BACKGROUND:Childhood maltreatment (CM), encompassing abuse and neglect, is highly prevalent and associated with elevated risk for major depressive disorder (MDD), posttraumatic stress disorder (PTSD), and other related conditions. However, the extent to which neuroanatomical alterations in MDD and PTSD are attributable to CM is uncertain. METHODS:Here, we analyzed CM and whole-brain magnetic resonance imaging (MRI) data from 3711 participants in the ENIGMA (Enhancing Neuro Imaging Genetics through Meta Analysis) MDD and PTSD Working Groups (25 sites; mean age = 33.3 ± 13.0 years; 59.9% female). Normative modeling estimated deviation z scores for 14 subcortical volume, 68 cortical thickness (CT), and 68 surface area (SA) measures. To identify transdiagnostic effects, associations between CM and brain deviation scores were evaluated across all participants (patients and healthy control participants) stratified by sex and 3 age bins (pediatric, young adult, older adult). RESULTS:In young adults (ages 18-35), abuse was associated with larger volumes in the thalamus and pallidum, thinner isthmus cingulate and middle frontal regions, and thicker medial orbitofrontal cortex; there were no significant effects in pediatric (≤18 years) participants. The strongest effects were observed in young female adults (|β| = 0.07-0.22, q < .05): Greater abuse and neglect were correlated with smaller hippocampus and putamen volumes, thinner entorhinal cortex, and smaller SA in fusiform/inferior parietal regions and with larger SA in the orbitofrontal and occipital cortices. In males, abuse had widespread effects on CT and SA (|β| = 0.1-0.18, q < .05); effects for neglect were minimal. CONCLUSIONS:Our findings of age- and sex-specific instantiations of CM on brain morphometry highlight the importance of developmental context in understanding how adverse experiences shape neurobiological vulnerability to MDD and PTSD.

BACKGROUND:Bipolar disorder (BD) is associated with clinical and biological markers of premature aging. In this largest study of brain age in BD to date, with 2919 participants, we compared brain-predicted age difference (brain-PAD) in individuals with BD and healthy comparison (HC) participants. Brain-PAD is a machine learning-estimated metric that quantifies the difference between an individual's predicted brain age and their chronological age, a potential clinical bio-signature of premature brain aging. Within individuals with BD, we also examined how medication and clinical characteristics were related to brain-PAD. METHODS:Age was predicted from 77 MRI measures of regional subcortical and lateral ventricle volumes, cortical thickness, and surface area for 1342 BD and 1577 HC adult participants, aged 18-75 yrs. old (μ = 37.2; SD = 12.3), from the curated ENIGMA Bipolar Disorder working group (ENIGMA-BD) and leveraging an ENIGMA machine learning model previously trained and validated using independent samples. Chronological age was subtracted from predicted age to produce an individual-level estimate known as brain-PAD. Linear mixed models (adjusting for sex and age as fixed effects and site as a random effect) were used to examine group differences and clinical associations. RESULTS:BD was associated with higher brain-PAD, compared to HC, primarily among older patients, as demonstrated by a significant age by diagnosis interaction (+0.05 [SE: 0.02] years). Individuals with BD on antiepileptic (AED) medications only (+3.20 [SE: 0.78] years) or on both AED and second-generation antipsychotics (SGA) (+3.74 [SE: 0.89] years) demonstrated greater brain-PAD compared to individuals who were not on any of the examined medications. Those taking lithium, whether alone or with AED and SGA independently, showed no difference in brain-PAD compared to individuals not taking any of the examined medications. However, individuals who were taking lithium showed lower brain-PAD compared to those on AED (-4.48 [SE: 0.84] years) or AED and SGA (-5.01 [SE:0.92] years). Individuals with a BD I subtype diagnosis had a higher brain-PAD (+1.50 [SE:0.55] years) compared to those with BDII or subtypes that are not otherwise specified (NOS). CONCLUSIONS:Results from this study suggest compounding effects of BD diagnosis and older age on brain-PAD, an ML-derived summary metric of structural alterations. Within BD, brain-PAD was differentially related to medication use, consistent with prior findings from ENIGMA-BD. Notably, AED use was generally related to more advanced brain age. Lithium use, alone or in combination with other medications, was not associated with advanced brain age, suggesting a possible neuroprotective effect of lithium. Brain-PAD as an ML-derived summary metric of structural alterations of the brain may provide clinical utility in assessing long-term holistic brain health to monitor the effectiveness of lifestyle modifications or treatments over time. LIMITATIONS:The cross-sectional nature of the study design and the limited granularity of the clinical data limit interpretation. Longitudinal studies with detailed chronicity data, medications and clinical measures overtime will improve brain-PAD modeling in BD.

