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Recent studies proposed a general psychopathology factor underlying common comorbidities among psychiatric disorders. However, its neurobiological mechanisms and generalizability remain elusive. In this study, we used a large longitudinal neuroimaging cohort from adolescence to young adulthood (IMAGEN) to define a neuropsychopathological (NP) factor across externalizing and internalizing symptoms using multitask connectomes. We demonstrate that this NP factor might represent a unified, genetically determined, delayed development of the prefrontal cortex that further leads to poor executive function. We also show this NP factor to be reproducible in multiple developmental periods, from preadolescence to early adulthood, and generalizable to the resting-state connectome and clinical samples (the ADHD-200 Sample and the Stratify Project). In conclusion, we identify a reproducible and general neural basis underlying symptoms of multiple mental health disorders, bridging multidimensional evidence from behavioral, neuroimaging and genetic substrates. These findings may help to develop new therapeutic interventions for psychiatric comorbidities.

Funder: Cambridge Trust (University of Cambridge); Id: http://dx.doi.org/10.13039/501100000735, Funder: National Institute for Health Research (NIHR) Biomedical Research Centre at South London, Funder: Maudsley NHS Foundation Trust and King's College London, Funder: Medical Research Foundation; Id: http://dx.doi.org/10.13039/501100009187, Funder: Mission Interministérielle de Lutte‐contre‐les‐Drogues‐et‐les‐Conduites‐Addictives (MILDECA), Funder: Assistance‐Publique‐Hôpitaux‐de‐Paris and INSERM, Funder: Paris Sud University IDEX 2012, Funder: Fédération pour la Recherche sur le Cerveau; Id: http://dx.doi.org/10.13039/501100006424, BACKGROUND: Stress exposure in childhood and adolescence has been linked to reductions in cortical structures and cognitive functioning. However, to date, most of these studies have been cross-sectional, limiting the ability to make long-term inferences, given that most cortical structures continue to develop through adolescence. METHODS: Here, we used a subset of the IMAGEN population cohort sample (N = 502; assessment ages: 14, 19, and 22 years; mean age: 21.945 years; SD = 0.610) to understand longitudinally the long-term interrelations between stress, cortical development, and cognitive functioning. To these ends, we first used a latent change score model to examine four bivariate relations - assessing individual differences in change in the relations between adolescent stress exposure and volume, surface area, and cortical thickness of cortical structures, as well as cognitive outcomes. Second, we probed for indirect neurocognitive effects linking stress to cortical brain structures and cognitive functions using rich longitudinal mediation modeling. RESULTS: Latent change score modeling showed that greater baseline adolescence stress at age 14 predicted a small reduction in the right anterior cingulate volume (Std. β = -.327, p = .042, 95% CI [-0.643, -0.012]) and right anterior cingulate surface area (Std. β = -.274, p = .038, 95% CI [-0.533, -0.015]) across ages 14-22. These effects were very modest in nature and became nonsignificant after correcting for multiple comparisons. Our longitudinal analyses found no evidence of indirect effects in the two neurocognitive pathways linking adolescent stress to brain and cognitive outcomes. CONCLUSION: Findings shed light on the impact of stress on brain reductions, particularly in the prefrontal cortex that have consistently been implicated in the previous cross-sectional studies. However, the magnitude of effects observed in our study is smaller than that has been reported in past cross-sectional work. This suggests that the potential impact of stress during adolescence on brain structures may likely be more modest than previously noted.

IntroductionDiscrimination toward ethnic minorities is a persistent societal problem. One reason behind this is a bias in trust: people tend to trust their ingroup and comparatively distrust outgroups.MethodsIn this study, we investigated whether and how people change their explicit trust bias with respect to ethnicity based on behavioral interactions with in- and outgroup members in a modified Trust Game.ResultsSubjects’ initial explicit trust bias disappeared after the game. The change was largest for ingroup members who behaved unfairly, and the reduction of trust bias generalized to a small sample of new in- and outgroup members. Reinforcement learning models showed subjects’ learning was best explained by a model with only one learning rate, indicating that subjects learned from trial outcomes and partner types equally during investment.DiscussionWe conclude that subjects can reduce bias through simple learning, in particular by learning that ingroup members can behave unfairly.

IMPORTANCE: Arsenic, a contaminant of groundwater and irrigated crops, is a global public health hazard. Exposure to low levels of arsenic through food extends well beyond the areas with high arsenic content in water.OBJECTIVE: To identify cognitive impairments following commonly prevalent low-level arsenic exposure and characterize their underlying brain mechanisms.DESIGN, SETTING, AND PARTICIPANTS: This multicenter population-based cohort study analyzed cross-sectional data of the Indian Consortium on Vulnerability to Externalizing Disorders and Addictions (cVEDA) cohort, recruited between November 4, 2016, and May 4, 2019. Participants aged 6 to 23 years were characterized using deep phenotyping measures of behavior, neuropsychology, psychopathology, brain neuroimaging, and exposure to developmental adversities and environmental neurotoxins. All analyses were performed between June 1, 2020, and December 31, 2021.EXPOSURE: Arsenic levels were measured in urine as an index of exposure.MAIN OUTCOMES AND MEASURES: Executive function measured using the cVEDA neuropsychological battery, gray matter volume (GMV) from T1-weighted magnetic resonance imaging, and functional network connectivity measures from resting state functional magnetic resonance imaging.RESULTS: A total of 1014 participants aged 6 to 23 years (589 male [58.1%]; mean [SD] age, 14.86 [4.79] years) were included from 5 geographic locations. Sparse-partial least squares analysis was used to describe a negative association of arsenic exposure with executive function (r = -0.12 [P = 5.4 × 10-4]), brain structure (r = -0.20 [P = 1.8 × 10-8]), and functional connectivity (within network, r = -0.12 [P = 7.5 × 10-4]; between network, r = -0.23 [P = 1.8 × 10-10]). Alterations in executive function were partially mediated by GMV (b = -0.004 [95% CI, -0.007 to -0.002]) and within-network functional connectivity (b = -0.004 [95% CI, -0.008 to -0.002]). Socioeconomic status and body mass index moderated the association between arsenic and GMV, such that the association was strongest in participants with lower socioeconomic status and body mass index.CONCLUSIONS AND RELEVANCE: The findings of this cross-sectional study suggest that low-level arsenic exposure was associated with alterations in executive functioning and underlying brain correlates. These results indicate potential detrimental consequences of arsenic exposure that are below the currently recommended guidelines and may extend beyond endemic risk areas. Precision medicine approaches to study global mental health vulnerabilities highlight widespread but potentially modifiable risk factors and a mechanistic understanding of the impact of low-level arsenic exposure on brain development.

Human brain morphology undergoes complex developmental changes with diverse regional trajectories. Various biological factors influence cortical thickness development, but human data are scarce. Building on methodological advances in neuroimaging of large cohorts, we show that population-based developmental trajectories of cortical thickness unfold along patterns of molecular and cellular brain organization. During childhood and adolescence, distributions of dopaminergic receptors, inhibitory neurons, glial cell populations as well as features of brain metabolism explain up to 50% of variance associated with regional cortical thickness trajectories. Cortical maturation patterns in later life are best explained by distributions of cholinergic and glutamatergic systems. These observations are validated in longitudinal data from over 8,000 adolescents, explaining up to 59% of developmental change at population- and 18% at single-subject level. Integrating multilevel brain atlases with normative modeling and population neuroimaging provides a biologically and clinically meaningful path to understand typical and atypical brain development in living humans.

Recent studies proposed a general psychopathology factor underlying common comorbidities among psychiatric disorders. However, its neurobiological mechanisms and generalizability remain elusive. In this study, we used a large longitudinal neuroimaging cohort from adolescence to young adulthood (IMAGEN) to define a neuropsychopathological (NP) factor across externalizing and internalizing symptoms using multitask connectomes. We demonstrate that this NP factor might represent a unified, genetically determined, delayed development of the prefrontal cortex that further leads to poor executive function. We also show this NP factor to be reproducible in multiple developmental periods, from preadolescence to early adulthood, and generalizable to the resting-state connectome and clinical samples (the ADHD-200 Sample and the Stratify Project). In conclusion, we identify a reproducible and general neural basis underlying symptoms of multiple mental health disorders, bridging multidimensional evidence from behavioral, neuroimaging and genetic substrates. These findings may help to develop new therapeutic interventions for psychiatric comorbidities.

