Relapse and recurrence of depression are common, contributing to the overall burden of depression globally. Accurate prediction of relapse or recurrence while patients are well would allow the identification of high-risk individuals and may effectively guide the allocation of interventions to prevent relapse and recurrence.

To review prognostic models developed to predict the risk of relapse, recurrence, sustained remission, or recovery in adults with remitted major depressive disorder.

We searched the Cochrane Library (current issue); Ovid MEDLINE (1946 onwards); Ovid Embase (1980 onwards); Ovid PsycINFO (1806 onwards); and Web of Science (1900 onwards) up to May 2021. We included development and external validation studies of multivariable prognostic models. We assessed risk of bias of included studies using the Prediction model risk of bias assessment tool (PROBAST).

We identified 12 eligible prognostic model studies (11 unique prognostic models): 8 model development-only studies, 3 model development and external validation studies and 1 external validation-only study. Multiple estimates of performance measures were not available and meta-analysis was therefore not necessary. Eleven out of the 12 included studies were assessed as being at high overall risk of bias and none examined clinical utility.

Due to high risk of bias of the included studies, poor predictive performance and limited external validation of the models identified, presently available clinical prediction models for relapse and recurrence of depression are not yet sufficiently developed for deploying in clinical settings. There is a need for improved prognosis research in this clinical area and future studies should conform to best practice methodological and reporting guidelines.

Anxiety disorders are leading contributors to the global disease burden, highly prevalent across the lifespan and associated with substantially increased morbidity and early mortality.

The aim of this study was to examine age-related changes across a wide range of physiological measures in middle-aged and older adults with a lifetime history of anxiety disorders compared with healthy controls.

The UK Biobank study recruited >500 000 adults, aged 37–73, between 2006 and 2010. We used generalised additive models to estimate non-linear associations between age and hand-grip strength, cardiovascular function, body composition, lung function and heel bone mineral density in a case group and in a control group.

The main data-set included 332 078 adults (mean age 56.37 years; 52.65% females). In both sexes, individuals with anxiety disorders had a lower hand-grip strength and lower blood pressure, whereas their pulse rate and body composition measures were higher than in the healthy control group. Case–control group differences were larger when considering individuals with chronic and/or severe anxiety disorders, and differences in body composition were modulated by depression comorbidity status. Differences in age-related physiological changes between females in the anxiety disorder case group and healthy controls were most evident for blood pressure, pulse rate and body composition, whereas this was the case in males for hand-grip strength, blood pressure and body composition. Most differences in physiological measures between the case and control groups decreased with increasing age.

Findings in individuals with a lifetime history of anxiety disorders differed from a healthy control group across multiple physiological measures, with some evidence of case–control group differences by age. The differences observed varied by chronicity/severity and depression comorbidity.

Mood disorders are characterised by pronounced symptom heterogeneity, which presents a substantial challenge both to clinical practice and research. Identification of subgroups of individuals with homogeneous symptom profiles that cut across current diagnostic categories could provide insights in to the transdiagnostic relevance of individual symptoms, which current categorical diagnostic systems cannot impart.

To identify groups of people with homogeneous clinical characteristics, using symptoms of manic and/or irritable mood, and explore differences between groups in diagnoses, functional outcomes and genetic liability.

We used latent class analysis on eight binary self-reported symptoms of manic and irritable mood in the UK Biobank and PROTECT studies, to investigate how individuals formed latent subgroups. We tested associations between the latent classes and diagnoses of psychiatric disorders, sociodemographic characteristics and polygenic risk scores.

Five latent classes were derived in UK Biobank (N = 42 183) and were replicated in the independent PROTECT cohort (N = 4445), including ‘minimally affected’, ‘inactive restless’, active restless’, ‘focused creative’ and ‘extensively affected’ individuals. These classes differed in disorder risk, polygenic risk score and functional outcomes. One class that experienced disruptive episodes of mostly irritable mood largely comprised cases of depression/anxiety, and a class of individuals with increased confidence/creativity reported comparatively lower disruptiveness and functional impairment.

