Understanding the neurobiological underpinnings of childhood maltreatment is vital given consistent links with poor mental health. Dimensional models of adversity purport that different types of adversity likely have distinct neurobiological consequences. Adolescence is a key developmental period, during which deviations from normative neurodevelopment may have particular relevance for mental health. However, longitudinal work examining links between different forms of maltreatment, neurodevelopment, and mental health is limited.

In the present study, we explored associations between abuse, neglect, and longitudinal development of within-network functional connectivity of the salience (SN), default mode (DMN), and executive control network in 142 community residing adolescents. Resting-state fMRI data were acquired at age 16 (T1; M = 16.46 years, s.d. = 0.52, 66F) and 19 (T2; mean follow-up period: 2.35 years). Mental health data were also collected at T1 and T2. Childhood maltreatment history was assessed prior to T1.

Abuse and neglect were both found to be associated with increases in within-SN functional connectivity from age 16 to 19. Further, there were sex differences in the association between neglect and changes in within-DMN connectivity. Finally, increases in within-SN connectivity were found to mediate the association between abuse/neglect and lower problematic substance use and higher depressive symptoms at age 19.

Our findings suggest that childhood maltreatment is associated with altered neurodevelopmental trajectories, and that changes in salience processing may be linked with risk and resilience for the development of depression and substance use problems during adolescence, respectively. Further work is needed to understand the distinct neurodevelopmental and mental health outcomes of abuse and neglect.

Anorexia nervosa (AN) is a disabling, deadly and costly mental disorder. Until recently, treatment recommendations were based on expert opinion and limited evidence. The aim of this systematic review is to synthesise recent evidence on established and emerging AN treatments and to forecast trends for future developments.

We systematically review trials of established treatments and associated process outcome studies from the last 5 years, published since a previous review in this journal. ‘Established’ treatments were those that are widely used in AN, recommended by guidelines and/or have been tested in at least one large randomised controlled trial. Secondly, we summarise emerging treatments for AN, i.e. those that have only been (or are currently being) tested in proof-of concept, feasibility or pilot trials.

We identified 19 published trials of established treatments (15 of high or moderate quality), mostly assessing psychological therapies (n = 17). We also found 11 published trials of emerging treatments, and a total of 34 registered, as yet unpublished trials. Promising emerging treatments include cognitive remediation therapy, exposure therapy and non-invasive neuromodulation.

Evidence generation on the treatment of AN has dramatically accelerated, with our understanding of the role of family-based approaches for adolescents more nuanced and a range of psychological approaches available for the treatment of adults. Evidence on emerging treatments and from forthcoming trials suggests that there is a shift towards more targeted brain-based interventions. Future studies need to focus on elucidating mechanisms of action of treatments and what works best for whom.

This review aimed to address the question of whether cognitive impairment should be considered a core feature of depression that may be a valuable target for treatment.

We conducted a systematic review and meta-analysis of cognitive function, assessed with a single neuropsychological test battery, the Cambridge Neuropsychological Test Automated Battery (CANTAB), in patients with depression during symptomatic and remitted states. Inclusion of studies comparing patients remitted from depression and controls enabled us to investigate whether cognitive impairment persists beyond episodes of low mood in depression.

Our meta-analysis revealed significant moderate cognitive deficits in executive function, memory and attention in patients with depression relative to controls (Cohen's d effect sizes ranging from −0.34 to −0.65). Significant moderate deficits in executive function and attention (Cohen's d ranging from −0.52 to −0.61) and non-significant small/moderate deficits in memory (Cohen's d ranging from −0.22 to −0.54) were found to persist in patients whose depressive symptoms had remitted, indicating that cognitive impairment occurs separately from episodes of low mood in depression.

Both low mood and cognitive impairment are associated with poor psychosocial functioning. Therefore, we argue that remediation of cognitive impairment and alleviation of depressive symptoms each play an important role in improving outcome for patients with depression. In conclusion, this systematic review and meta-analysis demonstrates that cognitive impairment represents a core feature of depression that cannot be considered an epiphenomenon that is entirely secondary to symptoms of low mood and that may be a valuable target for future interventions.

Previous research suggests an increase in schizophrenia population attributable risk fraction (PARF) for cannabis use disorder (CUD). However, sex and age variations in CUD and schizophrenia suggest the importance of examining differences in PARFs in sex and age subgroups.

We conducted a nationwide Danish register-based cohort study including all individuals aged 16–49 at some point during 1972–2021. CUD and schizophrenia status was obtained from the registers. Hazard ratios (HR), incidence risk ratios (IRR), and PARFs were estimated. Joinpoint analyses were applied to sex-specific PARFs.

