major depressive disorder and schizophrenia. Data on six mental disorders revealed copy number losses and gains to be more frequent in ASD cases than in controls. Consistently, RBFOX1 expression appeared decreased in post-mortem frontal and temporal cortices of individuals with ASD and prefrontal cortex of individuals with schizophrenia. Brain-functional MRI studies demonstrated that carriers of a common RBFOX1 variant, rs6500744, displayed increased neural reactivity to emotional stimuli, reduced prefrontal processing during cognitive control, and enhanced fear expression after fear conditioning, going along with increased avoidance behaviour. Investigating Rbfox1 neuron-specific knockout mice allowed us to further specify the role of this gene in behaviour. The model was characterised by pronounced hyperactivity, stereotyped behaviour, impairments in fear acquisition and extinction, reduced social interest, and lack of aggression; it provides excellent construct and face validity as an animal model of ASD. In conclusion, convergent translational evidence shows that common variants in RBFOX1 are associated with a broad spectrum of psychiatric traits and disorders, while rare genetic variation seems to expose to early-onset neurodevelopmental psychiatric disorders with and without developmental delay like ASD, in particular. Studying the pleiotropic nature of RBFOX1 can profoundly enhance our understanding of mental disorder vulnerability.

Recent large-scale, genome-wide association studies (GWAS) have identified hundreds of genetic loci associated with general intelligence. The cumulative influence of these loci on brain structure is unknown. We examined if cortical morphology mediates the relationship between GWAS-derived polygenic scores for intelligence (PSi) and g-factor. Using the effect sizes from one of the largest GWAS meta-analysis on general intelligence to date, PSi were calculated among 10 P value thresholds. PSi were assessed for the association with g-factor performance, cortical thickness (CT), and surface area (SA) in two large imaging-genetics samples (IMAGEN N = 1651; IntegraMooDS N = 742). PSi explained up to 5.1% of the variance of g-factor in IMAGEN (F1,1640 = 12.2–94.3; P

The reproducibility crisis in neuroimaging has led to an increased demand for standardized data processing workflows. Within the ENIGMA consortium, we developed HALFpipe (Harmonized AnaLysis of Functional MRI pipeline), an open-source, containerized, user-friendly tool that facilitates reproducible analysis of task-based and resting-state fMRI data through uniform application of preprocessing, quality assessment, single-subject feature extraction, and group-level statistics. It provides state-of-the-art preprocessing using fMRIPrep without the requirement for input data in Brain Imaging Data Structure (BIDS) format. HALFpipe extends the functionality of fMRIPrep with additional preprocessing steps, which include spatial smoothing, grand mean scaling, temporal filtering, and confound regression. HALFpipe generates an interactive quality assessment (QA) webpage to assess the quality of key preprocessing outputs and raw data in general. HALFpipe features myriad post-processing functions at the individual subject level, including calculation of task-based activation, seed-based connectivity, network-template (or dual) regression, atlas-based functional connectivity matrices, regional homogeneity (ReHo), and fractional amplitude of low frequency fluctuations (fALFF), offering support to evaluate a combinatorial number of features or preprocessing settings in one run. Finally, flexible factorial models can be defined for mixed-effects regression analysis at the group level, including multiple comparison correction. Here, we introduce the theoretical framework in which HALFpipe was developed, and present an overview of the main functions of the pipeline. HALFpipe offers the scientific community a major advance toward addressing the reproducibility crisis in neuroimaging, providing a workflow that encompasses preprocessing, post-processing, and QA of fMRI data, while broadening core principles of data analysis for producing reproducible results. Instructions and code can be found at https://github.com/HALFpipe/HALFpipe.

BackgroundLimbic-cortical imbalance is an established model for the neurobiology of major depressive disorder (MDD), but imaging genetics studies have been contradicting regarding potential risk and resilience mechanisms. Here, we re-assessed previously reported limbic-cortical alterations between MDD relatives and controls in combination with a newly acquired sample of MDD patients and controls, to disentangle pathology, risk, and resilience.MethodsWe analyzed functional magnetic resonance imaging data and negative affectivity (NA) of MDD patients (n = 48), unaffected first-degree relatives of MDD patients (n = 49) and controls (n = 109) who performed a faces matching task. Brain response and task-dependent amygdala functional connectivity (FC) were compared between groups and assessed for associations with NA.ResultsGroups did not differ in task-related brain activation but activation in the superior frontal gyrus (SFG) was inversely correlated with NA in patients and controls. Pathology was associated with task-independent decreases of amygdala FC with regions of the default mode network (DMN) and decreased amygdala FC with the medial frontal gyrus during faces matching, potentially reflecting a task-independent DMN predominance and a limbic-cortical disintegration during faces processing in MDD. Risk was associated with task-independent decreases of amygdala-FC with fronto-parietal regions and reduced faces-associated amygdala-fusiform gyrus FC. Resilience corresponded to task-independent increases in amygdala FC with the perigenual anterior cingulate cortex (pgACC) and increased FC between amygdala, pgACC, and SFG during faces matching.ConclusionOur results encourage a refinement of the limbic-cortical imbalance model of depression. The validity of proposed risk and resilience markers needs to be tested in prospective studies. Further limitations are discussed.

