Common mental disorders (CMDs) including depression, anxiety, and stress are very common, but it is unclear whether the last decades of social, economic, and political change have impacted incidence of CMD. This study explored temporal trends in the recorded incidence of CMD in the United Kingdom.

We used data from general practices in the United Kingdom (Clinical Practice Research Datalink) to estimate the annual recorded incidence of CMD for 2000–2020, including symptoms, diagnosis, or pharmaceutical treatment. Trends were explored by sex, age, ethnicity, region, deprivation, and comorbidity.

We included 29 480 164 individuals who were followed up for 12.5 years on average (s.d. = 6.4 years). The recorded incidence of CMD episodes was 55.9 per 1000 person-years in 2000 [95% confidence interval (CI) 55.8–56.1], increasing to 79.6 per 1000 person-years in 2019 (95% CI 79.5–79.8). Females had higher recorded incidence rates, as did those living in more deprived areas. We observed striking patterns by age over time, with rates in ages 16–24 increasing from 40.2 per 1000 in 2000 (95% CI 39.8–40.5), to 107.8 per 1000 in 2019 (95% CI 107.0–108.6). In contrast, the rates in those aged ≥55 years decreased since 2014. There were differing patterns of incidence by ethnic group, with a steeper increase in Asian, Black, and mixed groups in recent years.

Overall, the incidence of recorded CMD in the UK general practice increased between 2000 and 2019 with a small decrease in 2020. The overall trends obscured important differences across population subgroups, which may have implications for prevention.

Previous research on the changes in resting-state functional connectivity (rsFC) in anorexia nervosa (AN) has been limited by an insufficient sample size, which reduced the reliability of the results and made it difficult to set the whole brain as regions of interest (ROIs).

We analyzed functional magnetic resonance imaging data from 114 female AN patients and 135 healthy controls (HC) and obtained self-reported psychological scales, including eating disorder examination questionnaire 6.0. One hundred sixty-four cortical, subcortical, cerebellar, and network parcellation regions were considered as ROIs. We calculated the ROI-to-ROI rsFCs and performed group comparisons.

Compared to HC, AN patients showed 12 stronger rsFCs mainly in regions containing dorsolateral prefrontal cortex (DLPFC), and 33 weaker rsFCs primarily in regions containing cerebellum, within temporal lobe, between posterior fusiform cortex and lateral part of visual network, and between anterior cingulate cortex (ACC) and thalamus (p < 0.01, false discovery rate [FDR] correction). Comparisons between AN subtypes showed that there were stronger rsFCs between right lingual gyrus and right supracalcarine cortex and between left temporal occipital fusiform cortex and medial part of visual network in the restricting type compared to the binge/purging type (p < 0.01, FDR correction).

Stronger rsFCs in regions containing mainly DLPFC, and weaker rsFCs in regions containing primarily cerebellum, within temporal lobe, between posterior fusiform cortex and lateral part of visual network, and between ACC and thalamus, may represent categorical diagnostic markers discriminating AN patients from HC.

Less than a third of patients with depression achieve successful remission with standard first-step antidepressant monotherapy. The process for determining appropriate second-step care is often based on clinical intuition and involves a protracted course of trial and error, resulting in substantial patient burden and unnecessary delay in the provision of optimal treatment. To address this problem, we adopt an ensemble machine learning approach to improve prediction accuracy of remission in response to second-step treatments.

Data were derived from the Level 2 stage of the STAR*D dataset, which included 1439 patients who were randomized into one of seven different second-step treatment strategies after failing to achieve remission during first-step antidepressant treatment. Ensemble machine learning models, comprising several individual algorithms, were evaluated using nested cross-validation on 155 predictor variables including clinical and demographic measures.

The ensemble machine learning algorithms exhibited differential classification performance in predicting remission status across the seven second-step treatments. For the full set of predictors, AUC values ranged from 0.51 to 0.82 depending on the second-step treatment type. Predicting remission was most successful for cognitive therapy (AUC = 0.82) and least successful for other medication and combined treatment options (AUCs = 0.51–0.66).

