Journal of Affective Disorders Reports

Objectives To describe the prevalence of anxiety and depression symptoms among Black-Latines compared to other racial/ethnic groups in the United States. Methods This cross-sectional analysis leveraged data from the 2022 National Health Interview Survey (NHIS) to estimate the prevalence of anxiety and depression as measured by the GAD-7 and PHQ-8, respectively. Anxiety and depression scores in Black-Latine were compared to White-Latine, Non-Latine White, and Non-Latine Black respondents. Results Black-Latines were more likely to score higher on both the GAD-7 and PHQ-8 indicating higher prevalence of more than minimal anxiety (22.0%) and more than minimal depression (26.0%) than both Non-Latine Black, anxiety (18.0%) and depression (22.0%), and White-Latine, anxiety (16.0%) and depression (18.0%), respondents. Conclusions Black-Latines are more likely to report more depression and anxiety symptoms than their Non-Latine Black and White-Latine counterparts. Studies should try to disaggregate Black subgroups in order to identify the most vulnerable populations.

Adolescent suicidal ideation is a critical public health issue, yet empirical evidence on its prevalence and associated factors among adolescents in northern Bangladesh remains limited. This study estimated the 12-month prevalence of suicidal ideation and examined its associated factors among school-going adolescents in Nilphamari District, Bangladesh. A cross-sectional survey was conducted between May and June 2026 among 365 adolescents aged 13-19 years across eight secondary schools and colleges. Self-administered questionnaires assessed suicidal ideation alongside validated tools for depression (PHQ-9), anxiety (GAD-7), and perceived social support (MSPSS). Factors associated with suicidal ideation were examined using multivariable logistic regression. The 12-month prevalence of suicidal ideation was 30.7% (112/365; 95% CI 26.0%-35.7%). In the primary multivariable model, moderate depression (AOR = 6.05, 95% CI 1.03-35.38; p = 0.046) and moderately severe depression (AOR = 6.86, 95% CI 1.05-44.62; p = 0.044) were associated with higher odds of suicidal ideation compared to minimal depression. However, this association was not statistically significant in a sensitivity analysis using the PHQ-8 score after excluding Item 9 (AOR = 1.06, 95% CI: 0.96-1.17; p = 0.228). Loneliness "often" showed the strongest association with suicidal ideation (AOR = 10.59, 95% CI: 2.53-44.35; p = 0.001). Suicidal ideation was also associated with truancy of 6-9 days (AOR 4.44, 95% CI 1.24-15.90; p = 0.022), truancy of [≥]10 days (AOR 3.77, 95% CI 1.34-10.59; p = 0.012), intentional self-harm (AOR = 2.51, 95% CI: 1.17-5.41; p = 0.018), and physical harm exposure (AOR = 2.63, 95% CI: 1.24-5.57; p = 0.012). Maternal secondary education was associated with lower odds of suicidal ideation (AOR = 0.34, 95% CI: 0.12-0.99; p = 0.048). Suicidal ideation was common among school-going adolescents in northern Bangladesh, affecting nearly one in three participants. Early school-based identification and support for adolescents experiencing mental-health and psychosocial difficulties, together with family engagement and strengthened suicide-prevention services, are warranted.

Brief assessments reduce clinician and patient burden and increase utilization. Recently, five core depression symptoms (low mood, poor concentration, low self-worth, loss of interest, low energy) have been shown to be particularly sensitive to treatment effects in adults. How these core depressive symptom domains perform for screening or diagnostic purposes in youth is unknown. To address this question, we extracted five analogous symptom items from four widely used symptom assessment tools for children and adolescents (Child Behavior Checklist [CBCL], Youth Self Report [YSR], Parent General Behavior Inventory [P-GBI], and Adolescent General Behavior Inventory [A-GBI]) to create 5-item short forms. We evaluated their psychometric properties and diagnostic utility in a large (N=1185), racially diverse sample (48% Black youth) across community and academic outpatient mental health settings, with diagnoses established through LEAD consensus. All short forms correlated highly with full-length measures (r=.74-.92) and demonstrated comparable inter-item correlations (r =.19-.46). The short forms did not differ meaningfully from the full measures in correlations with psychiatric diagnoses, demographics, or diagnostic interviews. Area under the curve (AUC) values for identifying depression and any mood disorder diagnosis (determined by semi-structured diagnostic interviews) were fair to good (AUC=0.60-0.79), which were not worse than full measures, except for the P-GBI. The CBCL-5, YSR-5, P-GBI-5, and A-GBI-5 may be useful brief depression assessments in youth. How these five symptom domains perform as outcome measures in treatment deserves study.

