Environmental exposures play a critical role in shaping physical and mental health, yet integrating such data into biomedical research remains technically complex and fragmented. The EnvironMENTAL Climate, Urbanicity, Environment and Society (CLUES) framework is an open-source, end-to-end workflow for generating individual-level environmental exposure data. CLUES automates the selection and download of open-access geospatial datasets, standardises spatial and temporal formats, and maps projections, and links resulting environmental variables to individual-level biomedical data, requiring no prior expertise in geospatial data. CLUES covers key environmental domains, including urban and natural space, climate and weather extremes, air pollution, and regional socioeconomic conditions. Designed for extensibility and cross-cohort applicability, it enables multidimensional exposure mapping across global settings and adheres to FAIR (Findability, Accessibility, Interoperability and Reusability) and privacy-compliant data protection principles. In this work, we present the CLUES framework and evaluate its scalability, computational performance, and reproducibility for large-scale biomedical research.

Poor inhibitory control and decision-making are often considered as risks for substance use and other adverse psychiatric outcomes. The Stop-Signal Task (SST) is a widely used protocol, from which inhibitory control is indexed by stop signal reaction time (SSRT). However, heretofore models of SSRT may be too simplistic to capture complex processes underlying task performance. In contrast, the Racing Diffusion Ex-Gaussian ABCD (RDEX-ABCD) model provides a more mechanistic framework, capturing both inhibitory control and task-general decision-making processes during the SST. Here, we applied the RDEX-ABCD model to SST data from the IMAGEN cohort (n > 1000) at ages 19 and 23, and examined model parameters in relation to substance use via Elastic Net regression. Connectome-based predictive modeling was then performed to identify brain networks predicting parameters, and the association between these networks and substance use was examined. We found that parameters indexing inhibitory control had no associations with substance use and were only weakly associated with brain connectivity. In contrast, parameters reflecting general decision-making processes - such as efficiency of evidence accumulation, decision threshold (response caution), probability of go failure - and their associated brain activity were significant predictors of cannabis and cigarette use. These findings suggested that efficiency of evidence accumulation, a neurocognitive mechanism that facilitates adaptive decision making across many contexts, emerged as a robust predictor of substance use vulnerability. Overall, general decision-making mechanisms may act as more reliable indicators of vulnerability to substance use than the conventional inhibitory control measures.

ABSTRACT Understanding the dynamics of brain–behaviour relationships during adolescence is critical for elucidating the neurodevelopmental basis of mental health. Leveraging two large-scale longitudinal cohorts—the Adolescent Brain Cognitive Development (ABCD) and IMAGEN studies, comprising over 10,000 participants aged 10 to 22 years with six waves of multimodal neuroimaging and behavioural data, we applied multi-view sparse canonical correlation analysis to investigate evolving associations between structural MRI, resting-state functional connectivity, and multi-domain behavioural measures. Our findings reveal four fundamental patterns of developmental reorganisation in brain-psychopathology relationships. First, symptom profiles evolved from predominantly externalising features (aggression, attention problems) at ages 10-12 toward global psychopathology by age 14, then transitioned toward internalising features (e.g., anxiety, depression) by ages 19-22, reflecting fundamental shifts in vulnerability from behavioural dysregulation to affective disturbance. Second, cortical thickness exhibited negative associations with externalising symptom profiles throughout development. During early adolescence (ages 10-14) this was driven by broadly distributed decreases across sensorimotor, temporal, visual, and cingulate regions alongside overall mean cortical thickness. After 14, this diffuse pattern shifted towards late maturing association cortices, notably the dorsolateral prefrontal and lateral temporal cortices. Third, this was accompanied by subcortical effects that exhibited greater age-specificity: whilst cerebellar volume contributions were evident at most timepoints, basal ganglia volume influence was principally evident in early development (ages 10-12), with thalamic structures and global subcortical grey matter volume becoming dominant at age 14, marking a transition in which subcortical structures mediate psychopathology associations. Fourth, functional connectivity showed a more dynamic developmental trajectory. During early adolescence, symptom associations were driven by positive connectivity between cognitive control and sensorimotor networks, whereas late adolescence exhibited predominantly positive connectivity patterns, transitioning from dense sensorimotor-frontoparietal configurations to more specific patterns involving the central executive and default-mode networks. These findings fundamentally challenge static biomarker models, demonstrating that adolescent psychopathology reflects developmentally contingent brain-behaviour relationships rather than static neural markers. Age 14 emerges as a critical inflection point marked by convergent thalamic reconfiguration, global subcortical grey matter dominance, and symptom profile transitions. This work provides an empirical foundation for precision mental health strategies tailored to specific developmental windows, with implications for reducing psychiatric burden in youth.