Ensemble machine learning has potential to predict second-step treatment. In this study, predictive performance varied by type of treatment, with greater accuracy in predicting remission in response to behavioral treatments than to pharmacotherapy interventions. Future directions include considering more informative predictor modalities to enhance prediction of second-step treatment response.

Harsh parenting in early childhood is related to offspring's adverse behavioral outcomes. Due to the scarcity of longitudinal neuroimaging data, few studies have explored the neurobiological underpinnings of this association, focusing on within-person variability. This study examined the temporal associations among harsh parenting, later behavioral problems, and the developmental trajectories of amygdala volume and amygdala resting-state functional connectivity (RSFC) profiles, using longitudinal neuroimaging data.

The study was embedded in the Growing Up in Singapore Towards healthy Outcomes (GUSTO) cohort. T1-weighted (296 children, 642 scans) and resting-state functional scans (256 children, 509 scans) were collected at ages 4.5, 6, 7.5, and 10.5 years. Amygdala volume and RSFC between the amygdala and six brain regions that have leading roles in emotional regulation were extracted. Harsh parenting at 4.5 years and child behavioral problems at 10.5 years were assessed via parent-report questionnaires. Linear regression and linear mixed models were applied.

Harsh parenting was associated with more severe externalizing problems in girls (β = 0.24, 95% CI 0.08–0.40) but not boys (pint = 0.07). In the overall sample, harsh parenting was associated with the developmental trajectories of amygdala-ACC, amygdala-OFC, and amygdala-DLPFC RSFC. In addition, the developmental trajectory of amygdala-ACC RSFC mediated the harsh parenting–externalizing problems association in girls (indirect effect = 0.06, 95% CI 0.01–0.14).

Harsh parenting in early childhood was associated with amygdala neurocircuitry development and behavioral problems. The developmental trajectory of amygdala-ACC RSFC is a potential neural mechanism linking harsh parenting and externalizing problems in girls.

Researchers have identified genetic and neural risk factors for externalizing behaviors. However, it has not yet been determined if genetic liability is conferred in part through associations with more proximal neurophysiological risk markers.

Participants from the Collaborative Study on the Genetics of Alcoholism, a large, family-based study of alcohol use disorders were genotyped and polygenic scores for externalizing (EXT PGS) were calculated. Associations with target P3 amplitude from a visual oddball task (P3) and broad endorsement of externalizing behaviors (indexed via self-report of alcohol and cannabis use, and antisocial behavior) were assessed in participants of European (EA; N = 2851) and African ancestry (AA; N = 1402). Analyses were also stratified by age (adolescents, age 12–17 and young adults, age 18–32).

The EXT PGS was significantly associated with higher levels of externalizing behaviors among EA adolescents and young adults as well as AA young adults. P3 was inversely associated with externalizing behaviors among EA young adults. EXT PGS was not significantly associated with P3 amplitude and therefore, there was no evidence that P3 amplitude indirectly accounted for the association between EXT PGS and externalizing behaviors.

Both the EXT PGS and P3 amplitude were significantly associated with externalizing behaviors among EA young adults. However, these associations with externalizing behaviors appear to be independent of each other, suggesting that they may index different facets of externalizing.

Binge-eating disorder (BED) co-occurs with neurobehavioral alterations in the processing of disorder-relevant content such as visual food stimuli. Whether neurofeedback (NF) directly targeting them is suited for treatment remains unclear. This study sought to determine feasibility and estimate effects of individualized, functional near-infrared spectroscopy-based real-time NF (rtfNIRS-NF) and high-beta electroencephalography-based NF (EEG-NF), assuming superiority over waitlist (WL).

