Electroconvulsive therapy (ECT) is the most effective treatment of major depression, but autobiographical memory loss may limit its use. Despite previous attempts to synthesise the literature, the nature of autobiographical memory loss after ECT is still debated.

To provide an overview of the effect of ECT on autobiographical memory in patients with depression and explore whether the effect is temporary or permanent. Furthermore, we wanted to analyse if ECT parameters or clinical information are associated with this effect.

PubMed, EMBASE, PsycINFO and Web of Science databases were searched on 26 January 2024. We included longitudinal studies measuring autobiographical memory before and after ECT in patients with depression compared to patients with depression receiving other treatment or healthy controls. Synthesis approach was a meta-analysis. PROSPERO ID: CRD42021267901.

Nine studies were included (432 patients, 173 controls). At post-ECT, we found that ECT patients had larger autobiographical memory loss than controls (standardised mean difference (SMD) = 0.55; 95% CI = 0.35–0.75). Right unilateral (RUL) ECT entailed a small effect on autobiographical memory (SMD = 0.32; 95% CI = 0.06–0.57), while bilateral ECT yielded a large effect (SMD = 0.82; 95% CI = 0.49–1.15). Higher age was associated with smaller effect. Autobiographical memory was stable at long-term follow-up.

The studies suggest that ECT causes autobiographical memory loss in patients with depression. Results also suggest that lost memories are not regained. Furthermore, results support that RUL ECT is less detrimental to autobiographical memory. Strangely, a higher age might mitigate the autobiographical memory loss. Our findings are limited by studies being mainly observational and generally consisting of small sample sizes. Future studies should prioritise long-term follow-up assessments of autobiographical memory.

To understand the pathogenetic mechanisms shared among schizophrenia (SCZ), bipolar disorder (BP), and major depression (MDD), we investigated the pleiotropic mechanisms using large-scale genome-wide and brain transcriptomic data.

We analyzed SCZ, BP, and MDD genome-wide association datasets available from the Psychiatric Genomics Consortium using the PLEIO framework and characterized the pleiotropic loci identified using pathway and tissue enrichment analyses. Pleiotropic and disorder-specific loci were also assessed.

Our pleiotropy-informed genome-wide analysis identified 553 variants that included 192 loci not reaching genome-wide significance in input datasets. These were enriched for five molecular pathways: cadherin signaling (p = 2.18 × 10−8), Alzheimer’s disease-amyloid secretase (p = 4 × 10−4), oxytocin receptor-mediated signaling (p = 1.47 × 10−3), metabotropic glutamate receptor group III (p = 5.82 × 10−4) and Wnt signaling (p = 1.61 × 10−11). Pleiotropic loci demonstrated the strongest enrichment in the brain cortex (p = 5.8 × 10−28), frontal cortex (p = 3 × 10−31), and cerebellar hemisphere (p = 9.8 × 10−28). SCZ-BP-MDD pleiotropic variants were also enriched for neurodevelopmental brain transcriptomic profiles related to the second-trimester post-conception (week 21, p = 7.35 × 10−5; week 17, p = 6.36 × 10−4) and first year of life (p = 3.25 × 10−5).

Genetic mechanisms shared among SCZ, BP, and MDD appear to be related to early neuronal development. Because the genetic architecture of psychopathology transcends diagnostic boundaries, pleiotropy-focused analyses can lead to increased gene discovery and novel insights into relevant pathogenic mechanisms.

Understanding the effects of ketamine on depressive symptoms could help identify which patients might benefit and clarify its mechanism of action in both the early (≤1 day post-infusion) and late (e.g. 2–30 days post-infusion) post-infusion periods. Symptom network analyses could provide complementary information regarding relationships between symptoms.

To identify the effects of ketamine on symptom-level changes in depression across both the early and late post-infusion periods and on depressive symptom network changes.

In this secondary analysis of 152 adults with treatment-resistant depression (with 38.8% reporting suicidal ideation at baseline), we compared symptom changes in the early and late post-infusion periods between individuals randomised to a single 40 min infusion of intravenous ketamine 0.5 mg/kg (n = 103) or saline (n = 49) and identified changes in symptom networks between pre- and post-ketamine treatment using network analyses.

In the early post-infusion period, the greatest improvement (comparing ketamine with saline) was in depressive symptoms related to sadness. In network analyses, symptom network connectivity increased following ketamine infusion. Symptoms of sadness and lassitude showed persistent improvement in the first week post-infusion, whereas improvements in suicidal thoughts first emerged 3–4 weeks post-infusion.