The relationship between transdiagnostic, dimensional, and categorical approaches to psychiatric nosology is under intense debate. To inform this discussion, we studied neural systems linked to reward anticipation across a range of disorders and behavioral dimensions. We assessed brain responses to reward expectancy in a large sample of 221 participants, including patients with schizophrenia (SZ; n = 27), bipolar disorder (BP; n = 28), major depressive disorder (MD; n = 31), autism spectrum disorder (ASD; n = 25), and healthy controls (n = 110). We also characterized all subjects with an extensive test battery from which a cognitive, affective, and social functioning factor was constructed. These factors were subsequently related to functional responses in the ventral striatum (vST) and neural networks linked to it. We found that blunted vST responses were present in SZ, BP, and ASD but not in MD. Activation within the vST predicted individual differences in affective, cognitive, and social functioning across diagnostic boundaries. Network alterations extended beyond the reward network to include regions implicated in executive control. We further confirmed the robustness of our results in various control analyses. Our findings suggest that altered brain responses during reward anticipation show transdiagnostic alterations that can be mapped onto dimensional measures of functioning. They also highlight the role of executive control of reward and salience signaling in the disorders we study and show the power of systems-level neuroscience to account for clinically relevant behaviors., Schizophrenia Bulletin, 46 (3), ISSN:0586-7614, ISSN:1745-1701

Previous studies have linked the low expression variant of a variable number of tandem repeat polymorphism in the monoamine oxidase A gene (MAOA‐L) to the risk for impulsivity and aggression, brain developmental abnormalities, altered cortico‐limbic circuit function, and an exaggerated neural serotonergic tone. However, the neurobiological effects of this variant on human brain network architecture are incompletely understood. We studied healthy individuals and used multimodal neuroimaging (sample size range: 219–284 across modalities) and network‐based statistics (NBS) to probe the specificity of MAOA‐L‐related connectomic alterations to cortical‐limbic circuits and the emotion processing domain. We assessed the spatial distribution of affected links across several neuroimaging tasks and data modalities to identify potential alterations in network architecture. Our results revealed a distributed network of node links with a significantly increased connectivity in MAOA‐L carriers compared to the carriers of the high expression (H) variant. The hyperconnectivity phenotype primarily consisted of between‐lobe (“anisocoupled”) network links and showed a pronounced involvement of frontal‐temporal connections. Hyperconnectivity was observed across functional magnetic resonance imaging (fMRI) of implicit emotion processing (p FWE = .037), resting‐state fMRI (p FWE = .022), and diffusion tensor imaging (p FWE = .044) data, while no effects were seen in fMRI data of another cognitive domain, that is, spatial working memory (p FWE = .540). These observations are in line with prior research on the MAOA‐L variant and complement these existing data by novel insights into the specificity and spatial distribution of the neurogenetic effects. Our work highlights the value of multimodal network connectomic approaches for imaging genetics.

BackgroundAberrant brain connectivity during emotional processing, especially within the fronto-limbic pathway, is one of the hallmarks of major depressive disorder (MDD). However, a lack of systematic approaches in previous studies made it difficult to determine whether a specific alteration in brain connectivity reflects a cause, correlate, or effect of the disorder. The current study aimed to investigate neural mechanisms that correspond to disease, risk and resilience in major depression during implicit processing of emotion cues.MethodsForty-eight patients with MDD, 49 first-degree relatives of patients with MDD and 103 healthy controls performed a face-matching task during functional magnetic resonance imaging. We used dynamic causal modelling to estimate task-dependent effective connectivity at the subject level. Parametric empirical Bayes was then performed to quantify group differences in effective connectivity.ResultsDepressive pathology was associated with decreased effective connectivity from the left amygdala and left dorsolateral prefrontal cortex to the right fusiform gyrus, whereas familial risk for depression corresponded to decreased connectivity from the right orbitofrontal cortex to the left insula and from the left orbitofrontal cortex to the right fusiform gyrus. Resilience for depression was related to increased connectivity from the anterior cingulate cortex to the left dorsolateral prefrontal cortex.ConclusionsOur results suggest that the depressive state alters top-down control of higher visual regions during the processing of emotional faces, whereas increased connectivity within the cognitive control network promotes resilience to depression.

Background: Altered ventral striatal (vST) activation to reward expectancy is a well-established intermediate phenotype for psychiatric disorders, specifically schizophrenia (SZ). Preclinical research suggests that striatal alterations are related to a reduced inhibition by the hippocampal formation, but its role in human transdiagnostic reward-network dysfunctions is not well understood. Methods: We performed functional magnetic resonance imaging during reward processing in 728 individuals including healthy control subjects (n = 396), patients (SZ: n = 46; bipolar disorder: n = 45; major depressive disorder: n = 60), and unaffected first-degree relatives (SZ: n = 46; bipolar disorder: n = 50; major depressive disorder: n = 85). We assessed disorder-specific differences in functional vST-hippocampus coupling and transdiagnostic associations with dimensional measures of positive, negative, and cognitive symptoms. We also probed the genetic underpinning using polygenic risk scores for SZ in a subset of healthy participants (n = 295). Results: Functional vST-hippocampus coupling was 1) reduced in patients with SZ and bipolar disorder (pFWE < .05, small-volume corrected [SVC]); 2) associated transdiagnostically to dimensional measures of positive (pFWE = .01, SVC) and cognitive (pFWE = .02, SVC), but not negative, (pFWE > .05, SVC) symptoms; and 3) reduced in first-degree relatives of patients with SZ (pFWE = .017, SVC) and linked to the genetic risk for SZ in healthy participants (p = .035). Conclusions: We provide evidence that reduced vST-hippocampus coupling during reward processing is an endophenotype for SZ linked to positive and cognitive symptoms, supporting current preclinical models of the emergence of psychosis. Moreover, our data indicate that vST-hippocampus coupling is familial and linked to polygenic scores for SZ, supporting the use of this measure as an intermediate phenotype for psychotic disorders.

