Journal of Affective Disorders

Importance: The menopausal transition is associated with an increased risk of depression. Prior depression is a well-established risk factor, but studies do not distinguish between prior reproductive and non-reproductive depression. Objective: To compare the associations of reproductive (i.e., premenstrual mood disorder and perinatal depression) and non-reproductive (i.e., not related to hormonal transitions) histories of depression with depressive symptoms during the menopausal transition. Design: Cross-sectional analysis of questionnaire data from the Multidisciplinary Menopausal Outpatient Care Project (MOPP) collected between February 2023 and October 2025. Setting: Menopause outpatient clinics Amsterdam, the Netherlands, including a specialized multidisciplinary menopause clinic. Participants: In total 364 individuals were approached; 244 enrolled at baseline. After exclusions for age <40 (n=3), premature ovarian insufficiency (n=2), premenopausal status (n=1), age >58 with final menstruation >10 years earlier (n=12), bipolar disorder (n=5), and missing survey data (n=41), 180 participants were included. Exposures: Premenstrual mood disorder measured with Premenstrual Symptom Screening Tool, perinatal depression with Edinburgh Postnatal Depression Scale Lifetime version, and reported prior non-reproductive depression in medical records. Main outcome and measures: Depressive symptom severity measured with Inventory of Depressive Symptomatology-Self Rated. We used univariable and multivariable linear regressions; multivariable models accounted for overlap between exposures. Results: Among 180 participants (median age 51; 61% perimenopausal and 39% postmenopausal), premenstrual mood disorder showed the strongest association with depressive symptom severity (B = 9.0, 95% CI 5.1-12.9, p < 0.001), followed by perinatal depression (B = 7.8, 95% CI 3.4-12.1, p < 0.001) and prior non-reproductive depression (B = 4.7, 95% CI 0.7-8.7, p = 0.021). In multivariable analysis, only premenstrual mood disorder (B = 7.2, 95% CI 2.4-12.1, p = 0.0037) and perinatal depression (B = 5.7, 95% CI 1.2-10.1, p = 0.013) remained associated with depressive symptom severity. Conclusions and Relevance: Prior reproductive depression, but not prior non-reproductive depression, was associated with greater depressive symptom severity during the menopausal transition. A history of premenstrual mood disorder and/or perinatal depression may therefore help identify individuals at increased vulnerability to depressive symptoms during this period. Future studies should replicate these findings in population-based samples.

Depression is accompanied by considerable comorbidity and excess mortality. We examined multimorbidity data using the validated pharmacy-based Rx-Risk Comorbidity Index and examined healthcare costs associated with chronic illness burden in the Australian Genetics of Depression Study (AGDS). Australian Pharmaceutical Benefits Scheme (PBS) record linkage for 15,890 AGDS participants was available from 01/07/2013-31/12/2017. Forty-six health morbidities were inferred by mapping the prescription data using Anatomical Therapeutic Chemical Classification System codes and PBS Item Codes. Morbidity prevalence rates were then compared with an unselected 10% Australian representative population sample (10PCT) with PBS claims data available from 01/07/2010-31/12/2014. The average number of inferred comorbidities was higher among AGDS participants (4.6 {+/-} 2.9) than 10PCT individuals (3.0 {+/-} 3.0). Excluding depression, 89.1% of AGDS participants had one or more inferred comorbidity, most commonly pain (51.0%), inflammation/pain (40.3%), and anxiety (32.3%). In the AGDS, the number of comorbidities was higher among women compared to men and positively correlated with participant age, BMI, number of depressive episodes experienced, and annual health care costs. Compared to participants with no inferred comorbidities, the median annual health care costs were ~65% higher among those with 2-3 comorbidities. This study highlights the patterns of health morbidities experienced by individuals living with depression and shows that this chronic disease burden is significantly associated with increased health costs to the individual and the health system.

