Gene-environment correlations (rGEs) are often observed for psychiatric traits, including externalizing problems. Some researchers believe rGEs reflect the effects of offspring genotype on psychosocial environments. Others consider rGEs to be a nuisance, reflecting effects of confounders, such as population stratification, assortative mating, or parental genotypes. Our study aims to examine whether rGEs persist when confounding effects are controlled for in a family-based design.

We examined rGEs between a polygenic score (PGS) for externalizing problems and adolescent psychosocial environments (parent- and child-reported parental relationships, delinquent peer affiliation, and school connectedness) in the National Longitudinal Study of Adolescent to Adult Health. We estimated rGEs within 777 families (1,589 twins/full-siblings) and also at the population-level using 7,635 unrelated individuals.

rGEs were observed with parental relationships, delinquent peer affiliation, and school connectedness in the population sample. In family-based analyses, rGEs with parental relationships persisted at the within-family level. However, rGE with delinquent peer affiliation was attenuated and non-significant at the within-family level, suggesting the presence of confounding. Analyses regarding school connectedness were inconclusive.

While PGSs are often associated with psychosocial environments, some of these associations may be subject to substantial confounding. Our findings highlight the importance of testing rGE while controlling for potential confounds in large population-based studies.

Genetic liability of depressive disorder can be captured by psychopathology in relatives (family history). Various methods summarize family history in a single score, differing in included information as well as the underlying model. We systematically compared the performance of family history indicators, including promising new indicators based on the liability threshold model, in predicting depressive disorder.

We calculated selected family history indicators for depression (dichotomous, proportion, novel genetically informed method – Pearson–Aitkens Family Genetic Risk Score [PAFGRS]) in 1339 participants of the Netherlands Study of Depression and Anxiety (Ncase = 1086). Polygenic scores (PGS) were computed from the most recent GWAS for major depression. We assessed correlations between genetic liability indicators, as well as their prediction of lifetime depressive disorder diagnosis.

Correlations of family history indicators with each other were high (r = 0.71–0.99) and much lower with the PGS (r = 0.15). There was a suggested increase in predictive accuracy for more elaborately computed scores, ranging from proportion to PAFGRS. The best-performing family history indicator and the PGS were independently associated with depressive disorder.

Our analysis shows that more elaborate family history indicators, including family size, prevalence, heritability, and genetic theory, would be preferable over simpler methods. Family history and PGS were complementary in prediction, showing the added value of including both in future studies.

Schizophrenia (SCZ) is a heterogeneous disorder characterized by widespread brain dysconnectivity, yet it remains unclear whether variable structural dysconnectivity patterns reflect biologically and clinically meaningful subtypes.

We recruited 105 SCZ patients and 97 healthy controls, collecting magnetic resonance imaging, clinical assessment, and laboratory test data. Double generalized linear models were first applied to estimate group differences in the mean and inter-individual variability of structural connectivity between SCZ and controls. Subsequently, nonnegative matrix factorization was used to identify SCZ subtypes according to dysconnectivity profiles.

We observed a significantly altered subnetwork that exhibited increased inter-individual variability in SCZ, with its connections and regions predominantly enriched in the ventral-attention, frontal–parietal, and default-mode networks that support higher-order cognition. Connectivity-derived subtype analysis grouped patients into two subtypes, with subtype-II tending to show more severe positive symptoms (t = 2.272, nominal P = 0.025), a lower neutrophil/lymphocyte ratio (t = −2.383, nominal P = 0.025), a better treatment response (t = 2.366, nominal P = 0.021), and greater short-term symptom improvement (t = 3.509, nominal P = 0.006) within the olanzapine monotherapy subgroup. This subtype also demonstrated distinct neuroanatomical features, including a subtype-specific cortical thickness change pattern unrelated to SCZ or bipolar disorder, as well as subtype-specific brain epicenters, such as left posterior cingulate and rostral middle frontal regions.

Our study demonstrates increased inter-individual variability in the structural connectome of SCZ and highlights the potential of connectome-based subtyping approaches for characterizing disease heterogeneity related to pathophysiological processes.

