Journal of Affective Disorders

Background: Previous evidence suggests that higher body size is associated with bipolar disorders, however, whether this association is causal remains uncertain. Interpretation is further complicated by heterogeneity across age, variation in clinical presentation, and potentially distinct underlying aetiologies. Aims: To determine whether body size exerts heterogenous causal effects on bipolar disorder subtypes and symptom profiles. Methods: By leveraging genetic instruments that differentiate effects at different life stages, summary-level univariable and multivariable Mendelian randomisation (MR) analyses were used to estimate how age-specific body size relates to adult psychiatric and symptomatic bipolar features; major depressive disorder (MDD), depressive symptom scores, subthreshold mania symptoms, bipolar disorder, bipolar type I and bipolar type II. Genetic instruments derived from genome-wide association studies (GWASs) for adult body mass index (BMI) (n= 681,275), childhood body size (n= 453,169) and mid-to-later life body size (n= 453,169) served as proxies for prepubertal and adult BMI measures. Results: In univariable MR, higher genetically proxied adult BMI increased the odds of MDD (odds ratio (OR) = 1.13, 95% CI 1.09-1.16), subthreshold mania (OR = 1.09, 95% CI 1.0-1.19)), and depressive scores (Beta = 0.07, 95% CI 0.05-0.09). There was little evidence that childhood body size had an effect on any outcome. Robust evidence suggested bipolar disorder and MDD increased adult BMI in our reverse univariable analyses. Using multivariable MR, robust evidence indicated that increased adult body size after accounting for childhood body size increased the odds of MDD, subthreshold mania and depressive scores. Conclusions: Body size may exert different causal effects on bipolar disorder depending on age and symptoms, with detrimental effects occurring during adulthood. Weaker evidence suggested varying effects across bipolar subtypes. Triangulation of findings and higher powered GWASs to detect symptom-specific genetic variants are required to explore whether body size contributes to distinct aetiologies across bipolar patients, informing the identification of novel and personalised treatment targets.

BackgroundMajor depressive disorder (MDD), a leading cause of disability worldwide, exhibits substantial heterogeneity in treatment outcomes. Patients who do not respond to standard antidepressant therapy account for the majority of MDDs disease burden. Risk factors have been implicated in treatment response, including genes impacting on how antidepressants are metabolised. Yet, despite its clinical importance, risk factors for treatment-resistant depression (TRD) remain unexplored in low- and middle-income countries (LMIC). We used data from the DIVERGE study on MDD to investigate the risk factors of TRD in Pakistan. MethodsDIVERGE is a genetic epidemiological study that recruited adult MDD patients ([≥]18 years) between Sep 27,2021 to Jun 30, 2025, from psychiatric care facilities across Pakistan. Detailed phenotypic information was collected by trained interviewers and blood samples taken. Infinium Global Diversity Array with Enhanced PGx-8 from Illumina was used for genotyping followed by DRAGEN calling to infer metaboliser phenotypes for Cytochrome P450 (CYP) enzyme genes. We defined TRD as minimal to no improvement after [≥]12 weeks of adherent antidepressant therapy. We conducted multi-level logistic regression to test the association of demographic, clinical and pharmacogenetic variables with TRD. FindingsAmong 3,677 eligible patients, polypharmacy was rampant; 86% were prescribed another psychotropic drug along with an antidepressant. Psychological therapies were uncommon (6%) while 49% of patients had previously visited to a religious leader/faith healer in relation to their mental health problems. TRD was experienced by 34% (95%CI: 32-36%) patients. The TRD group was characterised by more psychotic symptoms and suicidal behaviour (OR=1.39, 95%CI=1.04-1.84, p=0.02; OR=1.03, 95%CI=1.01-1.05, p=0.005). Social support (OR=0.55, 95%CI=0.44-0.69, p=1.4x10-7) and parents being first cousins (OR=0.81, 95%CI=0.69-0.96, p=0.01) were associated with lower odds of TRD. In 1,085 patients with CYP enzyme data, poor (OR=1.85, 95%CI=1.11-3.07, p=0.01) and ultra-rapid (OR=3.11, 95%CI=1.59-6.12, p=0.0009) metabolizers for CYP2C19 had increased risk of TRD compared with normal metabolisers. InterpretationThere was an excessive use of polypharmacy in the treatment of depression while psychological therapies were uncommon highlighting the need for more evidence-based practice. This first large study of MDD from Pakistan uncovered the importance of culture-specific forms of social support in preventing TRD, highlighting opportunities for interventions in low-income settings. Pharmacogenetic markers can be leveraged to predict TRD.