We examined 6 907 859 individuals with 45 327 cases of incident schizophrenia during follow-up across 129 521 260 person-years. The overall adjusted HR (aHR) for CUD on schizophrenia was slightly higher among males (aHR = 2.42, 95% CI 2.33–2.52) than females (aHR = 2.02, 95% CI 1.89–2.17); however, among 16–20-year-olds, the adjusted IRR (aIRR) for males was more than twice that for females (males: aIRR = 3.84, 95% CI 3.43–4.29; females: aIRR = 1.81, 95% CI 1.53–2.15). During 1972–2021, the annual average percentage change in PARFs for CUD in schizophrenia incidence was 4.8 among males (95% CI 4.3–5.3; p < 0.0001) and 3.2 among females (95% CI 2.5–3.8; p < 0.0001). In 2021, among males, PARF was 15%; among females, it was around 4%.

Young males might be particularly susceptible to the effects of cannabis on schizophrenia. At a population level, assuming causality, one-fifth of cases of schizophrenia among young males might be prevented by averting CUD. Results highlight the importance of early detection and treatment of CUD and policy decisions regarding cannabis use and access, particularly for 16–25-year-olds.

Although mental disorders are significant predictors of educational attainment throughout the entire educational career, most research on mental disorders among students has focused on the primary and secondary school years.

The World Health Organization World Mental Health Surveys were used to examine the associations of mental disorders with college entry and attrition by comparing college students (n = 1572) and non-students in the same age range (18–22 years; n = 4178), including non-students who recently left college without graduating (n = 702) based on surveys in 21 countries (four low/lower-middle income, five upper-middle-income, one lower-middle or upper-middle at the times of two different surveys, and 11 high income). Lifetime and 12-month prevalence and age-of-onset of DSM-IV anxiety, mood, behavioral and substance disorders were assessed with the Composite International Diagnostic Interview (CIDI).

One-fifth (20.3%) of college students had 12-month DSM-IV/CIDI disorders; 83.1% of these cases had pre-matriculation onsets. Disorders with pre-matriculation onsets were more important than those with post-matriculation onsets in predicting subsequent college attrition, with substance disorders and, among women, major depression the most important such disorders. Only 16.4% of students with 12-month disorders received any 12-month healthcare treatment for their mental disorders.

Mental disorders are common among college students, have onsets that mostly occur prior to college entry, in the case of pre-matriculation disorders are associated with college attrition, and are typically untreated. Detection and effective treatment of these disorders early in the college career might reduce attrition and improve educational and psychosocial functioning.

This paper aims to synthesise the literature on machine learning (ML) and big data applications for mental health, highlighting current research and applications in practice.

We employed a scoping review methodology to rapidly map the field of ML in mental health. Eight health and information technology research databases were searched for papers covering this domain. Articles were assessed by two reviewers, and data were extracted on the article's mental health application, ML technique, data type, and study results. Articles were then synthesised via narrative review.

Three hundred papers focusing on the application of ML to mental health were identified. Four main application domains emerged in the literature, including: (i) detection and diagnosis; (ii) prognosis, treatment and support; (iii) public health, and; (iv) research and clinical administration. The most common mental health conditions addressed included depression, schizophrenia, and Alzheimer's disease. ML techniques used included support vector machines, decision trees, neural networks, latent Dirichlet allocation, and clustering.

Overall, the application of ML to mental health has demonstrated a range of benefits across the areas of diagnosis, treatment and support, research, and clinical administration. With the majority of studies identified focusing on the detection and diagnosis of mental health conditions, it is evident that there is significant room for the application of ML to other areas of psychology and mental health. The challenges of using ML techniques are discussed, as well as opportunities to improve and advance the field.

The coronavirus [coronavirus disease 2019 (COVID-19)] pandemic has introduced extraordinary life changes and stress, particularly in adolescents and young adults. Initial reports suggest that depression and anxiety are elevated during COVID-19, but no prior study has explored changes at the within-person level. The current study explored changes in depression and anxiety symptoms from before the pandemic to soon after it first peaked in Spring 2020 in a sample of adolescents and young adults (N = 451) living in Long Island, New York, an early epicenter of COVID-19 in the U.S.

Depression (Children's Depression Inventory) and anxiety symptoms (Screen for Child Anxiety Related Symptoms) were assessed between December 2014 and July 2019, and, along with COVID-19 experiences, symptoms were re-assessed between March 27th and May 15th, 2020.

Across participants and independent of age, there were increased generalized anxiety and social anxiety symptoms. In females, there were also increased depression and panic/somatic symptoms. Multivariable linear regression indicated that greater COVID-19 school concerns were uniquely associated with increased depression symptoms. Greater COVID-19 home confinement concerns were uniquely associated with increased generalized anxiety symptoms, and decreased social anxiety symptoms, respectively.

Adolescents and young adults at an early epicenter of the COVID-19 pandemic in the U.S. experienced increased depression and anxiety symptoms, particularly amongst females. School and home confinement concerns related to the pandemic were independently associated with changes in symptoms. Overall, this report suggests that the COVID-19 pandemic is having multifarious adverse effects on the mental health of youth.