Electroconvulsive therapy (ECT) may be accompanied by amnesia. However, the extent to which different memory subtypes are affected is unclear, as well-controlled longitudinal studies are sparse. We aimed to investigate short- and long-term effects of ECT on different aspects of memory.

We recruited patients with depression referred to ECT (n = 31) or other treatment (n = 21) and healthy participants (n = 29). We measured autobiographical (Columbia Autobiographical Memory Interview – Short Form), verbal (Verbal Learning Test – Delayed), and visual (Rey Complex Figure Test) anterograde memory at baseline (T1), post-ECT (T2), and 6-month follow-up (T3). We performed a repeated measures mixed ANOVA for each outcome. An ANCOVA explored whether age and cognitive reserve were associated with severity of amnesia. ClinicalTrials.gov: NCT04160286.

A substantial effect of ECT on autobiographical memory was indicated by a group-by-time interaction, F (3.45, 103.51) = 22.261, p < 0.001, partial η2 = 0.426. The effect remained stable, and inferior to control groups, at T3 (p < 0.001). A large effect of ECT on verbal anterograde memory was suggested by a group-by-time interaction, F (2, 68) = 6.138, p = 0.004, partial η2 = 0.153, reflecting transient impairment after ECT (p = 0.009). No ECT-related decline in visual anterograde memory was observed (p = 1.0). Neither age nor cognitive reserve was associated with degree of amnesia.

Our findings suggest that ECT may be associated with autobiographical amnesia, potentially lasting at least 6 months, and transient verbal, but not visual, anterograde amnesia.

The Five Factor Model (FFM) personality meta-traits, stability and plasticity, are thought to capture broad individual differences in the parameters of goal-directed, cybernetic mechanisms in the brain, which have implications for transdiagnostic psychopathology risk. It remains unknown whether these meta-traits represent genetically coherent dimensions. Here we conduct a genomic investigation into the FFM meta-traits and characterize their association with 11 psychiatric conditions.

We combined recent genome-wide association study (GWAS) summary statistics with data from the 23andMe Research Institute (European ancestry; total N = 279,240–682,707) and applied Genomic Structural Equation Modelling to conduct a multivariate GWAS of the FFM meta-traits. We then conducted transcriptome-wide association studies (TWAS) and stratified linkage disequilibrium score regression (S-LDSC) to prioritize candidate effector genes and brain cell types for personality traits. Finally, we used LDSC regression and CAUSE Mendelian randomization to characterize the relationship between personality traits and 11 psychiatric conditions.

We identified 83 (14 novel) and 13 (4 novel) independent genome-wide significant loci for stability and plasticity respectively. TWAS prioritized hundreds of candidate effector genes for personality, and S-LDSC showed heritability enrichment across several neuronal cell types. Finally, genetic correlation and Mendelian randomization analyses provided genetic evidence suggesting bidirectional links between stability and psychopathology: higher genetic propensity for stability was protective against psychopathology, whereas greater genetic liability to psychopathology was associated with reduced stability.

The personality meta-traits capture modest shared architecture across personality, with genetic liability for lower stability potentially representing a broad transdiagnostic link between personality and psychiatric vulnerability.

Suicide remains a leading cause of death worldwide, underscoring the importance of identifying neurobiological markers associated with suicidal behaviors. While nonsuicidal self-directed violence (NSSDV) is an established risk factor for future suicide attempts (SAs), the underlying neural distinctions between these behaviors remain insufficiently understood.

A total of 1,070 participants with a history of self-directed violence (SDV) were stratified into SA (n = 579) and NSSDV (n = 491) groups based on self-reported questionnaires. All data were collected from the UK Biobank between October 2016 and January 2024. Whole-brain connectome across 90 brain regions were analyzed. A general linear regression model was employed to identify significant functional connectivity (FC) correlations with SA. An exploratory analysis examined the correlation between FC and SDV frequency and recency.

Compared to NSSDV, the SA group exhibited decreased FC in the bilateral amygdala and right putamen, alongside increased FC in the right caudate. Higher frequency of SDV was correlated with increased FC between the orbitofrontal cortex (OFC) and occipital regions, whereas individuals reporting SDV within the past 12 months showed trends of decreased FC between the OFC and both the rectus gyrus and temporal lobe.