Findings suggest that data-driven investigations of psychopathological symptoms that include sub-diagnostic threshold conditions can complement research of clinical diagnoses. Improved classification systems of psychopathology could investigate a weighted approach to symptoms, toward a more dimensional classification of mood disorders.

NHS Psychiatric beds comprise mental illness and intellectual disability beds. Penrose hypothesised that the number of psychiatric in-patients was inversely related to prison population size.

To ascertain whether the Penrose hypothesis held true in England between 1960 and 2018–2019.

A time-series analysis explored the association between total prison population and NHS psychiatric beds; this was also tested for the male and female prison populations, using non-psychiatric beds as a comparator. Associations were explored with time lags of up to 20 years. Linear regression was conducted to estimate the size of the effect of bed closures.

NHS psychiatric beds decreased 93% and the prison population increased 208%. A strong (r =−0.96) and highly significant negative correlation between these changes was found. Annual reduction in psychiatric bed numbers was associated with an increase in prison population, strongest at a lag of 10 years. The closure of mental illness and intellectual disability beds was associated with increases in female prisoners 10 years later. The only significant explanatory variable for the increase in male prison population was intellectual disability bed reduction.

The Penrose hypothesis held true between 1960 and 2018–2019 in England: psychiatric bed closures were associated with increases in prison population up to 10 years later. For every 100 psychiatric beds closed, there were 36 more prisoners 10 years later: 3 more female prisoners and 33 more male prisoners. Our results suggest that the dramatic increase in the female prison population may relate to the closure of NHS beds.

Evidence for risk of dying by suicide and other causes following discharge from in-patient psychiatric care throughout adulthood is sparse.

To estimate risks of all-cause mortality, natural and external-cause deaths, suicide and accidental, alcohol-specific and drug-related deaths in working-age and older adults within a year post-discharge.

Using interlinked general practice, hospital, and mortality records in the Clinical Practice Research Datalink we delineated a cohort of discharged adults in England, 2001–2018. Each patient was matched to up to 20 general population comparator patients. Cumulative incidence (absolute risks) and hazard ratios (relative risks) were estimated separately for ages 18–64 and ≥65 years with additional stratification by gender and practice-level deprivation.

The 1-year cumulative incidence of dying post-discharge was 2.1% among working-age adults (95% CI 2.0–2.3) and 14.1% (95% CI 13.6–14.5) among older adults. Suicide risk was particularly elevated in the first 3 months, with hazard ratios of 191.1 (95% CI 125.0–292.0) among working-age adults and 125.4 (95% CI 52.6–298.9) in older adults. Older patients were vulnerable to dying by natural causes within 3 months post-discharge. Risk of dying by external causes was greater among discharged working-age adults in the least deprived areas. Relative risk of suicide in discharged working-age women relative to their general population peers was double the equivalent male risk elevation.

Recently discharged adults at any age are at increased risk of dying from external and natural causes, indicating the importance of close monitoring and provision of optimal support to all such patients, particularly during the first 3 months post-discharge.

Concerns persist that some ethnic minority groups experience longstanding mental health inequalities in England. It is unclear if these have changed over time.

To assess the prevalence of common mental disorders (CMDs) and treatment receipt by ethnicity, and changes over time, using data from the nationally representative probability sample in the Adult Psychiatric Morbidity Surveys.

We used survey data from 2007 (n = 7187) and 2014 (n = 7413). A Clinical Interview Schedule – Revised score of ≥12 indicated presence of a CMD. Treatment receipt included current antidepressant use; any counselling or therapy; seeing a general practitioner about mental health; or seeing a community psychiatrist, psychologist or psychiatric nurse, in the past 12 months. Multivariable logistic regression assessed CMD prevalence and treatment receipt by ethnicity.

CMD prevalence was highest in the Black group; ethnic variation was explained by demographic and socioeconomic factors. After adjustment for these factors and CMDs, odds ratios for treatment receipt were lower for the Asian (0.62, 95% CI 0.39−1.00) and White Other (0.58, 95% CI 0.38–0.87) groups in 2014, compared with the White British group; for the Black group, this inequality appeared to be widening over time (2007 treatment receipt odds ratio 0.68, 95% CI 0.38−1.23; 2014 treatment receipt odds ratio 0.23, 95% CI 0.13−0.40; survey year interaction P < 0.0001).