This study investigated associations between bullying victimization and brain development using longitudinal structural MRI data from the IMAGEN cohort (n = 2,094; 1,009 females) across three time points (~14, ~19, and ~22 years). A data-driven analysis revealed that higher bullying victimization was significantly associated with accelerated volumetric growth in subcortical and limbic regions, including the putamen (β = 0.12, 95% CI: 0.10-0.15), amygdala (β = 0.07, 95% CI: 0.05-0.09), hippocampus (β = 0.06, 95% CI: 0.04-0.08), and anterior cingulate cortex (caudal: β = 0.05, 95% CI: 0.03-0.07; rostral: β = 0.06, 95% CI: 0.04-0.08). In contrast, bullying victimization was also significantly associated with reduced volumetric growth in the cerebellum (β = -0.09, 95% CI: -0.11 to -0.07), entorhinal cortex (β = -0.10, 95% CI: -0.13 to -0.07), and insula (β = -0.08, 95% CI: -0.11 to -0.06). Exploratory analyses indicated that females exhibited more pronounced changes in emotional processing regions, while males showed greater changes in motor and sensory areas. Overall, the findings indicate that bullying victimization is associated with widespread structural differences in brain development from adolescence to early adulthood, with sex-specific trajectories.

Globally, 60% of the population has experienced at least one type of adversity (e.g., emotional abuse, bullying) across infancy, childhood, and adolescence. Such experiences have been linked to an increased risk for mental health disorders. Changes in brain structure following experiences of childhood adversity have been hypothesised to be a mechanistic pathway explaining later mental health issues. However, to understand how changes in brain structure might mediate the effects of adversity, it is essential to identify which underlying neuronal processes may be affected by different types of adverse experiences. A key open question is whether grey or white matter is more vulnerable to adversity, as these two structures reflect distinct neurobiological mechanisms. This study investigated whether differences in trajectories of grey and white matter development during adolescence can be explained by exposure to different types of adversity. We applied the Adverse Adolescent Experiences Framework (Pollmann et al., 2025) categorising adversity into four levels: Intrapersonal (e.g., accidents), Caregiver (e.g., emotional neglect), Peer (e.g., bullying), and Community (e.g., neighbourhood safety). Exposure to each of the four factors was estimated through principal components analyses. We analysed two large longitudinal datasets: the Adolescent Brain Cognitive Development study (~12,000 adolescents measured at ages 10, 12, and 14) and the IMAGEN study (~1,400 adolescents measured at ages 14, 19, and 22). Using latent growth curve models, we captured individual differences in brain development by estimating baseline levels (intercepts) and rates of change (slopes) for total grey matter volume and mean white matter fractional anisotropy. In both cohorts, we found significant interindividual variability in baseline levels and rates of change for both grey matter volume and fractional anisotropy. Caregiver, Peer, and Community adversities were negatively associated only with the intercepts of grey matter volume and white matter fractional anisotropy. Importantly, associations differed between grey and white matter. In ABCD, Peer and Community adversities were more strongly associated with grey matter volume intercepts. In contrast, in IMAGEN, Caregiver, Peer and Community adversities were more strongly linked to white matter fractional anisotropy intercepts. This suggests that adversity has unique associations with grey and white matter, rather than exerting a uniform influence on brain structure. By demonstrating that different environments generate distinct biological associations with brain maturation, this work underscores the need to consider both grey and white matter when assessing the neurodevelopmental pathways to outcomes across the lifespan.

