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.

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.

BACKGROUND:Narcissistic dimensions are partly shaped by genetic and non-shared environmental factors, yet the ways in which childhood maltreatment (CM) relates to narcissistic admiration and rivalry remain poorly understood. Despite the relevance of both CM and narcissistic dimensions for mental health, it remains unclear whether - and how - the association between CM and narcissism differs across clinical and non-clinical populations. METHODS:Data were drawn from five cohorts (N = 2157), including healthy controls (N = 418) and a transdiagnostic clinical sample (N = 1739) primarily with Major Depressive Disorder, alongside schizophrenia spectrum, bipolar, and stress-related disorders. Narcissism was assessed with the Narcissistic Admiration and Rivalry Questionnaire (NARQ), CM with the Childhood Trauma Questionnaire (CTQ), and depressive symptoms with the Beck Depression Inventory (BDI) or Patient Health Questionnaire-9 (PHQ-9). Cross-sectional linear regression models tested associations between narcissistic traits and CM, controlling for depression severity. RESULTS:Across the total sample, narcissistic rivalry was positively associated with CM (B = 0.108, p < .001), and admiration was negatively associated (B = -0.130, p < .001), both with small effect sizes. Subgroup analyses showed consistent negative associations of admiration with CM in both groups, while rivalry associations were weaker in clinical populations. Emotional abuse, emotional neglect, and physical neglect were most consistently linked to higher rivalry and lower admiration. CONCLUSION:CM is linked to lower narcissistic admiration across populations and to higher narcissistic rivalry in healthy individuals, highlighting differential associations with narcissistic trait dimensions.

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.

Positive and negative schizotypy reflect distinct patterns of subclinical traits in the general population associated with neurodevelopmental and schizophrenia-spectrum pathologies. Yet, a comprehensive characterization of the unique and shared neuroanatomical signatures of these schizotypy dimensions is lacking. Leveraging 3D brain MRI data from 2730 unmedicated healthy individuals, we identified neuroanatomical profiles of positive and negative schizotypy and systematically compared them with disorder-specific, microarchitectural, neurotransmitter-level, and connectome measures. Positive and negative schizotypy were associated with distinct cortical signatures, of predominantly thinner frontal and thicker paralimbic cortical areas, respectively. These cortical signatures of positive and negative schizotypy were differentially linked to brain-wide cortical patterns of schizophrenia-spectrum (clinical high-risk for psychosis, schizophrenia) and neurodevelopmental conditions (ADHD, autism spectrum disorder and 22q11.2 deletion syndrome). Additionally, the positive and negative schizotypy-related cortical profiles mapped onto different local attributes of gene expression, cortical myelination, D1, and histamine receptor distributions. Network models further showed that positive and negative schizotypy cortical signatures were spatially associated with cortical hubs, suggesting that highly interconnected regions are more vulnerable to the morphological differences associated with both schizotypy dimensions. Finally, predominantly sensorimotor-to-association and paralimbic areas emerged as epicenters with connectivity profiles significantly linked to the schizotypy-related cortical patterns. Collectively, this study identified cortical signatures of positive and negative schizotypy traits that are embedded along multiple scales of cortical organization and neuropsychiatric pathologies. Our work yields novel insights into how neurobiology and brain architecture may guide neuroanatomical vulnerability and resilience to psychopathology in the general population.

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.