International audience; Incomplete Hippocampal Inversion (IHI) is an atypical anatomical pattern of the hippocampus that has been associated with several brain disorders (epilepsy, schizophrenia). IHI can be visually detected on coronal T1 weighted MRI images. IHI can be absent, partial or complete (no IHI, partial IHI, IHI). However, visual evaluation can be long and tedious, justifying the need for an automatic method. In this paper, we propose, to the best of our knowledge, the first automatic IHI detection method from T1-weighted MRI. The originality of our approach is that, instead of directly detecting IHI, we propose to predict several anatomical criteria, which each characterize a particular anatomical feature of IHI, and that can ultimately be combined for IHI detection. Such individual criteria have the advantage of providing interpretable anatomical information regarding the morphological aspect of a given hippocampus. We relied on a large population of 2,008 participants from the IMAGEN study. The approach is general and can be used with different machine learning models. In this paper, we explored two different backbone models for the prediction: a linear method (ridge regression) and a deep convolutional neural network. We demonstrated that the interpretable, anatomical based prediction was at least as good as when predicting directly the presence of IHI, while providing interpretable information to the clinician or neuroscientist. This approach may be applied to other diagnostic tasks which can be characterized radiologically by several anatomical features.

Schizophrenia has a heritability of 60-80%1, much of which is attributable to common risk alleles. Here, in a two-stage genome-wide association study of up to 76,755 individuals with schizophrenia and 243,649 control individuals, we report common variant associations at 287 distinct genomic loci. Associations were concentrated in genes that are expressed in excitatory and inhibitory neurons of the central nervous system, but not in other tissues or cell types. Using fine-mapping and functional genomic data, we identify 120 genes (106 protein-coding) that are likely to underpin associations at some of these loci, including 16 genes with credible causal non-synonymous or untranslated region variation. We also implicate fundamental processes related to neuronal function, including synaptic organization, differentiation and transmission. Fine-mapped candidates were enriched for genes associated with rare disruptive coding variants in people with schizophrenia, including the glutamate receptor subunit GRIN2A and transcription factor SP4, and were also enriched for genes implicated by such variants in neurodevelopmental disorders. We identify biological processes relevant to schizophrenia pathophysiology; show convergence of common and rare variant associations in schizophrenia and neurodevelopmental disorders; and provide a resource of prioritized genes and variants to advance mechanistic studies., The work at Cardiff University was additionally supported by Medical Research Council Centre grant no. MR/L010305/1 and program grant no. G0800509. S. Xu also gratefully acknowledges the support of the National Natural Science Foundation of China (NSFC) grants (31525014, 91731303, 31771388, 31961130380 and 32041008), the UK Royal Society-Newton Advanced Fellowship (NAF\R1\191094), the Key Research Program of Frontier Sciences (QYZDJ-SSW-SYS009) and the Strategic Priority Research Program (XDB38000000) of the Chinese Academy of Sciences, and the Shanghai Municipal Science and Technology Major Project (2017SHZDZX01). O. A. Andreassen was supported by the Research Council of Norway (283798, 262656, 248980, 273291, 248828, 248778, 223273); KG Jebsen Stiftelsen, South-East Norway Health Authority, EU H2020 no. 847776. B. Melegh was supported in part by the National Scientific Research Program (NKFIH) K 138669. S. V. Faraone is supported by the European Union’s Seventh Framework Programme for research, technological development and demonstration under grant agreement no. 602805, the European Union’s Horizon 2020 research and innovation programme under grant agreements 667302 and 728018 and NIMH grants 5R01MH101519 and U01 MH109536-01. S. I. Belangero was supported by FAPESP (Fundação de Amparo à Pesquisa do Estado de São Paulo), grant numbers: 2010/08968-6; 2014/07280-1 2011/50740-5 (including R. A. Bressan). The Singapore team (J. Lee, J. Liu, K. Sim, S. A. Chong and M. Subramanian) acknowledges the National Medical Research Council Translational and Clinical Research Flagship Programme (grant no.: NMRC/TCR/003/2008). M. Macek was supported by LM2018132, CZ.02.1.01/0.0/0.0/18_046/0015515 and IP6003 –VZFNM00064203. C. Arango has been funded by the Spanish Ministry of Science and Innovation, Instituto de Salud Carlos III (SAM16PE07CP1, PI16/02012, PI19/024), co-financed by ERDF Funds from the European Commission, ‘A way of making Europe’, CIBERSAM, Madrid Regional Government (B2017/BMD-3740 AGES-CM-2), European Union Structural Funds, European Union Seventh Framework Program and European Union H2020 Program under the Innovative Medicines Initiative 2 Joint Undertaking (grant agreement no 115916, project PRISM; and grant agreement no. 777394, project AIMS-2-TRIALS), Fundación Familia Alonso and Fundación Alicia Koplowitz. E. Bramon acknowledges support from the National Institute of Health Research UK (grant NIHR200756); Mental Health Research UK John Grace QC Scholarship 2018; an ESRC collaborative award 2020; BMA Margaret Temple Fellowship 2016; Medical Research Council New Investigator Award (G0901310); MRC Centenary Award (G1100583); MRC project grant G1100583; National Institute of Health Research UK post-doctoral fellowship (PDA/02/06/016); NARSAD Young Investigator awards 2005 and 2008; Wellcome Trust Research Training Fellowship; Wellcome Trust Case Control Consortium awards (085475/B/08/Z, 085475/Z/08/Z); European Commission Horizon 2020 (747429); NIHR Biomedical Research Centre for Mental Health at the South London and Maudsley NHS Foundation Trust and King’s College London; and NIHR Biomedical Research Centre at University College London Hospitals NHS Foundation Trust and University College London (UCLH BRC - Mental Health Theme). D. Molto is funded by the European Regional Development Fund (ERDF)–Valencian Community 2014–2020, Spain. E. G. Atkinson was supported by the NIMH K01MH121659.

et al., Human brain structure changes throughout the lifespan. Altered brain growth or rates of decline are implicated in a vast range of psychiatric, developmental and neurodegenerative diseases. In this study, we identified common genetic variants that affect rates of brain growth or atrophy in what is, to our knowledge, the first genome-wide association meta-analysis of changes in brain morphology across the lifespan. Longitudinal magnetic resonance imaging data from 15,640 individuals were used to compute rates of change for 15 brain structures. The most robustly identified genes GPR139, DACH1 and APOE are associated with metabolic processes. We demonstrate global genetic overlap with depression, schizophrenia, cognitive functioning, insomnia, height, body mass index and smoking. Gene set findings implicate both early brain development and neurodegenerative processes in the rates of brain changes. Identifying variants involved in structural brain changes may help to determine biological pathways underlying optimal and dysfunctional brain development and aging., The ENIGMA-Plasticity Working Group is part of the ENIGMA World Aging Center, funded by NIA grants R56 AG058854 and R01 AG058854. The ENIGMA Consortium core funding was supported by NIH Consortium Grant U54 EB020403, supported by a cross-NIH alliance that funds Big Data to Knowledge Centers of Excellence. 1000BRAINS: 1000BRAINS is a population-based cohort based on the Heinz-Nixdorf Recall Study and is supported, in part, by the German National Cohort. We thank the Heinz Nixdorf Foundation (Germany) for their generous support in terms of the Heinz Nixdorf Study. The authors are supported by the Initiative and Networking Fund of the Helmholtz Association (Svenja Caspers) and the European Union’s Horizon 2020 Research and Innovation Programme under grant agreements 785907 (Human Brain Project SGA2; Svenja Caspers, Sven Cichon and Katrin Amunts). This work was further supported by the German Federal Ministry of Education and Research (BMBF) through the Integrated Network IntegraMent (Integrated Understanding of Causes and Mechanisms in Mental Disorders) under the auspices of the e:Med Program (grant 01ZX1314A; Sven Cichon) and by the Swiss National Science Foundation (SNSF, grant 156791; Sven Cichon). The authors acknowledge grants supporting their work from the European Union’s Horizon 2020 Research and Innovation Programme (H2020/2014–2020) under grant agreements 667302 (CoCA), 728018 (Eat2beNICE), 785907 (HBP SGA2) and 772376 (EScORIAL) and the Netherlands ALS Foundation.Additional support is received from the European Community’s Seventh Framework Programme (FP7/2007—2013) under grant agreements n° 602805 (Aggressotype), n° 603016 (MATRICS), n° 602450 (IMAGEMEND) and n° 278948 (TACTICS) and from the European Community’s Horizon 2020 Programme (H2020/2014—2020) under grant agreements n° 643051 (MiND) and n° 667302 (CoCA). FP7 Ideas: the European Research Council (ERC-230374 to D.B.). This work was supported by Spanish Ministry of Science and Innovation, Instituto de Salud Carlos III (SAM16PE07CP1, PI16/02012 and PI19/024), co-financed by ERDF Funds from the European Commission, ‘A way of making Europe’, CIBERSAM; Madrid Regional Government (B2017/BMD-3740 AGES-CM-2), European Union Structural Funds; European Union Seventh Framework Program under grant agreements FP7- HEALTH-2013-2.2.1-2-603196 (Project PSYSCAN) and European Union H2020 Program under the Innovative Medicines Initiative 2 Joint Undertaking (grant agreement 115916, Project PRISM and grant agreement 777394, Project AIMS-2-TRIALS), Fundación Familia Alonso, Fundación Alicia Koplowitz and Fundación Mutua Madrileña. The Horizon 2020 funded ERC Advanced Grant ‘STRATIFY’ (Brain network based stratification of reinforcement-related disorders) (695313). The FP7 project MATRICS (603016). The research leading to these results also received support from the European Community’s Seventh Framework Programme (FP7/2007-2013) under grant agreement 278948 (TACTICS), 602805 (Aggressotype), 603016 (MATRICS) and 602450 (Imagemend) and the Innovation Medicine Initiative grants 115300 (EU-AIMS) and 777394 (AIMS-2-TRIALS).PAFIP: PAFIP was supported by the Instituto de Salud Carlos III (PI14/00639, PI14/00918 and PI17/01056) and Fundación Instituto de Investigación Marqués de Valdecilla (NCT0235832 and NCT025KG Jebsen Stiftelsen and H2020 CoMorMent (847776).34363). European Union Marie Curie Research Training Network (MRTN-CT-2006-035987).