Background Somatic and psychological symptoms like depression, anxiety, and trauma-related stress often co-occur, especially in young adults, a group facing major life transitions and increased vulnerability. These overlapping symptoms pose diagnostic challenges that traditional disorder-specific models capture poorly. Transdiagnostic and dimensional approaches may offer a more meaningful framework. However, population-based data on symptom patterns in young adults remains sparse. This study investigated the patterns of psychological and somatic symptoms among young adults from Switzerland and compares these results to findings from populations with different stress exposure histories: Ukrainians who fled to Switzerland, and Ukrainians living in different regions in Ukraine during the war. Methods We analyzed cross-sectional baseline data collected in spring 2024 as part of the Mental Health Assessment of the Population (MAP) studies, where we enrolled randomly selected young adults aged 18-24 from Switzerland, Ukrainian refugees in Switzerland, and Ukrainians residing in regions with different degrees of proximity to active war zones. We assessed somatic (PHQ-15) and psychological symptoms (PHQ-9, GAD-7, PCL-5) and explored symptom patterns using descriptive statistics, correlations, and k-means clustering. Results Psychological symptom severity showed highly consistent moderate-to-strong correlations with somatic symptoms (range: 0.53-0.69), across all young adult subgroups and disorders. Rather than identifying disorder-specific patterns, symptoms clustered by overall symptom severity, emerging in three clusters: (1) high symptom burden, (2) moderate symptom burden, and (3) low symptom burden clusters with elevated somatic, depressive, anxiety, and PTSD symptoms. The cluster structure was remarkably stable across Swiss, Ukrainian, and refugee subsamples, despite markedly different stress exposure histories. Conclusion Our results support a symptom-based, dimensional approach to understanding mental health in young adults and to better capture the complexity and co-occurrence of psychological and somatic symptoms in this age group. These findings further suggest that prevention and early detection strategies should more systematically integrate both psychological and somatic symptomatology.

BackgroundDepression is a mood disorder frequently associated with episodic memory impairment. However, it remains unclear whether functional brain activity differs between depressed and non-depressed individuals during encoding or retrieval of autobiographical or non-autobiographical memories. Clarifying these differences is important for refining theoretical models of memory impairment in depression and, potentially, for developing targeted interventions. MethodsWe conducted three coordinate-based meta-analyses examining encoding and retrieval of autobiographical and non-autobiographical memory in control participants and individuals with current, remitted, or subthreshold depression, or those at risk for depression. Studies were identified via database searches and analysed using Seed-based d Mapping. ResultsWe included coordinates from 21 fMRI studies. During encoding, depression was associated with reduced activity in the thalamus, the caudate, the salience network, the frontoparietal executive control network, and motor-related areas (ten studies, N = 506). During non-autobiographical retrieval, depression was associated with higher activity in the right inferior frontal gyrus (six studies, N = 332). During autobiographical retrieval, depression was associated with reduced activity in the right insula and fusiform gyrus, alongside increased activity in the left anterior cingulate cortex and the left middle frontal gyrus (ten studies, N = 423). Between-study heterogeneity was low and no evidence for publication bias was found. DiscussionOur results indicate that depression may be associated with impaired salience integration during encoding and autobiographical retrieval. In contrast, during non-autobiographical retrieval, increased frontal activity suggests a more vigilant or self-monitoring retrieval mode. Functional brain activity changes in depression therefore appear stage- and content-specific.