Importance Childhood trauma is associated with increased risk for bipolar disorder, but the biological mechanisms of this association remain incompletely defined. Gray matter differences observed after trauma exposure overlap with those reported in bipolar disorder, suggesting that the association of childhood trauma with bipolar disorder might be mediated through brain morphology. Objective To determine whether cortical thickness, cortical surface, or subcortical volume mediate the association of childhood trauma with bipolar disorder. Design, Setting, and Participants This case-control study conducted a cross-sectional analysis of individuals with bipolar disorder and healthy controls from 19 international cohorts (Enhancing NeuroImaging Genetics Through Meta-Analyses [ENIGMA] Bipolar Disorder Working Group) from January 2010 to December 2022. Data were analyzed from January 2025 to January 2026. Exposures The primary exposure was the severity of total childhood trauma assessed with the Childhood Trauma Questionnaire, with secondary analyses of 5 subscales (emotional neglect and abuse, physical neglect and abuse, and sexual abuse). Main Outcomes and Measures The primary outcome was bipolar disorder diagnosis (case vs control). The primary measure was the mediation effects of childhood trauma on diagnosis via gray matter (75 bilateral-averaged cortical thickness, surface, and subcortical volume measures). The mediation pathway from severity of childhood trauma to bipolar disorder through brain morphology was specified a priori. High-dimensional mediation analysis, with leave-one-site-out cross-validation and permutation testing for significance (false discovery rate [FDR]), was conducted. Results The final sample included 2221 healthy controls (mean [SD] age, 35.6 [13.2] years; 1274 female [57%]) and 1031 participants with bipolar disorder (mean [SD] age, 38.6 [13.7] years; 579 female [56%]). Severity of childhood trauma was directly associated with higher likelihood of having a bipolar disorder diagnosis (median coefficient, 0.841; 95% CI, 0.834-0.851; range, 0.776-0.893; FDR P < .001). Less than 1% of the association between childhood trauma and bipolar disorder was mediated by brain morphology. Statistically significant mediators were hippocampal volume (median coefficient, 0.004; 95% CI, 0.002-0.005; range, 0-0.008; FDR P < .001), medial orbitofrontal gray matter thickness (median coefficient, 0.002; 95% CI, 0.002-0.003; range, 0-0.004; FDR P < .001), and superior frontal gyrus gray matter thickness (median coefficient, 0.002; 95% CI, 0.002-0.003; range, 0-0.005; FDR P < .001). Conclusions and Relevance This study found that severity of childhood trauma exposure was associated with bipolar disorder diagnosis in part through a smaller hippocampus, thinner cortex in the medial orbitofrontal gyrus, and thinner cortex in the superior frontal gyrus. The identification of this mechanistic pathway improves understanding of the disorder, could help to identify those at risk, and enable the development of new interventions.

Bipolar disorder's (BD) clinical heterogeneity has an unresolved genetic basis. We meta-analyzed genome-wide association studies (GWAS) of 16 BD subphenotypes in 226,032 individuals from 57 cohorts (38,022 cases); 10 advanced to multivariate and multi-trait analyses. Four factors (compulsive, psychotic, dysregulated, internalizing) explained 82.8% of shared genetic variance. BD1 and BD2 loaded on distinct factors despite a high genetic correlation; 87.0% of common-factor loci were significant in neither subtype. Unipolar mania aligned with psychosis over internalizing, and was distinguishable from BD1, and rapid cycling showed heritable cross-domain liability. We identified 356 risk loci, 158 novel, including the first univariate-GWAS associations for psychosis, unipolar mania, rapid cycling and schizoaffective disorder-and 249 credible genes (89 high-confidence), 12 with approved-drug or clinical-phase annotations. Cell-type association showed a midbrain dopaminergic-GABAergic gradient along the psychotic factor. BD's genetic architecture appears hierarchical-a general liability resolving into dimensions of course and comorbidity, beyond subtypes.