Inhibitory control matures progressively from childhood to early adulthood, yet the neural mechanisms driving this development and their relevance to psychiatric risk remain poorly understood. Guided by the Dual Cognitive Control model, we leveraged longitudinal fMRI from two independent cohorts in the US (ABCD, ages 9-12) and Europe (IMAGEN, ages 14-22) to map the spatiotemporal dynamics of reactive and proactive control using novel single-trial modeling and representational similarity analysis. We found both reactive and proactive stopping networks stabilize after mid-adolescence, tracking the developmental patterns of inhibitory control and behavioral stability. By decoding trial-by-trial fluctuations along a speedcaution continuum, we demonstrate that brain-behavior coupling to a proactive "Safe state" tightens progressively with age. Furthermore, network-level representational coherence of this Safe state emerged as a scanner-invariant, trait-like biomarker that robustly predicted inhibitory control, behavioral stability, and transdiagnostic psychopathology across multiple developmental windows, providing a validated neural phenotype for precision psychiatry.

BACKGROUND:Adolescence is a sensitive period when suicidal ideation can peak. Although traumatic life events are a well established risk factor for suicidal ideation, the role of cumulative ordinary life events across multiple dimensions, such as family, autonomy, and sexuality, remains underexplored. Crucially, no evidence is available on how multidimensional trajectories of cumulative exposure to such events relate to suicidal ideation throughout adolescence. We aimed to examine how cumulative and domain-specific trajectories of ordinary life events during adolescence relate to suicidal ideation. METHODS:In this multinational, prospective cohort study, we used longitudinal data from the IMAGEN cohort, gathered from eight centres in Germany, France, Ireland, and the UK between January, 2008, and January, 2010, to model trajectories of exposure to life events during adolescence, and to quantify the association between trajectory group and suicidal ideation. Participants were assessed at ages 14, 16, 19, and 23 years, and key inclusion criteria included having the capacity to provide informed assent or consent, and having parental completion of perinatal and developmental history. Exposure to 39 life events across seven dimensions (family, accidents, distress, autonomy, deviance, sexuality, and relocation) were assessed using the Life Events Questionnaire (LEQ), administered at each of the study timepoints. Childhood maltreatment was also assessed, using the Childhood Trauma Questionnaire, administered once at age 19 years. The primary outcome was the number of participants with suicidal ideation across trajectories, assessed via a dedicated item in the LEQ at the first three timepoints. We also compared self-harm and suicidal ideation responses from the Developmental and Well-Being Assessment at the first three timepoints. Latent trajectory analysis was used to identify classes of cumulative life event exposure across repeated assessments: high, mid-high, mid-low, and low. The association between trajectory group and suicidal ideation was quantified using logistic mixed model regressions, adjusting for childhood maltreatment and demographic covariates. No individuals with lived experience of suicidality or mental health conditions were involved in the design, conduct, or reporting of this research. FINDINGS:At baseline, 2161 adolescents, enrolled between January, 2008, and January, 2010, were included in the cohort (1106 [51·2%] female and 1055 [48·8%] male). Sample sizes at subsequent waves were 1581 at first follow-up (which took place between January, 2011, and January, 2012), 1474 at second follow-up (between January, 2013, and January, 2015), and 1331 at third follow-up (between January, 2016, and January, 2019). At each timepoint, the high trajectory group (1125 [52·1%] of 2161 participants) had the highest proportion of participants with suicidal ideation (90 [8·0%] of 1125 at pre-baseline, 127 [11·3%] of 1125 at age 14 years, 102 [13·4%] of 759 at age 16 years, and 78 [11·0%] of 709 at age 19 years). After controlling for demographics and childhood maltreatment, the high trajectory group had the highest predicted probability of suicidal ideation at each timepoint. Life events in the family dimension predicted adolescent suicidal ideation (marginal R2GLMM=0·078) more than those in all remaining dimensions combined (marginal R2GLMM=0·073). INTERPRETATION:We found that higher cumulative exposure to ordinary life events across multiple dimensions is associated with higher suicidal ideation in adolescence, with life events occurring within the family environment carrying the highest risk. These results point to the importance of evaluating different types of everyday life event exposure, not just childhood trauma or maltreatment, when assessing suicidal risk in adolescents. FUNDING:Abroad Advanced Study Fellowship, Chang Gung Medical Foundation, Taiwan; National Science and Technology Council, Taiwan; Medical Research Council, UK.