This large-sample rs-fMRI study highlights distinct FC alterations associated with different dimensions of self-harm. Results implicated that the limbic and reward circuits appear particularly relevant in differentiating SA from NSSDV and in capturing variations tied to SDV frequency and recency. These insights advance our understanding of suicidal behaviors’ neurobiological underpinnings and may inform targeted risk stratification.

Drug use may induce transient bursts of inspiration but is also associated with increasing cognitive rigidity. How neurological damage induced by drug addiction affects higher-order thinking processes in humans remains elusive. This gap limits our understanding of broader cognitive impairment in addiction and prolonged maladaptation during withdrawal.

The study investigated divergent thinking, a key component of flexible behavior, in heroin use disorder (HUD). Behavioral performance was assessed using the Realistic Presented Problem-Solving Task. Task-related neural signatures were recorded via functional near-infrared spectroscopy (fNIRS) in three groups of individuals: (1) HUD short withdrawal; (2) HUD long withdrawal; and (3) healthy controls (HC).

Both HUD groups exhibited poorer divergent thinking performance than the HC group. HUD participants showed hyperactivation in the prefrontal cortex (PFC) and temporoparietal junction (TPJ), accompanied by reduced functional connectivity within the PFC and between the PFC and TPJ. Network analysis further revealed reduced topological properties of PFC nodes and increased topological properties of TPJ nodes, indicating disrupted PFC-mediated top-down regulatory control. PFC subregional activation and functional connectivity with the bilateral TPJ served as shared neural features that predicted divergent thinking impairment and protracted withdrawal symptoms.

These findings reveal a sustained compensatory pathological mechanism and an imbalance in neural resource allocation during complex cognition in HUD individuals. The identified signatures of cortical activity by fNIRS offer reliable biomarkers for the rapid detection of both higher-order cognitive deficits and withdrawal symptoms, and further offer precise cortical targets for their integrated intervention.

China’s aging population faces increased prevalence in mid- and later life depression, with married middle-aged and older women vulnerable due to disproportionate caregiving and socioeconomic stress. Although emotional contagion between partners is well-documented, pathways through which depressive symptoms spread between these spouses remain unclear. This study examines the directional predictive patterns of depressive symptoms, wives to husbands specifically, within middle-aged and older Chinese couples.

Using the 2011–2013 longitudinal data from the China Health and Retirement Longitudinal Study (CHARLS), we analyzed 2,578 heterosexual couples aged 45 years or older. Depressive symptoms were assessed at two time points using the 10-item Center for Epidemiological Studies-Depression scale (CES-D-10). Cross-lagged panel network (CLPN) modeling estimated temporal changes in predictive pathways of each spouse’s symptoms in the other’s, capturing intra-personal symptom interactions and inter-spousal predictive dynamics while comparing patterns between couples.

Distinct symptom networks emerged for husbands and wives, with wives’ emotional states showing stronger cross-partner predictive associations, highlighting gender-specific patterns. Wives’ fearfulness emerged as a critical bridge symptom, significantly predicting increases in husbands’ depressive symptoms. Overall, significant cross-spousal spillover associations were observed; emotional contagion patterns were notably asymmetric, showing more pronounced symptom predictions from wives to husbands.

Findings indicate bidirectional associations of depressive symptoms with significant gender disparities in symptom dynamics among middle-aged and older couples, highlighting the necessity for gender-sensitive, relationship-focused research and interventions. Dyadic network analysis provides new insights into depression dynamics, advocating approaches addressing relational factors with individual symptoms in mental health strategies for China’s aging population.

Major depressive disorder (MDD) is a prevalent chronic mental illness often treated by serotonergic pharmacotherapy and complicated by sex differences and comorbid anxiety disorders (COMs). Deficits in inhibitory control are a transdiagnostic feature of MDD and COM, though the presentation of these deficits may be impacted by competing cognitive demands, symptoms severity, sexes, and medication status.

This project includes 233 participants from the Tulsa 1000 study who completed concurrent electroencephalography (EEG) and magnetic functional resonance imaging (MRI) during a stop signal task (SST). We examined how serotonergic antidepressants, biological sex, and comorbidity with an anxiety disorder uniquely relate to neural indices of inhibitory control for healthy controls (HCs), MDD, and COM using linear mixed effect models, type III ANOVAs, and optimal full propensity matching. N200, P300, and correct-related negativity (CRN) components were extracted and analyzed to index conflict monitoring, conflict resolution, and performance monitoring, respectively, in response to stop signals and their absence.