Abstract Modeling individual brain dynamics from resting-state fMRI (rs-fMRI) remains challenging due to substantial inter-subject variability, noise, and limited data length per subject. Here, we systematically evaluate whether hierarchical shallow piecewise-linear recurrent neural networks (shPLRNNs), recently introduced as interpretable dynamical system reconstruction models, can generate individualized rs-fMRI time series while preserving subject-specific functional connectivity structure. We applied the framework to 1,423 rs-fMRI samples from healthy participants of the Marburg-Münster Affective Disorders Cohort Study (MACS). Simulated rs-fMRI data reproduced substantial empirical FC structure, with comparable reconstruction accuracy on the validation and held-out test sets. Generalization to unseen individuals was heterogeneous and strongly depended on how typical a subject’s connectivity pattern was relative to the training cohort, with template similarity explaining 37% of variance in reconstruction accuracy. Learned subject-specific parameters exhibited significant test-retest stability and higher within-subject than between-subject similarity on longitudinal data from two different timepoints, supporting their interpretation as individualized dynamical markers. Associations between individual parameters and demographic or cognitive variables were statistically significant but modest in effect size, and predictive performance remained below that obtained using empirical rs-fMRI features directly. Empirical FC was used as a reference for static subject information rather than as a target to be outperformed. Together, these results suggest that hierarchical shPLRNNs can extract meaningful and partially stable individual-specific dynamical structure from rs-fMRI data. The findings delineate key trade-offs between model expressivity, generalization and subject specificity, and point to directions for future methodological refinement in individualized brain modeling. Graphical Abstract A hierarchical dynamical RNN captures substantial individual rs-fMRI functional connectivity structure using compact subject-specific parameters embedded in shared population dynamics. The resulting representations generalize to held-out subjects and show test-retest stability, but only modest associations with phenotypic variables.

BACKGROUND:Environmental adversity is linked to major depressive disorder (MDD), potentially via sustained low-grade inflammation. However, serum markers such as C-reactive protein (CRP) are transient and sensitive to acute states. In contrast, epigenetic signatures of inflammation may provide a more stable trace of how stress becomes biologically embedded and contributes to depression risk over time. METHODS:In a subsample of the Marburg-Münster Affective Disorders Cohort Study (MACS; N = 579; 320 healthy controls, 259 with MDD), we examined whether early life adversity (ELA; CTQ) and recent life stress (RLS; LEQ) are associated with CRP-related DNA methylation (CRPm) at baseline. We further tested whether CRPm predicts depressive symptom severity (HAMD) at baseline and at two-year follow-up (n = 407). DNA was extracted from whole blood, and CRPm scores were computed using publicly available genome-wide summary statistics. RESULTS:CRPm explained 21.3% of the variance in serum high-sensitivity CRP (hsCRP). Higher CRPm was significantly associated with both ELA (b = 0.01, SE = 0.003, p = 0.017) and RLS (b = 0.01, SE = 0.004, p = 0.032), after adjusting for age and sex. CRPm also predicted depressive symptom severity at baseline (b = 0.68, SE = 0.27, p = 0.013) and at follow-up (b = 0.79, SE = 0.25, p = 0.002). These associations remained after controlling for white blood cell-type composition but were attenuated after adjusting for BMI and smoking. In contrast, hsCRP was not associated with adversity or depressive symptoms. CONCLUSION:Our study indicates that a methylation-based index of chronic inflammation is associated with stress exposure and depressive symptoms over time, in contrast to fluctuating serum hsCRP. The findings are more consistent with an indirect pathway in which environmental adversity is linked to inflammatory biology via stress-related health behaviors, rather than with a model of direct biological embedding.