Background: Stroke survivors frequently experience post-stroke depression (PSD) and post-stroke anxiety (PSA), which may be underexamined by clinicians if patient mood status between discharge and follow-up is unknown. Digital monitoring may enable early identification of mood disturbances. Ecological momentary assessments (EMA) have been tested to capture mood fluctuations in patients regular environments, but no standardized procedure for monitoring PSA or PSD has been developed. Objective: Determine the feasibility and validity of EMA for evaluating subacute PSA and PSD in non-severe stroke patients. Methods: Patients with non-severe stroke discharged home responded to daily smartphone EMA questions concerning mood and activity for twelve weeks until follow-up. Questions based on symptoms of anxiety and depression were used to derive EMA Anx and EMA Dep indices, computed over five 15-day intervals. Convergent validity was assessed using follow-up Generalized Anxiety Disorder-7 (GAD-7) and Centre for Epidemiological Studies-Depression (CES-D) scores. Logistic regression models predicted PSA and PSD status at follow-up, with baseline models comprising stroke severity, age, sex, and mood status, to which EMA Anx or Dep were added. Results: Data from 118 patients were analyzed. EMA adherence was moderate, with 89 patients (75.4%) responding on 75 or more days. Correlations between EMA Anx and GAD-7 and between EMA Dep and CES-D generally strengthened at later intervals, with the average mood index generally showing the strongest associations. Adding EMA data from the third time interval (31-45 days) significantly improved model fit beyond the baseline models (Anxiety: {chi}-square = 7.08, p = 0.008, {delta}R-square = 0.060; Depression: {chi}-square = 28.0, p < 0.001, {delta}R-square = 0.256). Discussion: EMA is feasible in mild-to-moderate stroke patients and may facilitate early identification of post-stroke mood disturbances. Further analyses in more diverse populations are required to validate these findings.

Background/Aims: Pre-stroke depression has been shown to be associated with worse functional outcomes and depression after stroke. However, it is not currently well understood whether pre-stroke mood disorders (e.g., depression, anxiety, mood and psychotic disorders, substance use disorder) are associated with withdrawal of life-sustaining treatment (WOLST) after a severe stroke. Methods: This was a retrospective cohort study within the Tufts Vascular Neurology Registry of patients hospitalized with severe acute ischemic stroke (AIS, defined as NIHSS [&ge;]10) or severe nontraumatic intracerebral hemorrhage (ICH, defined as ICH score [&ge;]2) between 2018 and 2022. Multivariate logistic regression was performed to identify predictors of WOLST. Results: This study included 405 patients with severe stroke. Patients who opted for WOLST were more likely to be older, female, have AIS, and experience more severe strokes. In an adjusted analysis (basic demographics, stroke type), a pre-stroke mood disorder was associated with WOLST (OR 2.15, 95% CI 1.11-4.15). In models stratified by stroke type (AIS vs. ICH) and adjusted for stroke severity, WOLST was associated with age in both groups and with mood disorder in patients with ICH. The WOLST event rate was higher in the ICH group (19% vs 11%). Conclusion: Pre-stroke mood disorder was associated with increased rates of withdrawal of care after severe stroke in this patient population. Opportunities to actively treat depression may be useful to include in goals of care discussions to mitigate the potential impact of this factor in decision-making. Consistent with prior studies, we observed a higher proportion of WOLST in older, female, and ICH patients. Future studies regarding the quality and content of goals of care discussions may help elucidate the differing effects of pre-stroke mood disorders on WOLST by stroke type seen in this study.