Understanding depressive symptoms dynamics and their determinants is crucial for designing effective mental health support initiatives. This study compared two methods for describing youth depressive symptoms trajectories and investigated associations of early-life factors (maternal education, maternal perinatal depression, domestic violence, physical, emotional, or sexual abuse, bullying victimisation, psychiatric disorder) with trajectory features. Prospective data from 8,264 mostly White European participants (54% female), including self-reported Short Moods and Feelings Questionnaires on ten occasions between 10-25 years, were used. Trajectories were summarised using functional principal component analysis (FPCA) and P-splines linear mixed-effect (PLME) models. Estimated derivatives were used to obtain magnitude and age of peak symptoms and peak symptoms velocity. Both methods performed comparably, but PLME models tended to over-smooth trajectories. Peak symptoms and peak velocity were higher and occurred >1 year earlier in females than males. All early-life factors were associated with higher peak symptoms, and most associated with higher and earlier peak velocity. Abuse and bullying additionally associated with earlier age of peak symptoms. FPCA is a useful alternative for characterising depressive symptoms trajectories and informing time-sensitive preventative measures to reduce impact of depression before symptoms reach their peak. Early-life stressors may accelerate timeline and intensity of symptoms escalation during adolescence. Lay summaryUnderstanding development of depressive symptoms and factors shaping them is crucial for designing effective mental health support initiatives. This study used data from over 8,000 young people regularly followed up from before birth to compare two cutting-edge methods for describing depressive symptoms trajectories and examined how known risk factors for adulthood depression relate to the severity and rate of change of depressive symptoms in adolescence. We found that both methods performed well and that the peaks in depressive symptoms and their rate of change were, on average, higher and occurred over a year earlier in females than males. Our findings additionally suggest that early-life stressors (e.g., abuse, bullying) may accelerate the development of depression, highlighting the importance of early prevention.

Background: Bipolar disorder (BD) is increasingly conceptualized as a heterogeneous condition with a neurodevelopmental phenotype (NDP) identifying a subgroup with early neurodevelopmental vulnerability and poorer clinical outcomes. Sensory processing (SP) abnormalities are a core feature of neurodevelopmental disorders but remain poorly characterized in BD and may reflect underlying neurodevelopmental liability. We examined whether NDP load is associated with specific SP alterations in euthymic BD patients and whether NDP-based stratification explains SP variability better than conventional BD subtype (BD 1/2). Methods: We assessed 102 euthymic BD patients and 45 healthy controls (HC) using the Adolescent/Adult Sensory Profile (AASP). NDP load (0-3) was computed from nine clinical variables grouped into neonatal, comorbidity, and neurodevelopmental clusters; a median split defined BD without NDP (BD) and BD with NDP (BD-ND). Associations between NDP load and AASP quadrants were analyzed using Spearman correlations with FDR correction. Group differences (BD, BD-ND, HC) were assessed using Welch ANOVA and post-hoc tests. Nested and multivariable linear regressions examined whether NDP classification explained SP variance beyond BD subtype, adjusting for age, sex, anxiety, and residual mood symptoms. Results: Higher NDP load correlated with greater low registration (rho=0.35, p<0.001, q=0.004), sensory sensitivity (rho=0.30, p=0.001, q=0.004), and sensation avoiding (rho=0.23, p=0.014, q=0.040), but not sensation seeking. BD-ND showed higher low registration, sensory sensitivity, and sensation avoiding than BD and HC (all qs<0.01). NDP classification explained more SP variance than BD subtype; with robust associations after adjustment. Conclusions: Sensory processing alterations in BD are dimensionally associated with neurodevelopmental load and more accurately captured by NDP-based stratification than diagnostic subtype. SP alterations may represent a transdiagnostic marker of neurodevelopmental liability within BD, supporting biologically informed stratification approaches.