BACKGROUND:Language impairments are common in affective and psychotic disorders, yet their patterns and underlying pathomechanisms remain insufficiently understood. A transdiagnostic perspective provides a framework for identifying shared and disorder-specific language alterations across diagnostic boundaries. Combining natural language processing (NLP) with network analysis enables the investigation of complex associations between linguistic, cognitive, and psychopathological features. METHODS:Spontaneous speech from N = 372 participants (119 MDD, 27 BD, 48 SSD and 178 HC) was elicited using four Thematic Apperception Test pictures (~12 min per participant). NLP models were applied to extract latent linguistic variables across various levels, including lexical diversity, syntactic complexity, semantic coherence, and disfluencies. Network analysis was used to relate linguistic variables, psychopathology (SAPS, SANS, HAM-A, HAM-D, YMRS, TLI, GAF), and cognitive performance (attention, verbal memory, recognition, and verbal fluency). RESULTS:Linguistic variables formed the densest network cluster, with type-token ratio, mean length of utterance, and syntactic complexity emerging as central nodes. Psychopathology variables were less cohesive, while TLI "Impoverishment", coherence mean, and executive functioning bridged linguistic, cognitive, and psychopathological domains. Network comparison tests revealed no significant differences in linguistic-cognitive network structure across HC, MDD, BD, and SSD. CONCLUSIONS:Linguistic networks show high structural consistency across healthy individuals and patients, whereas psychopathological symptom networks reflect transdiagnostic profiles. These findings support a dimensional and transdiagnostic framework underscore shared language-cognition mechanisms, and highlight executive functioning as key cross-domain connection, which opens up new avenues for dimensional research into the pathophysiological and etiological mechanisms underlying language dysfunctions.

BACKGROUND:Peripheral low-grade inflammation has been implicated in the pathophysiology of various psychiatric disorders and has been associated with cortical brain structural alterations. However, it remains unclear whether inflammation-related cortical atrophy is disorder-specific or reflects shared, diagnosis-independent vulnerability across psychiatric conditions. METHODS:We investigated cross-sectional and longitudinal associations between baseline high-sensitivity C-reactive protein (hs-CRP) and cortical thickness in participants from the Marburg Affective Disorders Cohort Study (MACS). The baseline sample comprised 683 patients (524 with major depressive disorder [MDD], 82 with bipolar disorder [BD], 77 with schizophrenia [SCZ]) and 620 healthy controls (HC) (59.9% female). After two years, follow-up data were available for 163 patients (125 MDD, 18 BD, 20 SCZ) and 184 HC (57.3% female). Serum hs-CRP levels were measured in all participants, and cortical thickness was assessed using structural MRI with FreeSurfer parcellation. Models were adjusted for age, sex, BMI, site, and diagnosis, with multiple comparisons corrected using the false discovery rate. RESULTS:Higher baseline hs-CRP was significantly associated with reduced cortical thickness in the left paracentral lobule at baseline (β = -0.029; p FDR = 0.017) and with cortical thinning over time in the left fusiform gyrus (β = -0.014; p FDR = 0.038) in longitudinal analyses. No significant interaction effects were found for age, sex, diagnosis, or smoking status. CONCLUSIONS AND RELEVANCE:Peripheral low-grade inflammation was associated with progressive cortical thinning across diagnostic groups, supporting a diagnosis-independent neurobiological mechanism, that is not specific to any psychiatric disorder, highlighting peripheral inflammation as potential target for preventive and therapeutic strategies in psychiatric care.

Schizophrenia is often conceptualized as a brain network disorder, yet the organizational principles and heterogeneity underlying widespread cortical abnormalities remain poorly understood. Leveraging multisite MRI data from 3,958 individuals diagnosed with schizophrenia and 5,489 neurotypical individuals, we studied the cortical organization and its subtyping by analyzing individualized cortical network similarity. We used eigenvector decompositions to study spatial patterning of the gradients and graph theory to study small-world topology. Individuals with schizophrenia showed widespread alterations of gradient loadings, which followed inferior-superior and frontal-temporal axes. Alterations in small-world topology were localized in key network hubs, including the insula and anterior cingulate cortex. Brain-symptom association analyses identified a latent dimension linking disorganization symptoms to topological alterations. Finally, clustering cortical alterations identified two robust subtypes, characterized by divergent anterior cingulate (S1) versus temporoparietal (S2) thickness differences aligned with the intrinsic gradient-topology patterns. Both subtypes were present early in the illness and stable across disease stages and age groups. These findings reveal systematic disruptions of cortical organization in schizophrenia, providing a network-level framework for macroscale brain organization and inter-individual heterogeneity.

