Schools are central places for adolescent social lives, which is a major factor greatly affecting adolescent mental health; school climate (i.e. quality of the school social environments) can be a proximal social determinant for adolescent mental health. Supportive school environments may serve as a protective factor during crises like COVID-19, which disrupt social lives and worsen adolescent mental health. This is the first study examining whether the pandemic effects differed based on the levels of school climate on depressive symptoms (DS) and psychotic experiences (PEs) among adolescents.

School climate (score range: 0–28), DS (0–26), and PEs (0–5) were self-reported in a population-based cohort (Tokyo Teen Cohort; N = 3171) at four timepoints (10y, 12y, 14y, and 16y) before and during COVID-19. COVID-19 occurred midway through the 16y survey, allowing us to examine its impact and interaction effect with school climate while accounting for within-person changes over time using mixed-effects models.

Significant interaction effects were found on DS (unstandardized coefficient [B] = −0.166, 95% confidence interval [CI] −0.225 to −0.107) and PEs (B = −0.020, 95% CI −0.028 to −0.012). The pandemic effects were not significant for adolescents with high school climate scores (around the 80th percentile or higher), although the pandemic significantly worsened these outcomes among the overall sample.

The negative mental health effects of the pandemic were significantly mitigated among adolescents experiencing a supportive school climate. A positive school climate can protect adolescent mental health during challenging social conditions, such as pandemics.

At a time of increased demand for specialist mental health services, a more nuanced understanding of how adolescents navigate systems of care and support is essential. We mapped ‘networks of care’ to explore patterns of mental health help-seeking alongside the perceived helpfulness of support accessed.

We examined data from 23 927 adolescents aged 11–18 years who participated in the 2023 OxWell Student Survey, an English school-based, repeated cross-sectional survey of mental health and wellbeing. Students self-reported past-year access to 18 types of support across informal (e.g. friends and family), semi-formal (e.g. school and charities), and formal (e.g. health and social care) domains, alongside how helpful they found the support. We used a network approach to explore interconnections between sources of support accessed and perceived helpfulness.

One in four (27.0%, 6449/23927) adolescents reported past-year access to mental health support, of which 56.7% (3658/6449) reported accessing multiple types. Informal networks were the most commonly accessed (23.1%, 5523/23927), followed by semi-formal (9.7%, 2317/23927) and formal (6.8%, 1623/23927) supports. Informal sources had high acceptability, with around 80–90% reporting them as helpful, whereas child and adolescent mental health services (CAMHS), helplines, and online supports were perceived to be the least helpful. The networks also identified groups who might not be optimally served by current systems, including gender diverse adolescents and adolescents who found mental health support from their parents unhelpful.

Adolescents are accessing mental health support across informal, semi-formal, and formal sources of care. Services can no longer be developed, delivered, or evaluated in isolation from these networks.

The identification of predictors of treatment response is crucial for improving treatment outcome for children with anxiety disorders. Machine learning methods provide opportunities to identify combinations of factors that contribute to risk prediction models.

A machine learning approach was applied to predict anxiety disorder remission in a large sample of 2114 anxious youth (5–18 years). Potential predictors included demographic, clinical, parental, and treatment variables with data obtained pre-treatment, post-treatment, and at least one follow-up.

All machine learning models performed similarly for remission outcomes, with AUC between 0.67 and 0.69. There was significant alignment between the factors that contributed to the models predicting two target outcomes: remission of all anxiety disorders and the primary anxiety disorder. Children who were older, had multiple anxiety disorders, comorbid depression, comorbid externalising disorders, received group treatment and therapy delivered by a more experienced therapist, and who had a parent with higher anxiety and depression symptoms, were more likely than other children to still meet criteria for anxiety disorders at the completion of therapy. In both models, the absence of a social anxiety disorder and being treated by a therapist with less experience contributed to the model predicting a higher likelihood of remission.

These findings underscore the utility of prediction models that may indicate which children are more likely to remit or are more at risk of non-remission following CBT for childhood anxiety.

The relationship between adolescent alcohol use and emotional problems remains unclear and contradictory. These inconsistencies may in part be due to differences in the measurement and operationalization of alcohol use and emotional problems across studies, as well as confounder selection and missing data decisions. This study explores the associations between common specifications of adolescent alcohol use and emotional problems in a large sample of adolescents.

A multiverse analysis (also known as specification curve analysis or vibration of effects) was done with 7680 unique model specifications in a large longitudinal sample of 6639 Australian adolescents (aged ~14.7–15.7, 2021–2022).

