Autism spectrum disorder (ASD) is a highly heterogeneous disorder that affects nearly 1 in 189 females and 1 in 42 males. However, the neurobiological basis of gender differences in ASD is poorly understood, as most studies have neglected females and used methods ill-suited to capture such differences.

To identify robust functional brain organisation markers that distinguish between females and males with ASD and predict symptom severity.

We leveraged multiple neuroimaging cohorts (ASD n = 773) and developed a novel spatiotemporal deep neural network (stDNN), which uses spatiotemporal convolution on functional magnetic resonance imaging data to distinguish between groups.

stDNN achieved consistently high classification accuracy in distinguishing between females and males with ASD. Notably, stDNN trained to distinguish between females and males with ASD could not distinguish between neurotypical females and males, suggesting that there are gender differences in the functional brain organisation in ASD that differ from normative gender differences. Brain features associated with motor, language and visuospatial attentional systems reliably distinguished between females and males with ASD. Crucially, these results were observed in a large multisite cohort and replicated in a fully independent cohort. Furthermore, brain features associated with the motor network's primary motor cortex node predicted the severity of restricted/repetitive behaviours in females but not in males with ASD.

Our replicable findings reveal that the brains of females and males with ASD are functionally organised differently, contributing to their clinical symptoms in distinct ways. They inform the development of gender-specific diagnoses and treatment strategies for ASD, and ultimately advance precision psychiatry.

Neuroimaging- and machine-learning-based brain-age prediction of schizophrenia is well established. However, the diagnostic significance and the effect of early medication on first-episode schizophrenia remains unclear.

To explore whether predicted brain age can be used as a biomarker for schizophrenia diagnosis, and the relationship between clinical characteristics and brain-predicted age difference (PAD), and the effects of early medication on predicted brain age.

The predicted model was built on 523 diffusion tensor imaging magnetic resonance imaging scans from healthy controls. First, the brain-PAD of 60 patients with first-episode schizophrenia, 60 healthy controls and 21 follow-up patients from the principal data-set and 40 pairs of individuals in the replication data-set were calculated. Next, the brain-PAD between groups were compared and the correlations between brain-PAD and clinical measurements were analysed.

The patients showed a significant increase in brain-PAD compared with healthy controls. After early medication, the brain-PAD of patients decreased significantly compared with baseline (P < 0.001). The fractional anisotropy value of 31/33 white matter tract features, which related to the brain-PAD scores, had significantly statistical differences before and after measurements (P < 0.05, false discovery rate corrected). Correlation analysis showed that the age gap was negatively associated with the positive score on the Positive and Negative Syndrome Scale in the principal data-set (r = −0.326, P = 0.014).

The brain age of patients with first-episode schizophrenia may be older than their chronological age. Early medication holds promise for improving the patient's brain ageing. Neuroimaging-based brain-age prediction can provide novel insights into the understanding of schizophrenia.

Comorbidity with general medical conditions is common in individuals with eating disorders. Many previous studies do not evaluate types of eating disorder.

To provide relative and absolute risks of bidirectional associations between (a) anorexia nervosa, bulimia nervosa and eating disorders not otherwise specified and (b) 12 general medical conditions.

We included all people born in Denmark between 1977 and 2010. We collected information on eating disorders and considered the risk of subsequent medical conditions, using Cox proportional hazards regression. Absolute risks were calculated using competing risks survival analyses. We also considered risks for prior medical conditions and subsequent eating disorders.

An increased risk was seen for almost all disorder pairs (69 of 70). Hazard ratios for those with a prior eating disorder receiving a subsequent diagnosis of a medical condition ranged from 0.94 (95% CI 0.57−1.55) to 2.05 (95% CI 1.86−2.27). For those with a prior medical condition, hazard ratios for later eating disorders ranged from 1.35 (95% CI 1.26–1.45) to 1.98 (95% CI 1.71–2.28). Absolute risks for most later disorders were increased for persons with prior disorders, compared with reference groups.

This is the largest and most detailed examination of eating disorder–medical condition comorbidity. The findings indicate that medical condition comorbidity is increased among those with eating disorders and vice versa. Although there was some variation in comorbidity observed across eating disorder types, magnitudes of relative risks did not differ greatly.

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.

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.

Despite efforts to predict suicide risk in children, the ability to reliably identify who will engage in suicide thoughts or behaviours has remained unsuccessful.