Treatment receipt was lower for all ethnic minority groups compared with the White British group, and lowest among Black people, for whom inequalities appear to be widening over time. Addressing socioeconomic inequality could reduce ethnic inequalities in mental health problems, but this does not explain pronounced treatment inequalities.

Eating disorders are stigmatised. Little is known about whether stigma has decreased over time and which groups hold more stigmatising beliefs.

To explore whether stigma towards eating disorders has changed between 1998 and 2008 and whether it varies by sociodemographic characteristics.

We used the Office for National Statistics Omnibus surveys 1998 and 2008. As outcomes, we selected four questions eliciting participants’ views on issues of blame and ability to recover, and compared their mean scores across eating disorders, depression and alcohol dependence in both years. We used multivariable linear regressions to investigate associations between sociodemographic characteristics and each stigma domain.

In total, 2720 participants had data on all variables of interest. Compared with 1998, in 2008 stigmatising views towards eating disorders improved. In both years, participants believed it was easier to recover from eating disorders than depression or alcohol dependence. Respondents believed people with eating disorders were more to blame for their condition than those with depression, but less than those with alcohol dependence. Men, those with less formal education, and those from ethnic minority backgrounds were more likely to place greater blame on individuals for their mental illness. Men were more likely than women to think it was possible to recover from an eating disorder.

Stigmatising attitudes towards people with eating disorders have improved over time, but are still greater than those observed for other mental illnesses. Improving eating disorder mental health literacy could help to reduce these negative views and lead to improved quality of life, greater help-seeking and better prognosis.

Borderline personality disorder is associated with impaired quality of life and has a number of untoward public health associations. There is no established first-line pharmacological treatment for borderline personality disorder, and available options are not suitable for all individuals.

To evaluate brexpiprazole, which has effects on the dopaminergic and serotonergic systems, for the reduction of borderline personality disorder symptoms.

Eighty adults with borderline personality disorder were recruited for a randomised, double-blind placebo-controlled study. Participants received 12-week treatment with brexpiprazole (1 mg/day for 1 week, then increasing to 2 mg/day) or placebo in a parallel design. The primary efficacy outcome measure was the clinician-rated Zanarini Rating Scale for Borderline Personality Disorder (ZAN-BPD). Safety data were collected. Effects of active versus placebo treatment were characterised with linear repeated measures models.

There was a significant interaction between treatment and time on the ZAN-BPD scale (P = 0.0031), solely because of differentiation specifically at week 12. Brexpiprazole was generally well tolerated. Secondary measures did not result in statistically significant differences from placebo.

Brexpiprazole appears to have some possible effect on borderline personality disorder symptoms, but further studies are needed because of the significant effects evident, specifically at the final time point. These findings also need to be viewed cautiously, given the small sample size, large drop-out rate and robust placebo response.

Aripiprazole augmentation is proven effective for antidepressant-refractory depression, but its licensed dose range is wide and optimal dosage remains unclear.

To find the optimal dosage of aripiprazole augmentation.

Multiple electronic databases were searched (from inception to 16 February 2021) to identify all assessor-masked randomised controlled trials evaluating aripiprazole augmentation therapy in adults (≥18 years old, both genders) with major depressive disorder showing inadequate response to at least one antidepressant treatment. A random-effects, one-stage dose–effect meta-analysis with restricted cubic splines was conducted. Outcomes were efficacy (treatment response: ≥50% reduction in depression severity), tolerability (drop-out due to adverse effects) and acceptability (drop-out for any reason) after 8 weeks of treatment (range 4–12 weeks).