While alcohol use and emotional problems nearly universally co-occurred in minimally adjusted cross-sectional models (98–99%), the operationalization of emotional problems, temporality of prospective relationships, and choice of confounders substantially impacted findings. Emotional problems appeared to predict later alcohol use more-so than the reverse, depression-focused measures yielded more consistent associations with alcohol use than anxiety-focused measures, and certain confounders (i.e. conduct, ADHD, smoking) explained most of the associations between adolescent alcohol use and emotional problems. Missing data decisions and whether outcomes were modelled continuously v. dichotomously had minimal impact on findings.

While adolescent alcohol use and emotional problems commonly co-occur, inconsistencies in the magnitude, direction, and significance of effects are closely tied to researcher decisions that are often made arbitrarily.

The Personalized Advantage Index (PAI) shows promise as a method for identifying the most effective treatment for individual patients. Previous studies have demonstrated its utility in retrospective evaluations across various settings. In this study, we explored the effect of different methodological choices in predictive modelling underlying the PAI.

Our approach involved a two-step procedure. First, we conducted a review of prior studies utilizing the PAI, evaluating each study using the Prediction model study Risk Of Bias Assessment Tool (PROBAST). We specifically assessed whether the studies adhered to two standards of predictive modeling: refraining from using leave-one-out cross-validation (LOO CV) and preventing data leakage. Second, we examined the impact of deviating from these methodological standards in real data. We employed both a traditional approach violating these standards and an advanced approach implementing them in two large-scale datasets, PANIC-net (n = 261) and Protect-AD (n = 614).

The PROBAST-rating revealed a substantial risk of bias across studies, primarily due to inappropriate methodological choices. Most studies did not adhere to the examined prediction modeling standards, employing LOO CV and allowing data leakage. The comparison between the traditional and advanced approach revealed that ignoring these standards could systematically overestimate the utility of the PAI.

Our study cautions that violating standards in predictive modeling may strongly influence the evaluation of the PAI's utility, possibly leading to false positive results. To support an unbiased evaluation, crucial for potential clinical application, we provide a low-bias, openly accessible, and meticulously annotated script implementing the PAI.

Pediatric bipolar disorder (PBD) is characterized by abnormal functional connectivity among distributed brain regions. Increasing evidence suggests a role for the limbic network (LN) and the triple network model in the pathophysiology of bipolar disorder (BD). However, the specific relationship between the LN and the triple network in PBD remains unclear. This study aimed to investigate the aberrant causal connections among these four core networks in PBD.

Resting-state functional MRI scans from 92 PBD patients and 40 healthy controls (HCs) were analyzed. Dynamic Causal Modeling (DCM) was employed to assess effective connectivity (EC) among the four core networks. Parametric empirical Bayes (PEB) analysis was conducted to identify ECs associated with group differences, as well as depression and mania severity. Leave-one-out cross-validation (LOOCV) was used to test predictive accuracy.

Compared to HCs, PBD patients exhibited primarily excitatory bottom-up connections from the LN to the salience network (SN) and bidirectional excitatory connections between the default mode network (DMN) and SN. In PBD, top-down connectivity from the triple network to the LN was excitatory in individuals with higher depression severity but inhibitory in those with higher mania severity. LOOCV identified dysconnectivity circuits involving the caudate and hippocampus as being associated with mania and depression severity, respectively.

Disrupted bottom-up connections from the LN to the triple network distinguish PBD patients from healthy controls, while top-down disruptions from the triple network to LN relate to mood state differences. These findings offer insight into the neural mechanisms of PBD.

To refine the knowledge on familial transmission, we examined the (shared) familial components among neurodevelopmental problems (i.e. two attention-deficit/hyperactivity–impulsivity disorder [ADHD] and six autism spectrum disorder [ASD] subdomains) and with aggressive behavior, depression, anxiety, and substance use.

Data were obtained from a cross-sectional study encompassing 37 688 participants across three generations from the general population. ADHD subdomains, ASD subdomains, aggressive behavior, depression, anxiety, and substance use were assessed. To evaluate familial (co-)aggregation, recurrence risk ratios (λR) were estimated using Cox proportional hazards models. The (shared) familiality (f2), which is closely related to (shared) heritability, was assessed using residual maximum likelihood-based variance decomposition methods. All analyses were adjusted for sex, age, and age2.

The familial aggregation and familiality of neurodevelopmental problems were moderate (λR = 2.40–4.04; f2 = 0.22–0.39). The familial co-aggregation and shared familiality among neurodevelopmental problems (λR = 1.39–2.56; rF = 0.52–0.94), and with aggressive behavior (λR = 1.79–2.56; rF = 0.60–0.78), depression (λR = 1.45–2.29; rF = 0.43–0.76), and anxiety (λR = 1.44–2.31; rF = 0.62–0.84) were substantial. The familial co-aggregation and shared familiality between all neurodevelopmental problems and all types of substance use were weak (λR = 0.53–1.57; rF = −0.06–0.35).