Negative emotional bias, including reduced processing of positive information and negative interpretation of ambiguous stimuli, is a core feature of depression. Esketamine, an N-methyl-D-aspartate (NMDA) receptor antagonist with rapid antidepressant effects in treatment-resistant depression (TRD), reduces depressive symptoms, but its effects on distinct components of emotional processing remain unclear. Fifty-three patients with TRD and 53 healthy controls performed an Emotional Facial Expression Morphing task. Patients were assessed at baseline, 24 hours after the first esketamine administration, and after 4 weeks of treatment. Statistical models examined detection thresholds for happy and sad faces, negative categorisation of neutral faces, and signal detection indices separating emotion-neutral discrimination sensitivity (d') from response criterion (c), indexing the evidence required to report the presence of an emotion. At baseline, patients required greater happy-face intensity for emotion detection and were more likely to categorise detected neutral faces as negative. They also showed a more conservative response criterion without group difference in discrimination sensitivity. Responders showed an early increase in d'; and, by week 4, less negative categorisation of neutral faces than non-responders. A lower baseline response criterion was associated with responder status and, in secondary continuous analyses, with greater subsequent MADRS improvement. These findings indicate that TRD involves abnormalities across detection-related, valence-related, and decisional aspects of facial-emotion processing that are differently associated with esketamine response: baseline response criterion emerged as a candidate prognostic marker, whereas negative categorisation of neutral faces tracked clinical response. Assessing these domains may inform clinical evaluation, pending independent replication.

Individuals with major depressive disorder (MDD) show an attentional bias toward negatively valenced stimuli and thoughts. In this study, we applied a closed-loop neurofeedback procedure designed to reduce this bias. Participants were shown composite negative faces and neutral scenes with variable opacity and were instructed to attend to the neutral scene while ignoring the negative face. Internal attentional states were decoded in real time from functional magnetic resonance imaging (fMRI) data. When a participant's decoded attentional state indicated a failure to ignore the negative faces, the faces became more visible (higher opacity), thus externalizing the brain's attentional lapse in that moment. Forty-eight individuals with MDD were randomly assigned to a real neurofeedback training group (N = 24) or a sham control group (N = 24); the control group received feedback yoked to a participant in the real group. All participants completed three fMRI neurofeedback sessions. The main outcome quantified the extent to which participants got ``stuck'' in the most negative attentional state of focusing strongly on the negative faces. We hypothesized that the real neurofeedback group would learn to escape that state by the end of training, relative to the sham control group. Consistent with our hypothesis, neurofeedback training reduced the probability of getting stuck in the most negative attentional state. In offline analyses, training reduced fMRI activity in the precuneus/posterior cingulate and the medial prefrontal cortex when participants successfully attended to scenes and ignored faces. These results demonstrate the efficacy of remediating the negative attentional bias in depression with closed-loop neurofeedback from real-time fMRI.

Objective: Mindfulness is a robust predictor of mental health, yet the psychometric properties of the short-form Five Facet Mindfulness Questionnaire (FFMQ-15) have not been examined in Iranian populations. This study evaluated the psychometric properties, factor structure, and diagnostic utility of the FFMQ-15 among medical sciences students. Participants: A total of 348 students (178 females, 170 males) from Kermanshah University of Medical Sciences participated. Methods: Participants completed the FFMQ-15, Penn State Worry Questionnaire (PSWQ), Difficulties in Emotion Regulation Scale (DERS), and Emotion Regulation Questionnaire (ERQ). Data were analyzed using exploratory and confirmatory factor analyses, composite reliability, and ROC curve analysis. Results: Both EFA and CFA supported the five-factor model, revealing a culturally unique item loading pattern. The FFMQ-15 demonstrated strong convergent and discriminant validity. ROC analysis indicated acceptable diagnostic accuracy for distinguishing high- and low-worry groups (AUC = 0.72, 95% CI: 0.66-0.78). Conclusions: The FFMQ-15 demonstrated acceptable psychometric properties in Iranian medical students. The non-reactivity facet exhibited cultural variability, suggesting that cross-cultural adaptation is essential. The instrument shows promise for screening in high-stress educational settings.