Background: Major depressive disorder (MDD) is clinically heterogeneous, hindering identification of reproducible biomarkers. Using a semi-supervised machine learning approach, HYDRA, we previously identified two neuroanatomical dimensions from structural MRI in medication-free MDD from COORDINATE-MDD consortium. These dimensions (D1, D2) showed differential responses to selective serotonin reuptake inhibitor (SSRI) antidepressants and placebo. External replication in UK Biobank linked D2, characterized by widespread subtle neuroanatomical reductions, to an immuno-metabolic profile. Here, we examined whether these dimensions are detectable early in the course of illness. Methods: We applied the pre-trained model to structural MRI data from the multisite PRONIA cohort, comprising individuals with recent-onset depression (ROD; n = 377; mean age 25.8 years, SD 6.0; 51.3% female) and healthy controls (n = 267; mean age 25.5 years, SD 6.4; 61.0% female). Participants were assigned to clusters (C1, C2) corresponding to the previously identified dimensions (D1, D2). Clusters were compared on clinical symptom profiles, peripheral inflammatory markers, and in a subset (n = 107), proteomic ageing indices. Results: Two neuroanatomical clusters were identified in PRONIA. C1 (n = 265) showed higher negative symptom severity and elevated interleukin-2 levels. C2 (n = 140) was associated with higher residual proteomic age. Overall depressive symptom severity did not differ significantly between clusters. Conclusions: Neuroanatomical dimensions of MDD are reproducible and detectable at illness onset. Associations with negative symptom severity, inflammatory signalling, and proteomic ageing suggest these dimensions capture biologically meaningful heterogeneity early in depression. These findings support a biologically informed framework for stratified treatment approaches in MDD.

Background: Individuals diagnosed with depression during pregnancy are more likely to develop cardiovascular disease (CVD) later in life. However, it remains unclear whether subclinical depressive symptoms or symptom trajectories across time are associated with indicators of cardiovascular health (CVH). Therefore, the present study evaluated the relationship between longitudinal depressive symptom trajectories beginning in pregnancy and future CVH. Methods: This secondary analysis of the multisite prospective nuMoM2b-Heart Health Study and included participants with complete longitudinal data from early pregnancy to 2-7 years post-delivery. Participants self-reported depressive symptoms using the Edinburgh Postnatal Depression Scale (EPDS) at 6-13 weeks gestation (early pregnancy), 22-29 weeks gestation (mid- to late-pregnancy), and 2-7 years post-delivery. Latent class mixture modeling was conducted to identify longitudinal patterns of depressive symptoms across early pregnancy, mid-late pregnancy, and extended postpartum follow-up. Structural equation modeling was used to test whether EPDS trajectories were associated with latent CVH, adjusted for length of follow-up interval, pre-pregnancy BMI, gravidity, adverse pregnancy outcomes, smoking history, age, education, income, and use of psychiatric medications. Results: A total of 3,934 participants (mean (M) {+/-} standard deviation (SD) age=27.6{+/-}5.6 years) met inclusion criteria with a mean follow-up interval of 3.2{+/-}0.9 years. A 4-class model, which provided the best fit to the EPDS data (mean posterior probability across classes=0.81), produced the following trajectories: (1) stable low (n=2412; 61.1%), (2) increasing severity (n=848; 21.5%), (3) decreasing severity (n=476; 12.1%), and (4) stable high (n=212; 5.4%). Compared to the stable low group, all groups exhibited significantly lower CVH (stable high: {beta}=0.06, p<0.01; decreasing severity: {beta}=0.05, p=0.02; increasing severity: {beta}=0.08 p<0.01). Pairwise comparisons among the three elevated-symptom groups revealed no significant differences in latent CVH (all ps >0.24). Discussion: The longitudinal course of depressive symptoms from pregnancy to 2-7 years post-delivery varied across individuals. Compared to those with consistently low depressive symptoms, individuals with higher severity symptoms at any point all exhibited lower CVH, regardless of the specific trajectory of symptoms. These findings support a life-course perspective in which depressive symptom patterns may represent an early indicator of cardiometabolic vulnerability.

Abstract Background Ketamine has emerged as an effective rapid-acting treatment for treatment-resistant depression (TRD), producing significant antidepressant effects within hours of administration. Given ketamine's capacity to induce states of heightened neuroplasticity and psychological openness, psychotherapy may represent a meaningful complement to its pharmacological effects - facilitating emotional processing, cognitive restructuring, and the consolidation of therapeutic gains. However, the adjunctive potential of structured psychotherapeutic support in ketamine-based interventions remains largely unexplored. Methods This preliminary, non-randomized, open-label clinical trial evaluated the adjunctive effects of ketamine-assisted psychotherapy (KAP) in an outpatient setting. Forty-six patients with TRD received eight weekly sessions of subcutaneous esketamine (0.5-1.0 mg/kg) and were allocated into two groups: esketamine without psychotherapeutic support (n = 23) and esketamine combined with structured KAP encompassing preparation, dosing accompaniment, and post-session integration (n = 23). Depressive symptoms were assessed using the Montgomery-Asberg Depression Rating Scale (MADRS) and the Beck Depression Inventory-II (BDI-II) at multiple timepoints during treatment and at follow-up assessments up to six months after protocol completion. Results Both groups showed significant reductions in depressive symptoms throughout treatment. The KAP group demonstrated greater clinical improvement by the end of treatment, with between-group differences on the MADRS emerging at sessions 7 and 8. MADRS response and remission rates were 52.2% and 34.8% in the KET group, and 78.3% and 78.3% in the KAP group, respectively. BDI-II scores indicated earlier subjective improvement in the KAP group, with between-group differences emerging as early as the second session and persisting across multiple timepoints. No significant between-group differences were observed during the six-month follow-up, with both groups maintaining symptom reductions comparable to end-of-treatment levels. Conclusions These findings suggest that structured psychotherapeutic support may be associated with early clinical response and remission rates in subcutaneous esketamine treatment for TRD, potentially through facilitation of emotional processing, psychological flexibility, and behavioural change. Further controlled studies are needed to clarify the specific contribution of psychotherapy, investigate the mechanisms underlying this interaction, and optimize integrated treatment approaches for TRD. The trial was registered at https://ensaiosclinicos.gov.br/rg/RBR-1072m6nv . Keywords: esketamine; treatment-resistant depression; ketamine-assisted psychotherapy; innovative therapies.