Neurodevelopmental problems belonging to the same disorder were more akin than cross-disorder problems. That said, there is a clear (shared) familial component to neurodevelopmental problems, in part shared with other psychiatric problems (except for substance use). This suggests that neurodevelopmental disorders, disruptive behavior disorders, and internalizing disorders share genetic and environmental risk factors.

Executive dysfunction, including working memory deficits, is prominent in posttraumatic stress disorder (PTSD) and can impede treatment effectiveness. Intervention approaches that target executive dysfunction alongside standard PTSD treatments could boost clinical response. The current study reports secondary analyses from a randomized controlled trial testing combined PTSD treatment with a computerized training program to improve executive dysfunction. We assessed if pre-treatment neurocognitive substrates of executive functioning predicted clinical response to this novel intervention.

Treatment-seeking veterans with PTSD (N = 60) completed a working memory task during functional magnetic resonance imaging prior to being randomized to six weeks of computerized executive function training (five 30-minute sessions each week) plus twelve 50-minute sessions of cognitive processing therapy (CEFT + CPT) or placebo training plus CPT (PT + CPT). Using linear mixed effects models, we examined the extent to which the neurocognitive substrates of executive functioning predicted PTSD treatment response.

Results indicated that veterans with greater activation of working memory regions (e.g. lateral prefrontal and cingulate cortex) had better PTSD symptom improvement trajectories in CEFT + CPT v. PT + CPT. Those with less neural activation during working memory showed similar trajectories of PTSD symptom change regardless of treatment condition.

Greater activity of frontal regions implicated in working memory may serve as a biomarker of response to a novel treatment in veterans with PTSD. Individuals with greater regional responsiveness benefited more from treatment that targeted cognitive dysfunction than treatment that did not include active cognitive training. Clinically, findings could inform our understanding of treatment mechanisms and may contribute to better personalization of treatment.

England's primary care service for psychological therapy (Improving Access to Psychological Therapies [IAPT]) treats anxiety and depression, with a target recovery rate of 50%. Identifying the characteristics of patients who achieve recovery may assist in optimizing future treatment. This naturalistic cohort study investigated pre-therapy characteristics as predictors of recovery and improvement after IAPT therapy.

In a cohort of patients attending an IAPT service in South London, we recruited 263 participants and conducted a baseline interview to gather extensive pre-therapy characteristics. Bayesian prediction models and variable selection were used to identify baseline variables prognostic of good clinical outcomes. Recovery (primary outcome) was defined using (IAPT) service-defined score thresholds for both depression (Patient Health Questionnaire [PHQ-9]) and anxiety (Generalized Anxiety Disorder [GAD-7]). Depression and anxiety outcomes were also evaluated as standalone (PHQ-9/GAD-7) scores after therapy. Prediction model performance metrics were estimated using cross-validation.

Predictor variables explained 26% (recovery), 37% (depression), and 31% (anxiety) of the variance in outcomes, respectively. Variables prognostic of recovery were lower pre-treatment depression severity and not meeting criteria for obsessive compulsive disorder. Post-therapy depression and anxiety severity scores were predicted by lower symptom severity and higher ratings of health-related quality of life (EuroQol questionnaire [EQ5D]) at baseline.

Almost a third of the variance in clinical outcomes was explained by pre-treatment symptom severity scores. These constructs benefit from being rapidly accessible in healthcare services. If replicated in external samples, the early identification of patients who are less likely to recover may facilitate earlier triage to alternative interventions.

Determining whether the incidence of suicidal behavior during the COVID-19 pandemic changed for those with severe mental disorders is essential to ensure the provision of suicide preventive initiatives in the case of future health crises.

Using population-based registers, quarterly cohorts from the first quarter of 2018 (2018Q1) to 2021Q4 were formed including all Swedish-residents >10 years old. Interrupted time series and generalized estimating equations analyses were used to evaluate changes in Incidence Rates (IR) of specialised healthcare use for suicide attempt and death by suicide per 10 000 person-years for individuals with or without specific severe mental disorders (SMDs) during, compared to before the pandemic.