Background: Depression is a risk factor for overdose and is more commonly diagnosed among females. However, sex-differences in the association between depression, in isolation and in combination with substance use disorder (SUD), and overdose have not been examined at the population level in British Columbia (BC). This study described sex-and-age differences in associations between documented history of depression and/or SUD diagnoses and risk of fatal (FOD) and non-fatal overdose (NFOD). Methods: This study used linked administrative health data from the Provincial Health Services Authority Platform for Analytics and Data. The sample included BC residents aged 19-79 as of January 1st, 2015 (n=3,996,690). Exposures were classified as documented diagnosis history of 1) depression; 2) substance use disorder (SUD); 3) depression and SUD; and 4) neither condition, using physician billing and hospitalization records from 2010-2014. FOD (BC Coroners Service records) and NFOD (BC Emergency Health Services records) were ascertained from 2015 to 2024. Incidence rates per 10,000 person-years and unadjusted incidence rate ratios (IRRs) and 95% confidence intervals (CI) were estimated using Poisson regression, with neither diagnosis as reference, overall and stratified by sex and age. Results: There were 14,281 FOD (78.59% male) and 43,555 NFOD (69.45% male) cases. FOD incidence was higher among males overall; however, relative associations between diagnosis history and FOD risk were stronger among females. Depression diagnosis history was associated with a FOD IRR of 3.95 (CI: 3.56-4.38) among females and 2.46 (CI: 2.32-2.61) among males. IRRs for SUD and for depression and SUD were larger among females (108.07 [CI: 95.83-121.73] and 116.70 [CI: 106.51-127.96]) than males (26.69 [CI: 25.57-28.18] and 31.21 [CI: 29.81-32.67]). NFOD findings were similar but attenuated (depression IRR: 3.22 [CI: 3.08-3.38]) in females; 2.29 [CI: 2.21-2.37] in males). Conclusion: A documented history of depression and/or SUD was more strongly associated with FOD and NFOD risk among females, despite higher incidence overall among males. Future research should consider the potential role of other sociodemographic factors in explaining sex differences in associations between diagnosis history and overdose risk.

Background: Major depressive disorder (MDD) is a heterogeneous condition characterized by neuro-immune-metabolic-oxidative stress (NIMETOX) pathways. A systems-biology approach may help elucidate how these interconnected NIMETOX disturbances relate to early-life adversity and multidimensional clinical phenomes in adolescent MDD. Objectives: This study aimed to define the peripheral metabolic, vascular, and acute-phase, oxidative, and lipid biomarker profiles of adolescent MDD and to investigate associations with ACEs and clinical phenotypes. Methods: This cross-sectional study recruited 80 adolescents with MDD and 53 matched healthy controls and assessed C-peptide and insulin, vascular tone markers, the acute-phase inflammatory axis, oxidative stress, and lipid profiles. Results: MDD adolescents exhibited significant reductions in C-peptide, NO, ApoE, ApoA1, HDL-C, albumin, and transferrin, together with increased ApoB/ApoA1 ratio. These biomarkers correlated significantly with family dysfunction, childhood trauma, bullying exposure, and maladaptive parenting. These biomarkers and stressors accounted for 46.2% to 61.2% of the variance in psychological resilience, and major affective, physiosomatic, self-injurious, suicidal, and self-regulation phenomes. ACEs and bullying exposure, together with relevant socio-demographic data, explained 10.5% to 28.4% of the variance in selected metabolic, vascular, and acute-phase biomarkers. Multivariable classification models showed adequate discrimination between MDD and healthy controls, achieving up to 88.5% classification accuracy and an AUC of 0.948. Conclusion: Adolescent MDD is characterized by coordinated reductions in negative acute-phase proteins, metabolic and lipoprotein protective factors, and endothelial NO, without overt CRP-defined inflammation or conventional dyslipidemia. These findings support a systems-biology model of impaired peripheral homeostatic protection associated with developmental adversity and multidimensional depressive phenomes.