Alterations in regional subcortical brain volumes have been investigated as part of the efforts of an international consortium, ENIGMA, to identify reliable neural correlates of major depressive disorder (MDD). Given that subcortical structures are comprised of distinct subfields, we sought to build significantly from prior work by precisely mapping localized MDD-related differences in subcortical regions using shape analysis. In this meta-analysis of subcortical shape from the ENIGMA-MDD working group, we compared 1,781 patients with MDD and 2,953 healthy controls (CTL) on individual measures of shape metrics (thickness and surface area) on the surface of seven bilateral subcortical structures: nucleus accumbens, amygdala, caudate, hippocampus, pallidum, putamen, and thalamus. Harmonized data processing and statistical analyses were conducted locally at each site, and findings were aggregated by meta-analysis. Relative to CTL, patients with adolescent-onset MDD (≤ 21 years) had lower thickness and surface area of the subiculum, cornu ammonis (CA) 1 of the hippocampus and basolateral amygdala (Cohen's d = −0.164 to −0.180). Relative to first-episode MDD, recurrent MDD patients had lower thickness and surface area in the CA1 of the hippocampus and the basolateral amygdala (Cohen's d = −0.173 to −0.184). Our results suggest that previously reported MDD-associated volumetric differences may be localized to specific subfields of these structures that have been shown to be sensitive to the effects of stress, with important implications for mapping treatments to patients based on specific neural targets and key clinical features.

Preregistration of the analysis plan: "Reward sensitivity in the lab and daily life: Associations with resilience over time", using data from the "Dynamic Modelling of Resilience - Observational Study" (DynaM-OBS) study.

The promise of machine learning has fueled the hope for developing diagnostic tools for psychiatry. Initial studies showed high accuracy for the identification of major depressive disorder (MDD) with resting-state connectivity, but progress has been hampered by the absence of large datasets. Here we used regular machine learning and advanced deep learning algorithms to differentiate patients with MDD from healthy controls and identify neurophysiological signatures of depression in two of the largest resting-state datasets for MDD. We obtained resting-state functional magnetic resonance imaging data from the REST-meta-MDD (N = 2338) and PsyMRI (N = 1039) consortia. Classification of functional connectivity matrices was done using support vector machines (SVM) and graph convolutional neural networks (GCN), and performance was evaluated using 5-fold cross-validation. Features were visualized using GCN-Explainer, an ablation study and univariate t-testing. The results showed a mean classification accuracy of 61% for MDD versus controls. Mean accuracy for classifying (non-)medicated subgroups was 62%. Sex classification accuracy was substantially better across datasets (73-81%). Visualization of the results showed that classifications were driven by stronger thalamic connections in both datasets, while nearly all other connections were weaker with small univariate effect sizes. These results suggest that whole brain resting-state connectivity is a reliable though poor biomarker for MDD, presumably due to disease heterogeneity as further supported by the higher accuracy for sex classification using the same methods. Deep learning revealed thalamic hyperconnectivity as a prominent neurophysiological signature of depression in both multicenter studies, which may guide the development of biomarkers in future studies. Funding Agencies|Netherlands Organization for Scientific Research (NWO) [628.011.023]; Philips Research; ZonMW (Vidi) [016.156.318]; Region Ostergoetland; Phyllis and Jerome Lyle Rappaport Foundation; BIAL Foundation; Brain and Behavior Research Foundation; Center for Depression, Anxiety, and Stress Research at McLean Hospital; Ad Astra Chandaria Foundation [DA1151/5-2, SFB-TRR58, Dan3/012/17]; German Research Foundation (DFG); Interdisciplinary Center for Clinical Research (IZKF) of the medical faculty of Munster [1024570]; European Commission [KLI 597-827]; Australian National Health and Medical Research Council of Australia (NHMRC) [F3514-B1, GR 4510/2-1]; Austrian Science Fund (FWF) [WA1539/4-1]; Science Foundation Ireland (SFI); [FOR2107 DA1151/5-1]; [H2020-634541]; [1064643]; [KLI-148-B00]

Advances in the field of emotion regulation (ER) research support the notion that no clear distinction of functional and dysfunctional ER strategies exists (Sheppes, 2020). Rather, dynamic deployment of ER strategies has been proposed as a cornerstone of healthy emotion regulation (Bonanno & Burton 2013; Aldao et al., 2015). Aldao et al. (2015) define ER variability as variation in use of different ER strategies over time, while ER flexibility is defined as ER variability that is synchronized with situational changes. Following these definitions, ER variability is a precondition for ER flexibility. Both ER variability and ER flexibility remain understudied. Further, little is known about the cognitive determinants, or the longer-term consequences, of ER dynamics (Aldao et al., 2015; Kobylińska & Kusev, 2019). We aim to investigate both in the current study. Most ecological momentary assessment (EMA) studies of ER dynamics focus on momentary affective consequences (e.g., Bellingtier et al., 2022; Blanke et al., 2020; Wenzel et al., 2022). While short-term relief of negative emotions is certainly a goal of ER, this immediate effect does not necessarily imply adaptive ER in the long run. In this study, we will investigate associations between ER dynamics and resilience. Resilience has been defined as the maintenance or quick recovery of mental health during and after times of adversity. In the long run, better regulation of emotional responses to adverse events should result in less severe mental health problems, i.e., in a more resilient outcome (Kalisch et al., 2015). Following the frequent stressor and mental health monitoring (FRESHMO) paradigm (Kalisch et al, 2021), resilience is operationalized by relating an individual’s mental health problems to their experienced adversity. We here use the stressor reactivity (SR) score that relates the presence of internalizing symptoms to the amount of stressor exposure by means of normative modelling. Low stressor reactivity, meaning few internalizing symptoms relative to one’s stressor exposure, is used as a proxy for resilience (Kalisch et al., 2021). Variability and predictability in emotion regulation Blanke et al. (2020) and Wenzel et al. (2022) found between- and within-strategy variability as well as ER predictability robustly associated with negative affect. Between-strategy variability refers to the variability in selecting different strategies from a repertoire, while within-strategy variability is the variability in intensity of usage of one strategy over time. ER predictability assesses reoccurring patterns of endorsement (i.e., strategy use) spanning over more than two time points within individual strategies. Investigating how these indices relate to resilience will provide further insight into relevance and scope of these ER dynamic measures. Emotion regulation flexibility Following the definition by Aldao et al. (2015), the context in which a certain strategy is applied needs to be considered to assess ER flexibility. Bellingtier et al. (2022) calculated a global indicator for dominance of ER strategies over different stressor domains, interpreted as an inverse of ER flexibility. A dominant use of one or only few ER strategies over multiple stressor domains (interpersonal, financial, work-related, etc.) would indicate low ER flexibility. No significant associations were found between dominance across stressor domains and reactivity to hassles. A potential explanation is that there is no sufficiently high demand for ER differentiation between these chosen stressor domains. Here we will investigate measures based on other dimensions, namely stressor intensity, and the social as well as physical context in which a stressor is experienced. Dynamics in use of positive cognitive reappraisal The use of positive cognitive reappraisal has robustly been linked to various beneficial outcomes (Riepenhausen et al., 2022). There are multiple subtypes of positive cognitive reappraisal, often referred to as reappraisal tactics (Gross, 2015; McRae et al., 2012). Examples are perspective taking-based, acceptance-based, distancing-based, or growth-focused reappraisal. To date, no research has investigated dynamics within this super-category of positive cognitive reappraisal. In our work, we will assess ER dynamics both between different ER strategies and between subtypes of positive cognitive reappraisal. ‘Positive cognitive reappraisal’ will be referred to as ‘reappraisal’ in the remainder. Cognitive control and ER flexibility Finally, to advance our understanding of ER flexibility, we investigate its potential cognitive underpinnings. Prüssner et al. (2020) postulate that cognitive control is crucial for an adaptive ER and that a balance between flexibly shifting between strategies and shielding a strategy from distraction to maintain it is key. We here investigate how individual differences in cognitive control are associated with ER dynamics in daily life.