BACKGROUND:Bipolar disorder (BD) is a major mood disorder influenced by both genetic and environmental factors. While DNA methylation from peripheral tissues can reflect both genetic and environmental influences and reveal insights into disease biology, it remains understudied in BD. DNA methylation signatures may complement polygenic scores (PGS) and hold potential as biomarkers. Here, we conducted the largest epigenome-wide association study (EWAS) of BD to date and evaluated the predictive value of polymethylation scores (PMS) in classifying case-control status. METHODS:DNA methylation from peripheral blood of 1729 cases and 1747 controls, comprising twelve cohorts, was obtained. We performed meta-analyses for the total sample, male-only, and female-only analyses. Differentially methylated regions (DMRs) were identified using the comb-p method. Polymethylation scores for BD (BD-PMS) were tested for association with BD, and in combination with PGS. FINDINGS:We identified 47 differentially methylated CpG positions (DMPs) in the total and four in the female-only analysis. Ninety, fourteen and six DMRs were identified in the total sample, female-only, and male-only analyses, respectively. Genes annotated to the top DMPs were enriched for immune activation and phosphorylation pathways. DMRs were annotated to genes relevant to neurotransmission, including GABBR1 and CACNA2D4. BD-PMS explained 2% of the variance in BD case-control status, and improved the variance explained from 7.9 to 8.5% when combined with PGS. For bipolar I disorder, BD-PMS explained 4.9% of the variance, and improved the variance explained by PGS from 15.9 to 18.5%. Association of BD with PMS for schizophrenia and major depression suggests pleiotropic epigenetic effects. INTERPRETATION:DNA methylation signatures of BD are detectable in blood using adequately powered data and may reveal novel BD biology that is not captured by genetic studies. PMS from large cohorts have the potential to facilitate the development of prediction tools to aid clinical decision-making. FUNDING:This investigation was primarily funded by the Research Council of Norway (RCN #250299, #273446, #223273) and the University of Bergen. A complete list of funding organisations is provided in the Acknowledgements.

Voxel-based morphometry (VBM), a popular approach in neuroimaging research, uses magnetic resonance imaging data to assess variations in the local density of brain tissue and to examine its associations with biological and psychometric variables. Here we present deepmriprep, a preprocessing pipeline designed to leverage neural networks to perform all the necessary preprocessing steps for the VBM analysis of T1-weighted magnetic resonance imaging. Utilizing the graphics processing unit, deepmriprep is 37 times faster than CAT12, the leading VBM preprocessing toolbox. The proposed method matches CAT12 in accuracy for tissue segmentation and image registration across more than 100 datasets and shows strong correlations in the VBM results. Tissue segmentation maps from deepmriprep have more than 95% agreement with ground-truth maps, and its nonlinear registration predicts smooth deformation fields comparable to CAT12. The high computational speed of deepmriprep enables rapid preprocessing of large datasets and opens the door to real-time applications.

Abstract Formal thought disorder (FTD) is a core psychosis feature. Disentangling its dimensions requires tasks simple enough for formal modeling yet sensitive enough to capture individual variation across the psychosis spectrum. The semantic verbal fluency task offers precisely this: a structured behavioral trace of semantic memory sampling, amenable to computational analysis using distributed word embeddings. We hypothesized that this sampling process is governed by two dissociable mechanisms mapping onto FTD dimensions: initial retrieval drive ( d 0 ), quantifying the motivational resource sustaining production, and semantic search precision ( α ), quantifying how strongly similarity to the preceding word constrains each retrieval step from near-random to highly structured. We hypothesized that reduced d 0 would track negative psychosis symptoms and alogia, while degraded α would track language disorganization and left inferior longitudinal fasciculus (ILF) fractional anisotropy. We tested these predictions in a primary ( N = 120) and an independent replication sample ( N = 249) of German-speaking individuals across the psychosis spectrum. Both parameters decreased with greater psychosis severity and, in the primary sample, they dissociated regarding their clinical correlates. d 0 correlated negatively with negative symptoms, general psychopathology, and poverty of speech, consistent with a computational signature of alogia. α correlated negatively with positive symptoms and cognitive flexibility, and, in individuals with psychosis, positively with left ILF fractional anisotropy. The association between d 0 and negative symptoms was replicated in the independent sample. These findings pave the way for mechanistic, automatically derived FTD markers capturing subclinical variation across the psychosis spectrum and mapping onto underlying cognitive and neural processes.