Ketamine improved all symptoms but showed the greatest effect on symptoms of sadness, both immediately and in the initial week after treatment. Ketamine also rapidly altered the topology of symptom networks, strengthening interrelationships between residual symptoms. The efficacy of ketamine (compared with saline) regarding suicidal symptoms emerged later. Our findings suggest potentially divergent efficacy, time courses and mechanisms for different symptoms of depression.

Mental health conditions, particularly depression and anxiety, are highly prevalent and impose substantial health burdens globally. Despite advancements in machine learning, there is limited application of these methods in predicting common mental illnesses within community populations in low-resource settings.

This study aims to examine the prevalence and associated risk factors of common mental illnesses collectively (depression and anxiety) in a rural Bangladeshi community using machine learning models.

This cross-sectional study surveyed 490 adults aged 18–59 in a rural Bangladeshi community. Depression and anxiety were assessed using the Patient Health Questionnaire (PHQ-2) and Generalised Anxiety Disorder (GAD-2) scales. Machine learning models, including Categorical Boosting, the support vector machine, the random forest and XGBoost (eXtreme Gradient Boosting), were trained on 80% of the data-set and tested on 20% to evaluate predictive accuracy, precision, F1 score, log-loss and area under the receiver operating characteristic curve (AUC-ROC).

Some 20.4% of participants experienced at least one common mental illness. Feature importance analysis identified house type, age group and educational status as the most significant predictors. SHAP (Shapley Additive exPlanations) values highlighted their influence on model outputs, and the XGBoost gain metric confirmed the importance of marital status and house type, with gains of 0.76 and 0.73, respectively. XGBoost delivered the best performance, achieving an F1 score of 71.01%, precision of 71.58%, accuracy of 71.15% and the lowest log-loss value of 0.56. The random forest had an accuracy of 78.21% and an AUC-ROC of 0.90.

The findings of this study suggest targeted interventions addressing housing and social determinants could improve mental health outcomes in similar rural settings. Further studies should consider longitudinal data to explore causal relationships.

Mood and anxiety disorders co-occur and share symptoms, treatments and genetic risk, but it is unclear whether combining them into a single phenotype would better capture genetic variation. The contribution of common genetic variation to these disorders has been investigated using a range of measures; however, the differences in their ability to capture variation remain unclear, while the impact of rare variation is mostly unexplored.

We aimed to explore the contributions of common genetic variation and copy number variations associated with risk of psychiatric morbidity (P-CNVs) to different measures of internalising disorders.

We investigated eight definitions of mood and anxiety disorder, and a combined internalising disorder, derived from self-report questionnaires, diagnostic assessments and electronic healthcare records (EHRs). Association of these definitions with polygenic risk scores (PRSs) of major depressive disorder and anxiety disorder, as well as presence of a P-CNV, was assessed.

The effect sizes of both PRSs and P-CNVs were similar for mood and anxiety disorder. Compared to mood and anxiety disorder, internalising disorder resulted in higher prediction accuracy for PRSs, and increased significance of associations with P-CNVs for most definitions. Comparison across the eight definitions showed that PRSs had higher prediction accuracy and effect sizes for stricter definitions, whereas P-CNVs were more strongly associated with EHR- and self-report-based definitions.

Future studies may benefit from using a combined internalising disorder phenotype, and may need to consider that different phenotype definitions may be more informative depending on whether common or rare variation is studied.

We studied posttraumatic stress symptoms (PTSS) and disorder (PTSD), associated factors, and quality of life (QOL) of a group of passengers (n = 58) affected by the 2023 Odisha train accident, comparing it with health professionals (n = 42) such as doctors and nurses who treated them, and individuals from the local community (n = 65). We also checked the anxiety and depression of passengers.

In a cross-sectional study, we assessed accident experience and used the PTSD checklist, WHO-QOL-BREF, General Anxiety Disorder, and Patient Health Questionnaire scales.

The PTSS were common; specifically, intrusive memories (36.4%), feeling upset while reminded of the experience (33.9%), and avoidance of memories (30.9%). Strong negative feelings, loss of interest, feeling distant, and irritability or anger outbursts were significantly more common among passengers than others. PTSD was present in 20.7% of passengers, 19.0% of health professionals, and 7.7% of local participants. Seeing dead bodies significantly contributed to PTSD. Clinical levels of anxiety (58.3%) and depression (50%) were present in passengers, which were significantly associated with PTSD, along with fear of death. Passengers had the worst QOL and health satisfaction among the groups.

Following the train accident, stress-related psychiatric problems were common and highlighted the intervention needs of the affected people.

Cognitive–behavioural therapy (CBT) is a first-line treatment for depressive disorders, but research on its neurobiological mechanisms is limited. Given the heterogeneity in CBT response, investigating the neurobiological effects of CBT may improve response prediction and outcomes.