The IR (95% Confidence interval, CI) of suicide in individuals with SMDs decreased from 16.0 (15.0–17.1) in 2018Q1 to 11.6 (10.8–12.5) in 2020Q1 (i.e. the quarter before the start of the pandemic), after which it dropped further to 6.7 (6.3–7.2) in 2021Q2. In contrast, IRs of suicide attempt in SMDs showed more stable trends, as did the trends regarding suicide and suicide attempt for individuals without SMD. These discrepancies were most evident for individuals with substance use disorder and ASD/ADHD. Changes in IRs of suicide v. suicide attempt for one quarter during the pandemic for substance misuse were 11.2% v. 3.6% respectively. These changes for ASD/ADHD were 10.7% v. 3.6%.

The study shows pronounced decreases in suicide rates in individuals with SMDs during the pandemic. Further studies aiming to understand mechanisms behind these trends are warranted to consult future suicide prevention strategies.

Both childhood adversity (CA) and first-episode psychosis (FEP) have been linked to alterations in cortical thickness (CT). The interactive effects between different types of CAs and FEP on CT remain understudied.

One-hundred sixteen individuals with FEP (mean age = 23.8 ± 6.9 years, 34% females, 80.2% non-affective FEP) and 98 healthy controls (HCs) (mean age = 24.4 ± 6.2 years, 43% females) reported the presence/absence of CA <17 years using an adapted version of the Childhood Experience of Care and Abuse (CECA.Q) and the Retrospective Bullying Questionnaire (RBQ) and underwent magnetic resonance imaging (MRI) scans. Correlation analyses were used to assess associations between brain maps of CA and FEP effects. General linear models (GLMs) were performed to assess the interaction effects of CA and FEP on CT.

Eighty-three individuals with FEP and 83 HCs reported exposure to at least one CA. CT alterations in FEP were similar to those found in participants exposed to separation from parents, bullying, parental discord, household poverty, and sexual abuse (r = 0.50 to 0.25). Exposure to neglect (β = −0.24, 95% CI [−0.37 to −0.12], p = 0.016) and overall maltreatment (β = −0.13, 95% CI [−0.20 to −0.06], p = 0.043) were associated with cortical thinning in the right medial orbitofrontal region.

Cortical alterations in individuals with FEP are similar to those observed in the context of socio-environmental adversity. Neglect and maltreatment may contribute to CT reductions in FEP. Our findings provide new insights into the specific neurobiological effects of CA in early psychosis.

Neuroimaging studies have documented brain structural changes in schizophrenia at different stages of the illness, including clinical high-risk (cHR), genetic high-risk (gHR), first-episode schizophrenia (FES), and chronic schizophrenia (ChS). There is growing awareness that neuropathological processes associated with a disease fail to map to a specific brain region but do map to a specific brain network. We sought to investigate brain structural damage networks across different stages of schizophrenia.

We initially identified gray matter alterations in 523 cHR, 855 gHR, 2162 FES, and 2640 ChS individuals relative to 6963 healthy controls. By applying novel functional connectivity network mapping to large-scale discovery and validation resting-state functional magnetic resonance imaging datasets, we mapped these affected brain locations to four specific networks.

Brain structural damage networks of cHR and gHR had limited and non-overlapping spatial distributions, with the former mainly involving the frontoparietal network and the latter principally implicating the subcortical network, indicative of distinct neuropathological mechanisms underlying cHR and gHR. By contrast, brain structural damage networks of FES and ChS manifested as similar patterns of widespread brain areas predominantly involving the somatomotor, ventral attention, and subcortical networks, suggesting an emergence of more prominent brain structural abnormalities with illness onset that have trait-like stability over time.

Our findings may not only provide a refined picture of schizophrenia neuropathology from a network perspective, but also potentially contribute to more targeted and effective intervention strategies for individuals at different schizophrenia stages.

There is a lack of large-scale studies exploring labor market marginalization (LMM) among individuals diagnosed with bipolar disorder (BD). We aimed to investigate the association of BD with subsequent LMM in Sweden, and the effect of sex on LMM in BD.

Individuals aged 19–60 years living in Sweden with a first-time BD diagnosis between 2007 and 2016 (n = 25 231) were followed from the date of diagnosis for a maximum of 14 years. Risk of disability pension (DP), long-term sickness absence (SA) (>90 days), and long-term unemployment (>180 days) was compared to a matched comparison group from the general population, matched 1:5 on sex and birth year (n = 126 155), and unaffected full siblings (n = 24 098), using sex-stratified Cox regression analysis, yielding hazard ratios (HRs) with 95% confidence intervals (CIs).