OBJECTIVE:Although conduct problems (CPs) are continuously distributed, little is known about how dimensional measures of CPs map onto brain structure. Therefore a large sample was used to comprehensively assess associations between dimensionally measured CPs and brain structure. METHOD:T1-weighted structural brain magnetic resonance imaging scans from 14,160 youths (5-21 years old, 46.2% female) across 18 international case-control, community-based, and population-based cohorts were preprocessed using ENIGMA-standardized protocols. Regression models examined associations between CPs and cortical thickness, surface area, and subcortical volumes, adjusting for age, sex, and intracranial volume. Moderation by sex, age, and callous-unemotional traits was also investigated. RESULTS:Widespread but small (β = -0.02 to -0.07) negative associations were observed between CPs and surface area (total surface area, 23/34 regions), cortical thickness (average thickness, 15/34 regions), and amygdalar and hippocampal volumes. Sex was a key moderator, with many surface area associations limited to boys and some thickness associations limited to girls. Some associations were stronger in younger children and at lower levels of callous-unemotional traits. The impact of adjusting for IQ and other psychopathology varied by outcome (eg, most surface area findings survived IQ adjustment, whereas cortical thickness associations did not). CONCLUSION:CPs were associated with subtle, yet widespread, alterations in brain structure. Findings overlapped with differences observed in categorically measured conduct disorder, but novel associations with cortical thickness were identified. This provides further evidence that neuroanatomical differences are not limited to youth with clinically elevated CPs. Our findings have potential implications for neurocognitive models of CPs as they extend beyond the regions highlighted in these models. STUDY REGISTRATION INFORMATION:Investigating dimensional relationships between conduct problems and brain structure: an ENIGMA mega-analysis; https://osf.io/nzj3r/.

Human cognitive processing involves dynamic interactions across brain regions, evolving over time. Traditional neuroimaging analysis often overlooks this temporal aspect, limiting insights into how functional network connectivity (FNC) supports ongoing cognition and behaviour. Using sliding window analysis, we captured FNC changes during tasks, reflecting network reconfiguration in cognitive processes. We further determined behavioural relevance of time-varying FNC by relating network measurements with task performances and psychopathology. We found that several whole-brain FNC patterns, or states, persist across resting and task-based fMRI, with state occurrences fluctuating with the most prominent task stimuli. Regional FNC distinguishes specific task conditions, and time-varying FNC explains more variance in psychopathology symptoms compared to static connectivity. These findings highlight that cognitive tasks reshape regional and whole-brain connectivity. By considering the different FNC states, time-varying connectivity provides a more comprehensive representation of brain interactions and thus may represent a better neural proxy for cognition and behaviour.