Objective: Major depressive episodes frequently show limited response to first-line treatments, motivating the search for objective biomarkers. EEG/ECG-based support tools aggregating electrophysiological predictors may guide treatment selection. We examined whether antidepressant treatments concordant with an EEG/ECG-biomarker report were associated with higher response rates. Methods: We retrospectively analyzed adults with ICD-10 depressive disorder or bipolar depression treated with electroconvulsive therapy (ECT), repetitive transcranial magnetic stimulation (rTMS), (es)ketamine, or selective serotonin reuptake inhibitors (SSRIs) between 2022 and 2024. Resting-state EEG with simultaneous ECG generated individualized biomarker reports with modality-specific response likelihoods. Treatment chosen by clinical teams was classified as concordant or non-concordant; response was derived from routinely collected clinical scales. Results: Among 153 patients (ECT n=53, rTMS n=48, (es)ketamine n=36, SSRIs n=16), response rates were higher for concordant vs non-concordant treatments: ECT 70% vs 50%, rTMS 30% vs 13%, (es)ketamine 31% vs 10%, and SSRIs 100% vs 11%. Overall, 46% (42/92) of concordant vs. 26% (14/54) of non-concordant patients responded (absolute difference +20 percentage points; relative increase {approx}77%; number needed to treat {approx}5). Conclusion: Concordance with EEG/ECG biomarkers correlated with higher treatment response, warranting confirmation in prospective trials. Significance: EEG/ECG-based decision support may enhance antidepressant treatment response in everyday clinical practice.

Background: The etiology and nosological status of seasonal affective disorder (SAD) as a specifier of depressive episodes versus a transdiagnostic disorder are the subject of debate. In this study, we investigated the underlying etiology of SAD and dimensional seasonality by examining their association with latitude and genetic risk for a range of traits, and investigated gene-environment interactions. Methods: This study included 12,460 adults aged 18-90 with a history of depression from the Australian Genetics of Depression Study. Regression models included predictors for latitude (distance from equator) and polygenic scores for eight traits; major depressive disorder, bipolar disorder, anxiety disorders, chronotype, sleep duration, body mass index, vitamin D levels, and educational attainment. Outcomes were SAD status and general seasonality score. Results: SAD was positively associated with latitude (OR[95%CI] = 1.05[1.03-1.06], padjusted<0.001), and there was nominal evidence of additive and multiplicative interactions between chronotype genetic risk and latitude (OR = 0.99[0.99-0.99], padjusted=0.381; OR=0.98[0.97-0.99], padjusted=0.489). General seasonality score was associated with latitude (IRR=1.01[1.01-1.01], padjusted 0.001) and genetic risk for major depressive disorder (IRR =1.02[1.01-1.03], padjusted<0.001), bipolar disorder (IRR=1.02[1.01-1.03], padjusted=0.001), anxiety disorders (IRR=1.03[1.01-1.04], padjusted<0.001), vitamin D levels (OR=0.89[0.80-0.95], padjusted=0.048), and educational attainment (IRR=0.97[0.96-0.99], padjusted<0.001). Conclusions: These findings enhance understanding of SAD etiology, highlighting contributions of psychiatric genetic risk and geographic measures on seasonal behavior, and support examining seasonality as a continuous dimension.

Pediatric bipolar disorder is challenging to diagnose accurately due to symptom heterogeneity. More standardized and data-driven approaches are needed to enhance diagnostic reliability. We evaluated a clinical decision tool (nomogram), statistical methods (logistic regression, LASSO), machine learning (support vector machine, random forest, k-nearest neighbors, extreme gradient boosting), and deep learning model (multilayer perceptron) for pediatric bipolar disorder prediction across two datasets collected in academic (N=550) and community (N=511) clinical settings. We compared three modeling strategies: cross-dataset validation, cross-dataset with interaction terms, and mixed-dataset. We assessed model performance using discrimination ability, calibration, and predictor importance ranking. In the baseline cross-dataset approach, all models showed good internal discrimination in the academic dataset; but external discrimination in the community dataset substantially declined. Interaction-enhanced models slightly improved internal discrimination but not external performance or calibration. Recalibration prominently improved cross-dataset calibration without compromising discrimination, indicating that transportability problems were largely driven by probability scaling. Models trained on mixed datasets exhibited much stronger external discrimination and calibration. Across models and training strategies, family risk and PGBI-10M were consistently ranked as the most important predictors. Predictive models for pediatric bipolar disorder showed strong internal performance but limited cross-setting generalizability due to dataset shift and miscalibration. Increasing model complexity did not improve external performance, whereas training on pooled data substantially improved both discrimination and calibration. Findings suggest that sampling diversity, rather than model complexity, is more valuable for developing clinically useful and generalizable psychiatric prediction models, underscoring the importance of open and collaborative datasets.