Single-center, assessor-blinded feasibility study with randomization to rtfNIRS-NF, EEG-NF, or WL and assessments at baseline (t0), postassessment (t1), and 6-month follow-up (t2). NF comprised 12 60-min food-specific rtfNIRS-NF or EEG-NF sessions over 8 weeks. Primary outcome was the binge-eating frequency at t1 assessed interview-based. Secondary outcomes included feasibility, eating disorder symptoms, mental and physical health, weight management-related behavior, executive functions, and brain activity at t1 and t2.

In 72 patients (intent-to-treat), the results showed feasibility of NF regarding recruitment, attrition, adherence, compliance, acceptance, and assessment completion. Binge eating improved at t1 by −8.0 episodes, without superiority of NF v. WL (−0.8 episodes, 95% CI −2.4 to 4.0), but with improved estimates in NF at t2 relative to t1. NF was better than WL for food craving, anxiety symptoms, and body mass index, but overall effects were mostly small. Brain activity changes were near zero.

The results show feasibility of food-specific rtfNIRS-NF and EEG-NF in BED, and no posttreatment differences v. WL, but possible continued improvement of binge eating. Confirmatory and mechanistic evidence is warranted in a double-blind randomized design with long-term follow-up, considering dose–response relationships and modes of delivery.

Cross-sectional studies report high levels of depressive symptoms during the COVID-19 pandemic, especially in youth and females. However, longitudinal research comparing depressive symptoms before and during the pandemic is lacking. Little is known about how the pandemic affected individuals with familial history of mental illness. The present study examines the impact of the pandemic on youth depressive symptoms, including offspring of parents with major mood and psychotic disorders.

Between March 2018 and February 2020, we measured depressive symptoms in 412 youth aged 5–25 years. We measured depressive symptoms again in 371 (90%) of these youth between April 2020 and May 2022. Two thirds (249) participants had a biological parent with a major mood or psychotic disorder. We tested the effect of the pandemic by comparing depression symptoms before and after March 2020. We examined age, sex, and family history as potential moderators.

We found an overall small increase in youth depressive symptoms (b = 0.07, 95% CI −0.01 to 0.15, p = 0.062). This was driven by an increase in female youth without familial history of mental illness (b = 0.35, 95% CI 0.14 to 0.56, p = 0.001). There was no change in depressive symptoms among offspring of parents with mental illness or males.

Our results provide reassurance about the wellbeing of children of parents with mental illness during a period of restricted access to resources outside the family. Rather than increasing symptoms in established risk groups, the pandemic led to a redistribution of depression burden towards segments of the youth population that were previously considered to be low-risk.

Research suggests that most mental health conditions have their onset in the critically social period of adolescence. Yet, we lack understanding of the potential social processes underlying early psychopathological development. We propose a conceptual model where daily-life social interactions and social skills form an intermediate link between known risk and protective factors (adverse childhood experiences, bullying, social support, maladaptive parenting) and psychopathology in adolescents – that is explored using cross-sectional data.

N = 1913 Flemish adolescent participants (Mean age = 13.8; 63% girls) were assessed as part of the SIGMA study, a large-scale, accelerated longitudinal study of adolescent mental health and development. Self-report questionnaires (on risk/protective factors, social skills, and psychopathology) were completed during class time; daily-life social interactions were measured during a subsequent six-day experience-sampling period.

Registered uncorrected multilevel linear regression results revealed significant associations between all risk/protective factors and psychopathology, between all risk/protective factors and social processes, and between all social processes and psychopathology. Social processes (social skills, quantity/quality of daily social interactions) were uniquely predicted by each risk/protective factor and were uniquely associated with both general and specific types of psychopathology. For older participants, some relationships between social processes and psychopathology were stronger.

Unique associations between risk/protective factors and psychopathology signify the distinct relevance of these factors for youth mental health, whereas the broad associations with social processes support these processes as broad correlates. Results align with the idea of a social pathway toward early psychopathology, although follow-up longitudinal research is required to verify any mediation effect.