To examine brain functional changes during negative emotion processing following naturalistic CBT.

In this case-control study, 59 patients with depressive disorders were investigated before and after 20 CBT sessions using a negative-emotion-processing paradigm during functional magnetic resonance imaging, clinical interviews and depressive symptom questionnaires. Healthy controls (n = 60) were also assessed twice within an equivalent time interval. Patients were classified into subgroups based on changes in diagnosis according to DSM-IV criteria (n = 40 responders, n = 19 non-responders). Brain activity changes were examined using group × time analysis of variance for limbic areas, and at the whole-brain level.

Analyses yielded a significant group × time interaction in the hippocampus (P family-wise error [PFWE] = 0.022, ηP2 = 0.101), and a significant main effect of time in the dorsal anterior cingulate cortex (PFWE = 0.043, ηP² = 0.098), resulting from activity decreases following CBT (PFWE ≤ 0.024, ηP² ≤ 0.233), with no changes in healthy controls. Hippocampal activity decreases were driven by responders (PFWE ≤ 0.020, ηP² ≤ 0.260) and correlated with symptom improvement (r = 0.293, P = 0.024). Responders exhibited higher pre-treatment hippocampal activity (PFWE = 0.017, ηP² = 0.189).

Following CBT, reduced activity in emotion-processing regions was observed in patients with depressive disorders, with hippocampal activity decreases linked to treatment response. This suggests successful CBT could correct biased emotion processing, potentially by altering activity in key areas of emotion processing.Hippocampal activity may function as a predictive marker of CBT response.

Nutraceuticals have been taken as an alternative and add-on treatment for depressive disorders. Direct comparisons between different nutraceuticals and between nutraceuticals and placebo or antidepressants are limited. Thus, it is unclear which nutraceuticals are the most efficacious.

We conducted a network meta-analysis to estimate the comparative efficacy and tolerability of nutraceuticals for the treatment of depressive disorder in adults. The primary outcome was the change in depressive symptoms, as measured by the standard mean difference (SMD). Secondary outcomes included response rate, remission rate, and anxiety. Tolerability was defined as all-cause discontinuation and adverse events. Frequentist random-effect NMA was conducted.

Hundred and ninety-two trials involving 17,437 patients and 44 nutraceuticals were eligible for inclusion. Adjunctive nutraceuticals consistently showed better efficacy than antidepressants (ADT) alone in outcomes including SMD, remission, and response. Notable combinations were Eicosapentaenoic acid + Docosahexaenoic Acid plus ADT (EPA + DHA + ADT) (SMD 1.04, 95% confidence interval 0.64–1.44), S-Adenosyl Methionine (SAMe) + ADT (0.99, 0.31–1.68), curcumin + ADT (1.03, 0.55–1.51), Zinc + ADT (1.59, 0.63–2.55), tryptophan + ADT (1.24, 0.32–2.16), and folate + ADT (0.64, 0.17–1.10). Additionally, four nutraceutical monotherapies demonstrated superior efficacy compared to ADT: EPA + DHA (0.6, 0.32–0.88), SAMe (0.52, 0.18–0.87), curcumin (0.62, −0.17 to 1.40) and saffron (0.69, 0.34–1.04). It is noted that EPA + DHA, SAMe, and curcumin showed strong performance as either monotherapies or adjuncts to ADT. Most nutraceuticals showed comparable tolerability to placebo.

This extensive systematic review and NMA of nutraceuticals for treating depressive disorders indicated a number of nutraceuticals that could offer benefits, either as adjuncts or monotherapies.

Multimorbidity, especially physical–mental multimorbidity, is an emerging global health challenge. However, the characteristics and patterns of physical–mental multimorbidity based on the diagnosis of mental disorders in Chinese adults remain unclear.

A cross-sectional study was conducted from November 2004 to April 2005 among 13,358 adults (ages 18–65years) residing in Liaoning Province, China, to evaluate the occurrence of physical–mental multimorbidity. Mental disorders were assessed using the Composite International Diagnostic Interview (version 1.0) with reference to the Diagnostic and Statistical Manual of Mental Disorders (3rd Edition Revised), while physical diseases were self-reported. Physical–mental multimorbidity was assessed based on a list of 16 physical and mental morbidities with prevalence ≥1% and was defined as the presence of one mental disorder and one physical disease. The chi-square test was used to calculate differences in the prevalence and comorbidity of different diseases between the sexes. A matrix heat map was generated of the absolute number of comorbidities for each disease. To identify complex associations and potential disease clustering patterns, a network analysis was performed, constructing a network to explore the relationships within and between various mental disorders and physical diseases.