Background Chronic pain and depression are prevalent and burdensome conditions that frequently co-occur. Separate neuroimaging studies of each disorder suggest overlapping brain-structure alterations, however, relatively few studies have examined their comorbidity directly, and the neuroanatomical profile of co-occurring chronic pain and depression remains unclear. Methods Using UK Biobank data (n = 71,214), we conducted cross-sectional pairwise association analyses of brain structure (cortical measures, subcortical volumes, and white matter microstructure) comparing participants with current comorbid chronic pain and depression, current chronic pain only, current depression only, and controls. Results Compared with controls, the comorbidity group showed regional differences in cortical surface area and thickness ({beta} range = -0.096 to 0.098, pFDR < 0.05), widespread lower cortical volume ({beta} range = -0.096 to -0.050, pFDR < 0.05), lower thalamic (left: {beta} = -0.048, pFDR = 0.038; right: {beta} = -0.060, pFDR = 0.007), hippocampal (left: {beta} = -0.062, pFDR = 0.035; right: {beta} = -0.088, pFDR = 0.002) and left accumbens volume ({beta} = -0.073, pFDR = 0.011), and evidence of widespread white matter microstructure alterations (fractional anisotropy: {beta} range = -0.116 to -0.080, pFDR < 0.05; mean diffusivity: {beta} range = 0.063 to 0.137, pFDR < 0.05). Pairwise comparisons with the disorder-specific groups also identified several alterations unique to the comorbidity group. Compared to controls, those with chronic pain only had widespread lower cortical surface area and volume ({beta} range = -0.043 to -0.015, pFDR < 0.05), whereas non-comorbid depression showed more regionally specific lower cortical thickness and volume ({beta} range = -0.140 to -0.062, pFDR < 0.05) and lower thalamic volume (left: {beta} = -0.067, pFDR = 0.016; right: {beta} = -0.066, pFDR = 0.015), alongside widespread white matter microstructure deficits (fractional anisotropy: {beta} range = -0.104 to -0.083, pFDR < 0.05; mean diffusivity: {beta} range = 0.079 to 0.149, pFDR < 0.05). Conclusion These results provide a robust characterisation of brain structure alterations in comorbid chronic pain and depression, highlighting a distinct neuroanatomical profile and advancing understanding of its underlying neurobiology.

Background: Early adolescence is a common period of onset for depressive symptoms. In part, this may reflect a developmental manifestation of individual's genetic propensities as they undergo physiological and hormonal changes and interact with new environments. Many commonly proposed mechanisms assume direct effects of an individual's own genes on emerging variation in their depressive symptomatology. However, estimates of genetic influence based on analyses in unrelated individuals capture not only direct genetic effects but also genetic effects from parents and other biologically related family members. Aim: In data from the Norwegian Mother, Father and Child Cohort (MoBa), we used linear mixed models to distinguish developmentally-stable and adolescence-specific direct and parental indirect genetic effects. We examined effects of polygenic scores for major depressive disorder (MDD), ADHD, anxiety disorders, and educational attainment (EA) on depressive symptoms, which were assessed by maternal reports at ages 8 and 14. Results: Children's own MDD polygenic scores showed adolescence-specific effects on depressive symptoms ( b_PGS*wave=0.041, [95% CI: 0.017, 0.065]). Developmentally-stable direct effects from children's polygenic scores for MDD (b=0.016, [0.006, 0.039]), ADHD (b=0.024, [0.008, 0.041]) and EA (b=-0.02, [ -0.038, -0.002]) were also evident. The only evidence of indirect genetic effects was a stable effect of maternal EA polygenic scores (b=0.04, [0.024, 0.054]). Conclusion: Direct genetic effects linked to genetic liability to MDD accounted for emerging variation in depressive symptoms in adolescence. These results imply that specific etiological mechanisms related to MDD may become particularly relevant for depressive symptoms during early adolescence compared to at earlier ages.