Supplementary Material 1

This analysis will be conducted in subproject 7 of the DFG-funded research unit 5187 “Towards precision psychotherapy for non-respondent patients: From signatures to predictions to clinical utility” (see more information here: https://forschungsgruppe5187.de/en). The general aim of the unit is to foster precision psychotherapy by i) investigating clinical and bio-behavioral signatures of nonresponse to improve our understanding of this phenomenon, ii) applying state-of-the-art machine learning technology for single-case predictions, and iii) validating these for clinical utility in an ecologically valid treatment setting. In our subproject, we will investigate whether anterior cingulate cortex (ACC)-based neuroimaging measures are able to make accurate single-case predictions of nonresponse to cognitive behavioral therapy (CBT). For this endeavor, we will train several ACC-based models on a retrospective data set (see preregistration here: https://doi.org/10.17605/OSF.IO/CEF24) and validate these results on the here described prospective data set. The prospective data set will be collected between Spring 2023 (start mid of May 2023) and Spring 2025 within the research unit 5187 and will consist of approximately N = 330.

SP3 is a subproject of the DFG-funded research unit 5187 “Towards precision psychotherapy for non-respondent patients: From signatures to predictions to clinical utility” (see more information here: https://forschungsgruppe5187.de/en). The general aim of the unit is to foster precision psychotherapy by i) investigating clinical and bio-behavioral signatures of nonresponse to cognitive-behavioral therapy (CBT) to improve our understanding of this phenomenon, ii) applying state-of-the-art machine learning technology for single-case predictions, and iii) validating these for clinical utility in an ecologically valid treatment setting. SP1 (see preregistration https://osf.io/bcgax) describes the set-up of the prospective-longitudinal observational cohort of n = 500 patients with mental disorders from the internalizing spectrum who are treated with CBT. SP3 implements and coordinates the (f)MRI data acquisition and performs quality control and preprocessing of the (f)MRI data. Several subprojects, most importantly SP7, SP8, and SP9, will focus their analysis on different aspects of the acquired (f)MRI data. In contrast to the other subprojects, SP3 will conduct a data-driven transdiagnostic whole-brain connectome-based prediction analysis.

Preregistration of the analysis plan: "Functional resting-state networks and their associations with the stress response, daily stress and physical health", using data from the "Dynamic Modelling of Resilience - Observational Study" (DynaM-OBS) study.

This analysis will be conducted in subproject 7 of the DFG-funded research unit 5187 “Towards precision psychotherapy for non-respondent patients: From signatures to predictions to clinical utility” (see more information here: https://forschungsgruppe5187.de/en). The general aim of the unit is to foster precision psychotherapy by i) investigating clinical and bio-behavioral signatures of nonresponse to improve our understanding of this phenomenon, ii) applying state-of-the-art machine learning technology for single-case predictions, and iii) validating these for clinical utility in an ecologically valid treatment setting. In our subproject, we will investigate whether anterior cingulate cortex (ACC)-based neuroimaging measures are able to make accurate single-case predictions of nonresponse to cognitive behavioral therapy (CBT). ACC-based neuroimaging measures are highly promising as the ACC plays a key role in in emotion regulation (Etkin et al., 2015; Stevens, 2016), a construct that might be closely related to psychotherapeutic nonresponse (e.g., Sloan et al., 2017). Indeed, there are first promising studies that have shown that predictions of treatment response based on the ACC are possible on an individual level (e.g., Dunlop et al., 2017; Siegle et al., 2012). However, due to small sample sizes and lacking external validation, the interpretability of these studies is limited. Here, we will perform a retrospective analysis on a much larger, transdiagnostic sample, consisting of approximately 364 patients with internalized disorders. Furthermore, we will validate the results of the here-described analysis on a prospective data set, being collected between Spring 2023 and Spring 2025 within the research unit. There will be an independent preregistration for the prospective analysis linked to this document. We will build several individual predictors to make single-subject predictions, using different kinds of input data. More explicitly, based on structural covariance and resting-state functional connectivities, we will calculate gradients and graph metrics to describe the ACC’s functional division and its integration in crucial brain systems. Furthermore, in order to optimize prediction accuracies, we will integrate these separate models in a stacked ensemble-learning model.

Deficits in effective executive function, including inhibitory control are associated with risk for a number of psychiatric disorders and significantly impact everyday functioning. These complex traits have been proposed to serve as endophenotypes, however their genetic architecture is not yet well understood. To identify the common genetic variation associated with inhibitory control in the general population we performed the first trans-ancestry genome wide association study (GWAS) combining data across 8 sites and four ancestries (N=14,877) using behavioural traits derived from the stop-signal task, namely – go reaction time (GoRT), go reaction time variability (GoRT SD) and stop signal reaction time (SSRT). Although we did not identify genome wide significant associations for any of the three traits, GoRT SD and SSRT demonstrated significant and similar SNP heritability of 8.2%, indicative of an influence of genetic factors. Power analyses demonstrated that the number of common causal variants contributing to the heritability of these phenotypes is relatively high and larger sample sizes are necessary to robustly identify associations. The polygenic risk for ADHD was significantly associated with GoRT SD further supporting its suggested utility as an endophenotype for ADHD. Together these findings provide the first evidence indicating the influence of common genetic variation in the genetic architecture of inhibitory control quantified using objective behavioural traits derived from the stop-signal task.

BACKGROUND: Posttraumatic stress disorder (PTSD) is accompanied by disrupted cortical neuroanatomy. We investigated alteration in covariance of structural networks associated with PTSD in regions that demonstrate the case-control differences in cortical thickness (CT) and surface area (SA). METHODS: Neuroimaging and clinical data were aggregated from 29 research sites in >1,300 PTSD cases and >2,000 trauma-exposed controls (age 6.2-85.2 years) by the ENIGMA-PGC PTSD working group. Cortical regions in the network were rank-ordered by effect size of PTSD-related cortical differences in CT and SA. The top-n (n = 2 to 148) regions with the largest effect size for PTSD > non-PTSD formed hypertrophic networks, the largest effect size for PTSD < non-PTSD formed atrophic networks, and the smallest effect size of between-group differences formed stable networks. The mean structural covariance (SC) of a given n-region network was the average of all positive pairwise correlations and was compared to the mean SC of 5,000 randomly generated n-region networks. RESULTS: Patients with PTSD, relative to non-PTSD controls, exhibited lower mean SC in CT-based and SA-based atrophic networks. Comorbid depression, sex and age modulated covariance differences of PTSD-related structural networks. CONCLUSIONS: Covariance of structural networks based on CT and cortical SA are affected by PTSD and further modulated by comorbid depression, sex, and age. The structural covariance networks that are perturbed in PTSD comport with converging evidence from resting state functional connectivity networks and networks impacted by inflammatory processes, and stress hormones in PTSD.

The first interim analysis of the data set, preregistered on 01/04/2020 (osf.io/r6btn) and published as a preprint on 22/04/2020 (psyarxiv.com/4z62t/), has confirmed the main effects hypotheses H1 to H9 about resilience factors as well as the mediation hypotheses H10 and H11 in a sample of the first 5000 European respondents providing complete survey data. In particular, our primary hypothesis about positive appraisal style as a resilience factor (H1) was tested positively. Regularized regression analysis identified positive appraisal specifically of the consequences of the Corona crisis (PAC, see H9) as the strongest resilience factor. The data used for the analysis were acquired between 22/03/2020 (start of data collection) and 01/04/2020, that is, during a time window when measures of quarantine, social distancing, or curfew were already in place in many European countries. This amendment describes the plan for the second interim analysis, in which we will use the data acquired from study start on 22/03/2020 until 19/04/2020, that is, before the date (20/04/2020) when several of the European countries most strongly represented in our sample started to loosen the movement and contact restrictions. Thereby, we intend to obtain a complete picture of resilience factors during the most intense phase of the lockdown in response to the first wave of the pandemic. The independent and dependent variables, the main effects and mediation hypotheses H1 to H11, and the design plan remain identical (see sections 4 and 5 of the preregistration nr. 1). We will also use the same analysis methods (hypothesis testing with multiple regression and mediation analyses and combined regularized regression analysis) to determine the relative strengths of the hypothesized resilience factors (see section 7 of the preregistration). The considerably larger sample size than in the first interim analysis allows us to also perform exploratory subgroup analyses. In particular, we will here explore if and how the covariates age, gender, country of residence, education, household income, and past or present mental health diagnosis moderate the effects of the resilience factors (as formulated in the main effects hypotheses H1 to H9) on resilience. To this end, we will calculate additional multiple regression analyses, separately for each resilience factor (as above), and there introduce the interaction term of the resilience factor with the given covariate. We will then plot and interpret the resulting pattern of effect estimates that correspond to the main hypotheses for each level of the covariate. See section 6 of this amendment. Thereby, these subgroup analyses will shed first light on a) whether the hypothesized main effects H1 to H9 generalize across subgroups and b) whether subgroups differ in their main effect sizes. If effects generalize across subgroups, that is, if they are positive at all levels of a covariate (a), the identified resilience factors can be considered potentially valid in the broader population from which the current sample was drawn. If there are pronounced subgroup differences in a covariate (b), this would yield valuable information on which resilience factors might be the most promising targets of preventive interventions in different subgroups. These interaction analyses will be complemented with combined regularized regression analyses at each of the covariate levels, to learn more about which appear to be the strongest resilience factors in each subgroup.