Physical–mental multimorbidity was confirmed in 3.7% (498) of the participants, with a higher prevalence among women (4.2%, 282) than men (3.3%, 216). The top three diseases with the highest comorbidity rate and average number of comorbidities were dysphoric mood (86.3%; 2.86), social anxiety disorder (77.8%; 2.78) and major depressive disorder (77.1%; 2.53). A physical–mental multimorbidity network was visually divided into mental and physical domains. Additionally, four distinct multimorbidity patterns were identified: ‘Affective-addiction’, ‘Anxiety’, ‘Cardiometabolic’ and ‘Gastro-musculoskeletal-respiratory’, with the digestive-respiratory-musculoskeletal pattern being the most common among the total sample. The affective-addiction pattern was more prevalent in men and rural populations. The cardiometabolic pattern was more common in urban populations.

The physical–mental multimorbidity network structure and the four patterns identified in this study align with previous research, though we observed notable differences in the proportion of these patterns. These variations highlight the importance of tailored interventions that address specific multimorbidity patterns while maintaining broader applicability to diverse populations.

Psychological and existential distress is prevalent among patients with life-threatening cancer, significantly impacting their quality of life. Psilocybin-assisted therapy has shown promise in alleviating these symptoms. This systematic review aims to synthesize the evidence on the efficacy and safety of psilocybin in reducing cancer-related distress.

We searched MEDLINE, APA PsycINFO, Cochrane database, Embase, and Scopus from inception to February 8, 2024, for randomized controlled trials (RCTs), open-label trials, qualitative studies, and single case reports that evaluated psilocybin for cancer-related distress. Data were extracted on study characteristics, participant demographics, psilocybin and psychotherapy intervention, outcome measures, and results. Two authors independently screened, selected, and extracted data from the studies. Cochrane Risk of Bias for RCTs and Methodological Index for Non-Randomized Studies criteria were used to evaluate study quality. This study was registered with PROSPERO (CRD42024511692).

Fourteen studies met the inclusion criteria, comprising three RCTs, five open-label trials, five qualitative studies, and one single case report. Psilocybin therapy consistently showed significant reductions in depression, anxiety, and existential distress, with improvements sustained over several months. Adverse effects were generally mild and transient.

This systematic review highlights the potential of psilocybin-assisted therapy as an effective treatment for reducing psychological and existential distress in cancer patients. Despite promising findings, further large-scale, well-designed RCTs are needed to confirm these results and address existing research gaps.

This study aimed to refine the content of a new patient-reported outcome (PRO) measure via cognitive interviewing techniques to assess the unique presentation of depressive symptoms in older adults with cancer (OACs).

OACs (≥ 70years) with a history of a depressive disorder were administered a draft measure of the Older Adults with Cancer – Depression (OAC-D) Scale, then participated in a semi-structured cognitive interview to provide feedback on the appropriateness, comprehensibility, and overall acceptability of measure. Interviews were audio-recorded and transcribed, and qualitative methods guided revision of scale content and structure.

OACs (N = 10) with a range of cancer diagnoses completed cognitive interviews. Participants felt that the draft measure took a reasonable amount of time to answer and was easily understandable. They favored having item prompts and response anchors repeated with each item for ease of completion, and they helped identify phrasing and wording of key terms consistent with the authors’ intended constructs. From this feedback, a revised version of the OAC-D was created.

The OAC-D Scale is the first PRO developed specifically for use with OACs. The use of expert and patient input and rigorous cognitive interviewing methods provides a conceptually accurate means of assessing the unique symptom experience of OACs with depression.

Mindfulness is a promising psychological resource that can alleviate dysfunctional fear responses and promote mental health. We investigated how mindfulness affects fear and depression in isolated patients with coronavirus disease 2019 (COVID-19), and whether it acts as a mediator.

We conducted an online survey of COVID-19 patients undergoing at-home treatment from February to April 2022. The survey included a questionnaire on fear of COVID-19 (measured by the Fear of COVID-19 Scale), mindfulness (measured by the Mindful Attention Awareness Scale), and depression (measured by the Patient Health Questionnaire). A total of 380 participants completed the questionnaire. We analyzed the correlation between each variable and performed a mediation analysis using hierarchical regression and bootstrapping to verify the statistical significance of the mediating effects.

Each variable was significantly correlated. Hierarchical regression analysis showed that the association between the fear of COVID-19 and depression decreased from 0.377-0.255, suggesting that mindfulness partially mediates the relationship between fear of COVID-19 and depression. Bootstrapping analysis showed that the indirect effect of the mediating variable (mindfulness) is 0.121, which accounts for 32.3% of the total effect.

Interventions that promote mindfulness in patients with acute COVID-19 may be beneficial for their mental health.