Abstract Elucidating the neurobiological basis of neurodevelopmental and psychiatric conditions (NDPCs) remains challenging because brain alterations vary within diagnoses and overlap across them. Whether diverse alterations follow a systematic organization that may reflect shared vulnerabilities remains unknown. Here, we assembled 10,135 individuals with schizophrenia, autism, bipolar, obsessive-compulsive, generalized anxiety, and major depressive disorders, and 11,998 reference participants across six continents through the ENIGMA consortium. Using normative modeling, we quantified individual deviations in cortical thickness, surface area, and subcortical volumes relative to lifespan reference trajectories (5 to 80 years). We show that structural deviations converged along cortical axes reflecting connectome organization, maturation, and cytoarchitectonic diversity. These axes mirrored typical population variation, but their expression differed across diagnoses and partly scaled with symptom severity. Even rare and highly individualized extreme deviations followed this organization, concentrating in densely connected regions. Finally, brain structural deviations overlapped substantially across diagnoses, while differences between them increased toward the association cortex. Together, we provide large-scale evidence that structural deviations across NDPCs are systematically constrained by the brain’s intrinsic architecture. This shared organization provides a framework for reconciling individual variability with transdiagnostic similarities and motivates an integrative, systems-level understanding of mental health.

Background:Large-scale T1-weighted MRI studies have established grey-matter abnormalities in bipolar disorder (BD), with our group contributing to consensus findings. However, structural connectivity, particularly within emotion- and reward-related circuits, remains poorly understood. Diffusion-weighted MRI (dMRI) enables investigation of white-matter pathways, yet prior work is constrained by small samples, methodological heterogeneity, and unclear medication effects. We conducted the largest dMRI network analysis in BD, relating symptom burden and polypharmacy to tractography-derived connectivity and graph-theoretic metrics. Methods:Cross-sectional structural and diffusion MRI scans from 449 individuals with BD (35.7±12.6 years) and 510 controls (33.3±12.6 years), aged 18-65, were analyzed across 16 ENIGMA-BD sites. Standardized segmentation/parcellation and constrained spherical deconvolution tractography generated individual structural connectivity matrices. Graph-theoretic metrics of global and subnetwork organization were related to symptom severity and medications. Results:BD showed widespread network alterations (lower density and efficiency, longer path length, and higher betweenness centrality), altered microstructural organization in a limbic-basal ganglia circuit, and abnormal streamline counts in a default-mode/salience/fronto-limbic-basal ganglia network. Longer illness duration, later onset, and psychosis history were associated with greater abnormalities in network architecture, whereas more manic episodes were associated with greater fronto-limbic connectivity. Antidepressant (particularly SSRI), anticonvulsant, and antipsychotic use related to poorer global and fronto-limbic connectivity; no clear lithium effects emerged. Conclusions:As the largest structural connectivity study in BD, we reveal widespread disruption in reward and emotion-regulation networks influenced by illness severity and medication use. Results show that multisite harmonization is feasible and highlight ENIGMA-BD as a scalable framework for identifying reproducible neurobiological markers.