Importance: Obsessive-Compulsive Disorder (OCD) has a 2% global prevalence, but only about 50% of patients respond to conventional treatment, underscoring the need for additional effective interventions. Objective: To assess the feasibility, safety, and preliminary evidence for efficacy of repeated psilocybin administration in OCD patients who had previously attempted psychotherapy or pharmacotherapy. Design: Waitlist-controlled open label randomized clinical trial. Setting: Data were collected at the Johns Hopkins University School of Medicine. Participants: Data were analyzed from 30 participants (total randomized N=37, by age, sex and OCD severity) with a failed previous attempt at either pharmacotherapy or evidence-based psychotherapies such as exposure response prevention (ERP). Intervention: Participants received two psilocybin doses over two weeks (20 mg followed by 30 mg, if well-tolerated) under supportive conditions. The immediate-treatment arm received the drug within a month following enrollment, and the waitlist-control arm received identical support and dosing after an 8-week waiting period. Main Outcomes: Clinician-administered Y-BOCS scores was collected one week post each drug administration and one month post session 2. Self-assessed reports were collected for acute subjective effects (Mystical Experience Questionnaire (MEQ), Challenging Experience Questionnaire (CEQ), and 11-Dimensional Altered States of Consciousness (11D-ASC)), State-Trait Anxiety Inventory (STAI), Beck Depression Inventory-II (BDI-II), and Quality of Life Enjoyment and Satisfaction Questionnaire (Q-LES-Q). Results: A total of 37 participants were enrolled in the study (age[SD] years:38.4[11.6] years; 21 [56.75%] female), with 30 [81.08%] completing the 1-month follow-up. We show that two successive doses of psilocybin over two weeks were well-tolerated, leading to improvements in OCD symptoms. Compared with the waitlist group (N=14), the immediate-treatment group (N=16) showed a greater reduction in the Y-BOCS scores (F(2,56)=16.17, p<0.001). After crossover, this improvement persisted one month after the second psilocybin dose for both groups (F(3,84)=29.91, p<0.001). Reductions in Y-BOCS score post sessions were correlated with several acute subjective effects measures, including total scores on MEQ, and individual factors on CEQ and 11D-ASC. Conclusions and Relevance: Repeated psilocybin administration is tolerable and potentially efficacious in reducing OCD symptoms. These results support further investigation of multidose psilocybin protocols as a viable therapeutic approach. Trial Registration: ClinicalTrials.gov Identifier NCT05546658

Background: Neuroanatomy poses a significant challenge for Psychology students due to its spatial and conceptual complexity. Educational approaches that enhance the relevance and visualization of neuroanatomical content may improve students learning experiences. This study implemented a tractography-based activity focused on the virtual dissection of the arcuate fasciculus, a major white matter pathway, in undergraduate Psychology students and examined the relationships between perceived learning and students perceptions of utility, difficulty and handling, and organizational aspects of the activity. Methods: First-year undergraduate Psychology students participated in a two-session tractography-based activity combining instruction on white matter anatomy and diffusion tractography with a hands-on virtual dissection of the arcuate fasciculus using research-grade software routinely employed in neuroscience research. Following the activity, students completed an anonymous questionnaire assessing perceived learning, utility, difficulty and handling, and organizational aspects of the activity. Pearson correlations, multiple regression analyses, and relative importance analyses were performed. Results: Sixty-eight students completed the questionnaire. Students reported generally positive perceptions of the activity across the evaluated dimensions, with perceived learning receiving the highest mean score (M = 3.44, SD = .85). Perceived utility showed the strongest association with perceived learning (r = .64, p < .001). The regression model explained 41% of the variance in perceived learning (R2 = .41, adjusted R2 = .38, p < .001). Perceived utility was the only significant predictor in the model ({beta} = .59, p = .001), accounting for 67.9% of the explained variance. Conclusions: The findings support the feasibility of integrating authentic neuroimaging tools into undergraduate neuroanatomy teaching. Students who perceived the activity as more useful also reported higher perceived learning outcomes, with perceived utility emerging as the strongest predictor of perceived learning. In contrast, perceived difficulty and handling, and organizational aspects did not make significant independent contributions. These results suggest that students perceptions of educational relevance may play an important role in technology-enhanced STEM learning experiences.