Background: Sleep debt and irregular sleep patterns are highly prevalent amongst adolescents. However, whether the absence of these sleep behaviours protects against subsequent depression remains unclear. Here, we examined the association of sleep debt, weekend catch-up sleep (WCS), and social jetlag (SJL) in adolescence with depression in young adulthood and identified underlying biopsychosocial mechanisms. Methods: Secondary data analyses were conducted using the Avon Longitudinal Study of Parents and Children. Bedtimes and wake-up times on school days and weekends (i.e., sleep duration) and sleep need were self-reported at 15 years. This was used to generate sleep debt (sleep need minus school day sleep duration), WCS (weekend sleep duration minus school day sleep duration), and SJL (absolute difference in the midpoint of sleep times between school days and weekends). Depression was assessed at 24 years with the Clinical Interview Schedule-Revised. Common mental health symptoms, biological, and school-related factors at 17 years were the mediators. Results: Logistic regression analyses revealed that greater WCS (adjusted odds ratio [AOR]=0.90; 95% CI=0.84-0.97; p=0.004) and lower sleep debt (AOR=1.10; 95% confidence interval [CI]=1.03-1.18; p=0.005) at age 15 reduced the likelihood of depression at 24 years. Irritability at 17 years partially mediated the relationship between sleep debt and depression (bias-corrected estimate=0.003; 95% CI=0.002-0.004; p<0.001). Conclusions: Adolescents who experience less sleep debt (i.e., less discrepancies between their actual sleep and their perceived sleep need) and those who extend their sleep duration on weekends are at reduced risk for depression in young adulthood. These findings underscore the need for greater opportunities for adolescents to obtain more hours of sleep to protect them against later poor mental health outcomes, such as depression. Keywords: Sleep; longitudinal studies; depression; ALSPAC

Background: Internet-based cognitive behavioral therapy (iCBT) is efficacious for panic disorder (PD), yet the mechanisms of change remain underspecified. Anxiety sensitivity (AS) is theoretically central to PD maintenance, but its role as a mediator has not been formally tested in Spanish-speaking populations using minimal-contact formats. This study evaluates the efficacy of the "Free from Anxiety" iCBT program and examines AS as a mediator of clinical outcomes. Methods: In a randomized controlled trial, 95 adults meeting DSM-IV-TR criteria for PD were assigned to an 8-week iCBT program with optional email support (n = 49) or a waiting-list control (n = 46). Primary outcome was PD severity (PDSS); secondary outcomes included anxiety sensitivity (ASI-3), general anxiety (BAI), and depression (BDI-II). Mediation was assessed via Baron and Kenny's framework with bootstrapping (5,000 resamples) to estimate the indirect effect of ASI-3 change on PDSS reduction. Results: The treatment group showed significant improvements across all measures compared to controls (PDSS: d = 0.76, 95% CI [0.10, 1.42]; mean d = 1.30). Mediation analysis confirmed that ASI-3 change partially mediated the treatment effect on PDSS (indirect effect = 1.85, 95% CI [0.36, 3.70]), accounting for 27.4% of the total effect. The direct effect remained significant (b = 4.89, p < .001). Intent-to-treat (ITT) analyses supported robustness (d = 0.47 to 1.47). Gains were maintained at 6-month follow-up (d = 1.19 to 1.26). Conclusions: iCBT reduces anxiety sensitivity as a partial mechanism of change, aligning with cognitive models of panic. These findings support Free from Anxiety as an evidence-based, viable first-step intervention for Spanish-speaking clinical populations within stepped-care pathways.