Abstract Changes in personality during adolescence may shape future alcohol use, especially in times of societal crisis. The COVID-19 pandemic disrupted social norms and stress regulation, making it a natural experiment to examine how early-life personality trajectories predict later substance use. Using longitudinal data from the European IMAGEN cohort (N = 968), we investigated whether intra-individual personality changes during adolescence predicted alcohol use at three timepoints: pre-pandemic, immediately after COVID-19 onset (T1), and several months into the pandemic (T2). Generalized Additive Mixed Models (GAMMs) were used to model non-linear relationships between adolescent personality change (e.g., in anxiety sensitivity and openness) and alcohol use frequency across time. Participants were drawn from the IMAGEN cohort (N = 968; 54.4% female), assessed longitudinally from early adolescence (BL: M = 13.93, SD = 0.41) through middle (FU1: M = 16.04, SD = 0.60) and late adolescence (FU2: M = 18.41, SD = 0.65) to early adulthood (FU3: M = 22.00, SD = 0.66), with additional follow-ups during the COVID-19 pandemic (COVID-T1: M = 25.00, SD = 0.81; COVID-T2: M = 25.56, SD = 0.75). Assessments were conducted at approximately 2-year intervals during adolescence and at shorter intervals (several months) during the COVID-19 period. Analyses controlled for sex and country-level lockdown stringency. Intra-individual fluctuations in anxiety sensitivity significantly predicted increased alcohol use several months into the pandemic (T2; EDF = 1.86, p < 0.05), but not immediately after lockdown (T1). Pre-pandemic alcohol use was instead associated with changes in openness (EDF = 1.00, p < 0.05). Exploratory analyses suggested gender differences in anxiety-related drinking risk. Our findings suggest that personality dynamics in adolescence shape alcohol use responses during societal crises. Different traits appear to matter depending on the temporal and emotional context of the stressor. These findings have implications for personalized prevention efforts targeting stress-related substance use in youth.

BACKGROUND:Although the initiation of alcohol consumption is common during adolescence, some individuals engage in binge drinking behavior that could lead to harmful consequences such as developing alcohol use disorder later in life. Evidence suggests a relationship between depressive symptoms and harmful alcohol consumption that seems to vary depending on sex. Furthermore, it has been suggested that amygdala activation in response to negative emotional stimuli influences drinking due to depressive mood states. Therefore, we expected a sex-dependent effect of neuronal activation and depressive symptoms on risky drinking. METHODS:Here, we tested our hypothesis using a large dataset of 19-year-old participants (n = 958) in the IMAGEN study. Amygdala activation during an emotional faces task was extracted and entered into sex-moderated mediation models that also included scores from the Alcohol Use Disorders Identification Test and the Adolescent Depression Rating Scale. RESULTS:Moderated mediation models indicated that amygdala activation was associated with hazardous drinking through enhanced depressive symptoms in males, while amygdala reactivity in females was associated with decreased risky drinking. CONCLUSIONS:Taken together, our findings reveal sex differences in negative emotional processing in at-risk adolescents. These associations have the potential to inform the development of sex-specific strategies as well as the detection of early neuronal risk factors to effectively curtail alcohol risk behavior.

Brain charts have emerged as a highly useful approach for understanding brain development and aging on the basis of brain imaging and have shown substantial utility in describing typical and atypical brain development with respect to a given reference model. However, all existing models are fundamentally cross-sectional and cannot capture change over time at the individual level. We address this using velocity centiles, which directly map change over time and can be overlaid onto cross-sectionally derived population centiles. We demonstrate this by modelling rates of change for 24,062 scans from 10,795 healthy individuals with up to 8 longitudinal measurements across the lifespan. We provide a method to detect individual deviations from a stable trajectory, generalising the notion of 'thrive lines', which are used in pediatric medicine to declare 'failure to thrive'. Using this approach, we predict transition from mild cognitive impairment to dementia more accurately than by using either time point alone, replicated across two datasets. Last, by taking into account multiple time points, we improve the sensitivity of velocity models for predicting the future trajectory of brain change. This highlights the value of predicting change over time and makes a fundamental step towards precision medicine.