Although low-grade inflammatory processes have traditionally been studied in affective disorders, they are increasingly recognized as relevant across diagnostic categories. Genetic predisposition and environmental exposures such as childhood trauma (CT) may influence inflammation and shape vulnerability to psychopathology. Understanding how genetic predisposition for inflammation relates to specific symptom dimensions may clarify biological mechanisms underlying psychopathology. In N = 1790 individuals from the Marburg-Münster Affective Disorders Cohort Study (MACS), including patients with affective, anxiety, and psychotic disorders, as well as healthy controls, five transdiagnostic psychopathological syndrome factors were derived using factor analysis of clinical ratings. Polygenic scores (PGS) for circulating tumor necrosis factor TNF-α, interleukin IL-6, IL-10, and CRP were computed to indicate genetic predisposition to low-grade inflammation. Using network analyses, associations between inflammatory PGS and psychopathological syndrome factors were estimated while adjusting for age, sex, and BMI and including CT as a potential moderator. Six direct PGS-syndrome associations emerged, all with small but stable effect sizes. IL-6 PGS had the broadest connectivity, showing negative associations with increased appetite, paranoid-hallucinatory syndrome, and depression, as well as a positive association with negative syndrome. It also had the highest bridging centrality. IL-10 PGS was connected to negative and paranoid hallucinatory syndromes. These associations were largely independent of diagnosis and CT exposure. Integrating inflammatory genetic predisposition into networks of transdiagnostic symptom dimensions reveals small but consistent links between immune-related genetic risk and psychopathology, highlighting shared and distinct immunological pathways across psychiatric disorders.

Importance:Childhood trauma is associated with increased risk for bipolar disorder, but the biological mechanisms of this association remain incompletely defined. Gray matter differences observed after trauma exposure overlap with those reported in bipolar disorder, suggesting that the association of childhood trauma with bipolar disorder might be mediated through brain morphology. Objective:To determine whether cortical thickness, cortical surface, or subcortical volume mediate the association of childhood trauma with bipolar disorder. Design, Setting, and Participants:This case-control study conducted a cross-sectional analysis of individuals with bipolar disorder and healthy controls from 19 international cohorts (Enhancing NeuroImaging Genetics Through Meta-Analyses [ENIGMA] Bipolar Disorder Working Group) from January 2010 to December 2022. Data were analyzed from January 2025 to January 2026. Exposures:The primary exposure was the severity of total childhood trauma assessed with the Childhood Trauma Questionnaire, with secondary analyses of 5 subscales (emotional neglect and abuse, physical neglect and abuse, and sexual abuse). Main Outcomes and Measures:The primary outcome was bipolar disorder diagnosis (case vs control). The primary measure was the mediation effects of childhood trauma on diagnosis via gray matter (75 bilateral-averaged cortical thickness, surface, and subcortical volume measures). The mediation pathway from severity of childhood trauma to bipolar disorder through brain morphology was specified a priori. High-dimensional mediation analysis, with leave-one-site-out cross-validation and permutation testing for significance (false discovery rate [FDR]), was conducted. Results:The final sample included 2221 healthy controls (mean [SD] age, 35.6 [13.2] years; 1274 female [57%]) and 1031 participants with bipolar disorder (mean [SD] age, 38.6 [13.7] years; 579 female [56%]). Severity of childhood trauma was directly associated with higher likelihood of having a bipolar disorder diagnosis (median coefficient, 0.841; 95% CI, 0.834-0.851; range, 0.776-0.893; FDR P < .001). Less than 1% of the association between childhood trauma and bipolar disorder was mediated by brain morphology. Statistically significant mediators were hippocampal volume (median coefficient, 0.004; 95% CI, 0.002-0.005; range, 0-0.008; FDR P < .001), medial orbitofrontal gray matter thickness (median coefficient, 0.002; 95% CI, 0.002-0.003; range, 0-0.004; FDR P < .001), and superior frontal gyrus gray matter thickness (median coefficient, 0.002; 95% CI, 0.002-0.003; range, 0-0.005; FDR P < .001). Conclusions and Relevance:This study found that severity of childhood trauma exposure was associated with bipolar disorder diagnosis in part through a smaller hippocampus, thinner cortex in the medial orbitofrontal gyrus, and thinner cortex in the superior frontal gyrus. The identification of this mechanistic pathway improves understanding of the disorder, could help to identify those at risk, and enable the development of new interventions.