BackgroundDepression is associated with social dysfunction, but the mechanisms linking affective symptoms to disrupted close relationships remain poorly understood. One possibility is that depression alters how people experience rewards shared with close others and how they interpret partners actions. It remains unclear whether neural sensitivity to shared reward predicts social valuation during more complex interactions such as reciprocated trust. MethodsIn this preregistered fMRI study, participants completed a reward-sharing task and a Trust Game with a close friend, a stranger, and a computer. We measured striatal shared reward sensitivity (SRS; friend > computer) and tested whether it related to subsequent investment behavior and brain responses to trust reciprocation. Depressive symptoms and perceived closeness were assessed via self-report. ResultsIn a final sample of n = 123, participants reporting more depressive symptoms invested more in their friend than in the computer. Striatal SRS predicted temporoparietal junction responses to reciprocated trust, but this association depended jointly on social closeness and depression -- with depression reversing the expected pattern among individuals reporting closer relationships. Striatal SRS was also inversely associated with connectivity between the default mode network and cerebellum during reciprocity. ConclusionsThese findings suggest that closeness calibrates the striatal SRS link to regional activity and network-level responses during social exchange, while depression alters how striatal SRS relates to regional activity, potentially disrupting how individuals interpret and respond to close others.

An inflammatory subtype of major depressive disorder (MDD) is associated with treatment resistance pointing to an unmet need for adjunctive treatments. To evaluate treatment-related changes in brain inflammation, diffusion basis spectrum imaging (DBSI) is a promising non-radiation-based technique for longitudinal designs which has been verified with histopathology. However, its use as an endpoint in clinical trials is dependent on its individual-level reliability to robustly track changes. Here, we evaluated two DBSI runs acquired in 94 participants (including 43 participants with MDD) on the same day about 1.5 h apart to assess short-term test-retest reliability. Fiber fraction (reflecting axonal/dendrite density) and hindered fraction (reflecting edema) showed moderate to high test-retest reliability in both gray and white matter regions, whereas restricted fraction (reflecting cellularity) showed lower values in gray and white matter. Group-level reliability was similar in participants with MDD, except for lower reliability of hindered fraction in gray matter. Re-identification rates of individual brain maps were higher using voxel-level white matter signatures compared to gray matter regions of interest (ROIs) (p<.001). Crucially, participants with MDD showed reduced fiber fraction (tmax=4.68, k=38) and elevated hindered fraction (tmax=4.74, k=32) in the cingulate bundle, consistent with increased white matter inflammation, while gray matter ROI-based classification failed to identify cases. We conclude that DBSI is a promising technique to track inflammatory signatures in MDD, particularly in white matter tracts. Since several frontal and subcortical gray matter ROIs showed insufficient reliability, their assessment would require multiple DBSI runs to provide robust estimates.

Despite the global prevalence of postpartum depression (PPD), current referral uptake rates are far from satisfactory. While some qualitative studies have investigated factors affecting PPD referrals, a gap in quantitative analysis remains. Addressing this, our study utilized a discrete choice experiment (DCE) to understand the procedural elements influencing PPD referral uptake among diagnosed women. The DCE was conducted via home visits by healthcare providers and a comprehensive mobile app questionnaire. We constructed seven distinct referral attributes to explore participants' preferences, analyzed using mixed logit models and latent class analysis. This analysis identified key determinants and revealed the heterogeneities in referral preferences. A total of 698 individuals completed the DCE questionnaire. All assessed attributes, except for Accompaniment (going to clinic with a family member), were important determinants of preference. Participants generally preferred referrals to psychiatric clinics, face-to-face consultations, lower costs, and shorter waiting times. Significantly, participants' personal and socio-demographic characteristics also played a critical role in their referral preferences. Latent class analysis categorized participants into four distinct groups based on their preferences, with treatment cost and waiting times being the most decisive factors. In conclusion, the preference for PPD referrals is predominantly driven by convenience and access to specialist care. To enhance referral uptake, developing flexible and personalized referral programs that cater to these preferences is crucial.