The present study aims to (1) replicate the findings of differential fronto-amygdala coupling depending on FAAH C385A genotype (rs324420) as reported in Gärtner et al. (2019). Furthermore, the study aims to extent these findings and investigates 2) habituation patterns in fronto-amygdala circuits depending on FAAH genotypes. Given the failure to replicate findings from candidate gene studies (e.g., Border et al., 2019), which partly relies on the small effect sizes of single candidate genes, we further 3) investigate whether a polygenic score (PGS) comprising genetic variation from the endocannabinoid system (PGS 1), the serotonin system (PGS 2) and anxiety-related genetic variation derived from recent GWAS findings (PGS 3) explains a larger amount of variance in the investigated phenotypes than single polymorphisms.

The current Corona pandemic is not only a threat to physical health. First data from China and the United Kingdom indicate that symptoms of anxiety and depression and perceptions of stress rise significantly as a consequence of the pandemic. There are also anecdotal reports in the general media of increased domestic violence, divorce and suicide rates. Hence, the Corona crisis is also a mental health crisis. Threats to public mental health are likely to further increase with the spread of the virus across the world and the potentially severe economic, societal and political side effects of the pandemic. In this situation, there is urgent need for knowledge about factors that can protect mental health in this world-wide crisis and that can be harnessed in the service of public mental health promotion. DynaCORE-C attempts to identify these protective factors (resilience factors) with the help of an internet-based cross-sectional questionnaire survey for adults available in multiple languages. Our dependent variable is resilience. In keeping with current conceptualizations of this construct (Bonanno et al., 2015; Kalisch et al., 2017), we define resilience as an outcome of good mental health despite exposure to adversity (outcome-based resilience) and measure it by relating mental health outcomes to experienced stressors (Kalisch et al., 2015), which we extensively sample. That is, we do not rely on resilience questionnaires to obtain our dependent variable. In defining our hypotheses about independent variables (resilience factors), we focus on modifiable factors that can be targeted by prevention measures. Specifically, we hypothesize that social support, an increase in social support during the Corona crisis, optimism, general self-efficacy, good stress recovery, positive appraisal style (primary hypothesis), positive appraisal specifically of the Corona crisis, and behavioral coping are positively associated with resilience (outcome), while neuroticism is negatively associated with resilience. We also hypothesize that the expected positive effect of social support on resilience is positively mediated by its effect on positive appraisal style, and that the expected positive effect of positive appraisal style on resilience is positively mediated by its effect on stress recovery. Open questions in the survey allow for detecting potential non-hypothesized resilience factors. Analyses will be conducted in the whole sample. Subgroup analyses will be conducted for socio-demographic and health characteristics such as gender, country of residence, occupation, or belonging to a risk group. Further, different phases of the crisis (during official general movement or contact restrictions and after the loosening of these restrictions) will be considered. This will allow us to gain insights into which resilience factors are most effective in which groups and at what times of the crisis and how preventive interventions can be optimally targeted.

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).

While there is substantial evidence that cannabis use is associated with differences in human brain development, most of this evidence is correlational in nature. Bayesian causal network (BCN) modeling attempts to identify probable causal relationships in correlational data using conditional probabilities to estimate directional associations between a set of interrelated variables. In this study, we employed BCN modeling in 637 adolescents from the IMAGEN study who were cannabis naïve at age 14 to provide evidence that the accelerated prefrontal cortical thinning found previously in adolescent cannabis users by Albaugh et al. [1] is a result of cannabis use causally affecting neurodevelopment. BCNs incorporated data on cannabis use, prefrontal cortical thickness, and other factors related to both brain development and cannabis use, including demographics, psychopathology, childhood adversity, and other substance use. All BCN algorithms strongly suggested a directional relationship from adolescent cannabis use to accelerated cortical thinning. While BCN modeling alone does not prove a causal relationship, these results are consistent with a body of animal and human research suggesting that adolescent cannabis use adversely affects brain development.

BACKGROUND AND AIMS: Graph theoretic analysis of structural covariance networks (SCN) provides an assessment of brain organization that has not yet been applied to alcohol dependence (AD). We estimated whether SCN differences are present in adults with AD and heavy-drinking adolescents at age 19 and age 14, prior to substantial exposure to alcohol. DESIGN: Cross-sectional sample of adults and a cohort of adolescents. Correlation matrices for cortical thicknesses across 68 regions were summarized with graph theoretic metrics. SETTING AND PARTICIPANTS: A total of 745 adults with AD and 979 non-dependent controls from 24 sites curated by the Enhancing NeuroImaging Genetics through Meta Analysis (ENIGMA)-Addiction consortium, and 297 hazardous drinking adolescents and 594 controls at ages 19 and 14 from the IMAGEN study, all from Europe. MEASUREMENTS: Metrics of network segregation (modularity, clustering coefficient and local efficiency) and integration (average shortest path length and global efficiency). FINDINGS: The younger AD adults had lower network segregation and higher integration relative to non-dependent controls. Compared with controls, the hazardous drinkers at age 19 showed lower modularity [area-under-the-curve (AUC) difference?=?-0.0142, 95% confidence interval (CI)?=?-0.1333, 0.0092; P-value?=?0.017], clustering coefficient (AUC difference?=?-0.0164, 95% CI?=?-0.1456, 0.0043; P-value?=?0.008) and local efficiency (AUC difference?=?-0.0141, 95% CI?=?-0.0097, 0.0034; P-value?=?0.010), as well as lower average shortest path length (AUC difference?=?-0.0405, 95% CI?=?-0.0392, 0.0096; P-value?=?0.021) and higher global efficiency (AUC difference?=?0.0044, 95% CI?=?-0.0011, 0.0043; P-value?=?0.023). The same pattern was present at age 14 with lower clustering coefficient (AUC difference?=?-0.0131, 95% CI?=?-0.1304, 0.0033; P-value?=?0.024), lower average shortest path length (AUC difference?=?-0.0362, 95% CI?=?-0.0334, 0.0118; P-value?=?0.019) and higher global efficiency (AUC difference?=?0.0035, 95% CI?=?-0.0011, 0.0038; P-value?=?0.048). CONCLUSIONS: Cross-sectional analyses indicate that a specific structural covariance network profile is an early marker of alcohol dependence in adults. Similar effects in a cohort of heavy-drinking adolescents, observed at age 19 and prior to substantial alcohol exposure at age 14, suggest that this pattern may be a pre-existing risk factor for problematic drinking.

BackgroundAdolescent onset of depression is associated with long-lasting negative consequences. Identifying adolescents at risk for developing depression would enable the monitoring of risk-factors and the development of early intervention strategies. Using machine learning to combine several risk factors from multiple modalities might allow prediction of depression onset at the individual level.MethodsA subsample of a multi-site longitudinal study in adolescents, the IMAGEN study, was used to predict future (subthreshold) major depressive disorder (MDD) onset in healthy adolescents. Based on 2-year and 5-year follow-up data, participants were grouped into: 1) developing an MDD diagnosis or subthreshold MDD and 2) healthy controls. Baseline measurements of 145 variables from different modalities (clinical, cognitive, environmental and structural magnetic resonance imaging [MRI]) at age 14 were used as input to penalized logistic regression (with different levels of penalization) to predict depression onset in a training dataset (N=407). The features contributing highest to the prediction were validated in an independent hold-out sample (3 independent IMAGEN sites; N=137).ResultsThe area under the receiver operating characteristics curve (AUROC) for predicting depression onset ranged between 0.70-0.72 in the training dataset. Baseline severity of depressive symptoms, female sex, neuroticism, stressful life events and surface area of the supramarginal gyrus contributed most to the predictive model and predicted onset of depression with an AUROC between 0.68-0.72 in the independent validation sample.ConclusionsThis study showed that depression onset in adolescents can be predicted based on a combination multimodal data of clinical, life events, personality traits, brain structure variables.

It has been suggested that autistic traits are associated with less frequent alcohol use in adolescence. Our study seeks to examine the relationship between autistic traits and alcohol use in a large adolescent population. Leveraging data from the IMAGEN cohort, including 2045 14-year-old adolescents that were followed-up to age 18, we selected items on social preference/skills and rigidity from different questionnaires. We used linear regression models to (1) test the effect of the sum scores on the prevalence of alcohol use (AUDIT-C) over time, (2) explore the relationship between autistic traits and alcohol use patterns, and (3) explore the specific effect of each autistic trait on alcohol use. Higher scores on the selected items were associated with trajectories of less alcohol use from the ages between 14 and 18 (b = ? 0.030; CI 95% = ? 0.042, ? 0.017; p < 0.001). Among adolescents who used alcohol, those who reported more autistic traits were also drinking less per occasion than their peers and were less likely to engage in binge drinking. We found significant associations between alcohol use and social preference (p < 0.001), nervousness for new situations (p = 0.001), and detail orientation (p < 0.001). Autistic traits (social impairment, detail orientation, and anxiety) may buffer against alcohol use in adolescence.