Major Depressive Disorder (MDD) is a highly prevalent, severe mental health condition that constitutes one of the leading causes of disability worldwide. While recent animal studies suggest a causal role of the gut microbiome in the pathophysiology of MDD models, evidence in humans is still unclear due to small sample sizes, inconsistent clinical assessment of MDD diagnosis, and methodological limitations regarding causal inference in cross-sectional data. Here, we explicitly address these shortcomings to investigate the potential causal link between the gut microbiome and MDD: First, we replicate previously reported microbiome-depression associations using one of the largest multicenter MDD cohorts for which microbiome data and in-depth diagnostic assessment are available (N = 1,269 MDD patients and controls). We find a significant difference between healthy controls and MDD patients for the relative abundance of four taxa: Eggerthella, Hungatella, Coprobacillus, and Lachnospiraceae FCS020. Second, we employ state-of-the-art, fully data-driven causal inference tools within Judea Pearl's framework, allowing us to derive model constraints from the data rather than relying on potentially strong, unrealistic assumptions. Using this approach, we found evidence for Eggerthella and Hungatella as potential causal contributors to MDD. Furthermore, we show that the potential causal effects of Eggerthella and Hungatella on MDD persist beyond the influence of body mass index, revealing two distinct potential causal pathways linking the gut microbiome to MDD. Finally, the difference in relative abundance of these taxa between healthy and MDD patients was independent of antidepressant medication. Our study provides the first data-driven evidence for a potential causal role of gut microbiota in the pathophysiology of depression in humans.

Background Affective and psychotic disorders share overlapping symptom constellations, environmental and genetic risk factors, and neurocognitive profiles. However, the nature of this association is not well understood, and accumulating evidence suggests a dimensional rather than categorical distinction between healthy and clinical populations. Objective This study investigated the relationships linking childhood trauma, positive and negative symptoms, depression symptoms, anxiety symptoms, and neurocognitive functioning such as verbal intelligence, executive functioning, and semantic processing within a unified transdiagnostic network. Methods We employed a partial correlation network and directed acyclic graph (DAG) analysis in a large psychiatric sample of 2444 participants, including 1364 patients with affective disorders or psychotic disorders, and 1080 healthy controls. Using self-report scales, clinician ratings, and neurocognitive tests, we analyzed risk and symptom clusters and developed a preliminary predictive model of these associations. Results Distinct, interconnected clusters of psychopathological symptoms emerged in the Gaussian Graphical Model (GGM), with strong connections between depression and anxiety clusters. Neurocognition and childhood trauma showed sparser associations with psychopathological symptoms. Nodes from self- and observer-ratings formed surprisingly strong bridges, especially concerning libido problems, lack of hobbies, social anhedonia, and loss of interest. DAG analyses indicated symptoms of low mood, reduced wellbeing, not feeling safe, and having difficulties initiating work or activities in general as potential predictors of downstream symptoms. Conclusion Complex associations between psychopathological and neurocognitive functioning emerged, with (the lack of) self-reported happiness, and well-being as key nodes. Future research should validate the clinical utility by using longitudinal network analyses and experimental data.

BACKGROUND:Negative expectations towards the future are frequently observed in patients with major depressive disorder (MDD) and linked to less favourable outcomes like worse treatment response and suicidal behaviours. Despite clinical significance, the neurobiological underpinnings of negative expectations remain largely unexplored. METHODS:This study compared structural MRI morphometric gray matter volume (SPM/CAT12) and two-year longitudinal clinical data between two groups of patients with DSM-IV-TR MDD (total N = 330), matched for age, sex, severity of depressive symptoms (HAMD), and global functioning (GAF scale), but differing in presence or absence of negative expectations assessed with the BDI-I item 2 "pessimism". RESULTS:In patients with negative expectations, the gray matter volume of the right middle frontal gyrus (rMFG) was increased compared to patients without negative expectations. Patients who expressed negative expectations at baseline had significantly longer duration of depressive episodes during the two-year follow-up period. Average duration of subclinical depressive episodes during follow-up was significantly predicted by the rMFG volume. CONCLUSION:This study showed a correlation between negative expectations and brain structure, as well as with the course of illness. Clinically it sets the stage for future research into therapeutic interventions targeting patients' expectations as a potential modulator towards a more favourable outcome in MDD patients with negative expectations.