Ten studies met the inclusion criteria. All were individually randomised, placebo-controlled, multi-centre, parallel studies including 2625 participants in total. The maximum target dose–efficacy curve showed an increase up to doses between 2 mg (odds ratio OR = 1.46, 95% CI 1.15–1.85) and 5 mg (OR = 1.93, 95% CI 1.33–2.81), and then a non-increasing trend through the higher licensed doses up to 20 mg (OR = 1.90, 95% CI 1.52–2.37). Tolerability showed a similar trend with greater uncertainty. Acceptability showed no significant difference through the examined dose range. Certainty of evidence was low to moderate.

Low-dose aripiprazole as augmentation treatment might achieve the optimal balance between efficacy, tolerability and acceptability in the acute treatment of antidepressant-refractory depression. However, the small number of included studies and the overall moderate to high risk of bias seriously compromise the reliability of the results. Further research is required to investigate the benefits of low versus high dose.

Psychosis is associated with many forms of adversity, deprivation and living in urban areas.

To investigate whether psychosis is part of a syndemic of multiple adversities.

Drawing on UK Biobank (UKBB) data (Project ID: 57601), we sought to understand mechanisms by which childhood, recent/contemporary and place-based adversities might cluster and interact to be implicated in pathways by which psychoses evolve. We investigated the associations between adversities, potential mediating inflammatory markers and ICD-10 diagnoses (F20–F31) of psychotic disorders. We fitted logistic regression models initially including all relevant candidate variables and used backwards deletion to retain theoretically plausible and statistically significant (P < 0.05) associations with psychotic disorders. The candidate variables were entered in a partial least squares structural equation model (PLS-SEM) to test for syndemic interactions between risk factors. We tested whether the findings were sensitive to demographics, gender and ethnicity.

We fitted a PLS-SEM including psychosis as a syndemic outcome, and identified three latent constructs: lifetime adversity, current adversity and biomarkers. Factor loadings were above 0.30, and all structural paths were significant (P < 0.05). There were moderate associations between lifetime adversity and current adversity (standardised coefficient s.c. = 0.178) and between current adversity and biomarkers (s.c. = 0.227). All three latent constructs showed small but significant associations with psychosis (s.c. < 0.04). Lifetime adversity and current adversity were more strongly associated among ethnic minorities (combined) than White British people.

Our findings stress the importance of interactions between childhood and contemporary adversities in preventive and therapeutic interventions for psychotic disorders, especially among ethnic minorities.

Psychotherapies are the treatment of choice for panic disorder, but which should be considered as first-line treatment is yet to be substantiated by evidence.

To examine the most effective and accepted psychotherapy for the acute phase of panic disorder with or without agoraphobia via a network meta-analysis.

We conducted a systematic review and network meta-analysis of randomised controlled trials (RCTs) to examine the most effective and accepted psychotherapy for the acute phase of panic disorder. We searched MEDLINE, Embase, PsycInfo and CENTRAL, from inception to 1 Jan 2021 for RCTs. Cochrane and PRISMA guidelines were used. Pairwise and network meta-analyses were conducted using a random-effects model. Confidence in the evidence was assessed using Confidence in Network Meta-Analysis (CINeMA). The protocol was published in a peer-reviewed journal and in PROSPERO (CRD42020206258).

We included 136 RCTs in the systematic review. Taking into consideration efficacy (7352 participants), acceptability (6862 participants) and the CINeMA confidence in evidence appraisal, the best interventions in comparison with treatment as usual (TAU) were cognitive–behavioural therapy (CBT) (for efficacy: standardised mean differences s.m.d. = −0.67, 95% CI −0.95 to −0.39; CINeMA: moderate; for acceptability: relative risk RR = 1.21, 95% CI −0.94 to 1.56; CINeMA: moderate) and short-term psychodynamic therapy (for efficacy: s.m.d. = −0.61, 95% CI −1.15 to −0.07; CINeMA: low; for acceptability: RR = 0.92, 95% CI 0.54–1.54; CINeMA: moderate). After removing RCTs at high risk of bias only CBT remained more efficacious than TAU.

CBT and short-term psychodynamic therapy are reasonable first-line choices. Studies with high risk of bias tend to inflate the overall efficacy of treatments. Results from this systematic review and network meta-analysis should inform clinicians and guidelines.