Clinical heterogeneity in the symptom trajectories of attention deficit hyperactivity disorder (ADHD) is well documented, but their neurodevelopmental mechanisms remain unclear. We used a longitudinal cohort of adolescents (ABCD; n = 7,436) to show that persistent, remitting and emergent ADHD symptom trajectories correlated with persistent, improving and worsening behavioral changes, respectively. Each trajectory had distinct brain signatures: faster cortical thinning (persistence), slower thinning (emergence) and faster subcortical expansion (remission). Slower cortical thinning in the right posterior cingulate was associated with inattention symptom increase, whereas faster hippocampal expansion was associated with inattention symptom decrease. These signatures enhance ADHD symptom prediction at age 13 and generalize to young adults (age 23) in the IMAGEN cohort. The hippocampal signature for remitting symptoms was replicated in IMAGEN and two clinical cohorts (ADHD-200 and ADHD-1000). Given that baseline ADHD medication use was not significantly associated with the remitting trajectory, our findings suggest that current treatments may not facilitate sustained remission, highlighting the potential for new interventions.

Abstract Environmental exposures are increasingly examined in relation to mental health, yet large-scale epidemiological analyses remain constrained by fragmented geospatial data, heterogeneous spatial and temporal resolutions, and privacy-preserving linkage requirements, limiting systematic investigation of multiple environmental domains at the population level. We present environMAP, a harmonised set of analysis-ready environmental exposure layers derived from open, global sources. environMAP spans the built environment, green and blue spaces, light exposure (solar radiation and night-time light), terrain, weather and extremes, and air pollution. We document data provenance, spatial buffers, preprocessing, projection alignment, and metadata, and provide a reproducible workflow for privacy-preserving linkage to cohort residential locations. To demonstrate utility, we linked environMAP to >200,000 adults in the German National Cohort (NAKO) and summarised self-reported lifetime doctor-diagnosed depression across exposure gradients using sex-stratified descriptive analyses. Gradients were interpretable and broadly consistent with prior evidence, supporting feasibility, scalability, and hypothesis generation. The framework is adaptable to other outcomes, cohorts, and regions.

Conduct disorder (CD) is the leading global cause of mental health burden in children and adolescents and has recently been hypothesized to be a neurodevelopmental disorder. Although prior research has identified neuroanatomical differences associated with CD, it remains unclear whether these differences reflect atypical brain development. Here, we investigated the difference between an individual's brain age and chronological age as a proxy for variations in brain maturation. Using a pretrained model, we estimated brain age from structural neuroimaging data obtained from 1,119 youth with CD and 1,183 typically developing controls across 14 international cohorts participating in the ENIGMA-Antisocial Behavior Working Group. Youth with CD exhibited a statistically robust but small acceleration in brain age compared to typically developing youth (around 0.50 years), which was restricted to the adolescence-onset subtype of the disorder. Our large-scale, coordinated analysis provides the first evidence of accelerated neurodevelopment as a potential mechanism underlying CD.

Abstract Human reward processing varies along cue-centric and outcome-centric axes, but a reproducible mechanistic account of individual variation in incentive salience attribution has been lacking. Using fMRI across five cohorts (N-total=1,251; N1=890; N2=245; N3=34; N4=48; N5=34), we identified two robust imaging phenotypes mirroring sign- and goal-tracking (ST-like, GT-like). ST-like individuals showed dominant ventral striatal responses to reward-anticipation cues and sustained incentive salience attribution; GT-like individuals showed heightened responses to reward outcomes. This distinction was replicable across sites and independent samples. Single-dose and repeated-dose D 2 /D 3 antagonism (risperidone, haloperidol, amisulpride) selectively reduced anticipatory ventral striatal activity in ST, with single-dose antagonism additionally producing a parallel drop in self-reported energy. Instead, D 2 /D 3 partial agonism (aripiprazole) increased anticipatory and reduced outcome-phase responses in GT. In a psychosis cohort, antipsychotic D 2 affinity was associated with blunted anticipatory signals and higher negative symptom burden, offering a neuroimaging-driven basis for stratifying patients and predicting response to dopaminergic agents.