Special Issue: The ENIGMA Consortium: the first 10 years.-- Karolinska Schizophrenia Project (KaSP)., Age has a major effect on brain volume. However, the normative studies available are constrained by small sample sizes, restricted age coverage and significant methodological variability. These limitations introduce inconsistencies and may obscure or distort the lifespan trajectories of brain morphometry. In response, we capitalized on the resources of the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) Consortium to examine age-related trajectories inferred from cross-sectional measures of the ventricles, the basal ganglia (caudate, putamen, pallidum, and nucleus accumbens), the thalamus, hippocampus and amygdala using magnetic resonance imaging data obtained from 18,605 individuals aged 3–90 years. All subcortical structure volumes were at their maximum value early in life. The volume of the basal ganglia showed a monotonic negative association with age thereafter; there was no significant association between age and the volumes of the thalamus, amygdala and the hippocampus (with some degree of decline in thalamus) until the sixth decade of life after which they also showed a steep negative association with age. The lateral ventricles showed continuous enlargement throughout the lifespan. Age was positively associated with inter-individual variability in the hippocampus and amygdala and the lateral ventricles. These results were robust to potential confounders and could be used to examine the functional significance of deviations from typical age-related morphometric patterns., National Institute of Mental Health, Grant/Award Numbers: MH104284, MH116147, R01MH113619, R01 MH090553, R01MH117014, R01MH042191; Karolinska Institutet; Stockholm County Council; Southern and Eastern Norway Regional Health Authority; German Centre for Cardiovascular Research; DZHK; Siemens Healthineers; University of Queensland; US National Institute of Child Health and Human Development, Grant/Award Number: RO1HD050735; Australian National Health and Medical Research Council; Eunice Kennedy Shriver National Institute of Child Health & Human Development; National Institute on Drug Abuse, Grant/Award Numbers: UL1 TR000153, 1 U24 RR025736-01, 1 U24 RR021992; Brain Injury Fund Research Grant Program; Indiana State Department of Health Spinal Cord; Parents Against Childhood Epilepsy; Epilepsy Therapy Project, Fight Against Childhood Epilepsy and Seizures; Epilepsy Foundation; American Epilepsy Society; Knut and Alice Wallenberg Foundation; European Community's Horizon 2020 Programme; Vici Innovation Program; NWO Brain & Cognition Excellence Program; Netherlands Organization for Scientific Research; Hersenstichting Nederland; Netherlands Organization for Health Research and Development; Geestkracht Programme of the Dutch Health Research Council, Grant/Award Number: 10-000-1001; Universiteit Utrecht; FP7 Ideas: European Research Council; Nederlandse Organisatie voor Wetenschappelijk Onderzoek; National Center for Advancing Translational Sciences; National Institutes of Health; National Center for Research Resources; Consortium grant, Grant/Award Number: U54 EB020403; U.S. National Institutes of Health, Grant/Award Numbers: R01 CA101318, P30 AG10133, R01 AG19771; Medical Research Council, Grant/Award Number: G0500092; 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; the Research Council, Grant/Award Number: 223273; South Eastern Norway Regional Health Authority, Grant/Award Numbers: 2017-112, 2019107; Swedish Research Council; European Community's Seventh Framework Programme, Grant/Award Number: 602450; King's College London; South London and Maudsley NHS Foundation Trust; Biomedical Research Centre; National Institute for Health Research.

Introduction: Emotion regulation (ER), the ability to actively modulate one’s own emotion reactions, likely depends on the individual’s current emotional state. Here, we investigated whether negative emotions induced by an interpersonal autobiographic script affect the neuronal processes underlying ER. Methods: Twenty healthy participants were recruited and underwent functional magnetic resonance imaging (fMRI) during performance of distancing, a specific ER strategy, while viewing emotionally arousing pictures. Participants were instructed to either naturally experience (“permit” condition) or to actively downregulate (“regulate” condition) their emotional responses to the presented stimuli. Before each of the 4 runs in total, a neutral or negative autobiographical audio script was presented. The negative script comprised an emotionally negative event from childhood or adolescence that represented either emotional abuse or emotional neglect. The second event comprised an everyday neutral situation. We aimed at identifying the neural correlates of ER and their modulation by script-driven imagery. Results: fMRI analyses testing for greater responses in the “regulate” than the “permit” condition replicated previously reported neural correlates of ER in the right dorsolateral prefrontal cortex and the right inferior parietal lobule. A significant ER effect was also observed in the left orbitofrontal cortex. In the amygdala, we found greater responses in the “permit” compared to the “regulate” condition. We did not observe a significant modulation of the ER effects in any of these regions by the negative emotional state induced by autobiographical scripts. Bayesian statistics confirmed the absence of such modulations by providing marginal evidence for null effects. Discussion: While we replicated previously reported neural correlates of ER, we found no evidence for an effect of mood induction with individualized autobiographical scripts on the neural processes underlying ER in healthy participants.

Delineating age-related cortical trajectories in healthy individuals is critical given the association of cortical thickness with cognition and behaviour. Previous research has shown that deriving robust estimates of age-related brain morphometric changes requires large-scale studies. In response, we conducted a large-scale analysis of cortical thickness in 17,075 individuals aged 3-90 years by pooling data through the Lifespan Working group of the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) Consortium. We used fractional polynomial (FP) regression to characterize age-related trajectories in cortical thickness, and we computed normalized growth centiles using the parametric Lambda, Mu, and Sigma (LMS) method. Inter-individual variability was estimated using meta-analysis and one-way analysis of variance. Overall, cortical thickness peaked in childhood and had a steep decrease during the first 2-3 decades of life; thereafter, it showed a gradual monotonic decrease which was steeper in men than in women particularly in middle-life. Notable exceptions to this general pattern were entorhinal, temporopolar and anterior cingulate cortices. Inter-individual 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 reconcile uncertainties about age-related trajectories of cortical thickness; the centile values provide estimates of normative variance in cortical thickness, and may assist in detecting abnormal deviations in cortical thickness, and associated behavioural, cognitive and clinical outcomes.