Background: Adverse childhood experiences (ACEs) are traumatic or adverse events in early life that can have lasting effects on behavioral, emotional, and psychological functioning. Prior research suggests ACEs relate to later psychiatric outcomes through threshold, cumulative, and individual-specific risk patterns. Few studies, however, have operationalized all three models to test ACE-specific associations with diagnosed psychiatric disorders in individuals who are adopted or with foster care histories. Methods: We conducted a cross-sectional retrospective study using electronic health record data from foster care and adopted patients aged 0-21 years old seen at the University of Minnesota Adoption Medicine Clinic (UMN-AMC) between 2014-2024. Extracted measures included ACE history, demographics, and psychiatric diagnoses. We used latent class analysis and logistic regression to identify clusters of adversity and estimate associations with psychiatric diagnosis domains, adjusting for Sex and Age at Initial Visit. Results: ACEs showed a threshold pattern across psychiatric domains, with higher ACE counts associated with greater odds of psychiatric diagnoses. Individual risk modeling indicated that exposure to abuse or violence was associated with higher odds of psychiatric diagnoses. Across cumulative and individual risk approaches, Anxiety Disorders, Mood Disorders, and Behavioral or Emotional Disorders showed the greatest sensitivity to adversity. Conclusion: Current ACE models may not fully capture neurodevelopmental impacts reflected in diagnosed psychiatric disorders among adolescents, particularly in high-risk groups such as foster and adopted individuals. In a large clinic sample our findings support a nuanced association between ACEs and later psychiatric diagnoses and highlight the need for ACE-focused assessment, prevention, and treatment strategies tailored to foster care and adopted populations.

Background: While counterfactual thinking ('what could have been') guides adaptive decision-making, it remains unclear how this process is altered by the negative biases and motivational deficits characteristic of Major Depressive Disorder (MDD). Methods: We used a sequential economic decision-making task designed to emulate a volatile stock market to assess choice behavior in adults with or without MDD (Total N=178); a subset of these participants completed the task during functional MRI (N=53). The task allowed participants to make either positive ('invest') or negative ('short') bets, under either positive or negative contextual valence, defined by whether the immediately preceding stock price change was positive or negative. Fictive errors were defined as the difference between realized and best-possible outcomes. Results: Across the full cohort, group differences in behavioral adjustments to fictive error signals emerged exclusively under negative contextual valence, when stock prices decreased. Compared with controls, participants with MDD showed heightened sensitivity to invest-and-loss fictive errors, reflected in a greater reduction in subsequent bets (interaction beta = -0.63, p < .001), but blunted adjustment to short-and-gain fictive errors (beta = -0.86, p < .001). In the imaging cohort, blunted short-and-gain adjustment was accompanied by heightened anterior cingulate (ACC) activity and attenuated ventromedial prefrontal (vmPFC)-to-ACC coupling in MDD. vmPFC activity following negative market returns also tracked depression symptom severity. Conclusions: Depression selectively disrupts the use of counterfactual outcomes to guide adaptive choice under negative contextual valence, implicating altered frontocingulate function in maladaptive decision-making.

Objective: Biopsychosocial models recognize multiple determinants of post-trauma mental disorders, but their relative and interactive effects remain unclear. We quantified the independent contribution of traumatic event severity, preexisting vulnerability, social support, and coping capacity, and tested mediation pathways. Methods: In a Brazilian clinical sample reporting traumatic or stressful events (N = 612), constructs were operationalized as composite scores and a dichotomous clinical outcome was derived from intake assessments. Logistic regression (n = 594) and structural equation modeling evaluated prediction and mediation. Results: Vulnerability was the strongest risk factor (OR = 1.46, p < .001) and social support the main protective factor (OR = 0.60, p < .001). Traumatic event severity remained an independent predictor (OR = 1.39, p < .001), whereas coping capacity was not significant (OR = 0.94, p = .410). Discrimination was good (AUC = 0.80). Mediation indicated vulnerability reduced social support and coping capacity, with a significant indirect effect via social support. Conclusions: Findings support a multifactorial model centered on a triad of vulnerability, social support, and traumatic exposure. Risk is shaped primarily by preexisting vulnerability and relational context, alongside a direct trauma effect, providing a clinically relevant framework for assessment and intervention.