Major depressive disorder (MDD) is a highly prevalent psychiatric disorder with changes in motivation to work for rewards being a core symptom. Transcutaneous vagus nerve stimulation (tVNS) has emerged as a promising therapy but its effects on the core features of MDD, such as changes in motivation, remained relatively unexplored. In this randomised, single-blind, cross-over, controlled trial, we used a grip strength effort task to investigate how tVNS impacted choices to exert different levels of physical effort for varying monetary rewards in MDD patients (n=53) and a non-depressed control group (n=45). Compared to sham stimulation, tVNS enhanced the efficiency with which participants with severe depressive symptoms allocated physical effort for rewards (reward-effort efficiency). These effects were not seen in participants with less severe symptoms. Specifically, we found that the effect of tVNS on reward-effort efficiency was driven by reduced unnecessary effort, i.e., a reduction in choices to exert additional effort when this was not required to gain a larger reward. These findings suggest a potential motivational mechanism by which tVNS exerts its therapeutic effects in MDD. Determining whether the effects of tVNS are linked to broader changes in executive functioning, such as improvements in cognitive flexibility in MDD, should be a key aim for future work.

Major depressive disorder (MDD) is a highly prevalent neuropsychiatric disorder characterized by depressed mood, feelings of sadness, loss of interest, and reduced pleasure related to daily activities. The clinical etiology of depression has been extensively studied, with research indicating biological, social, and psychological factors related to onset of depressive symptoms. Despite increased knowledge related to MDD, there is no tangible biomarker developed for MDD. Neuroimaging modalities such as single photon emission computed tomography (SPECT) have been utilized to characterize regional cerebral perfusion (rCBF). Functional dysconnectivity in depressed patients have been examined, with depressed individuals showing elevated depression scores and decreased rCBF in cognition and executive functioning networks. While SPECT can be utilized to monitor rCBF changes with respect to symptom severity, it alone cannot be utilized to develop a potent biomarker. Advanced multivariate methods such as independent component analysis (ICA) have been used to visualize disconnected functional patterns across disorders including depression and schizophrenia. Given no current SPECT studies examine transdiagnostic clinical profiles, the current study aims to bridge this gap. We utilized the 68 NeuroMark SPECT template across six patient groups. Factor scores investigating three key symptoms of depression: worry/rumination, moodiness, and social disinterest, and measured the loading parameter strength (i.e. component expression for each NeuroMark domain/subdomain) across the 68 components were examined. We identified significant relationships between symptoms and frontal, triple network, sensorimotor, and visual components across the three symptom profiles. Future studies should examine these trends across larger sample sizes, and increased clinical samples.

BackgroundLarge-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. MethodsCross-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. ResultsBD 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. ConclusionsAs 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.

Introduction Experiential acceptance refers to the capacity to be open to internal experiences without attempting to change or avoid them. Although acceptance is a core emotion regulation strategy within mindfulness- and acceptance-based interventions (MABIs) and a protective factor for mental health, its conceptualization and implementation remain unclear and ambiguous. The aim of this study was to clarify and develop a comprehensive model of accepting anxiety. Method Twenty-six participants from a non-clinical sample with prior experience in MABIs took part in semi-structured interviews exploring their experience of accepting anxiety. Data collection and analysis followed the principles of Grounded Theory to generate a data-driven model of the acceptance process. Results We identified a five-stage dynamic model involving distinct processes: (Stage 1) observing through the body with attentional focus on interoceptive experience; (Stage 2) identifying and acknowledging anxiety; (Stage 3) validating and normalizing the experience through validation and self-compassion; (Stage 4) not reacting characterized by decentering and nonreactivity; and (Stage 5) staying with the experience via exposure. We also identified facilitating factors that support engagement in the acceptance process. Conclusion These findings refine the understanding of acceptance as a multidimensional emotion regulation process by highlighting an active dynamic involving multiple mechanisms underlying the acceptance of anxiety. This model provides a framework for developing more targeted clinical interventions and for investigating individual and contextual variability in these subprocesses.