Age has a major effect on brain volume. However, the normative studies available are constrained by small sample sizes, restricted age coverage and significant methodological variability. These limitations introduce inconsistencies and may obscure or distort the lifespan trajectories of brain morphometry. In response, we capitalised on the resources of the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) Consortium to examine the age-related morphometric trajectories of the ventricles, the basal ganglia (caudate, putamen, pallidum, and nucleus accumbens), the thalamus, hippocampus and amygdala using magnetic resonance imaging data obtained from 18,605 individuals aged 3-90 years. All subcortical structure volumes were at their maximum early in life; the volume of the basal ganglia showed a gradual monotonic decline thereafter while the volumes of the thalamus, amygdala and the hippocampus remained largely stable (with some degree of decline in thalamus) until the sixth decade of life followed by a steep decline thereafter. The lateral ventricles showed a trajectory of continuous enlargement throughout the lifespan. Significant age-related increase in inter-individual variability was found for the hippocampus and amygdala and the lateral ventricles. These results were robust to potential confounders and could be used to derive risk predictions for the early identification of diverse clinical phenotypes.

Establishing reliable, robust, and unique brain signatures from neuroimaging data is a prerequisite for precision psychiatry, and therefore a highly sought-after goal in contemporary neuroscience. Recently, the procedure of connectome fingerprinting, using brain functional connectivity profiles as such signatures, was shown to be able to accurately identify individuals from a group of 126 subjects from the Human Connectome Project (HCP). However, the specificity and generalizability of this procedure were not tested. In this replication study, we show both for the original and an extended HCP data set (n = 900 subjects), as well as for an additional data set of more commonly acquired imaging quality (n = 84) that (i) although the high accuracy can be replicated for the larger HCP 900 data set, accuracy is (ii) lower for standard neuroimaging data, and, that (iii) connectome fingerprinting may not be specific enough to distinguish between individuals. In addition, both accuracy and specificity are projected to drop considerably as the size of a data set increases. Although the moderate-to-high accuracies do suggest there is a portion of unique variance, our results suggest that connectomes may actually be quite similar across individuals. This outcome may be relevant to how precision psychiatry could benefit from inferences based on functional connectomes.

The serotonin transporter-linked polymorphic region 5-HTTLPR is a key genetic regulator of 5-HTT expression in the human brain where the short allele S has been implicated in emotion dysregulation. However, the neural mechanism underlying the association between this variant and emotion processing is still unclear. Earlier studies suggested an effect of 5-HTTLPR on amygdala activation during emotional face processing. However, this association has been questioned in recent studies employing larger sample sizes and meta-analyses. Here, we examined a sample of 223 healthy subjects with a well-established fMRI emotional face processing task to (1) re-evaluate the association between 5-HTTLPR and amygdala activation, (2) explore potential network-based functional connectivity phenotypes for associations with 5-HTTLPR, and (3) probe the reliability, behavioral significance and potential structural confounds of the identified network phenotype. Our results revealed no significant effect of 5-HTTLPR on amygdala activation (P40.79). However, the number of S alleles was significantly correlated with functional connectivity of a visual-limbic subnetwork (PFWE= 0.03). The subnetwork cluster included brain regions that are pivotal to emotion regulation such as the hippocampus, orbitofrontal cortex, anterior cingulate gyrus, fusiform gyrus, and subcortex. Notably, individuals with lower subnetwork connectivity had significantly higher emotion suppression scores (P= 0.01). Further, the connectivity metrics were test-retest reliable and independent from subnetwork gray matter volume and white matter anisotropy. Our data provide evidence for a functional network-based phenotype linking genetic variation in 5-HTTLPR to emotion regulation, and suggest that further critical evaluations of the association between 5-HTTLPR and amygdala activation are warranted.

Threshold-free cluster enhancement (TFCE) is a sensitive means to incorporate spatial neighborhood information in neuroimaging studies without using arbitrary thresholds. The majority of methods have applied TFCE to voxelwise data. The need to understand the relationship among multiple variables and imaging modalities has become critical. We propose a new method of applying TFCE to vertexwise statistical images as well as cortexwise (either voxel- or vertexwise) mediation analysis. Here we present TFCE_mediation, a toolbox that can be used for cortexwise multiple regression analysis with TFCE, and additionally cortexwise mediation using TFCE. The toolbox is open source and publicly available (https://github.com/trislett/TFCE_mediation). We validated TFCE_mediation in healthy controls from two independent multimodal neuroimaging samples (N = 199 and N = 183). We found a consistent structure-function relationship between surface area and the first independent component (IC1) of the N-back task, that white matter fractional anisotropy is strongly associated with IC1 N-back, and that our voxel-based results are essentially identical to FSL randomise using TFCE (all PFWE