Specific phobia (SPH) is a prevalent anxiety disorder and may involve advanced biological aging. However, limited brain age research has been conducted in anxiety disorders. This mega-analysis investigated brain aging in SPH participants within the ENIGMA-Anxiety Working Group. 3D brain structural MRI scans from 17 international samples (600 SPH individuals, of whom 504 formally diagnosed and 96 questionnaire-based cases; 1134 controls; age range: 22-75 years) were processed with FreeSurfer. Brain age was estimated from 77 subcortical and cortical regions with a publicly available ENIGMA brain age model. The brain-predicted age difference (brain-PAD) was calculated as brain age minus chronological age. Linear mixed-effects models examined group differences in brain-PAD and moderation by age. No significant group difference in brain-PAD manifested (βdiagnosis [SE] = 0.37 years [0.43], p = 0.39). A negative diagnosis-by-age interaction was identified, which was most pronounced in formally diagnosed SPH (βdiagnosis-by-age = -0.08 [0.03], pFDR = 0.02). This interaction remained significant when excluding participants with anxiety comorbidities, depressive comorbidities, and medication use. Post hoc analyses revealed a group difference for formal SPH diagnosis in younger participants (22-35 years; βdiagnosis = 1.20 [0.60], p < 0.05, mixed-effects d [95% confidence interval] = 0.14 [0.00-0.28]), but not older participants (36-75 years; βdiagnosis = 0.07 [0.65], p = 0.91). Brain aging did not relate to SPH in the full sample. However, a diagnosis-by-age interaction was observed across analyses, and was strongest in formally diagnosed SPH. Post hoc analyses showed subtle advanced brain aging in young adults with formally diagnosed SPH. Taken together, these findings indicate the importance of clinical severity, impairment, and persistence, and may suggest a slightly earlier end to maturational processes or subtle decline of brain structure in SPH.

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.

Abstract Background Childhood maltreatment is a traumatic interpersonal stressor that increases vulnerability for depression in adulthood. However, some individuals show secure attachment despite childhood maltreatment, a pattern that can be described as interpersonal resilience. The present study examined the behavioral and neural correlates of interpersonal resilience, defined as secure attachment in adults exposed to childhood maltreatment. Methods We analyzed structural 3T MRI data from 1,317 adults, including healthy participants and individuals with partially or fully remitted major depression. Gray matter volume was estimated from structural MRI data using voxel-based morphometry. Attachment style and childhood maltreatment were assessed using the Relationship Scales Questionnaire and the Childhood Trauma Questionnaire, respectively. A 2×2 design (childhood maltreatment by attachment style) tested main and interaction effects on behavioral outcomes and brain structure. Results Interpersonally resilient individuals with secure attachment and maltreatment reported significantly better mental health outcomes compared to insecurely attached adults with maltreatment. Differences included lower self-reported and rater-based depressive symptoms, lower global symptom severity, and higher global functioning. In the neuroimaging analyses, we identified a significant childhood maltreatment by attachment style interaction in the left supramarginal gyrus, with larger gray matter volume in resilient individuals compared to all other groups. This effect remained robust across multiple sensitivity analyses, controlling for medication load, antidepressant intake, diagnosis group, as well as in a complementary dimensional analysis. Conclusions The results identify a potential neural correlate of interpersonal resilience. Larger gray matter volume in the left supramarginal gyrus, a region previously implicated in perspective taking and self-other distinction among other functions, may be relevant to more adaptive interpersonal functioning after early adversity. Together with the robust behavioral effects, these findings are consistent with secure attachment as a protective factor that may be associated with attenuated effects of childhood maltreatment on mental health.

OBJECTIVES:Patients with major depressive disorder (MDD) frequently report burdensome interpersonal difficulties and altered empathy and perspective-taking. Previous findings on binary comparisons between patients and healthy controls are limited in their clinical translation as they neglect dissociable risk and disease states. In this preregistered study (https://osf.io/k6mdh/overview?view_only=04b7f39f59a8434292cd472120f661ca), we extend case-control comparisons by additionally investigating empathic distress, empathic concern, and perspective-taking in association with (a) familial MDD risk, (b) acute symptom severity, and (c) cumulative MDD severity. METHOD:We analyzed data from n = 499 MDD patients and n = 670 healthy controls from the Marburg-Münster-Affective-Cohort Study. Robust linear regressions were run to investigate group differences in self-reported empathy and perspective-taking and their associations with acute symptom severity and familial MDD risk. Analyses on cumulative MDD severity were run in a structural equation model. RESULTS:Empathic distress was positively associated with both acute and cumulative MDD severity and was elevated in patients compared to healthy controls. A positive association between empathic distress and familial MDD risk became non-significant after adjustment for age and sex. Patients also reported higher levels of empathic concern-likely influenced by acute symptom severity-and slightly lower levels of perspective-taking. CONCLUSIONS:Our associational findings indicate empathic distress to constitute a correlate of acute MDD and longer-term MDD progression, while its relevance for MDD risk requires further investigation. We provide new indications of a generalized heightened emotional attunement during acute depression, entailing both empathic concern and empathic distress. Longitudinal studies are warranted to probe causal associations to prospectively inform psychotherapeutic interventions addressing maladaptive empathic processes.