We apply a novel machine-learning approach and examine whether children with suicide thoughts or behaviours could be differentiated from children without suicide thoughts or behaviours based on a combination of traditional (sociodemographic, physical health, social–environmental, clinical psychiatric) risk factors, but also more novel risk factors (cognitive, neuroimaging and genetic characteristics).

The study included 5885 unrelated children (50% female, 67% White, 9–11 years of age) from the Adolescent Brain Cognitive Development (ABCD) study. We performed penalised logistic regression analysis to distinguish between: (a) children with current or past suicide thoughts or behaviours; (b) children with a mental illness but no suicide thoughts or behaviours (clinical controls); and (c) healthy control children (no suicide thoughts or behaviours and no history of mental illness). The model was subsequently validated with data from seven independent sites involved in the ABCD study (n = 1712).

Our results showed that we were able to distinguish the suicide thoughts or behaviours group from healthy controls (area under the receiver operating characteristics curve: 0.80 child-report, 0.81 for parent-report) and clinical controls (0.71 child-report and 0.76–0.77 parent-report). However, we could not distinguish children with suicidal ideation from those who attempted suicide (AUROC: 0.55–0.58 child-report; 0.49–0.53 parent-report). The factors that differentiated the suicide thoughts or behaviours group from the clinical control group included family conflict, prodromal psychosis symptoms, impulsivity, depression severity and history of mental health treatment.

This work highlights that mostly clinical psychiatric factors were able to distinguish children with suicide thoughts or behaviours from children without suicide thoughts or behaviours. Future research is needed to determine if these variables prospectively predict subsequent suicidal behaviour.

Youth who attempt suicide are more at risk for later mental disorders and suicide. However, little is known about their long-term socioeconomic outcomes.

We investigated associations between youth suicide attempts and adult economic and social outcomes.

Participants were drawn from the Quebec Longitudinal Study of Kindergarten Children (n = 2140) and followed up from ages 6 to 37 years. Lifetime suicide attempt was assessed at 15 and 22 years. Economic (employment earnings, retirement savings, welfare support, bankruptcy) and social (romantic partnership, separation/divorce, number of children) outcomes were assessed through data linkage with government tax return records obtained from age 22 to 37 years (2002–2017). Generalised linear models were used to test the association between youth suicide attempt and outcomes adjusting for background characteristics, parental mental disorders and suicide, and youth concurrent mental disorders.

By age 22, 210 youths (9.8%) had attempted suicide. In fully adjusted models, youth who attempted suicide had lower annual earnings (average last 5 years, US$ −4134, 95% CI −7950 to −317), retirement savings (average last 5 years, US$ −1387, 95% CI −2982 to 209), greater risk of receiving welfare support (risk ratio (RR) = 2.05, 95% CI 1.39 to 3.04) and were less likely to be married/cohabiting (RR = 0.82, 95% CI 0.73 to 0.93), compared with those who did not attempt suicide. Over a 40-year working career, the loss of individual earnings attributable to suicide attempts was estimated at US$98 384.

Youth who attempt suicide are at risk of poor adult socioeconomic outcomes. Findings underscore the importance of psychosocial interventions for young people who have attempted suicide to prevent long-term social and economic disadvantage.

Long-term ‘not in education, employment or training’ (NEET) status is an important indicator of youth marginalisation.

To carry out a comprehensive overview of the associations between different psychiatric illnesses and long-term NEET status.

We used the register-based 1987 Finnish Birth Cohort study, which includes all live births in Finland during that year. The analyses comprised 55 273 individuals after exclusions for intellectual disability, death or emigration. We predicted that psychiatric disorders, diagnosed by specialist services between 1998 and 2007 when the cohort were 10–20 years of age, would be associated with subsequent long-term NEET (defined as NEET for at least 5 years between 2008 and 2015, when they were 20–28 years of age).

In total, 1438 individuals (2.6%) were long-term NEET during follow-up and the associations between long-term NEET and the 11 diagnostic categories we studied were statistically significant (P < 0.001). In multivariate models we included sociodemographic characteristics and upper secondary education as covariates, and the highest effect sizes, measured by odds ratios (OR) with 95% confidence intervals (CI), were found for psychosis (OR = 12.0, 95% CI 9.5–15.2) and autism spectrum disorder (OR = 17.3, 95% CI 11.5–26.0). If individuals had not successfully completed this education, 70.6% of those with autism spectrum disorder and 48.4% of those with psychosis were later long-term NEET.

Adolescents who receive treatment for psychiatric disorders, particularly autism spectrum disorder or psychosis, need support to access education and employment. This could help to prevent marginalisation in early adulthood.