The STRATIFY (Brain Network-Based Stratification of Reinforcement-Related Disorders) and ESTRA (Eating Disorders Stratification) studies were established as harmonised “sibling” cohorts to develop a mechanistically informed framework for stratifying psychiatric disorders. Here, we describe the study design, methodology, and cohort characteristics. Both studies investigate how network properties of brain structure and function, together with biological markers derived from blood-based genomics, epigenetics, and proteomics, relate to reinforcement-related behaviours that cut across major depressive disorder, alcohol use disorder, psychosis, and eating disorders. A further objective is to identify discriminative multimodal features that predict disease onset, symptom course, and functional outcomes, thereby supporting the development of targeted interventions. STRATIFY and ESTRA recruited 674 patients and 70 healthy controls aged 18–30 years (76% females), supplemented by 199 age- and sex-matched healthy controls from the population-based IMAGEN cohort assessed at the same sites using harmonised protocols. Multimodal assessment included structured clinical interviews, self-report measures, cognitive testing, biosamples for molecular analyses, and multimodal MRI (structural, diffusion, resting-state, and task-based fMRI). ESTRA participants additionally completed longitudinal follow-up, and all cohorts were assessed during the COVID-19 pandemic. STRATIFY and ESTRA together constitute a large-scale, open-science resource integrating multimodal brain, behavioural, and biological data across transdiagnostic patient cohorts in early adulthood. The anonymised dataset is available to the research community through managed access, supporting international collaboration and accelerating the development of mechanistically informed classification systems and predictive tools in psychiatry.

QuestionWhat is the global evidence on climate-related and nature-based mental health interventions?FindingsAlthough psychosocial interventions are currently recommended in treatment guidelines worldwide to reduce the adverse mental health effects of climatic impact drivers, this umbrella review found the credibility of the evidence basis for this recommendation to be very low. Nature-based interventions were associated with favorable mental health outcomes generally but evidence specifically for climatic impact drivers was lacking.MeaningGuidelines and policies for mental health interventions in the context of climate change need to be largely informed by global evidence from contexts other than climate change. This umbrella review and meta-analysis evaluates associations of climate-related and nature-based mental health interventions with mental health outcomes. ImportanceClimate change is associated with increasing mental health morbidity and mortality. However, an umbrella review to classify and quantify the global evidence on climate-related and nature-based mental health interventions is lacking.ObjectiveTo assess associations of climate-related and nature-based mental health interventions with mental health outcomes.Data SourcesPubMed, PsycINFO, Web of Science, and Cochrane databases were searched from inception to November 17, 2024.Study SelectionSystematic reviews with meta-analyses (SRMAs) with controlled climate-related or nature-based mental health interventions and mental health outcomes were included.Data Extraction and SynthesisStandardized mean differences (SMDs; intervention vs control) and 95% CIs were synthesized, evidence was stratified according to the level of credibility, and associations were assessed using meta-regression.Main Outcomes and MeasuresOutcomes were mental disorders, psychiatric symptoms, and positive mental health.ResultsTwenty-eight SRMAs were included that examined 344 studies and 91 associations between psychosocial or nature-based interventions and outcomes. Of the 91 associations, 10 (11%) had a moderate credibility of evidence and 81 (89%) had low or very low credibility. Psychosocial interventions addressing climatic impact drivers were associated with very low credibility, based on limited data. Nature-based interventions were associated with reductions in tension (SMD, -0.87; 95% CI, -1.31 to -0.43), fatigue (SMD, -0.80; 95% CI, -1.16 to -0.44), confusion (SMD, -0.65; 95% CI, -1.12 to -0.19), and negative affect (SMD, -0.51; 95% CI, -0.85 to -0.16), as well as increases in positive affect (SMD, 0.98; 95% CI, 0.65 to 1.30), vigor (SMD, 0.83; 95% CI, 0.37 to 1.28), and well-being (SMD, 0.40; 95% CI, 0.07 to 0.73), with moderate credibility of evidence and not addressing climatic impact drivers. Older participants and study locations with lower tree cover, better health care access and quality, and lower systemic vulnerability to climate change were associated with stronger improvements in negative affect following nature-based interventions.Conclusions and RelevanceThere is limited evidence for mental health interventions to reduce adverse mental health impacts of climatic impact drivers, but there is promising potential for future research in this field based on evidence from contexts other than climate change. Currently, strategies for mental health interventions in the context of climate change, such as those for implementing and scaling interventions, need to rely largely on global evidence from contexts other than climate change.