Background: Aberrations in neuro-immune, metabolic, and oxidative stress (NIMETOX) pathways are implicated in major depressive disorder (MDD). First-episode simple dysmood disorder (FE-SDMD) without metabolic syndrome offers a unique model to investigate early lipid alterations underlying NIMETOX pathophysiology. Methods: Plasma samples were collected from 88 university students (44 FE-SDMD, 44 healthy controls). Participants underwent comprehensive psychiatric and psychological assessments, including adverse childhood experiences (ACEs), negative life events (NLEs), depression, anxiety, suicidal behaviors, and insomnia. Untargeted lipid profiling was performed using LC-QTOF-MS, while indices of oxidative and nitrosative stress (ONS) and lecithin-cholesterol acyltransferase (LCAT) activity were assessed. Data was analyzed using machine learning approaches with recursive feature elimination and cross-validation. Results: FE-SDMD was characterized by increased ceramides (CER), diacylglycerides (DAG), triacylglycerides (TG), sphingomyelins (SM), bis-monoacylglycerol phosphates (BMP), cholestone, and fatty-acyl amino acids (FAAA). DAG, CER, and BMP were the strongest predictors of depression severity and physiosomatic symptoms, whereas cholestone, CER, and SM predicted suicidal behaviors. These lipid modules, together with lowered LCAT and increased ONS, explained substantial variance in depression severity (46.4%), physiosomatic symptoms (42.4%), cognitive-affective symptoms (37.9%), suicidal behaviors (30.1%), insomnia (32%), and anxiety (19.5%). ACEs and NLEs were strongly associated with CER (p<0.001), DAG (p<0.01), and cholestone (p<0.01). Conclusion: Early-stage MDD is characterized by distinct lipid dysregulations linked to psychosocial stress exposure, oxidative and nitrosative stress, and an indicant of impaired reverse cholesterol transport. These lipid modules may serve as early biomarkers and therapeutic targets in vulnerable populations.

Structured AbstractO_ST_ABSBackgroundC_ST_ABSPost-Traumatic Stress Disorder (PTSD) is a mental health condition affecting people who experience traumatic events. Trauma-exposed occupational groups report higher rates of PTSD than the general population. Current treatments, and access, often take months and may cause distress when people are required to talk about the trauma. ObjectiveTo determine the proof of concept of FIRST, a brief, non-trauma focussed therapy, in two separate populations with employment-associated PTSD. MethodTwo independent, single-arm, experimental therapy pilot trials were conducted. Trial one recruited 20 military veterans who received FIRST therapy via trained third-sector therapists. Trial two recruited 16 health and social care workers with FIRST therapy delivered by healthcare provider therapists. All participants were adults with PTSD (confirmed via CAPS-5 in trial one, and symptom score of [&ge;]33 on the PCL5 in trial two). Primary outcomes were recruitment feasibility, retention, data quality and reduction in PTSD symptoms. Secondary outcomes were anxiety and depression symptoms, daily life functioning and perceived health status. Veterans were followed up at 12 weeks post-enrolment and healthcare workers at 8 weeks. ResultsThe veteran trial progression criteria to main trial were met. Seventy-nine people screened eligible, 43 attended a CAPS-5 assessment; 20 had confirmed PTSD and were enrolled. Seventeen completed therapy and 12-week outcome measures. Mean PCL-5 scores decreased from 48.7 (SD = 13.02, n=20) at baseline to 23.5 (SD = 15.30, n=17) at 12-weeks. The healthcare worker trial obtained informed consent from 16 participants, 10 commenced therapy and were included in analysis with eight completing therapy. Mean PCL-5 scores decreased from 42.60 (12.23, (n=10) at baseline to 22.00 (19.92, n=8) at 8-weeks. ConclusionsProof of concept of FIRST was established. PTSD symptom reductions exceeded the PCL-5 minimal clinically important difference. Undertaking a fully powered randomised controlled trial of FIRST therapy is feasible within both healthcare and third sectors. HighlightsO_LIPost-traumatic stress disorder (PTSD) is more common in military veterans and health workers than the general population C_LIO_LITherapy can be challenging to commence and complete when it requires a focus on the trauma incident C_LIO_LIFIRST offers a promising, brief, non-trauma focused therapy for the treatment of PTSD C_LI