Background: AI powered cognitive behavioral therapy CBT chatbots represent a scalable approach to addressing the global mental health treatment gap However causal evidence on their population level effectiveness in low and middle income countries LMICs remains limited and patient perspectives on acceptability and engagement are critical determinants of sustained use Brazils Estrategia de Saude da Familia ESF deployed an AI powered CBT chatbot Saude Mental Digital SMD to registered patients aged 18 and older at participating primary care units with eligibility determined by a composite vulnerability score exceeding a predetermined threshold Objective: To estimate the causal effect of AI powered CBT chatbot access on anxiety and depressive symptoms among primary care patients in Minas Gerais Brazil leveraging the eligibility score threshold as an exogenous source of variation Methods: We conducted a fuzzy regression discontinuity design fuzzy RDD study using linked administrative and clinical data from 312 ESF primary care units across Minas Gerais N 43287 patients January 2022 December 2024 The running variable was the composite vulnerability score with a threshold of 60 points determining chatbot eligibility The primary outcome was the 12 week change in the Patient Health Questionnaire Anxiety and Depression Scale PHQ ADS composite score Two stage least squares 2SLS estimation was used with local polynomial regression and triangular kernel weighting Bandwidth selection followed the Calonico Cattaneo Titiunik CCT optimal procedure Results: The fuzzy RDD estimated a local average treatment effect LATE of 473 points 95 CI 691 to 255 p 0001 on the PHQ ADS composite score at the eligibility threshold indicating clinically meaningful symptom reduction among compliers First stage estimates confirmed a strong 312 percentage point jump in chatbot uptake at the threshold F statistic 1274 Subgroup analyses revealed larger treatment effects among patients in rural municipalities 618 95 CI 902 to 334 those with lower educational attainment 582 95 CI 844 to 320 and women 537 95 CI 761 to 313 McCrary density tests confirmed no evidence of running variable manipulation p 067 Results were robust across alternative bandwidths polynomial orders and kernel specifications Conclusions: AI powered CBT chatbot access causally reduces anxiety and depressive symptoms among primary care patients near the eligibility threshold in Brazil with particularly pronounced benefits for rural less educated and female populations These findings provide quasi experimental evidence supporting the scalable deployment of AI powered CBT tools within public primary care systems in LMICs while underscoring the importance of incorporating patient perspectives on acceptability to maximize engagement and sustained therapeutic benefit

Background: Attention problems are common transdiagnostic symptoms of psychiatric illness. Although environmental exposures and experiences influence attention during adolescent development, the underlying neural pathways by which they do so is unclear. Methods: We measured attention problems, attention-related brain networks, and multidimensional environmental experiences (the exposome) using data from the ABCD Study (N = 11,878). We tested whether the exposome is associated with 9-10-year-olds attention-related brain network strength and current and future attention problems. We further examined cross-sectional indirect pathways linking the exposome, brain network strength, and attention problems. Results: The exposome predicted youths current and future self-, caregiver-, and teacher-reported attention problems as well as their current attention-related brain network strength. This brain network signature of sustained attention also predicted attention problems from all three reporters. Indirect effects models revealed that the exposome was associated with current reported attention problems both directly and indirectly though this brain signature. Conversely, predictive brain network strength was related to attention problems both directly and indirectly through the exposome. Conclusion: Interactions between environmental exposures, experiences, and brain network organization are associated with attention problems in early adolescence. These findings support a bidirectional framework linking the environment and functional brain networks in the development of attention problems.