Altered connectivity of dorsolateral prefrontal cortex (DLPFC) and hippocampus during working memory is considered an intermediate phenotype for schizophrenia (SCZ), but the relevance for other mental disorders with shared genetic background remains unknown. Here we investigated its presence in unaffected first-degree relatives of patients with bipolar disorder (BD) or major depressive disorder (MDD). Furthermore, we aimed to provide an independent replication of this phenotype in first-degree relatives of SCZ patients. We acquired functional magnetic resonance imaging (fMRI) data from 309 healthy controls and 218 healthy first-degree relatives of index patients with SCZ (n = 62), BD (n = 66) and MDD (n = 90), who completed the n-back working memory paradigm. We observed a significant group effect on DLPFC–hippocampus coupling (PFWE = .031, all P-values region of interest [ROI] corrected). Post hoc comparisons revealed that this effect was driven by the SCZ relatives, who showed a significant increase in the negative functional connectivity of the DLPFC and right hippocampus compared to controls (PFWE = .001), BD relatives (PFWE = .015) and trend-wise also MDD relatives (PFWE = .082). Comparison of BD and MDD relatives to the controls revealed no difference (PFWE-values > .451). Supplementary analyses suggested that the SCZ relatives finding is robust to a range of potential confounds, including structural differences. Our data further support altered DLPFC–hippocampus connectivity during working memory as an intermediate phenotype for SCZ. This suggests that this phenotype is relatively specific to SCZ and does not translate to other genetically related disorders in the mood-psychosis spectrum.