Response to lithium in patients with bipolar disorder is associated with clinical and transdiagnostic genetic factors. The predictive combination of these variables might help clinicians better predict which patients will respond to lithium treatment.

To use a combination of transdiagnostic genetic and clinical factors to predict lithium response in patients with bipolar disorder.

This study utilised genetic and clinical data (n = 1034) collected as part of the International Consortium on Lithium Genetics (ConLi+Gen) project. Polygenic risk scores (PRS) were computed for schizophrenia and major depressive disorder, and then combined with clinical variables using a cross-validated machine-learning regression approach. Unimodal, multimodal and genetically stratified models were trained and validated using ridge, elastic net and random forest regression on 692 patients with bipolar disorder from ten study sites using leave-site-out cross-validation. All models were then tested on an independent test set of 342 patients. The best performing models were then tested in a classification framework.

The best performing linear model explained 5.1% (P = 0.0001) of variance in lithium response and was composed of clinical variables, PRS variables and interaction terms between them. The best performing non-linear model used only clinical variables and explained 8.1% (P = 0.0001) of variance in lithium response. A priori genomic stratification improved non-linear model performance to 13.7% (P = 0.0001) and improved the binary classification of lithium response. This model stratified patients based on their meta-polygenic loadings for major depressive disorder and schizophrenia and was then trained using clinical data.

Using PRS to first stratify patients genetically and then train machine-learning models with clinical predictors led to large improvements in lithium response prediction. When used with other PRS and biological markers in the future this approach may help inform which patients are most likely to respond to lithium treatment.

There are concerns that keyworkers may be at a greater risk for psychological distress than non-keyworkers during the COVID-19 pandemic. However, little research has included keyworkers outside of the healthcare sector or has disaggregated keyworkers into different subgroups.

To examine longitudinal changes in mental health over 12 months during the COVID-19 pandemic comparing four different groups of keyworkers with non-keyworkers.

Longitudinal data were from 21 874 adults living in England (21 March 2020 to 22 February 2021). Latent growth modelling was utilised to compare growth trajectories of depressive and anxiety symptoms in non-keyworkers and four types of keyworkers: (a) health and social care workers, (b) teachers and child care workers, (c) public service workers, and (d) essential services keyworkers (such as food chain or utility workers).

When accounting for both time-invariant and time-varying covariates, keyworkers in the essential services category had consistently higher levels of depressive and anxiety symptoms than non-keyworkers across the whole of the study period. There was little difference in the mental health trajectories between health/social care, teachers/child care and public service worker categories and non-keyworkers.

Our findings suggest that the risk for poorer mental health during the COVID-19 pandemic varies within the broad category of keyworkers generally, and that those working in utility, food chain and transport roles are especially at risk. Future research should focus on identifying which aspects of working conditions may be contributing to occupational stress in these groups.

Although recognised as the most effective antipsychotic for treatment-resistant schizophrenia, clozapine remains underused. One reason is the widespread concern about non-adherence to clozapine because of poor adherence before initiating clozapine.

To determine if prior poor out-patient adherence to treatmentbefore initiating clozapine predisposes to poor out-patient adherence to clozapine or to any antipsychotics (including clozapine) after its initiation.

This cohort study included 3228 patients with schizophrenia living in Quebec (Canada) initiating (with a 2-year clearance period) oral clozapine (index date) between 2009 and 2016. Using pharmacy data, out-patient adherence to treatment was measured by the medication possession ratio (MPR), over a 1-year period preceding and following the index date. Five groups of patients were formed based on their prior MPR level (independent variable). Two dependent variables were defined after clozapine initiation (good out-patient adherence to any antipsychotics and to clozapine only). Along with multiple logistic regressions, state sequence analysis was used as a visual representation of antipsychotic-use trajectories over time, before and after clozapine initiation.

Although prior poor adherence to antipsychotics was associated with poor adherence after clozapine initiation, the absolute risk of subsequent poor adherence remained low, regardless of previous adherence level. Most patients adhered to their treatment after initiating clozapine (>68% to clozapine and >84% to any antipsychotics).

Despite the fact that poor adherence prior to initiating clozapine is widely recognised by clinicians as a barrier for the prescription of clozapine, the current study supports the initiation of clozapine in all eligible patients.

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.

Clozapine-induced gastrointestinal hypomotility (CIGH) affects some 75% of patients treated with clozapine.

To document the incidence of potentially harmful CIGH in the UK.