Background: Childhood adversity (CA) is recognized as a distal risk-factor for suicide attempts (SA) in individuals with psychiatric disorders. However, not all individuals with experiences of CA will engage in SA. Contributing to this relationship may be proximal factors such as impulsivity, inward anger and self-aggression. However, these factors are often conceptually blended and measured in different samples. We sought to clarify association among CA and personality factors in persons with SA. Methods: Participants from two studies comprised individuals with a diagnosed psychiatric disorder and history of SA (n= 139) and individuals with depressive disorder (clinical controls, CC; n= 24). We investigated self-reported levels of CA, impulsivity, inward anger, and self-aggression between the SA and CC (pcorr< .012). We tested the relationship among the factors using regression (pcorr<.017) and mediation model (indirect effects, p<.05) within the SA group. Sensitivity models were run controlling for age, gender, symptom severity, trait anger, and externally oriented aggression. Results: SA group had higher impulsivity (pcorr=.067) in a model controlled for age and gender. Other factors did not differ among groups. Within the SA group the analyses revealed positive association among CA and personality factors (pcorr<.06) in basic and model with age and gender, however the association was not specific for internally (self) oriented factors (coefficient comparison, p<.07). Parallel mediation model indicated that CA had indirect effect on self-aggression through impulsivity (p=.001) and to a lesser extent through inward anger (p=.066). Generally, models controlling for cognitive depression symptoms showed less prominent effects (pcorr>.1). Limitations: The study was cross-sectional and did not include behavioral tasks (state) measures of proximal factors. Conclusions: CA and personality factors showed similar severity levels among the SA and CC groups suggesting they may relate to broader psychopathologies, rather than specifically to SA. The association of CA with anger and aggression was unspecific to internally oriented factors indicating the need for more precise measuring instruments developed specifically for individuals with SA. Overall, the study highlights personality factors as being associated with risk in broader vulnerable populations.

Post-traumatic stress disorder (PTSD) remains a significant psychiatric burden; despite growing biomarker research, no blood-based molecular diagnostic tool has been clinically validated for routine use. In this study, we developed a machine learning classifier for PTSD using peripheral blood leukocyte RNA-seq data from combat-exposed U.S. Marines (GSE64813), diagnosed via the Clinician-Administered PTSD Scale (CAPS) under DSM-IV criteria. Differentially expressed genes (DEGs) were identified and further refined through additional filtering criteria, yielding a 90-gene feature set used to train and compare multiple machine learning models. The support vector machine (SVM) classifier achieved the best performance, with an accuracy of 89% and an AUC of 0.95, outperforming logistic regression and random forest approaches. Furthermore we evaluated our model on independent external datasets to assess generalizability. These findings highlight the promise of transcriptomic signatures as a foundation for objective, blood-based PTSD diagnostics, while emphasizing the critical need for robust cross-dataset generalizability. Code availabilityhttps://www.kaggle.com/code/persianexxx/ptsd-final

Background: Generalized anxiety disorder (GAD) is associated with substantial psychological burden, autonomic dysregulation, and limitations of existing pharmacological and psychotherapeutic treatments. Transcutaneous auricular vagus nerve stimulation (taVNS) has emerged as a promising non-invasive neuromodulation approach, but evidence regarding home-based application in GAD remains limited. Objective: To evaluate the feasibility, safety, and preliminary clinical and physiological outcomes of a home-based taVNS intervention in adults with psychologist-confirmed moderate-to-severe GAD. Methods: In this prospective single-arm feasibility study, 48 participants initiated a 4-week home-based taVNS intervention consisting of two daily stimulation sessions performed five days per week. Clinical assessments were conducted at baseline, Week 2, Week 4, and follow-up visits at Weeks 6 and 8. Ambulatory electrocardiographic monitoring was performed before treatment initiation, at Week 2, and at the end of treatment to assess heart rate variability (HRV) using the root mean square of successive differences (RMSSD). Primary outcomes included feasibility, safety, adherence, and change in clinician-rated anxiety severity (HAM-A). Results: Thirty-four participants completed the study and were included in the primary analyses. HAM-A scores decreased significantly from baseline to Week 4 ([EMD] -6.9, 95% CI -10.4 to -3.4, p = 0.001), with partial maintenance during follow-up. Improvements were also observed in Beck Anxiety Inventory scores, whereas changes in GAD-7, perceived stress, depressive symptoms, and sleep quality were not statistically significant. RMSSD increased significantly from baseline to Week 4 (EMD 6.7 ms, 95% CI 2.1-11.3, p = 0.009). Greater increases in RMSSD were associated with larger reductions in HAM-A (R^2 = 0.18, p = 0.031) and BAI scores (R^2 = 0.21, p = 0.019). No serious adverse events occurred. Mean adherence was 79.8%, and 73.5% of participants completed at least 70% of prescribed stimulation sessions. Conclusions: Home-based taVNS was feasible and generally well tolerated in adults with moderate-to-severe GAD. Preliminary improvements in clinician-rated anxiety severity and autonomic physiological measures were observed; however, the single-arm design precludes causal inference. These findings support further evaluation of home-based taVNS in adequately powered randomized sham-controlled trials.