Dynamic interactions between brain regions, either during rest or performance of cognitive tasks, have been studied extensively using a wide variance of methods. Although some of these methods allow elegant mathematical interpretations of the data, they can easily become computationally expensive or difficult to interpret and compare between subjects or groups. Here, we propose an intuitive and computationally efficient method to measure dynamic reconfiguration of brain regions, also termed flexibility. Our flexibility measure is defined in relation to an a-priori set of biologically plausible brain modules (or networks) and does not rely on a stochastic data-driven module estimation, which, in turn, minimizes computational burden. The change of affiliation of brain regions over time with respect to these a-priori template modules is used as an indicator of brain network flexibility. We demonstrate that our proposed method yields highly similar patterns of whole-brain network reconfiguration (i.e., flexibility) during a working memory task as compared to a previous study that uses a data-driven, but computationally more expensive method. This result illustrates that the use of a fixed modular framework allows for valid, yet more efficient estimation of whole-brain flexibility, while the method additionally supports more fine-grained flexibility analyses restricted to biologically plausible brain networks.

Urbanicity is a growing environmental challenge for mental health. Here, we investigate correlations of urbanicity with brain structure and function, neuropsychology and mental illness symptoms in young people from China and Europe (total n = 3,867). We developed a remote-sensing satellite measure (UrbanSat) to quantify population density at any point on Earth. UrbanSat estimates of urbanicity were correlated with brain volume, cortical surface area and brain network connectivity in the medial prefrontal cortex and cerebellum. UrbanSat was also associated with perspective-taking and depression symptoms, and this was mediated by neural variables. Urbanicity effects were greatest when urban exposure occurred in childhood for the cerebellum, and from childhood to adolescence for the prefrontal cortex. As UrbanSat can be generalized to different geographies, it may enable assessments of correlations of urbanicity with mental illness and resilience globally.

Emerging evidence suggests that obesity impacts brain physiology at multiple levels. Here we aimed to clarify the relationship between obesity and brain structure using structural MRI (n = 6420) and genetic data (n = 3907) from the ENIGMA Major Depressive Disorder (MDD) working group. Obesity (BMI > 30) was significantly associated with cortical and subcortical abnormalities in both mass-univariate and multivariate pattern recognition analyses independent of MDD diagnosis. The most pronounced effects were found for associations between obesity and lower temporo-frontal cortical thickness (maximum Cohen´s d (left fusiform gyrus) = −0.33). The observed regional distribution and effect size of cortical thickness reductions in obesity revealed considerable similarities with corresponding patterns of lower cortical thickness in previously published studies of neuropsychiatric disorders. A higher polygenic risk score for obesity significantly correlated with lower occipital surface area. In addition, a significant age-by-obesity interaction on cortical thickness emerged driven by lower thickness in older participants. Our findings suggest a neurobiological interaction between obesity and brain structure under physiological and pathological brain conditions.

Behavioral studies found evidence for superior cognitive empathy (CE) in pedophilic men without a history of child sexual offending (P - CSO) compared to pedophilic men with a history of child sexual offending (P + CSO). Functional magnetic resonance imaging (fMRI) studies also point to differences between P - CSO and P + CSO. Neural processing associated with CE has not yet been investigated. Therefore, the present study aimed to explore the neural correlates of CE in subjects with pedophilia with (P + CSO) and without (P - CSO) child sexual offending. 15 P + CSO, 15 P - CSO and 24 teleiophilic male controls (TC) performed a CE task during fMRI. We observed reduced activation in the left precuneus (Pcu) and increased activation in the left anterior cingulate cortex (ACC) in P - CSO compared to P + CSO. P - CSO also showed stronger connectivity between these regions, which might reflect a top-down modulation of the Pcu by the ACC toward an increased self-focused emotional reaction in social situations. There was also evidence for increased right superior temporal gyrus activation in P - CSO that might constitute a potentially compensatory recruitment due to the dampened Pcu activation. These findings provide first evidence for altered neural processing of CE in P - CSO and underline the importance of addressing CE in pedophilia and CSO in order to uncover processes relevant to effective prevention of child sexual abuse. CA extern

Importance Alcohol consumption (AC) leads to death and disability worldwide. Ongoing discussions on potential negative effects of the COVID-19 pandemic on AC need to be informed by real-world evidence. Objective To examine whether lockdown measures are associated with AC and consumption-related temporal and psychological within-person mechanisms. Design, Setting, and Participants This quantitative, intensive, longitudinal cohort study recruited 1743 participants from 3 sites from February 20, 2020, to February 28, 2021. Data were provided before and within the second lockdown of the COVID-19 pandemic in Germany: before lockdown (October 2 to November 1, 2020); light lockdown (November 2 to December 15, 2020); and hard lockdown (December 16, 2020, to February 28, 2021). Main Outcomes and Measures Daily ratings of AC (main outcome) captured during 3 lockdown phases (main variable) and temporal (weekends and holidays) and psychological (social isolation and drinking intention) correlates. Results Of the 1743 screened participants, 189 (119 [63.0%] male; median [IQR] age, 37 [27.5-52.0] years) with at least 2 alcohol use disorder (AUD) criteria according to the Diagnostic and Statistical Manual of Mental Disorders (Fifth Edition) yet without the need for medically supervised alcohol withdrawal were included. These individuals provided 14 694 smartphone ratings from October 2020 through February 2021. Multilevel modeling revealed significantly higher AC (grams of alcohol per day) on weekend days vs weekdays (β = 11.39; 95% CI, 10.00-12.77; P < .001). Alcohol consumption was above the overall average on Christmas (β = 26.82; 95% CI, 21.87-31.77; P < .001) and New Year’s Eve (β = 66.88; 95% CI, 59.22-74.54; P < .001). During the hard lockdown, perceived social isolation was significantly higher (β = 0.12; 95% CI, 0.06-0.15; P < .001), but AC was significantly lower (β = −5.45; 95% CI, −8.00 to −2.90; P = .001). Independent of lockdown, intention to drink less alcohol was associated with lower AC (β = −11.10; 95% CI, −13.63 to −8.58; P < .001). Notably, differences in AC between weekend and weekdays decreased both during the hard lockdown (β = −6.14; 95% CI, −9.96 to −2.31; P = .002) and in participants with severe AUD (β = −6.26; 95% CI, −10.18 to −2.34; P = .002). Conclusions and Relevance This 5-month cohort study found no immediate negative associations of lockdown measures with overall AC. Rather, weekend-weekday and holiday AC patterns exceeded lockdown effects. Differences in AC between weekend days and weekdays evinced that weekend drinking cycles decreased as a function of AUD severity and lockdown measures, indicating a potential mechanism of losing and regaining control. This finding suggests that temporal patterns and drinking intention constitute promising targets for prevention and intervention, even in high-risk individuals., Zweitveröffentlichungen der Universität Potsdam : Humanwissenschaftliche Reihe; 805

Supplemental material, sj-docx-1-emr-10.1177_17540739221114642 for Positive Cognitive Reappraisal in Stress Resilience, Mental Health, and Well-Being: A Comprehensive Systematic Review by Antje Riepenhausen, Carolin Wackerhagen, Zala C. Reppmann, Hans-Christian Deter, Raffael Kalisch, Ilya M. Veer and Henrik Walter in Emotion Review

MRI-derived brain measures offer a link between genes, the environment and behavior and have been widely studied in bipolar disorder (BD). However, many neuroimaging studies of BD have been underpowered, leading to varied results and uncertainty regarding effects. The Enhancing Neuro Imaging Genetics through Meta-Analysis (ENIGMA) Bipolar Disorder Working Group was formed in 2012 to empower discoveries, generate consensus findings and inform future hypothesis-driven studies of BD. Through this effort, over 150 researchers from 20 countries and 55 institutions pool data and resources to produce the largest neuroimaging studies of BD ever conducted. The ENIGMA Bipolar Disorder Working Group applies standardized processing and analysis techniques to empower large-scale meta- and mega-analyses of multimodal brain MRI and improve the replicability of studies relating brain variation to clinical and genetic data. Initial BD Working Group studies reveal widespread patterns of lower cortical thickness, subcortical volume and disrupted white matter integrity associated with BD. Findings also include mapping brain alterations of common medications like lithium, symptom patterns and clinical risk profiles and have provided further insights into the pathophysiological mechanisms of BD. Here we discuss key findings from the BD working group, its ongoing projects and future directions for large-scale, collaborative studies of mental illness.