WOS: 000485217300010, PubMed ID: 31443932, BACKGROUND: Schizophrenia and bipolar disorder share genetic liability, and some structural brain abnormalities are common to both conditions. First-degree relatives of patients with schizophrenia (FDRs-SZ) show similar brain abnormalities to patients, albeit with smaller effect sizes. Imaging findings in first-degree relatives of patients with bipolar disorder (FDRs-BD) have been inconsistent in the past, but recent studies report regionally greater volumes compared with control subjects. METHODS: We performed a meta-analysis of global and subcortical brain measures of 6008 individuals (1228 FDRs-SZ, 852 FDRs-BD, 2246 control subjects, 1016 patients with schizophrenia, 666 patients with bipolar disorder) from 34 schizophrenia and/or bipolar disorder family cohorts with standardized methods. Analyses were repeated with a correction for intracranial volume (ICV) and for the presence of any psychopathology in the relatives and control subjects. RESULTS: FDRs-BD had significantly larger ICV (d = +10.16, q < .05 corrected), whereas FDRs-SZ showed smaller thalamic volumes than control subjects (d = -0.12, q < .05 corrected). ICV explained the enlargements in the brain measures in FDRs-BD. In FDRs-SZ, after correction for ICV, total brain, cortical gray matter, cerebral white matter, cerebellar gray and white matter, and thalamus volumes were significantly smaller; the cortex was thinner (d < -0.09, q < .05 corrected); and third ventricle was larger (d = +0.15, q < .05 corrected). The findings were not explained by psychopathology in the relatives or control subjects. CONCLUSIONS: Despite shared genetic liability, FDRs-SZ and FDRs-BD show a differential pattern of structural brain abnormalities, specifically a divergent effect in ICV. This may imply that the neurodevelopmental trajectories leading to brain anomalies in schizophrenia or bipolar disorder are distinct., Research Council of NorwayResearch Council of Norway [223273]; National Institutes of Health (NIH)United States Department of Health & Human ServicesNational Institutes of Health (NIH) - USA [R01 MH117601, R01 MH116147, R01 MH111671, P41 EB015922, 5T32MH073526, U54EB020403, R03 MH105808]; National Institute on Aging (NIA)United States Department of Health & Human ServicesNational Institutes of Health (NIH) - USANIH National Institute on Aging (NIA) [T32AG058507]; Canadian Institutes of Health ResearchCanadian Institutes of Health Research (CIHR) [103703, 106469, 341717]; National Centre for Mental Health; 2010 National Alliance for Research on Schizophrenia and Depression (NARSAD) Young Investigator Award [17319]; Dokuz Eylul University Department of Scientific Research Projects FundingDokuz Eylul University [2012.KB.SAG.062]; National Institute for Health Research (NIHR) Biomedical Research Centre at South London and Maudsley National Health Service Foundation Trust and King's College London; Ege University School of Medicine Research Foundation [2009-D-00017]; Medical Research CouncilMedical Research Council UK (MRC) [G0901310]; Geestkracht program of the Netherlands Organisation for Health Research and Development [10-000-1002]; National Institute of Mental HealthUnited States Department of Health & Human ServicesNational Institutes of Health (NIH) - USANIH National Institute of Mental Health (NIMH) [R01 MH 085667]; Nova Scotia Health Research Foundation; Dalhousie Clinical Research Scholarship; 2007 Brain and Behavior Research Foundation Young Investigator Award; Ministry of Health of the Czech RepublicMinistry of Health, Czech Republic [NR8786, NT13891]; 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]; Stockholm County CouncilStockholm County Council; Karolinska InstitutetKarolinska Institutet; Knut and Alice Wallenberg FoundationKnut & Alice Wallenberg Foundation; HUBIN project; Spanish Ministry of Economy and Competitiveness/Instituto de Salud Carlos III [PI070066, PI1100683, PI1500467]; Fundacio Marato TV3 [091630]; ERDF Funds from the European Commission A Way of Making Europe"); Brain and Behaviour Research Foundation (NARSAD Young Investigator Award); Alicia Koplowitz Foundation; Stanley Medical Research Institute; NARSADNARSAD [20244]; Wellcome TrustWellcome Trust [064971, 085475/B/08/Z, 085475/Z/08/Z]; NARSAD Young Investigator AwardNARSAD; European CommunityEuropean Community (EC); NIMH Intramural Research ProgramUnited States Department of Health & Human ServicesNational Institutes of Health (NIH) - USANIH National Institute of Mental Health (NIMH); NIHR Biomedical Research Centre at University College London Hospital; British Medical Association Margaret Temple Fellowship 2016; German Federal Ministry for Education and ResearchFederal Ministry of Education & Research (BMBF) [O1ZX1314B, O1ZX1314G]; Deutsche ForschungsgemeinschaftGerman Research Foundation (DFG) [1617]; NIMHUnited States Department of Health & Human ServicesNational Institutes of Health (NIH) - USANIH National Institute of Mental Health (NIMH) [R01 MH116147, R01 MH113619]; NIHUnited States Department of Health & Human ServicesNational Institutes of Health (NIH) - USA [U01 MH097435, R01 MH084803, R01 EB020062, R01 MH080912, R01 MH052857]; National Science FoundationNational Science Foundation (NSF) [1636893, 1734853]; Australian National Health and Medical Research CouncilNational Health and Medical Research Council of Australia [510135, 1037196, 1063960, 1066177]; ZonMwNetherlands Organization for Health Research and Development [908-02-123]; VIDINetherlands Organization for Scientific Research (NWO) [452-11-014, 917-46-370]; Janssen PharmaceuticaJohnson & Johnson USAJanssen Biotech Inc; Otsuka PharmaceuticalOtsuka Pharmaceutical; Bipolar Disorder Research Network, The researchers and studies included in this article were supported by the Research Council of Norway (Grant No. 223273), National Institutes of Health (NIH) (Grant No. R01 MH117601 [to NJ], Grant Nos. R01 MH116147, R01 MH111671, and P41 EB015922 [to PMT], Grant Nos. 5T32MH073526 and U54EB020403 [to CRKC], and Grant No. R03 MH105808 [to CEB and SCF]) and National Institute on Aging (NIA) (Grant No. T32AG058507 [to CRKC]).; C-SFS: This work was supported by Canadian Institutes of Health Research.; Cardiff: This work was supported by the National Centre for Mental Health, Bipolar Disorder Research Network, 2010 National Alliance for Research on Schizophrenia and Depression (NARSAD) Young Investigator Award (Grant No. 17319).; DEU: This work was supported by Dokuz Eylul University Department of Scientific Research Projects Funding (Grant No. 2012. KB. SAG. 062). This report represents independent research funded by the National Institute for Health Research (NIHR) Biomedical Research Centre at South London and Maudsley National Health Service Foundation Trust and King's College London. The views expressed are those of the authors and not necessarily those of the National Health Service, NIHR, or Department of Health.; EGEU: This work was supported by the Ege University School of Medicine Research Foundation (Grant No. 2009-D-00017).; EHRS: The Edinburgh High Risk Study was supported by the Medical Research Council.; GROUP: The infrastructure for the GROUP study was supported by the Geestkracht program of the Netherlands Organisation for Health Research and Development (Grant No. 10-000-1002).; ENBD_UT/BPO_FLB: This work was supported by the National Institute of Mental Health (Grant No. R01 MH 085667).; HHR/PHHR: This work was supported by the Canadian Institutes of Health Research (Grant Nos. 103703, 106469, and 341717), Nova Scotia Health Research Foundation, Dalhousie Clinical Research Scholarship (to TH), 2007 Brain and Behavior Research Foundation Young Investigator Award (to TH), and Ministry of Health of the Czech Republic (Grant Nos. NR8786 and NT13891).; HUBIN: This work was supported by the Swedish Research Council (Grant Nos. K2007-62X-15077-04-1, K2008-62P-20597-01-3, K2010-62X-15078-07-2, K2012-61X-15078-09-3), regional agreement on medical training and clinical research between Stockholm County Council and the Karolinska Institutet, Knut and Alice Wallenberg Foundation, and HUBIN project.; IDIBAPS: This work was supported by the Spanish Ministry of Economy and Competitiveness/Instituto de Salud Carlos III (Grant Nos. PI070066, PI1100683, and PI1500467) and Fundacio Marato TV3 (Grant No. 091630), co-financed by ERDF Funds from the European Commission A Way of Making Europe"), Brain and Behaviour Research Foundation (NARSAD Young Investigator Award), and Alicia Koplowitz Foundation.; IoP-BD: The Maudsley Bipolar Twin Study was supported by the Stanley Medical Research Institute and NARSAD.; IoP-SZ: This work was supported by a Wellcome Trust Research Training Fellowship (Grant No. 064971 to MMP), NARSAD Young Investigator Award (to TT), and European Community's Sixth Framework Programme through a Marie Curie Training Network called the European Twin Study Network on Schizophrenia.; Lieber Institute for Brain Development (LIBD): This work was supported by the NIMH Intramural Research Program (to DRW's laboratory). LIBD is a nonprofit research institute located in Baltimore, MD. The work performed at LIBD was performed in accordance with an NIMH material transfer agreement with LIBD.; MFS: The Maudsley Family Study cohort collection was supported by the Wellcome Trust (Grant Nos. 085475/B/08/Z and 085475/Z/08/Z), NIHR Biomedical Research Centre at University College London Hospital, Medical Research Council (Grant No. G0901310), and British Medical Association Margaret Temple Fellowship 2016.; MooDS: This work was supported by the German Federal Ministry for Education and Research grants NGFNplus MooDS (Systematic Investigation of the Molecular Causes of Major Mood Disorders and Schizophrenia) and Integrated Network IntegraMent (Integrated Understanding of Causes and Mechanisms in Mental Disorders) under the auspices of the e: Med program (Grant Nos. O1ZX1314B and O1ZX1314G) and Deutsche Forschungsgemeinschaft (Grant No. 1617 [to AH]).; MSSM: This work was supported by NIMH (Grant Nos. R01 MH116147 and R01 MH113619).; NU: This work was supported by NIH (Grant Nos. U01 MH097435, R01 MH084803, and R01 EB020062) and National Science Foundation (Grant Nos. 1636893 and 1734853).; OLIN: This work was supported by NIH (Grant No. R01 MH080912).; STAR: This work was supported by NIH (Grant No. R01 MH052857).; SydneyBipolarGroup: The Australian cohort collection was supported by the Australian National Health and Medical Research Council Program Grants (Grant No. 510135 [to PBM] and Grant No. 1037196 [to PBM and PRS]) and Project Grants (Grant No. 1063960 [to JMF and PRS] and Grant No. 1066177 [to JMF]).; UMCU: This work was supported by NARSAD (Grant No. 20244 [to MHJH]), ZonMw (Grant No. 908-02-123 [to HEHP]), VIDI (Grant No. 452-11-014 [to NEMvH] and Grant No. 917-46-370 [to HEHP]), and Stanley Medical Research Institute.; CliNG: We thank Anna Fanelli, Kathrin Jakob, and Maria Keil for help with data acquisition.; All authors have contributed to and approved the contents of this manuscript.; GS has received research and travel support from Janssen Pharmaceutica and Otsuka Pharmaceutical and honoraria from Adamed Pharma. NY has been an investigator in clinical studies conducted together with Janssen-Cilag, Corcept Therapeutics, and COMPASS Pathways in the last 3 years. AM-L has received consultant fees from Boehringer Ingelheim, BrainsWay, Elsevier, Lundbeck International Neuroscience Foundation, and Science Advances. CRKC has received partial research support from Biogen, Inc. (Boston, MA) for work unrelated to the topic of this manuscript. The remaining authors report no biomedical financial interests or potential conflicts of interest.