Hoarding disorder (HD) affects approximately 2-3% of adults and is associated with substantial functional disability and limited access to evidence-based care. The aim of the current analysis was to examine the naturalistic effectiveness of therapist-delivered video cognitive-behavioral therapy (CBT) for HD in a large real-world sample, and to characterize individual-level treatment response, time-to-response, and moderators of outcome. This retrospective, observational analysis examined clinical data from 305 adults diagnosed with HD who received therapist-delivered video CBT through an online specialty therapy platform between September 2021 and February 2026. Hoarding symptom severity was assessed using the Hoarding Rating Scale-Self Report (HRS-SR). Linear mixed models examined symptom change from baseline to three timepoints: session 10, session 20, and each patient's final session. HRS-SR scores decreased from M = 22.4 (SD = 7.6) at baseline to M = 16.4 (SD = 8.2) at final session (Hedges' g = 0.81, 95% CI: 0.68-0.94). By the final session, median percent improvement was 25.0% [IQR: 3.0-46.7%]. A total of 39.3% of patients achieved [&ge;]35% HRS-SR reduction, 27.4% of patients who began above the clinical threshold achieved remission, 36.4% demonstrated reliable improvement, and 22.9% of eligible patients achieved clinically significant change. Among patients who achieved and maintained [&ge;]35% reduction through their final session (n = 120), median time to first response was session 9, with 54.2% responding within 10 sessions. Analyses of secondary outcomes showed significant improvements in clutter severity, depressive and anxiety symptoms, stress, quality of life, and functional disability (Hedges' g = 0.21-0.47). Greater baseline severity, more sessions, and longer treatment duration significantly moderated outcomes; prior OCD treatment history did not. Findings suggest that therapist-delivered video CBT for HD, delivered remotely in a real-world setting, produces outcomes consistent with controlled trials and may be a clinically effective and scalable approach for a condition historically underserved by mental health systems.

Background: Observational studies have consistently reported associations between social media use (SMU) and poorer mental health outcomes; however, such designs cannot establish causality. This study synthesised evidence from randomized experiments to estimate the effects of restricting SMU on mental health outcomes. Methods: A systematic search was conducted across MEDLINE, Embase, PsycINFO, and Cochrane CENTRAL to identify experimental trials evaluating interventions that constrained SMU for at least 24 hours and included an unconstrained control condition. Multilevel random-effects meta-analyses were used to synthesise effect estimates. Prespecified meta-regressions explored study-level moderators, and population-level impact fractions were estimated relative to global SMU prevalence. Results: From 7,784 screened records, 37 reports representing 35 distinct studies were included (pooled N = 7,160). Most interventions lasted one to three weeks and targeted college-aged youth. Pooled estimates favoured SMU constraints across outcomes, with magnitude and precision varying by domain. Confidence intervals were entirely above zero, consistent with a beneficial response for depressive symptoms (g = 0.22; 95% CI, 0.12 to 0.32), perceived stress (g = 0.15; 95% CI, 0.01 to 0.29), anxiety symptoms (g = 0.19; 95% CI, 0.05 to 0.34), fear of missing out/nomophobia (g = 0.14; 95% CI, 0.04 to 0.24), and well-being (g = 0.36; 95% CI, 0.10 to 0.63). Heterogeneity was substantial for several outcomes (I2 > 75%). In bivariate meta-regressions, higher baseline SMU was associated with larger effects for anxiety symptoms ({beta} = 0.13; 95% CI, 0.03 to 0.22), and longer interventions were associated with larger effects for depressive symptoms ({beta} = 0.16; 95% CI, 0.02 to 0.30). Inferences revealed that a short-term reduction in SMU globally could plausibly mitigate 17.5% and 15.4% of depressive and anxiety symptom cases, respectively. Conclusions: Experimental design-based evidence supports the causal case for an effect of SMU on mental health, with constraints producing improvements across multiple outcomes and no evidence of harm. Population-level inferences suggest that even individually modest effects may translate into meaningful public health benefits given the high prevalence of SMU exposure. These findings suggest that reducing SMU may represent a low-intensity, low-cost, scalable strategy to support mental health and improve well-being.