People with borderline intellectual functioning (BIF) encounter greater social adversities than the general population and have an increased prevalence of mental illness. However, little is known about the socio-demographic characteristics and mental health of parents with BIF.

A secondary data analysis of the Adult Psychiatric Morbidity Survey 2014 was conducted. Logistic regression models were fitted to compare differences in socio-demographic, mental health and service-use characteristics between parents and non-parents with and without BIF, and to investigate if the relationship between parent status and mental health outcomes was modified by BIF status, sex, and employment.

Data from 6872 participants was analyzed; 69.1% were parents. BIF parents had higher odds of common mental disorder, severe mental illness, post-traumatic stress disorder, self-harm/suicide and were more likely to see their General Practitioner (GP) and to receive mental health treatment than non-BIF parents. BIF parents did not have a higher prevalence of mental health problems than BIF non-parents. Being a parent, after adjusting for BIF status and other confounders, was associated with increased odds of having a common mental disorder, visits to see a GP and treatment for mental health. Female parents had higher odds of treatment for mental health problems.

Being a parent is associated with elevated rates of common mental disorders. There is a higher burden of mental health problems and service use in people with BIF. A greater provision of specialist support services including ascertainment is indicated for this group.

Eating disorders (ED) are serious psychiatric disorders, taking a life every 52 minutes, with high relapse. There are currently no support or effective intervention therapeutics for individuals with an ED in their everyday life. The aim of this study is to build idiographic machine learning (ML) models to evaluate the performance of physiological recordings to detect individual ED behaviors in naturalistic settings.

From an ongoing study (Final N = 120), we piloted the ability for ML to detect an individual's ED behavioral episodes (e.g. purging) from physiological data in six individuals diagnosed with an ED, all of whom endorsed purging. Participants wore an ambulatory monitor for 30 days and tapped a button to denote ED behavioral episodes. We built idiographic (N = 1) logistic regression classifiers (LRC) ML trained models to identify onset of episodes (~600 windows) v. baseline (~571 windows) physiology (Heart Rate, Electrodermal Activity, and Temperature).

Using physiological data, ML LRC accurately classified on average 91% of cases, with 92% specificity and 90% sensitivity.

This evidence suggests the ability to build idiographic ML models that detect ED behaviors from physiological indices within everyday life with a high level of accuracy. The novel use of ML with wearable sensors to detect physiological patterns of ED behavior pre-onset can lead to just-in-time clinical interventions to disrupt problematic behaviors and promote ED recovery.

Psychedelic drugs are a focus of interest in the treatment of depression and other disorders but there are longstanding concerns about possible adverse psychiatric consequences. Because the relevant literature is largely informal, the seriousness of these risks is difficult to evaluate.

Searches were made for case reports of schizophrenia-spectrum, affective or other psychiatric disorders after use of psychedelic drugs. Case reports of flashbacks were also searched for. Individuals with recent use of other drugs (apart from cannabis and alcohol) and/or a previous history of major psychiatric disorder were excluded. Symptoms were tabulated using the Syndrome Check List of the Present State Examination (PSE-9).

We found 17 case reports of schizophrenia spectrum disorder, 17 of affective disorder (depression, mania, or both), 3 cases of anxiety, 1 of depersonalization, and 1 of unclassifiable illness. The states could develop after a single use of the drug (5/17 schizophrenia; 6/17 affective disorder), and duration was highly variable. Recovery was the rule in cases of affective disorder but not in schizophrenia spectrum disorder. Twelve of 29 cases of flashbacks showed psychiatric symptomatology definitely outlasting the attacks, mainly anxiety (5 cases) and depression (8 cases). Flashback symptoms resolved within twelve months in approximately half of the cases but in a few persisted for years.

Reliable descriptions of schizophrenia spectrum disorder and major affective disorder after psychedelic drug use disorder exist but are relatively uncommon. Flashbacks are sometimes but not always associated with psychiatric symptomatology, mainly anxiety or depression.