Approximately 60 000 people in England have coexisting type 2 diabetes mellitus (T2DM) and severe mental illness (SMI). They are more likely to have poorer health outcomes and require more complex care pathways compared with those with T2DM alone. Despite increasing prevalence, little is known about the healthcare resource use and costs for people with both conditions.

To assess the impact of SMI on healthcare resource use and service costs for adults with T2DM, and explore the predictors of healthcare costs and lifetime costs for people with both conditions.

This was a matched-cohort study using data from the Clinical Practice Research Datalink linked to Hospital Episode Statistics for 1620 people with comorbid SMI and T2DM and 4763 people with T2DM alone. Generalised linear models and the Bang and Tsiatis method were used to explore cost predictors and mean lifetime costs respectively.

There were higher average annual costs for people with T2DM and SMI (£1930 higher) than people with T2DM alone, driven primarily by mental health and non-mental health-related hospital admissions. Key predictors of higher total costs were older age, comorbid hypertension, use of antidepressants, use of first-generation antipsychotics, and increased duration of living with both conditions. Expected lifetime costs were approximately £35 000 per person with both SMI and T2DM. Extrapolating nationally, this would generate total annual costs to the National Health Service of around £250 m per year.

Our estimates of resource use and costs for people with both T2DM and SMI will aid policymakers and commissioners in service planning and resource allocation.

Depression treatments are typically less effective for young people than for adults. However, treatments rarely target loneliness, which is a key risk factor in the onset, maintenance and development of depression.

This study evaluated the efficacy of a novel loneliness intervention, Groups 4 Health (G4H), relative to the best-practice treatment of cognitive–behavioural therapy (CBT) in reducing loneliness and depression over a 12-month period (Australian New Zealand Clinical Trial Registry: ACTRN12618000440224).

The study was a phase 3 randomised non-inferiority trial comparing G4H with dose-controlled group CBT. Participants were 174 people aged 15–25 years experiencing loneliness and clinically significant symptoms of depression, who were not in receipt of adjunct treatment. Participants were recruited from mental health services in Southeast Queensland, Australia. Randomisation was conducted using computer software. Follow-up assessments and statistical analyses were masked to allocation. Both interventions consisted of five 75 min group-based psychotherapy sessions. The primary outcomes were depression and loneliness, with a non-inferiority margin of 2.20 for depression.

The trial enrolled 174 participants between 24 April 2018 and 25 May 2019, with 84 in the G4H condition and 90 in the CBT condition. All randomised participants were included in the intention-to-treat analyses (n = 174). The pre–post effect sizes for depression were dG4H = −0.71 and dCBT = −0.91. For loneliness, they were dG4H = −1.07 and dCBT = −0.89. At 12-month follow-up, the absolute difference between groups on depression was 1.176 (95% CI −1.94 to 4.29) and on loneliness it was −0.679 (95% CI −1.43 to 0.07). No adverse effects were observed.

G4H was non-inferior to CBT for depression and showed a slight advantage over CBT for loneliness that emerged after treatment completion.

There is concern that the COVID-19 pandemic and its aftermath will result in excess suicides by increasing known risk factors such as self-harm, but evidence on how pandemic-related risk factors contribute to changes in these outcomes is lacking.

To examine how different COVID-19-related experiences of and worries about adversity contribute to changes in self-harm thoughts and behaviours.

Data from 49 324 UK adults in the University College London COVID-19 Social Study were analysed (1 April 2020 to 17 May 2021). Fixed-effects regressions explored associations between weekly within-person variation in five categories of adversity experience and adversity worries with changes in self-harm thoughts and behaviours across age groups (18–29, 30–44, 45–59 and 60+ years).

In total, 26.1% and 7.9% of respondents reported self-harm thoughts and behaviours respectively at least once over the study period. The number of adverse experiences was more strongly related to outcomes than the number of worries. The largest specific adversity contributing to increases in both outcomes was having experienced physical or psychological abuse. Financial worries increased the likelihood of both outcomes in most age groups, and having had COVID-19 increased the likelihood of both outcomes in young (18–29 years) and middle-aged (45–59 years) adults.