Suicidal ideation in obsessive-compulsive disorder (OCD) is common and clinically significant, yet much of the existing literature conceptualizes suicide risk through the lens of comorbid depressive symptomatology. The present study examined whether other clinical features can identify clinically meaningful patterns associated with SI. Participants included 231 individuals with clinically significant OCD symptoms. SI was operationalized using Item 9 of the Beck Depression Inventory-II and binarized to reflect the presence or absence of suicidal thoughts. Depression severity scores were intentionally excluded from the predictive feature set, and three machine learning models (ElasticNet, Random Forest, and Explainable Boosting Machines) were evaluated using repeated nested cross-validation. All three algorithms showed comparable predictive performance. Given this overlap, the EBM was selected for interpretation due to its ability to model nonlinear relationships and interaction effects transparently. The model identified quality of life, obsessive-compulsive trait severity, somatic burden, and conscientiousness as prominent predictors of SI. Risk functions suggested nonlinear increases in estimated suicide risk at elevated levels of obsessive-compulsive traits and reduced quality of life. Additionally, interaction analyses indicated that severe obsessive-compulsive traits combined with elevated somatic burden were associated with higher estimated suicide risk than either factor alone. These findings suggest that interpretable machine learning can support clinically relevant phenotypic hypothesis generation. They also highlight somatic burden, functional impairment, obsessive-compulsive trait severity, and conscientiousness as potentially underappreciated targets for SI risk assessment in OCD, beyond the traditional focus on depressive comorbidity.

Background: Conventional psychiatric screening instruments summarize symptoms within individual scales and prioritize cases with high single-instrument additive score severity. This design treats items as independent within instruments and ignores cross-instrument covariance structure, making it insensitive to respondents whose responses are distributed across multiple domains in unusual combinations that remain below threshold on every individual scale. Methods: We analyzed two cohorts spanning older and younger adults. Item prompts from depression, stress, anxiety, and sleep instruments were embedded into a shared semantic space using a pretrained sentence encoder. Principal component analysis of the item-prompt embeddings alone---with no use of respondent data at this stage---was used to construct a low-dimensional subspace retaining 80\% of variance in the item embedding matrix. Normalized participant responses were then projected into this subspace, with Jaccard-based stability analysis used as a check on dimensional robustness. Multivariate deviation from the cohort norm was quantified with Mahalanobis distance using Ledoit-Wolf covariance regularization. Candidate outliers were defined by the empirical 95th percentile of the cohort-specific distance distribution. To isolate response configurations not already captured by conventional single-instrument extreme-value logic, we excluded all outlier respondents who had endorsed any individual item at the maximum value of its Likert scale on any instrument. For the remaining outliers, anomalous components were backtracked to their original item loadings for interpretation. Results: In the older-adult Health and Retirement Study (HRS) cohort, principal component analysis of 27 item-prompt embeddings showed that a 10-dimensional subspace provided a stable representation of cross-instrument semantic structure. In the younger-adult Xinxiang cohort the corresponding stable solution was 16-dimensional. In each cohort, seven respondents remained as multivariate outliers despite falling below every single-instrument extreme-value threshold. These cases were not characterized by uniformly severe symptom scores but by unusual cross-domain response configurations that became visible only in the shared semantic covariance subspace. The response structure of the retained configurations differed across cohorts: older-adult cases more often involved weak endorsement of mood-labeled items alongside nonzero body- and sleep-related responses, whereas younger-adult cases more often involved incomplete response configurations spanning mood, sleep, stress, and self-harm-related items. Conclusions: A semantically aligned, auditable covariance subspace provides a practical tool for flagging unusual multivariate response configurations that single-instrument additive screening may not flag. The method is interpretable at the level of original item contributions. It should be understood as a hypothesis-generating screen for unusual response configurations requiring further clinical assessment, not as a diagnostic instrument. Outcome validity remains to be established by prospective study.

Brain similarity networks (BSNs), extracted from structural magnetic resonance imaging, provide a validated framework for studying brain network organization and encode neurodevelopmental information relevant for psychiatric disorders. Recently, a neurodevelopmental hypothesis has been proposed for bipolar disorder (BD), where evidence demonstrates neuroprogression phenotypes differing from controls. BSNs offer a promising framework for investigating BD's neural correlates but remain largely underexplored. Parallelly, graph neural networks (GNNs) have emerged as suitable deep learning models for exploiting network-level information. This study aimed to investigate BSNs for discriminating subjects with BD from controls within a GNN framework using the multi-site StratiBip network, composed of 605 controls and 501 subjects with BD. Leveraging advanced analysis tools, we developed a multi-site classification framework including: i) the state-of-the-art MIND algorithm for computing morphometric similarity (MS) networks based on gray matter volumes (GMV), ii) MS integration with age, sex, and GMV, iii) a leave-one-site-out cross-validation for multi-site model generalizability evaluation. The best model achieved a mean multi-site accuracy of 68%. Explainability analyses revealed meaningful MS patterns in the basal ganglia, frontal and temporal lobes, and a particularly relevant integration with age. This study provides interpretable insights into the role of MS in BD and unveils evidence supporting ageing-related processes as a significant component of BD pathophysiology.