Background: Dissociative identity disorder (DID) is associated with frequent psychiatric comorbidity. Its complex and overlapping symptom profile can complicate recognition of both DID and co-occurring psychiatric conditions, contributing to diagnostic uncertainty. Evidence regarding psychiatric comorbidity is limited and prevalence estimates vary across studies. Aims: This meta-analysis aimed to examine the prevalence of psychiatric comorbidities in DID and factors associated with variation in prevalence estimates. Methods: Eligible studies were identified through PubMed, Scopus, Embase, PsycINFO, and MEDLINE from 1980 to 25 April 2026 (PROSPERO, no. CRD420261335819). Studies reporting psychiatric comorbidity in adults with DID were independently screened by two reviewers. Risk of bias was assessed. Random-effects meta-analyses using logit-transformed prevalence estimates were conducted. Meta-regressions examined moderators, whilst influence and sensitivity analyses assessed robustness. Certainty was evaluated using the GRADE approach. Results: Twenty-four studies (N = 858) contributed to the meta-analysis across 23 psychiatric comorbidities. Among individuals with DID, posttraumatic stress disorder had the highest pooled prevalence (87.44%, 95% CI 71.79-94.96), followed by major depressive disorder (62.64%, 95% CI 33.45-84.84), and borderline personality disorder (47.05%, 95% CI 34.21-60.32). Substantial heterogeneity was noted across most estimates. Meta-regressions identified significant associations with participant characteristics, recruitment, study quality, and diagnostic method. Risk of bias and certainty of evidence were heterogenous. Conclusions: Psychiatric comorbidity in DID is substantial and spans multiple diagnostic categories, although considerable heterogeneity and methodological variation limit confidence in prevalence estimates. Standardised diagnostic assessment, more representative sampling, and longitudinal research are needed to clarify the extent and clinical significance of comorbidity in DID.

Major depressive disorder (MDD) is marked by disorganization in electrophysiological brain networks, yet it remains unclear whether such disorganization reflects the state-dependent pathological effects or a trait-like vulnerability. We analyzed resting-state electroencephalographic (EEG) delta-beta phase-amplitude coupling (PAC) in two sibling cohorts. Current-MDD families included individuals with current MDD and their currently unaffected siblings (n=36), and remitted-MDD families comprised individuals with remitted MDD and their lifetime unaffected siblings (n=110). Delta-Betahigh PAC was significantly elevated (pcorr[&le;]0.05) over the left-medial frontal region in participants with current MDD relative to their unaffected siblings. In the remitted-MDD families, no significant differences were observed. Regrouping all individuals irrespective of family ties into current MDD, remitted MDD, and never-depressed controls confirmed significantly elevated frontal delta-Betahigh PAC at rest in the current-MDD subgroup compared to other subgroups. Overall, delta-Betahigh coupling reflects the depressive state rather than a trait-like or familial vulnerability.

Importance. Suicide is a leading cause of mortality, yet its prevention remains a challenge. Suicide attempters exhibit disrupted decision-making, but the specific cognitive processes driving this impairment and their utility for risk stratification are unclear. Objective. To determine which computational decision-making mechanisms are disrupted in high-risk suicide attempters, and whether they distinguish attempters from depressed and healthy controls. Design. This cross-sectional study fit reinforcement-learning models to Iowa Gambling Task performance across two independent studies: SPAD (August 2017 to May 2020); SUICIDE-DECIDE (March 2021 to September 2023). Data was analyzed between January 2025 and July 2026. Setting. SPAD was conducted in Nimes (France). SUICIDE-DECIDE was a muticenter study across Jena (Germany) and Nimes. Clinical groups were recruited from academic inpatient psychiatry units and outpatient clinics, and healthy controls through local advertisement. Participants. SPAD included adults aged 60 years or older; SUICIDE-DECIDE middle-aged adults stratified by attempt means (violent/nonviolent). Consecutive participants were recruited into three groups: suicide attempters, patient controls, healthy controls. Main outcomes and measures. The primary outcomes were group differences on parameters reflecting learning from rewards and losses, sensitivity to win frequency, choice perseverance, and forgetting of past choices. We trained a multinomial logistic regression model in the SUICIDE-DECIDE study using self-reported depression and parameters that differentiated attempters from controls across studies, and evaluated its performance in the SPAD sample. Results. SPAD included 57 participants (40 females [70.2%]; mean [SD] age, 70.1 [8.2]). SUICIDE-DECIDE included 124 participants (77 females [62.1%]; 32.1 [11.0]). Suicide attempters forgot past choices faster than controls (SPAD: F(2,36) = 13.5; P < .001; SUICIDE-DECIDE: F(3,57) = 46.6; P < .001). No other parameter specifically differed in attempters across studies. In SUICIDE-DECIDE, greater forgetting of past choices was associated with attempter status only (RRR = 4.2, 95% CI [1.5, 11.5]). This parameter paired with depression severity correctly classified 87.2% of participants in SPAD (95% CI [74.3%, 95.2%], 41/47). Conclusions and relevance. Short-sighted decision-making may represent a cognitive phenotype associated with suicide in high-risk populations and a potential target for prevention and treatment.