Our results revealed that HCs exhibited larger stop-P300 amplitudes than MDD and COM (η2 = 0.068); females had smaller stop-N200 amplitudes than males (η2 = 0.027); MDD/COM participants taking a serotonergic antidepressant exhibited less negative N200 amplitudes than those who were not (LME, B = 1.95; η2 = 0.044; ATE, B = 2.47).

Our findings suggest being treated with serotonergic antidepressants are associated with neural differences in the conflict monitoring of inhibitory control in pure and comorbid depression.

Repeated exposure to adversity is universal to the human experience and may impact mental health negatively. However, competing hypotheses predict that repeated adversity leads to either sensitisation or habituation.

Using a 22-year Australian population-based panel study, we examined changes in mental health following repeated exposure to seven common adverse life events in adulthood. Mixed-effects regression models estimated the relationship between repeated exposure to adverse life events of the same type and its impact on the Mental Health Inventory-5 score.

All seven adverse life events were associated with significant negative mental health impacts at each occurrence. Patterns of mental health response to repeated exposure varied by event type in the primary analysis. Sensitisation was observed for major worsening in finances and victimisation by property crime. Habituation was observed for separation/divorce and serious personal injury/illness. For physical violence, serious family injury/illness, and firing/redundancy, we found no clear associations. Sensitisation and habituation effects appeared to ‘plateau’ after the second adverse event rather than accumulating with exposure. Strength and direction of associations were robust to adjustments for pre-event mental health and a Mundlak correction.

In a population-based sample over two decades we found event-specific sensitisation and habituation in response to repeated adverse life events. Both effects appeared to ‘plateau’ after the second event. Our findings provide no consistent support for a single universal model of stress-response, indicating that how individuals respond psychologically to adversity is more nuanced and may depend on contextual factors such as individual meaning, resilience, or resources.

Understanding the factors that influence depression is crucial for both its treatment and prevention. Residential greenspace has received increasing attention for its potential role in mitigating depressive symptoms. However, traditional statistical methods are fundamentally constrained to evaluating simple bivariate associations between greenspace exposure and depression risk, overlooking the complex interactions among key health-related factors and failing to comprehensively assess the relative importance of greenspace compared to other contributing factors.

We employed machine learning (ML) methods to assess the effects of multiple factors, including greenspace, on the development of depressive symptoms among 1,542 urban middle-aged adults in the China Health and Retirement Longitudinal Study, with depressive symptoms measured using the 10-item Center for Epidemiological Studies Depression Scale. Individual greenspace exposure for each subject was quantified using vegetation indices obtained from the NASA satellite database.

Among the five ML models, the Support Vector Machines model achieved the best predictive performance, with an AUC of 0.776. Life satisfaction, EVI250m (a measure of greenspace), social activity, pain, sleep duration, and impairment in instrumental activities of daily living were significant predictors of depressive symptoms. EVI250m, identified by the ML model, indicated that closer proximity to greenspace was associated with a greater reduction in depressive symptoms. Furthermore, Shapley Additive Explanations values confirmed that greenspace ranked among the top influential factors.

Residential greenspace is associated with fewer depressive symptoms and ranks prominently among the most influential factors within the examined health-related variables. The potential beneficial association with greenspace appears to be stronger with closer proximity to residential areas.

Impaired control over alcohol consumption is a core feature of alcohol use disorder (AUD), yet its mechanisms remain unclear. Habit theories posit a shift from goal-directed to habitual control with repeated alcohol use, but human evidence is mixed and often lacks translational alignment with animal models. Recent accounts therefore call for paradigms assessing contingency sensitivity with disorder-relevant reinforcers.

We adapted a free-operant contingency degradation task to compare goal-directed control when responding for a natural (juice) versus disorder-relevant reinforcer (alcohol). Eighty-one participants were tested, comprising 49 non-treatment-seeking individuals with AUD (31.22 ± 8.35 years old; 28 female) and 32 healthy control individuals (27.44 ± 6.71 years old; 22 female). We expected reduced goal-directed control in AUD, indexed by weaker response-rate and causality judgment adaptation to changed contingencies, particularly for alcohol.