Findings suggest that a significant portion of UK adults may be at increased risk for self-harm thoughts and behaviours during the pandemic. Given the likelihood that the economic and social consequences of the pandemic will accumulate, policy makers can begin adapting evidence-based suicide prevention strategies and other social policies to help mitigate its consequences.

Studying phenotypic and genetic characteristics of age at onset (AAO) and polarity at onset (PAO) in bipolar disorder can provide new insights into disease pathology and facilitate the development of screening tools.

To examine the genetic architecture of AAO and PAO and their association with bipolar disorder disease characteristics.

Genome-wide association studies (GWASs) and polygenic score (PGS) analyses of AAO (n = 12 977) and PAO (n = 6773) were conducted in patients with bipolar disorder from 34 cohorts and a replication sample (n = 2237). The association of onset with disease characteristics was investigated in two of these cohorts.

Earlier AAO was associated with a higher probability of psychotic symptoms, suicidality, lower educational attainment, not living together and fewer episodes. Depressive onset correlated with suicidality and manic onset correlated with delusions and manic episodes. Systematic differences in AAO between cohorts and continents of origin were observed. This was also reflected in single-nucleotide variant-based heritability estimates, with higher heritabilities for stricter onset definitions. Increased PGS for autism spectrum disorder (β = −0.34 years, s.e. = 0.08), major depression (β = −0.34 years, s.e. = 0.08), schizophrenia (β = −0.39 years, s.e. = 0.08), and educational attainment (β = −0.31 years, s.e. = 0.08) were associated with an earlier AAO. The AAO GWAS identified one significant locus, but this finding did not replicate. Neither GWAS nor PGS analyses yielded significant associations with PAO.

AAO and PAO are associated with indicators of bipolar disorder severity. Individuals with an earlier onset show an increased polygenic liability for a broad spectrum of psychiatric traits. Systematic differences in AAO across cohorts, continents and phenotype definitions introduce significant heterogeneity, affecting analyses.

Healthcare decision makers require accurate long-term economic models to evaluate the cost-effectiveness of new mental health interventions.

To assess the suitability of current patient-level economic models to estimate long-term economic outcomes in severe mental illness.

We undertook pre-specified systematic searches in MEDLINE, Embase and PsycINFO to identify reviews and stand-alone publications of economic models of interventions for schizophrenia, bipolar disorder and major depressive disorder (PROSPERO: CRD42020158243). We screened paper titles and abstracts to identify unique patient-level economic models. We conducted a structured extraction of identified models, recording the presence of key predefined model features. Model quality and validation were appraised using the 2014 ISPOR and 2016 AdViSHE model checklists.

We identified 15 unique patient-level models for psychosis and major depressive disorder from 1481 non-duplicate records. Models addressed schizophrenia (n = 6), bipolar disorder (n = 2) and major depressive disorder (n = 7). The predominant model type was discrete event simulation (n = 9). Model complexity and incorporation of patient heterogeneity varied considerably, and only five models extrapolated costs and outcomes over a lifetime horizon. Key model parameters were often based on low-quality evidence, and checklist quality assessment revealed weak model verification procedures.

Existing patient-level economic models of interventions for severe mental illness have considerable limitations. New modelling efforts must be supplemented by the generation of good-quality, contemporary evidence suitable for model building. Combined effort across the research community is required to build and validate economic extrapolation models suitable for accurately assessing the long-term value of new interventions from short-term clinical trial data.

Growing evidence suggests that air pollution exposure may adversely affect the brain and increase risk for psychiatric disorders such as schizophrenia and depression. However, little is known about the potential role of air pollution in severity and relapse following illness onset.

To examine the longitudinal association between residential air pollution exposure and mental health service use (an indicator of illness severity and relapse) among individuals with first presentations of psychotic and mood disorders.