Depression is one of the most prevalent mental health disorders globally. Social media content can reflect emotional states, offering real-time signals of depressive symptoms. This study aimed to develop a deep learning model to predict depression using Twitter/X data, and to characterize potential causes of depression using a large language model (LLM). Twitter/X posts from April 20, 2023, to July 24, 2024, were obtained using the key phrase ("I" or "me") and "diagnosed with depression". After data cleaning and GPT-4o labelling, 2,275 depressive Twitter users, and an additional 1,661 non-depressive users from tweets using the keyword "today", were identified. A deep learning RoBERTa model for predicting depression was built on tweets from both user groups, with an 80/20 training-testing split. GPT-4o models were subsequently employed to analyze depressive users posts to understand potential causes for depression. The RoBERTa model achieved strong performance in predicting depression among Twitter users from their tweets, with an accuracy of 0.822, an F1 score of 0.855, and an AUC of 0.809. Common reasons for depression identified by GPT-4o models included societal pressure, low self-esteem, cultural influences, and identity-related challenges. These findings highlight the potential of deep learning models in early screening of depression using social media data. Insights into potential reasons for depression may inform targeted prevention strategies, public health interventions, and improved mental health support for at-risk populations.

Background: Pregnancy involves profound immune adaptations that may contribute to the development of postpartum depression (PPD); however, the relevant biomarkers and critical time window remain unclear. Therefore, we investigated whether third-trimester and postpartum cytokines and kynurenine pathway (KP) metabolites predict PPD and whether these biological signatures are related to postpartum brain morphology. Methods: Circulating cytokines and KP metabolites were assessed in 140 pregnant women during the third trimester and postpartum. Partial least squares (PLS) regression models were used to identify multivariate biological profiles predictive of PPD, followed by classification and regression tree (CART) analyses to evaluate predictive performance. In a neuroimaging subsample (n=61), voxel-based morphometry examined associations among PPD status, significant PLS-derived components, selected individual biomarkers, and postpartum grey matter volume. Results: A third-trimester cytokine signature including 21 cytokines significantly predicted PPD with high specificity. A third-trimester KP profile, driven primarily by the kynurenic acid/quinolinic acid ratio, also predicted PPD, and the combination of cytokine and KP components improved overall predictive performance to 93.3%. Postpartum biomarkers showed substantially weaker associations with PPD. The cytokine component was differentially associated with postpartum grey matter volume according to PPD status, with opposite associations observed in occipital regions between women who did and did not develop PPD. KP measures also showed distinct associations within sensorimotor regions based on PPD status Conclusions: Late pregnancy immune-inflammatory alterations are associated with subsequent PPD and postpartum brain morphology, supporting the potential utility of pregnancy-derived biological signatures for identifying women at increased risk for PPD.