We studied spontaneous UK pharmacovigilance reports recorded as clozapine-related gastrointestinal adverse drug reactions, 1992–2017.

There were 527 patients reported with potentially harmful CIGH; 33% (n = 172) died. Deaths averaged 1 per year 1992–1999, 5 per year 2000–2009 and 15 per year 2010–2017. Those who died were older (median 52 years v. 49 years) and had been prescribed clozapine for longer than those who recovered (median 11.3 years v. 4.8 years), but there was no difference in prescribed dose. Within the first 4 years of clozapine treatment, there were 169 reports of CIGH, of which 3% (n = 5) were fatal. At 10–14 years there were 63 reports of CIGH, of which 25% (n = 16) were fatal. Among the deaths, males were younger (median 51, range 22–89 v. median 57, range 24–89 years) with higher clozapine doses (median 450, range 100–900 v. median 300, range 12.5–800 mg/d) than females. In non-fatal CIGH, surgery was the most frequent outcome (n = 92). The procedures included appendectomy, ileostomy, total/partial colectomy, colostomy/stoma and proctosigmoidectomy. Clozapine dosage was reduced in 6 patients, stopped and restarted in 23, ‘continued’ in 6 and discontinued permanently in at least 76 patients.

The risk of serious morbidity/mortality from CIGH is substantial. The need to actively monitor bowel function and give laxatives to patients treated with clozapine is clear.

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.

Young people with social disability and severe and complex mental health problems have poor outcomes, frequently struggling with treatment access and engagement. Outcomes may be improved by enhancing care and providing targeted psychological or psychosocial intervention.

We aimed to test the hypothesis that adding social recovery therapy (SRT) to enhanced standard care (ESC) would improve social recovery compared with ESC alone.

A pragmatic, assessor-masked, randomised controlled trial (PRODIGY: ISRCTN47998710) was conducted in three UK centres. Participants (n = 270) were aged 16–25 years, with persistent social disability, defined as under 30 hours of structured activity per week, social impairment for at least 6 months and severe and complex mental health problems. Participants were randomised to ESC alone or SRT plus ESC. SRT was an individual psychosocial therapy delivered over 9 months. The primary outcome was time spent in structured activity 15 months post-randomisation.

We randomised 132 participants to SRT plus ESC and 138 to ESC alone. Mean weekly hours in structured activity at 15 months increased by 11.1 h for SRT plus ESC (mean 22.4, s.d. = 21.4) and 16.6 h for ESC alone (mean 27.7, s.d. = 26.5). There was no significant difference between arms; treatment effect was −4.44 (95% CI −10.19 to 1.31, P = 0.13). Missingness was consistently greater in the ESC alone arm.

We found no evidence for the superiority of SRT as an adjunct to ESC. Participants in both arms made large, clinically significant improvements on all outcomes. When providing comprehensive evidence-based standard care, there are no additional gains by providing specialised SRT. Optimising standard care to ensure targeted delivery of existing interventions may further improve outcomes.

Clinical high-risk states for psychosis (CHR) are associated with functional impairments and depressive disorders. A previous PRONIA study predicted social functioning in CHR and recent-onset depression (ROD) based on structural magnetic resonance imaging (sMRI) and clinical data. However, the combination of these domains did not lead to accurate role functioning prediction, calling for the investigation of additional risk dimensions. Role functioning may be more strongly associated with environmental adverse events than social functioning.

We aimed to predict role functioning in CHR, ROD and transdiagnostically, by adding environmental adverse events-related variables to clinical and sMRI data domains within the PRONIA sample.

Baseline clinical, environmental and sMRI data collected in 92 CHR and 95 ROD samples were trained to predict lower versus higher follow-up role functioning, using support vector classification and mixed k-fold/leave-site-out cross-validation. We built separate predictions for each domain, created multimodal predictions and validated them in independent cohorts (74 CHR, 66 ROD).

Models combining clinical and environmental data predicted role outcome in discovery and replication samples of CHR (balanced accuracies: 65.4% and 67.7%, respectively), ROD (balanced accuracies: 58.9% and 62.5%, respectively), and transdiagnostically (balanced accuracies: 62.4% and 68.2%, respectively). The most reliable environmental features for role outcome prediction were adult environmental adjustment, childhood trauma in CHR and childhood environmental adjustment in ROD.

Findings support the hypothesis that environmental variables inform role outcome prediction, highlight the existence of both transdiagnostic and syndrome-specific predictive environmental adverse events, and emphasise the importance of implementing real-world models by measuring multiple risk dimensions.