Cognitive control represents an essential neuropsychological characteristic that allows for the rapid adaption of a changing environment by constant re-allocation of cognitive resources. This finely tuned mechanism is impaired in psychiatric disorders such as schizophrenia and contributes to cognitive deficits. Neuroimaging has highlighted the contribution of the anterior cingulate cortex (ACC) and prefrontal regions (PFC) on cognitive control and demonstrated the impact of genetic variation, as well as genetic liability for schizophrenia. In this study, we aimed to examine the influence of the functional single-nucleotide polymorphism (SNP) rs6265 of a plasticity-related neurotrophic factor gene, BDNF (Val 66 Met), on cognitive control. Strong evidence implicates BDNF Val 66 Met in neural plasticity in humans. Furthermore, several studies suggest that although the variant is not convincingly associated with schizophrenia risk, it seems to be a modifier of the clinical presentation and course of the disease. In order to clarify the underlying mechanisms using functional magnetic resonance imaging (fMRI), we studied the effects of this SNP on ACC and PFC activation, and the connectivity between these regions in a discovery sample of 85 healthy individuals and sought to replicate this effect in an independent sample of 253 individuals. Additionally, we tested the identified imaging phenotype in relation to schizophrenia familial risk in a sample of 58 unaffected first-degree relatives of schizophrenia patients. We found a significant increase in interregional connectivity between ACC and PFC in the risk-associated BDNF 66 Met allele carriers. Furthermore, we replicated this effect in an independent sample and demonstrated its independence of structural confounds, as well as task specificity. A similar coupling increase was detectable in individuals with increased familial risk for schizophrenia. Our results show that a key neural circuit for cognitive control is influenced by a plasticity-related genetic variant, which may render this circuit particular susceptible to genetic and environmental risk factors for schizophrenia.

Schizophrenia is increasingly recognized as a disorder of distributed neural dynamics, but the molecular and genetic contributions are poorly understood. Recent work highlights a role for altered N-methyl-d-aspartate (NMDA) receptor signaling and related impairments in the excitation-inhibitory balance and synchrony of large-scale neural networks. Here, we combined a pharmacological intervention with novel techniques from dynamic network neuroscience applied to functional magnetic resonance imaging (fMRI) to identify alterations in the dynamic reconfiguration of brain networks related to schizophrenia genetic risk and NMDA receptor hypofunction. We quantified "network flexibility," a measure of the dynamic reconfiguration of the community structure of time-variant brain networks during working memory performance. Comparing 28 patients with schizophrenia, 37 unaffected first-degree relatives, and 139 healthy controls, we detected significant differences in network flexibility [F(2,196) = 6.541, P = 0.002] in a pattern consistent with the assumed genetic risk load of the groups (highest for patients, intermediate for relatives, and lowest for controls). In an observer-blinded, placebo-controlled, randomized, cross-over pharmacological challenge study in 37 healthy controls, we further detected a significant increase in network flexibility as a result of NMDA receptor antagonism with 120 mg dextromethorphan [F(1,34) = 5.291, P = 0.028]. Our results identify a potential dynamic network intermediate phenotype related to the genetic liability for schizophrenia that manifests as altered reconfiguration of brain networks during working memory. The phenotype appears to be influenced by NMDA receptor antagonism, consistent with a critical role for glutamate in the temporal coordination of neural networks and the pathophysiology of schizophrenia.