We identified individuals aged ≥15 years who had first contact with the South London and Maudsley NHS Foundation Trust for psychotic and mood disorders in 2008–2012 (n = 13 887). High-resolution (20 × 20 m) estimates of nitrogen dioxide (NO2), nitrogen oxides (NOx) and particulate matter (PM2.5 and PM10) levels in ambient air were linked to residential addresses. In-patient days and community mental health service (CMHS) events were recorded over 1-year and 7-year follow-up periods.

Following covariate adjustment, interquartile range increases in NO2, NOx and PM2.5 were associated with 18% (95% CI 5–34%), 18% (95% CI 5–34%) and 11% (95% CI 3–19%) increased risk for in-patient days after 1 year. Similarly, interquartile range increases in NO2, NOx, PM2.5 and PM10 were associated with 32% (95% CI 25–38%), 31% (95% CI 24–37%), 7% (95% CI 4–11%) and 9% (95% CI 5–14%) increased risk for CMHS events after 1 year. Associations persisted after 7 years.

Residential air pollution exposure is associated with increased mental health service use among people recently diagnosed with psychotic and mood disorders. Assuming causality, interventions to reduce air pollution exposure could improve mental health prognoses and reduce healthcare costs.

Psychotic experiences predict adverse health outcomes, particularly if they are persistent. However, it is unclear what distinguishes persistent from transient psychotic experiences.

In a large population-based cohort, we aimed to (a) describe the course of hallucinatory experiences from childhood to adolescence, (b) compare characteristics of youth with persistent and remittent hallucinatory experiences, and (c) examine prediction models for persistence.

Youth were assessed longitudinally for hallucinatory experiences at mean ages of 10 and 14 years (n = 3473). Multi-informant-rated mental health problems, stressful life events, self-esteem, non-verbal IQ and parental psychopathology were examined in relation to absent, persistent, remittent and incident hallucinatory experiences. We evaluated two prediction models for persistence with logistic regression and assessed discrimination using the area under the curve (AUC).

The persistence rate of hallucinatory experiences was 20.5%. Adolescents with persistent hallucinatory experiences had higher baseline levels of hallucinatory experiences, emotional and behavioural problems, as well as lower self-esteem and non-verbal IQ scores than youth with remittent hallucinatory experiences. Although the prediction model for persistence versus absence of hallucinatory experiences demonstrated excellent discriminatory power (AUC-corrected = 0.80), the prediction model for persistence versus remittance demonstrated poor accuracy (AUC-corrected = 0.61).

This study provides support for the dynamic expression of childhood hallucinatory experiences and suggests increased neurodevelopmental vulnerability in youth with persistent hallucinatory experiences. Despite the inclusion of a wide array of psychosocial parameters, a prediction model discriminated poorly between youth with persistent versus remittent hallucinatory experiences, confirming that persistent hallucinatory experiences are a complex multifactorial trait.

Major depressive disorder (MDD) is a clinically and biologically heterogeneous syndrome. Identifying discrete subtypes of illness with distinguishing neurobiological substrates and clinical features is a promising strategy for guiding personalised therapeutics.

This study aimed to identify depression subtypes with correlated patterns of functional network connectivity and clinical symptoms by clustering patients according to a weighted linear combination of both features in a relatively large, medication-naïve depression sample.

We recruited 115 medication-naïve adults with MDD and 129 matched healthy controls, and evaluated all participants with magnetic resonance imaging. We used regularised canonical correlation analysis to identify component mapping relationships between functional network connectivity and symptom profiles, and K-means clustering was used to define distinct subtypes of patients.

Two subtypes of MDD were identified: insomnia-dominated subtype 1 and anhedonia-dominated subtype 2. Subtype 1 was characterised by abnormal hyperconnectivity within the ventral attention network and sleep maintenance insomnia. Subtype 2 was characterised by abnormal hypoconnectivity in the subcortical and dorsal attention networks, and prominent anhedonia symptoms.

Our study identified two distinct subtypes of patients with specific neurobiological and clinical symptom profiles. These findings advance understanding of the biological and clinical heterogeneity of MDD, offering a pathway for defining categorical subtypes of illness via consideration of both biological and clinical features.