Supplemental material, sj-docx-1-emr-10.1177_17540739221114642 for Positive Cognitive Reappraisal in Stress Resilience, Mental Health, and Well-Being: A Comprehensive Systematic Review by Antje Riepenhausen, Carolin Wackerhagen, Zala C. Reppmann, Hans-Christian Deter, Raffael Kalisch, Ilya M. Veer and Henrik Walter in Emotion Review

Traditional models of future alcohol use in adolescents have used variable-centered approaches, predicting alcohol use from a set of variables across entire samples or populations. Following the proposition that predictive factors may vary in adolescents as a function of family history, we used a two-pronged approach by first defining clusters of familial risk, followed by prediction analyses within each cluster. Thus, for the first time in adolescents, we tested whether adolescents with a family history of drug abuse exhibit a set of predictors different from adolescents without a family history. We apply this approach to a genetic risk score and individual differences in personality, cognition, behavior (risk-taking and discounting) substance use behavior at age 14, life events, and functional brain imaging, to predict scores on the alcohol use disorders identification test (AUDIT) at age 14 and 16 in a sample of adolescents (N = 1659 at baseline, N = 1327 at follow-up) from the IMAGEN cohort, a longitudinal community-based cohort of adolescents. In the absence of familial risk (n = 616), individual differences in baseline drinking, personality measures (extraversion, negative thinking), discounting behaviors, life events, and ventral striatal activation during reward anticipation were significantly associated with future AUDIT scores, while the overall model explained 22% of the variance in future AUDIT. In the presence of familial risk (n = 711), drinking behavior at age 14, personality measures (extraversion, impulsivity), behavioral risk-taking, and life events were significantly associated with future AUDIT scores, explaining 20.1% of the overall variance. Results suggest that individual differences in personality, cognition, life events, brain function, and drinking behavior contribute differentially to the prediction of future alcohol misuse. This approach may inform more individualized preventive interventions.

Traditional models of future alcohol use in adolescents have used variable-centered approaches, predicting alcohol use from a set of variables across entire samples or populations. Following the proposition that predictive factors may vary in adolescents as a function of family history, we used a two-pronged approach by first defining clusters of familial risk, followed by prediction analyses within each cluster. Thus, for the first time in adolescents, we tested whether adolescents with a family history of drug abuse exhibit a set of predictors different from adolescents without a family history. We apply this approach to a genetic risk score and individual differences in personality, cognition, behavior (risk-taking and discounting) substance use behavior at age 14, life events, and functional brain imaging, to predict scores on the alcohol use disorders identification test (AUDIT) at age 14 and 16 in a sample of adolescents (N = 1659 at baseline, N = 1327 at follow-up) from the IMAGEN cohort, a longitudinal community-based cohort of adolescents. In the absence of familial risk (n = 616), individual differences in baseline drinking, personality measures (extraversion, negative thinking), discounting behaviors, life events, and ventral striatal activation during reward anticipation were significantly associated with future AUDIT scores, while the overall model explained 22% of the variance in future AUDIT. In the presence of familial risk (n = 711), drinking behavior at age 14, personality measures (extraversion, impulsivity), behavioral risk-taking, and life events were significantly associated with future AUDIT scores, explaining 20.1% of the overall variance. Results suggest that individual differences in personality, cognition, life events, brain function, and drinking behavior contribute differentially to the prediction of future alcohol misuse. This approach may inform more individualized preventive interventions.

Prompted by recent evidence of neural circuitry in rodent models, functional magnetic resonance imaging and functional connectivity analyses were conducted for a large adolescent population at two ages, together with alcohol abuse measures, to characterize a neural network that may underlie the onset of alcoholism. A network centered on the medial orbitofrontal cortex (mOFC), as well as including the dorsal periaqueductal gray (dPAG), central nucleus of the amygdala, and nucleus accumbens, was identified, consistent with the rodent models, with evidence of both inhibitory and excitatory coregulation by the mOFC over the dPAG. Furthermore, significant relationships were detected between raised baseline excitatory coregulation in this network and impulsivity measures, supporting a role for negative urgency in alcohol dependence.

BACKGROUND:Eating disorders are common in adolescence, devastating and strongly comorbid with other psychiatric disorders. Yet, little is known about their etiology to develop effective preventive measures.METHODS:Longitudinal assessments of disordered eating behaviors (DEBs; binge-eating, purging and dieting) and comorbid psychopathology were measured in 1,386 adolescents from the IMAGEN study. Development of DEBs and associated mental health problems were investigated by comparing participants who reported symptoms at ages 16 or 19, but not at age 14 to asymptomatic controls. Voxel-based morphometry and psychopathological differences at age 14 were investigated to identify risk factors for the development of DEBs and associated mental health problems.RESULTS:DEBs and depressive symptoms developed together. Emotional and behavioral problems, including symptoms of attention deficit hyperactivity disorder (ADHD) and conduct disorder (CD), predated their development. Alterations in fronto-striatal brain areas also predated the development of DEBs and depressive symptoms. Specifically, development of binge-eating was predicted by higher grey matter volumes in the right putamen/globus pallidus at age 14. Conversely, development of purging and depressive symptoms was predicted by lower volumes in the medial orbitofrontal, dorsomedial and dorsolateral prefrontal cortices. Lower grey matter volumes in the orbitofrontal and anterior cingulate cortices mediated the relationship between ADHD and CD symptoms and future purging and depressive symptoms. CONCLUSIONS:These findings suggest that alterations in frontal brain circuits are part of the shared etiology between eating disorders, ADHD, CD and depression and highlight the importance of a transdiagnostic approach to treating these conditions.?

Translational Psychiatry 11(1), 511 (2021). doi:10.1038/s41398-021-01619-w, Published by Nature Publishing Group, London

The ability to extract cues from faces is fundamental for social animals, including humans. An individual's profile of functional connectivity across a face network can be shaped by common organizing principles, stable individual traits, and time-varying mental states. In the present study, we used data obtained with functional magnetic resonance imaging in two cohorts, IMAGEN (N = 534) and ALSPAC (N = 465), to investigate - both at group and individual levels - the consistency of the regional profile of functional connectivity across populations (IMAGEN, ALSPAC) and time (Visits 1 to 3 in IMAGEN; age 14 to 22 years). At the group level, we found a robust canonical profile of connectivity both across populations and time. At the individual level, connectivity profiles deviated from the canonical profile, and the magnitude of this deviation related to the presence of psychopathology. These findings suggest that the brain processes faces in a highly stereotypical manner, and that the deviations from this normative pattern may be related to the risk of mental illness.

The SARS-CoV-2 pandemic is not only a threat to physical health but is also having severe impacts on mental health. Although increases in stress-related symptomatology and other adverse psycho-social outcomes, as well as their most important risk factors have been described, hardly anything is known about potential protective factors. Resilience refers to the maintenance of mental health despite adversity. To gain mechanistic insights about the relationship between described psycho-social resilience factors and resilience specifically in the current crisis, we assessed resilience factors, exposure to Corona crisis-specific and general stressors, as well as internalizing symptoms in a cross-sectional online survey conducted in 24 languages during the most intense phase of the lockdown in Europe (22 March to 19 April) in a convenience sample of N = 15,970 adults. Resilience, as an outcome, was conceptualized as good mental health despite stressor exposure and measured as the inverse residual between actual and predicted symptom total score. Preregistered hypotheses (osf.io/r6btn) were tested with multiple regression models and mediation analyses. Results confirmed our primary hypothesis that positive appraisal style (PAS) is positively associated with resilience (p

Adolescence is a period of vulnerability for the maturation of gray matter (GM) and also for the onset of psychiatric disorders such as major depressive disorder (MDD), bipolar disorder and schizophrenia. Chronic neuroinflammation is considered to play a role in the etiology of these illnesses. However, the involvement of neuroinflammation in the observed link between regional GM volume reductions and psychiatric symptoms is not established yet. Here, we investigated a possible common immune-related genetic link between these two phenomena in european adolescents recruited from the community. Hippocampal and medial prefrontal cortex (mPFC) were defined a priori as regions of interest (ROIs). Their GM volumes were extracted in 1,563 14-year-olds from the IMAGEN database. We found a set of 26 SNPs that correlated with the hippocampal volumes and 29 with the mPFC volumes at age 14. We formed two ROI-Related Immune-gene scores (RRI) with the inflammation SNPs that correlated to hippocampal GM volume and to mPFC GM volume. The predictive ability of both RRIs with regards to the presence of psychiatric symptoms at age 18 was investigated by correlating the RRIs with psychometric questionnaires obtained at age 18. The RRIs (but not control scores constructed with random SNPs) correlated with the presence of depressive symptoms, positive psychotic symptoms, and externalizing symptoms in later adolescence. In addition, the effect of childhood maltreatment, one of the major environmental risk factors for depression and other mental disorders, interacted with the RRI effect. We next sought to validate this finding by investigating our set of inflammatory genes in a translational animal model of early life adversity. Mice were subjected to a protocol of maternal separation at an early post-natal age. We evaluated depressive behaviors in separated and non-separated mice at adolescence and their correlations with the concomitant expression of our genes in whole blood samples. We show that in mice, early life adversity affected the expression of our set of genes in peripheral blood, and that levels of expression correlated with symptoms of negative affect in adolescence. Overall, our translational findings in adolescent mice and humans provide a novel validated gene-set of immune-related genes for further research in the early stages of mood disorders.