Formal thought disorder (FTD), involving disruptions in language and thought, is commonly linked to schizophrenia but is also prevalent and debilitating in Major Depressive Disorder (MDD). Despite its clinical relevance, few studies have comprehensively examined FTD in MDD using operationalized rating scales and MR neuroimaging. FTD was assessed in 379 acute MDD patients using the Scales for the Assessment of Positive and Negative Symptoms. Whole-brain MRI was used to examine associations between FTD severity and gray and white matter structures, as well as resting-state functional connectivity (seed-to-voxel analysis) with CAT12, FSL, and CONN toolboxes. Overall, 37.5% of patients presented with FTD symptoms (29% negative; 14.5% positive; 6.1% both). Negative FTD was more common and driven by increased response latency and poverty of speech. FTD severity correlated negatively with gray matter volume of the right posterior cingulate gyrus and with white matter integrity of the right corticospinal tract. Functional resting state analyses showed FTD severity positively correlated with connectivity between the amygdala-hippocampus complex and the right pre- and postcentral gyri. Positive FTD was correlated with decreased connectivity between the orbitofrontal cortex and left occipital, temporal, and subgenual cingulate regions. Negative FTD was correlated with increased connectivity between the inferior frontal and middle frontal gyri. Neural correlates were unrelated to depression severity, illness duration, or psychotropic medication. This study highlights the prevalence and importance of FTD in acute MDD with structural and functional brain correlates being linked to language-related areas previously reported in schizophrenia.

MRI studies in bipolar disorder (BD) have yielded inconsistent findings, partly due to the varied use of psychotropic medications. This study utilised a mega-analysis approach, accounting for concurrent medication status (syndrome-based and Neuroscience-based Nomenclature (NbN) classifications), in order to assess the association of medication status with subcortical brain volumes in BD. Data from 2,664 BD patients and 4,065 controls (CN) were pooled from 34 research groups as part of the ENIGMA Bipolar Disorder Working Group. Standardized ENIGMA protocols were used to measure subcortical brain volumes. Linear-mixed-effects regression evaluated the association between psychotropic medications and subcortical volumes, and moderation analyses explored interactions. Medication-free patients (n = 410) showed mild ventricular enlargement (d = 0.07) and increased putamen volume (d = 0.06) compared to CN. Patients taking psychotropic medications exhibited smaller subcortical volumes (d = -0.06 to -0.11) and larger ventricles (d = 0.11 to 0.19). Use of antiepileptic and antipsychotic medications was associated with smaller hippocampal and thalamic volumes (d = -0.07 to -0.14), while NbN classification indicated that the categories of ‘valproate’ and ‘dopamine and other monoamine receptor antagonists’ are key variables when considering volume differences between BD and CN. Concurrent lithium use weakened the negative association between antiepileptic use and hippocampal volume (β = 0.19, q = 0.038) in patients. Medication status is associated with altered subcortical brain volumes in BD. The NbN classification provides a useful framework for future studies, emphasizing the need for comprehensive longitudinal research to further unravel complex clinical-pharmacological-neurobiological interactions in BD.

BACKGROUND:The gut microbiome has been linked to major depressive disorder (MDD), yet it remains unclear whether antidepressant treatment influences these associations. This study aimed to clarify the role of serotonin reuptake inhibitors (SSRI/SNRI) in shaping gut microbiome changes observed in MDD. METHODS:We conducted cross-sectional analyses in two independent patient cohorts (total N = 1802) and a meta-analysis across both cohorts, comparing the gut microbiome of MDD patients with and without SSRI/SNRI treatment. RESULTS:Here we show that SSRI/SNRI treatment is consistently associated with reduced Clostridium sensu stricto 1 abundance. This effect is specific to SSRI/SNRI treatment and not observed with other psychotropic medications. Importantly, reductions in Clostridium sensu stricto 1 in MDD compared to unaffected controls are explained by SSRI/SNRI medication status. CONCLUSIONS:Antidepressant treatment is an important factor shaping gut microbiome alterations linked to MDD, underscoring the need to account for medication effects and potentially informing future microbiome-based strategies to improve treatment response.