While Artificial intelligence (AI) is increasingly being integrated into psychotherapy, little is known about how prospective patients evaluate different levels of AI involvement. Participants (n = 1,003) with depression evaluated vignettes describing psychotherapy options, from treatment delivered without AI integration (Human therapist) to treatments involving different degrees of AI integration (Assistive, Collaborative, Fully Autonomous AI). Participants consistently evaluated Human therapists more favorably, reporting a greater likelihood of seeking treatment, less hesitancy, and greater treatment acceptability. Economically, compared with a Human therapist, participants were willing to pay 31.6% less for therapists using Assistive or Collaborative AI and 57.2% less for Fully Autonomous AI. 18-19% of participants indicated that they would refuse treatment involving Assistive or Collaborative AI, compared with 31% for Fully Autonomous AI. Preference for Human over AI therapists was particularly pronounced at higher levels of therapist experience (doctoral/masters vs. peer/trainee). Overall, participants valued psychotherapy delivered by Human therapists more highly than AI integrated psychotherapy. These findings suggest that, despite increasing AI integration in mental health care, prospective patients continue to view psychotherapy as a distinctly human endeavor.

Objective: To evaluate the psychometric properties, factor structure, validity, and age- and sex-specific norms of the 21- and 10-item parent-report Child Mania Rating Scale (CMRS-P) in a nationally representative U.S. sample of youth aged 6-18 years. Method: Parents of 2,229 youth aged 6-18 years [weighted M(SD)=12.0(3.3) years; 45.9% female] completed the CMRS-P and related measures between July 2025 and March 2026. Data were weighted to represent U.S. demographics. A clinical comparison sample included parents of 522 youth seeking outpatient mental health services. Complete-case analyses evaluated reliability, factor structure, item response theory, measurement equivalence, retest stability, and convergent and discriminant validity. GAMLSS estimated age- and sex-specific norms. Results: Weighted mean scores were 8.5(SD=9.0) for CMRS-P-21 and 4.0(4.3) for CMRS-P-10. Internal consistency was high ({omega}=.97/.93), and forms correlated .95. A general severity factor explained most reliable variance ({omega}H=.94). Three items differed in response thresholds across samples, with an average impact below one point on expected total scores. Both forms correlated strongly with attention problems, suicidality, depression, externalizing symptoms, and emotion dysregulation (r=.51-.63 for CMRS-P-21) and negligibly with pubertal status. Reliable-change thresholds were 14.5 (CMRS-P-21) and 6.8 (CMRS-P-10) points. Weighted GAMLSS CMRS-P-21 95th percentiles declined with age in males (30-23) and increased in females (22-30), with wide bootstrap intervals at age extremes; the female trend was not replicated without weights. Conclusions: Findings support both CMRS-P forms for assessing youth manic symptoms. The first nationally representative U.S. norms provide age- and sex-adjusted benchmarks for interpreting scores and informing further clinical assessment.

Abstract Background The neuropsychiatric sequelae (cognitive deficits, anxiety and depression) post-COVID-19 exist as part of the spectrum of the multisystemic long COVID. People with these symptoms have been shown to have worse functional outcomes. Hence, this significantly impacts the economy, primarily due to productivity losses. Yet, the precise underlying pathogenesis remains unknown, as do any diagnostic or prognostic biomarkers. Aim This study aims to identify functionally impacted brain regions in those with post-COVID-19 neurological sequelae and to evaluate how alterations in brain function relate to acute serum inflammatory mediators and brain injury biomarkers. Methods The study will utilise data from the COVID-19 Clinical Neuroscience Study (CNS), a large UK multicentre study conducted during the pandemic. This sub-study will be a secondary analysis of the COVID-19 CNS, a prospective observational study that compared COVID-19-positive participants with negative controls. Participants had an MRI, blood tested for acute serum inflammatory mediators and brain injury biomarkers, and neuropsychiatric assessment (cognitron test, PHQ-9 and GAD-7) post-acutely and at follow-up. The DPABI (Data Processing & Analysis for Brain Imaging) toolbox will be used for pre-processing and analysis of the neuroimaging data (resting state functional MRI) to obtain regional homogeneity (ReHo). The association between ReHO and neuropsychiatric symptoms, acute inflammatory mediators and brain injury biomarkers will be explored. Regression models would be used to determine the risk and protective factors for developing these symptoms. Conclusion It is anticipated that the study will provide new insights into the functional basis of post-acute COVID-19 neuropsychiatric manifestations and identify new diagnostic biomarkers. Key words: Regional Homogeneity, COVID-19, functional MRI, neuropsychiatry, biomarkers