Background It has not yet been determined if the commonly reported cannabis-psychosis association is limited to individuals with pre-existing genetic risk for psychotic disorders. Methods We examined whether the relationship between polygenic risk score for schizophrenia (PRS-Sz) and psychotic-like experiences (PLEs), as measured by the Community Assessment of Psychic Experiences-42 (CAPE-42) questionnaire, is mediated or moderated by lifetime cannabis use at 16 years of age in 1740 of the individuals of the European IMAGEN cohort. Secondary analysis examined the relationships between lifetime cannabis use, PRS-Sz and the various sub-scales of the CAPE-42. Sensitivity analyses including covariates, including a PRS for cannabis use, were conducted and results were replicated using data from 1223 individuals in the Dutch Utrecht cannabis cohort. Results PRS-Sz significantly predicted cannabis use (p = 0.027) and PLE (p = 0.004) in the IMAGEN cohort. In the full model, considering PRS-Sz and covariates, cannabis use was also significantly associated with PLE in IMAGEN (p = 0.007). Results remained consistent in the Utrecht cohort and through sensitivity analyses. Nevertheless, there was no evidence of a mediation or moderation effects. Conclusions These results suggest that cannabis use remains a risk factor for PLEs, over and above genetic vulnerability for schizophrenia. This research does not support the notion that the cannabis-psychosis link is limited to individuals who are genetically predisposed to psychosis and suggests a need for research focusing on cannabis-related processes in psychosis that cannot be explained by genetic vulnerability.

Major depressive disorder (MDD) is associated with an increased risk of brain atrophy, aging-related diseases, and mortality. We examined potential advanced brain aging in adult MDD patients, and whether this process is associated with clinical characteristics in a large multicenter international dataset. We performed a mega-analysis by pooling brain measures derived from T1-weighted MRI scans from 19 samples worldwide. Healthy brain aging was estimated by predicting chronological age (18-75 years) from 7 subcortical volumes, 34 cortical thickness and 34 surface area, lateral ventricles and total intracranial volume measures separately in 952 male and 1236 female controls from the ENIGMA MDD working group. The learned model coefficients were applied to 927 male controls and 986 depressed males, and 1199 female controls and 1689 depressed females to obtain independent unbiased brain-based age predictions. The difference between predicted "brain age" and chronological age was calculated to indicate brain-predicted age difference (brain-PAD). On average, MDD patients showed a higher brain-PAD of +1.08 (SE 0.22) years (Cohen's d = 0.14, 95% CI: 0.08-0.20) compared with controls. However, this difference did not seem to be driven by specific clinical characteristics (recurrent status, remission status, antidepressant medication use, age of onset, or symptom severity). This highly powered collaborative effort showed subtle patterns of age-related structural brain abnormalities in MDD. Substantial within-group variance and overlap between groups were observed. Longitudinal studies of MDD and somatic health outcomes are needed to further assess the clinical value of these brain-PAD estimates.

A growing number of studies have examined alterations in white matter organization in people with posttraumatic stress disorder (PTSD) using diffusion MRI (dMRI), but the results have been mixed which may be partially due to relatively small sample sizes among studies. Altered structural connectivity may be both a neurobiological vulnerability for, and a result of, PTSD. In an effort to find reliable effects, we present a multi-cohort analysis of dMRI metrics across 3047 individuals from 28 cohorts currently participating in the PGC-ENIGMA PTSD working group (a joint partnership between the Psychiatric Genomics Consortium and the Enhancing NeuroImaging Genetics through Meta-Analysis consortium). Comparing regional white matter metrics across the full brain in 1426 individuals with PTSD and 1621 controls (2174 males/873 females) between ages 18-83, 92% of whom were trauma-exposed, we report associations between PTSD and disrupted white matter organization measured by lower fractional anisotropy (FA) in the tapetum region of the corpus callosum (Cohen's d = -0.11, p = 0.0055). The tapetum connects the left and right hippocampus, for which structure and function have been consistently implicated in PTSD. Results were consistent even after accounting for the effects of multiple potentially confounding variables: childhood trauma exposure, comorbid depression, history of traumatic brain injury, current alcohol abuse or dependence, and current use of psychotropic medications. Our results show that PTSD may be associated with alterations in the broader hippocampal network.

DNA methylation, which is modulated by both genetic factors and environmental exposures, may offer a unique opportunity to discover novel biomarkers of disease-related brain phenotypes, even when measured in other tissues than brain, such as blood. A few studies of small sample sizes have revealed associations between blood DNA methylation and neuropsychopathology, however, large-scale epigenome-wide association studies (EWAS) are needed to investigate the utility of DNA methylation profiling as a peripheral marker for the brain. Here, in an analysis of eleven international cohorts, totalling 3337 individuals, we report epigenome-wide meta-analyses of blood DNA methylation with volumes of the hippocampus, thalamus and nucleus accumbens (NAcc)-three subcortical regions selected for their associations with disease and heritability and volumetric variability. Analyses of individual CpGs revealed genome-wide significant associations with hippocampal volume at two loci. No significant associations were found for analyses of thalamus and nucleus accumbens volumes. Cluster-based analyses revealed additional differentially methylated regions (DMRs) associated with hippocampal volume. DNA methylation at these loci affected expression of proximal genes involved in learning and memory, stem cell maintenance and differentiation, fatty acid metabolism and type-2 diabetes. These DNA methylation marks, their interaction with genetic variants and their impact on gene expression offer new insights into the relationship between epigenetic variation and brain structure and may provide the basis for biomarker discovery in neurodegeneration and neuropsychiatric conditions.

IMPORTANCE Animal studies have shown that the adolescent brain is sensitive to disruptions in endocannabinoid signaling, resulting in altered neurodevelopment and lasting behavioral effects. However, few studies have investigated ties between cannabis use and adolescent brain development in humans.OBJECTIVE To examine the degree to which magnetic resonance (MR) imaging–assessed cerebral cortical thickness development is associated with cannabis use in a longitudinal sample of adolescents.DESIGN, SETTING, AND PARTICIPANTS Data were obtained from the community-based IMAGEN cohort study, conducted across 8 European sites. Baseline data used in the present study were acquired from March 1, 2008, to December 31, 2011, and follow-up data were acquired from January 1, 2013, to December 31, 2016. A total of 799 IMAGEN participants were identified who reported being cannabis naive at study baseline and had behavioral and neuroimaging data available at baseline and 5-year follow-up. Statistical analysis was performed from October 1, 2019, to August 31, 2020.MAIN OUTCOMES AND MEASURES Cannabis use was assessed at baseline and 5-year follow-up with the European School Survey Project on Alcohol and Other Drugs. Anatomical MR images were acquired with a 3-dimensional T1-weighted magnetization prepared gradient echo sequence. Quality-controlled nativeMR images were processed through the CIVET pipeline, version 2.1.0.RESULTS The study evaluated 1598 MR images from 799 participants (450 female participants [56.3%]; mean [SD] age, 14.4 [0.4] years at baseline and 19.0 [0.7] years at follow-up). At 5-year follow-up, cannabis use (from 0 to >40 uses) was negatively associated with thickness in left prefrontal (peak: t785 = –4.87, cluster size = 1558 vertices; P = 1.10 × 10−6, random field theory cluster corrected) and right prefrontal (peak: t785 = –4.27, cluster size = 1551 vertices; P = 2.81 × 10−5, random field theory cluster corrected) cortices. There were no significant associations between lifetime cannabis use at 5- year follow-up and baseline cortical thickness, suggesting that the observed neuroanatomical differences did not precede initiation of cannabis use. Longitudinal analysis revealed that age-related cortical thinning was qualified by cannabis use in a dose-dependent fashion such that greater use, from baseline to follow-up, was associated with increased thinning in left prefrontal (peak: t815.27 = –4.24, cluster size = 3643 vertices; P = 2.28 × 10−8, random field theory cluster corrected) and right prefrontal (peak: t813.30 = –4.71, cluster size = 2675 vertices; P = 3.72 × 10−8, random field theory cluster corrected) cortices. The spatial pattern of cannabis-related thinning was associated with age-related thinning in this sample (r = 0.540; P less than< .001), and a positron emission tomography–assessed cannabinoid 1 receptor–binding map derived from a separate sample of participants (r = −0.189; P less than .001). Analysis revealed that thinning in right prefrontal cortices, from baseline to follow-up, was associated with attentional impulsiveness at follow-up.CONCLUSIONS AND RELEVANCE Results suggest that cannabis use during adolescence is associated with altered neurodevelopment, particularly in cortices rich in cannabinoid 1 receptors and undergoing the greatest age-related thickness change in middle to late adolescence.