After adjusting for socioeconomic factors, baseline labor market status, and comorbid disorders, individuals with BD had a significantly higher risk of DP compared to the general population (HR = 16.67, 95% CI 15.33–18.13) and their unaffected siblings (HR = 5.54, 95% CI 4.96–6.18). Individuals with BD were also more likely to experience long-term SA compared to the general population (HR = 3.19, 95% CI 3.09–3.30) and their unaffected siblings (HR = 2.83, 95% CI 2.70–2.97). Moreover, individuals diagnosed with BD had an elevated risk of long-term unemployment relative to both comparison groups (HR range: 1.75–1.78). Men with BD had a higher relative risk of SA and unemployment than women. No difference was found in DP.

Individuals with BD face elevated risks of LMM compared to both the general population and unaffected siblings.

Markers of inflammation and cannabis exposure are associated with an increased risk of mental disorders. In the current study, we investigated associations between cannabis use and biomarkers of inflammation.

Utilizing a sample of 914 participants from the Avon Longitudinal Study of Parents and Children, we investigated whether interleukin-6 (IL-6), tumor necrosis factor α (TNFα), C-reactive protein (CRP), and soluble urokinase plasminogen activator receptor (suPAR) measured at age 24 were associated with past year daily cannabis use, less frequent cannabis use, and no past year cannabis use. We adjusted for a number of covariates including sociodemographic measures, body mass index, childhood trauma, and tobacco smoking. We found evidence of a strong association between daily or near daily cannabis use and suPAR.

We did not find any associations between less frequent cannabis use and suPAR. We did not find evidence of an association between IL-6, TNFα or CRP, and cannabis use.

Our finding that frequent cannabis use is strongly associated with suPAR, a biomarker of systemic chronic inflammation implicated in neurodevelopmental and neurodegenerative processes is novel. These findings may provide valuable insights into biological mechanisms by which cannabis affects the brain and impacts the risk of serious mental disorders.

Dialectical behavior therapy (DBT) is a specialized treatment that has a growing evidence base for binge-spectrum eating disorders. However, cost and workforce capacity limit wide-scale uptake of DBT since it involves over 20 in-person sessions with a trained professional (and six sessions for guided self-help format). Interventions translated for delivery through modern technology offer a solution to increase the accessibility of evidence-based treatments. We developed the first DBT-specific skills training smartphone application (Resilience: eDBT) for binge-spectrum eating disorders and evaluated its efficacy in a randomized clinical trial.

Participants reporting recurrent binge eating were randomized to Resilience (n = 287) or a waitlist (n = 289). Primary outcomes were objective binge eating episodes and global levels of eating disorder psychopathology. Secondary outcomes were behavioral and cognitive symptoms, psychological distress, and the hypothesized processes of change (mindfulness, emotion regulation, and distress tolerance).

Intention-to-treat analyses showed that the intervention group reported greater reductions in objective binge eating episodes (incidence rate ratio = 0.69) and eating disorder psychopathology (d = −0.68) than the waitlist at 6 weeks. Significant group differences favoring the intervention group were also observed on secondary outcomes, except for subjective binge eating, psychological distress, and distress tolerance. Primary symptoms showed further improvements from 6 to 12 weeks. However, dropout rate was high (48%) among the intervention group, and engagement decreased over the study period.

A novel, low-intensity DBT skills training app can effectively reduce symptoms of eating disorders. Scalable apps like these may increase the accessibility of evidence-based treatments.

Accumulating evidence suggests that rapid eye movement sleep (REM) supports the consolidation of extinction memory. REM is disrupted in posttraumatic stress disorder (PTSD), and REM abnormalities after traumatic events increase the risk of developing PTSD. Therefore, it was hypothesized that abnormal REM in trauma-exposed individuals may pave the way for PTSD by interfering with the processing of extinction memory. In addition, PTSD patients display reduced vagal activity. Vagal activity contributes to the strengthening of memories, including fear extinction memory, and recent studies show that the role of vagus in memory processing extends to memory consolidation during sleep. Therefore, it is plausible that reduced vagal activity during sleep in trauma-exposed individuals may be an additional mechanism that impairs extinction memory consolidation. However, to date, the contribution of sleep vagal activity to the consolidation of extinction memory or any emotional memory has not been investigated.

Trauma-exposed individuals (n = 113) underwent a 2-day fear conditioning and extinction protocol. Conditioning and extinction learning phases were followed by extinction recall 24 h later. The association of extinction recall with REM characteristics and REM vagal activity (indexed as heart rate variability) during the intervening consolidation night was examined.

Consistent with our hypotheses, REM disruption was associated with poorer physiological and explicit extinction memory. Furthermore, higher vagal activity during REM was associated with better explicit extinction memory, and physiological extinction memory in males.

These findings support the notion that abnormal REM, including reduced REM vagal activity, may contribute to PTSD by impairing the consolidation of extinction memory.