Childhood trauma (CT) is associated with cognitive impairment across major psychiatric disorders. We tested a novel transdiagnostic hypothesis that atypical connectivity of the default mode network (DMN) mediates the association between childhood trauma (CT) and cognitive impairment. The sample of 1851 individuals aged 18-25 included 433 patients with depression, eating disorders, alcohol use disorder, psychosis and ADHD) and were recruited as part of the ESTRA/STRATIFY/IMAGEN studies. CT was measured using the Childhood Trauma Questionnaire (CTQ). The CANTAB spatial working memory task was administered to assess cognition. Four a priori seeds of the default mode network (DMN) were measured during face processing, namely the medial prefrontal cortex (PFC), right lateral parietal (LP), left lateral parietal (LP) and posterior cingulate cortex (PCC), according to the Harvard-Oxford Cortical and Subcortical Atlas (http://www.cma.mgh.harvard.edu/fsl_atlas.html) as implemented in CONN. Patients had significantly reduced DMN connectivity between the four chosen DMN seeds and the rest of the brain. Reduced DMN connectivity mediated the association between higher CT and worse cognitive performance. Our findings are transdiagnostic in nature with stronger effects in some regions observed in depression, and suggest one transdiagnostic cortical network via which CT's effects on cognition are transmitted.

The biological mechanisms underlying major neuropsychiatric disorders remain largely elusive. Given the frequent association of immune dysregulation with these conditions, we used blood-derived multi-omics data from 1,274 healthy adolescents in the IMAGEN cohort to identify transdiagnostic biomarkers and mechanisms that could inform diagnosis and treatment. We first conducted genome-wide analyses to identify single nucleotide polymorphisms associated with DNA methylation and gene expression, with findings replicated in external datasets. These quantitative trait loci were further explored through Mendelian randomization analyses across 6 neuropsychiatric disorders (attention deficit hyperactivity disorder, autism spectrum disorder, bipolar disorder, major depressive disorder, schizophrenia and insomnia), leading to the identification of 73 putatively causal CpG sites and 62 genes that were either unique to individual disorders or, as in the case of MRPL2, shared among the disorders. Notably, the identified genes were significantly enriched in pathways linked to both psychiatric and autoimmune diseases, suggesting a shared genomic architecture between autoimmune and neuropsychiatric disorders. Two-step Mendelian randomization and colocalization analyses revealed potential transdiagnostic regulatory pathways, in which the expression of three genes (MAD1L1, MRPL2 and HLA-DRB1) mediated the effects of CpG methylation on schizophrenia and insomnia. Specifically, DNA methylation at cg06770790 repressed MRPL2, which was putatively causal for insomnia (β = -0.38, P = 1.29 × 10-4) and schizophrenia (β = -0.38, P = 1 × 10-4). Conversely, increased expression of MAD1L1 and HLA-DRB1, driven by methylation at several CpG sites, was potentially causal for schizophrenia. Our findings highlight key molecular mechanisms and genes implicated in neuropsychiatric disorders, offering promising new targets for therapeutic intervention.