Contrary to our hypothesis, goal-directed control was preserved in both groups, with behavioral responding and causality judgments remaining contingency-sensitive. Rather than a generalized deficit, a reinforcer-specific attenuation emerged: across groups, behavioral contingency sensitivity was selectively reduced when responding for alcohol. Explicit contingency judgments remained intact and were stronger in individuals with AUD than in healthy control individuals.

These findings do not support a generalized deficit in goal-directed control in AUD. Instead, goal-directed control appears selectively attenuated in alcohol-related contexts across groups, potentially reflecting reinforcement-history-dependent reductions in contingency-sensitive responding. Alcohol-specific reductions in goal-directed control may emerge early in alcohol use, underscoring the importance of reinforcer context.

Biological factors affecting motor, sensory, and integrative processing may predispose to functional neurological disorder (FND) and functional somatic symptoms. While early-life influences have traditionally been conceptualized within psychological frameworks, the contribution of early-life biological vulnerabilities to symptom expression remains unclear.

Using TriNetX, a large electronic health records database, we assessed proportions of inherited, congenital, perinatal, and neurodevelopmental diagnoses, capturing a range of early-life conditions. We compared 188,868 individuals with FND and 406,202 with functional somatic symptoms to demographically matched migraine and depression cohorts. Subgroup analyses contrasted motor FND with functional seizures and system-predominant functional somatic symptom clusters (e.g. bowel and bladder) with the general cluster.

Compared with migraine and depression, individuals with FND and functional somatic symptoms showed higher proportions of multiple inherited and early-life vulnerability factors. Congenital and inherited conditions, including Ehlers–Danlos syndrome (OR = 2.1–3.6 across comparisons), congenital musculoskeletal malformations (1.7–2.0), and chromosomal abnormalities (2.5–3.2), were more prevalent. Perinatal factors such as low birth weight (1.8–2.4) were enriched, alongside neurodevelopmental disorders, including intellectual disability (1.9–5.4) and autism spectrum disorder (1.2–3.5). Subgroup analyses revealed phenotype-specific patterns: joint hypermobility and musculoskeletal malformations were more common in functional motor disorder and musculoskeletal functional somatic symptoms, whereas neurodevelopmental disorders were enriched in functional seizures and the general functional somatic symptom cluster.

Inherited and early-life biological vulnerabilities were more common in FND and functional somatic symptom cohorts. Their prevalence differed among phenotypes. Our findings suggest that developmental variation in brain–body systems may influence the risk and clinical expression of functional symptoms.

Immune-inflammatory dysregulation and cortical structural vulnerability have been increasingly implicated in major depressive disorder (MDD). However, how they interact with each other, and whether immune heterogeneity in MDD is systematically organized by structural networks, remain unclear.

Genome-wide association studies data of MDD, gray matter volume (GMV) phenotypes, and 19 immune-related traits were utilized to assess causal relationships. A prospective cohort comprising 122 patients with MDD and 66 healthy controls underwent structural magnetic resonance imaging and assessment of 40 inflammatory and immune-related markers. Individualized structural covariance network analysis, machine-learning approaches, and survival analyses were used to identify neurobiological subtypes and evaluate treatment outcomes.

Genetically predicted MDD was associated with lower levels of circulating IGF-1 (pFDR = 0.013). Levels of IL-5 and leptin were negatively associated with GMV alterations in the right cingulate gyrus and bilateral putamen, respectively (pFDR < 0.05). Structural covariance abnormalities were predominantly localized to connectivity between the default mode and limbic networks and within the limbic network in MDD, and they were classified into a hypoconnectivity subtype (n = 49) and a hyperconnectivity subtype (n = 73). The hypoconnectivity subtype exhibited greater anxiety and anhedonia symptoms, whereas the hyperconnectivity subtype showed broadly elevated inflammatory and immune state and achieved antidepressant response more rapidly during follow-up (p = 0.032).

This study supports a bidirectional immune–brain framework in MDD and suggests that structure-derived subtypes classify different immune signaling heterogeneity, which may provide a biologically informed basis for stratification and prognosis in MDD.