Objectives Behavioral deficits in the Theory of Mind (ToM) have been robustly demonstrated in bipolar disorder. These deficits may represent an intermediate phenotype of the disease. The aim of this study was: (i) to investigate alterations in neural ToM processing in euthymic patients with bipolar disorder, and (ii) to examine whether similar effects are present in unaffected relatives of patients with bipolar disorder suggesting that ToM functional activation may be, in part, due to genetic risk for the disease. Methods A total of 24 euthymic patients with bipolar disorder, 21 unaffected first-degree relatives, and 81 healthy controls completed a ToM task while undergoing functional magnetic resonance imaging. Results We observed reduced bilateral activation of the temporoparietal junction (TPJ) and diminished functional fronto-temporoparietal connectivity in patients compared to controls. Relatives tended towards intermediate temporoparietal activity and functional coupling with medial prefrontal areas. There was also evidence for a potentially compensatory enhanced recruitment of the right middle temporal gyrus and stronger connectivity between this region and the medial prefrontal cortex in relatives. Conclusions These findings provide further evidence of altered neural ToM processing in euthymic patients with bipolar disorder. Further, our findings in relatives lend support to the idea that altered ToM processing may act as an intermediate phenotype of the disorder.

Aims/hypothesis MODY can be wrongly diagnosed as type 1 diabetes in children. We aimed to find the prevalence of MODY in a nationwide population-based registry of childhood diabetes. Methods Using next-generation sequencing, we screened the HNF1A, HNF4A, HNF1B, GCK and INS genes in all 469 children (12.1%) negative for both GAD and IA-2 autoantibodies and 469 antibody-positive matched controls selected from the Norwegian Childhood Diabetes Registry (3882 children). Variants were classified using clinical diagnostic criteria for pathogenicity ranging from class 1 (neutral) to class 5 (pathogenic). Results We identified 58 rare exonic and splice variants in cases and controls. Among antibody-negative patients, 6.5% had genetic variants of classes 3–5 (vs 2.4% in controls; p = 0.002). For the stricter classification (classes 4 and 5), the corresponding number was 4.1% (vs 0.2% in controls; p = 1.6 × 10−5). HNF1A showed the strongest enrichment of class 3–5 variants, with 3.9% among antibody-negative patients (vs 0.4% in controls; p = 0.0002). Antibody-negative carriers of variants in class 3 had a similar phenotype to those carrying variants in classes 4 and 5. Conclusions/interpretation This is the first study screening for MODY in all antibody-negative children in a nationwide population-based registry. Our results suggest that the prevalence of MODY in antibody-negative childhood diabetes may reach 6.5%. One-third of these MODY cases had not been recognised by clinicians. Since a precise diagnosis is important for treatment and genetic counselling, molecular screening of all antibody-negative children should be considered in routine diagnostics.

Adaptive recovery from a stressor fosters resilience. So far, however, few studies have examined brain functional connectivity in the aftermath of stress, with inconsistent results reported. Focusing on the immediate recovery from psychosocial stress, the current study compared amygdala resting-state functional connectivity (RSFC) before and immediately after psychosocial stress between cortisol responders and non-responders. Differences between groups were expected for amygdala RSFC with regions involved in down-regulation of the physiological stress response, emotion regulation, and memory consolidation. Eighty-six healthy participants (36 males/50 females) underwent a social stress paradigm inside the MRI scanner. Before and immediately after stress, resting-state (RS) fMRI scans were acquired to determine amygdala RSFC. Next, changes in connectivity from pre- to post-stress were compared between cortisol responders and non-responders. Responders demonstrated a cortisol increase, higher negative affect, and decreased heart rate variability (HRV) in response to stress compared to non-responders. A significant Sex-by-Responder-by-Time interaction was found between the bilateral amygdala and posterior cingulate cortex (PCC) and precuneus (p < 0.05, corrected). As males were also more likely to show a cortisol increase to the stress task than females, follow-up analyses were conducted for both sexes separately. Whereas no difference was observed between female responders and non-responders, male non-responders showed an increase in FC after stress between the bilateral amygdala and the PCC and precuneus (p < 0.05, corrected). The increased coupling of the amygdala with the PCC/precuneus, a core component of the default mode network (DMN), might indicate an increased engagement of the amygdala within the DMN directly after stress in non-responders. Although this study was carried out in healthy participants, and the results likely reflect normal variations in the neural response to stress, understanding the mechanisms that underlie these variations could prove beneficial in revealing neural markers that promote resilience to stress-related disorders.

The fundamental role of the brain-specific myelin transcription factor 1-like (MYT1L) gene in cases of intellectual disability and in the aetiology of neurodevelopmental disorders is increasingly recognised. Yet, its function remains under-investigated. Here, we identify a network of helix-loop-helix (HLH) transcriptional regulators controlled by MYT1L, as indicated by our analyses in human neural stem cells and in the human brain. Using cell-based knockdown approaches and microarray analyses we found that (i) MYT1L is required for neuronal differentiation and identified ID1, a HLH inhibitor of premature neurogenesis, as a target. (2) While MYT1L prevented expression of ID1, it induced expression of a large number of terminal differentiation genes. (3) Consistently, expression of MYT1L in the human brain coincided with neuronal maturation and inversely correlated with that of ID1 and ID3 throughout the lifespan. (4) Genetic polymorphisms that reduced expression of MYT1L in the hippocampus resulted in increased expression of ID1 and ID3, decreased levels of the proneural basic HLH (bHLH) transcriptional regulators TCF4 and NEUROD6 and decreased expression of genes involved in long-term potentiation and synaptic transmission, cancer and neurodegeneration. Furthermore, our neuroimaging analyses indicated that MYT1L expression associated with hippocampal volume and activation during episodic memory recall, as measured by blood-oxygen-level dependent (BOLD) signals. Overall, our findings suggest that MYT1L influences memory-related processes by controlling a neuronal proliferation/differentiation switch of ID-bHLH factors.Neuropsychopharmacology accepted article preview online, 04 May 2017. doi:10.1038/npp.2017.91.