Learning from feedback provided by others is important for navigating complex social environments. Aberrant processing of such social feedback is implicated in the psychopathology of several mental health disorders including social anxiety and depression. Recent psychiatric research increasingly adopts transdiagnostic approaches that examine mechanisms and constructs cutting across traditional diagnostic categories.

Here, we applied reinforcement learning models together with transdiagnostic measures of psychopathology to formally assess social evaluation learning. Human participants (n = 193) completed the Social Evaluation Learning Task, learning whether computer personas ‘liked’, ‘disliked’, or were ‘neutral’ toward them. Participants also completed six questionnaires assessing socially relevant psychiatric traits.

Computational modeling revealed that a model with separate learning parameters for positive and negative feedback best accounted for the learning behavior. Exploratory factor analysis identified three transdiagnostic factors. A factor reflecting social avoidance predicted enhanced learning from negative feedback and a reduced positive learning bias. Socially avoidant individuals with heightened negative learning rates additionally showed slower decision times specifically in positive social contexts. A second factor linking with emotional insensitivity for others predicted a lower choice accuracy in both positively and negatively valenced conditions. No significant associations emerged for a third factor reflecting depressive and mood-related symptoms.

These results demonstrate how transdiagnostic trait dimensions shape social learning mechanisms through specific behavioral and computational processes.

Bipolar disorders (BD) are associated with a wide range of brain white matter abnormalities, but results vary across studies. This heterogeneity likely relates to effects of additional clinical/diagnostic variables, including medications and obesity, on white matter in BD. In this mega-analysis, we studied the links between relevant clinical variables and four diffusivity measures.

We obtained diffusion tensor imaging-derived fractional anisotropy (FA), mean, axial, and radial diffusivity (MD, AD, RD) values from 910 individuals with BD and 1256 healthy controls across 16 research sites in the ENIGMA-BD Working Group. We used principal component analysis to detect patterns of covariance across white matter tracts and studied associations between multivariate patterns of white matter alterations and demographic and clinical variables, including body mass index (BMI), medications, and diagnostic information, using mixed regression models.

Higher BMI was associated with lower PC1 FA and higher PC1 RD; BD was associated with higher PC1 MD, while treatment with antiepileptic medications was associated with lower PC1 FA, higher PC1 MD, and PC1 RD.

We found FA and MD are weakly associated, providing complementary information, and there are unique associations between specific diffusivity measures and clinical predictors. Treatment with antiepileptics may inflate the statistical effects of BD on white matter. By using all diffusivity measures and accounting for additional clinical factors, we can gain new insights into brain correlates of severe mental illness.

Explore/exploit decision-making is a crucial component of motivated behavior. Explore/exploit behaviors are related to disparate cognitive and reward-related functions and have been implicated in multiple neuropsychiatric conditions, including major depressive disorder (MDD). Several mediators of explore/exploit decision-making have been proposed; metabolic mechanisms have been theorized to potentially affect its underlying processes. Here, we examined the effects of a GLP-1 receptor agonist (GLP-1RA), semaglutide, on explore/exploit behavior in patients with MDD, using a four-armed restless bandit task.

In this secondary analysis of a randomized clinical trial, 72 participants with MDD were randomized either to oral semaglutide (n = 35) or placebo (n = 37), and completed the bandit task at baseline and following 16 weeks of treatment. Explore/exploit decision-making was assessed using computational modeling of choice behavior, with a trial-by-trial reinforcement-learning model.

Treatment with semaglutide resulted in increased exploitation (i.e. choosing the option with the highest expected reward) (Average Treatment Effect [ATE] = 0.0242, p < 0.001), directed exploration (i.e. exploring a choice with higher uncertainty) (ATE = 0.0619, p = 0.013), and perseveration (i.e. the value-independent tendency to repeat the previous action) (ATE = 0.3017, p = 0.013). These effects were moderated by, but ultimately independent of, baseline characteristics and weight loss during the trial.

Treatment with semaglutide modulated explore/exploit behaviors in patients with MDD. These results highlight the role of metabolic mechanisms in decision-making processes in MDD and expand knowledge of the potential neuropsychiatric effects of GLP-1RAs.