Depression is a risk factor for dementia and weight change can appear as a symptom of depression. However, the association between weight change after the diagnosis of depression and the risk of dementia is poorly established. This study aimed to investigate the association between weight change before and after a diagnosis of depression with the subsequent risk of dementia.

The National Health Insurance Sharing Service database was used. 1 308 730 patients aged ⩾40 years diagnosed with depression were identified to be eligible. Weight changes after their depression diagnosis were categorized and subsequent incidence of dementia was followed up.

During an average follow-up period of 5.2 years (s.d., 2.0 years), 69 373 subjects were newly diagnosed with all-cause dementia (56 351 were Alzheimer's disease and 6877 were vascular dementia). Regarding all outcomes, compared to those with a minimal weight change (−5 to 5%), all groups with weight gain or loss showed increased risks of dementia after adjusting potential risk factors for dementia, in all analysis models with a dose–response relationship, showing a U-shaped association.

Weight change as a symptom of depression could be a predictor for the future development of dementia.

The incidence of adolescent depressive disorder is globally skyrocketing in recent decades, albeit the causes and the decision deficits depression incurs has yet to be well-examined. With an instrumental learning task, the aim of the current study is to investigate the extent to which learning behavior deviates from that observed in healthy adolescent controls and track the underlying mechanistic channel for such a deviation.

We recruited a group of adolescents with major depression and age-matched healthy control subjects to carry out the learning task with either gain or loss outcome and applied a reinforcement learning model that dissociates valence (positive v. negative) of reward prediction error and selection (chosen v. unchosen).

The results demonstrated that adolescent depressive patients performed significantly less well than the control group. Learning rates suggested that the optimistic bias that overall characterizes healthy adolescent subjects was absent for the depressive adolescent patients. Moreover, depressed adolescents exhibited an increased pessimistic bias for the counterfactual outcome. Lastly, individual difference analysis suggested that these observed biases, which significantly deviated from that observed in normal controls, were linked with the severity of depressive symoptoms as measured by HAMD scores.

By leveraging an incentivized instrumental learning task with computational modeling within a reinforcement learning framework, the current study reveals a mechanistic decision-making deficit in adolescent depressive disorder. These findings, which have implications for the identification of behavioral markers in depression, could support the clinical evaluation, including both diagnosis and prognosis of this disorder.

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.

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.

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.

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.

Identifying adolescents at risk of internalizing problems is a key priority. However, studies have tended to consider such problems in simple ways using diagnoses, or item summaries. Network theory and methods instead allow for more complex interaction between symptoms. Two key hypotheses predict differences in global network properties for those at risk: altered structure and increased connectivity.

The current study evaluated these hypotheses for nine risk factors (e.g. income deprivation and low parent/carer support) individually and cumulatively in a large sample of 12–15 year-olds (N = 34 564). Recursive partitioning and bootstrapped networks were used to evaluate structural and connectivity differences.

The pattern of network interactions was shown to be significantly different via recursive partitioning for all comparisons across risk-present/absent groups and levels of cumulative risk, except for income deprivation. However, the magnitude of differences appeared small. Most individual risk factors also showed relatively small effects for connectivity. Exceptions were noted for gender and sexual minority risk groups, as well as low parent/carer support, where larger effects were evident. A strong linear trend was observed between increasing cumulative risk exposure and connectivity.

A robust approach to considering the effect of risk exposure on global network properties was demonstrated. Results are consistent with the ideas that pathological states are associated with higher connectivity, and that the number of risks, regardless of their nature, is important. Gender/sexual minority status and low parent/carer support had the biggest individual impacts on connectivity, suggesting these are particularly important for identification and prevention.

There are many barriers to engaging in current psychological treatments, including time, cost, and availability. Ultra-brief treatments overcome some of these barriers by delivering therapeutic information and skills using significantly less time than standard-length treatments. We developed a therapist-guided online ultra-brief treatment for depression and anxiety and compared it to an existing 8-week, 5-lesson therapist-guided standard-length treatment and a waitlist control.

In a randomized controlled trial, adults with self-reported depression or anxiety were randomized (1:1:1) to the ultra-brief treatment, standard-length treatment, or waitlist control. The primary outcomes were depression symptoms and anxiety symptoms assessed at baseline, 5-weeks later, 9-weeks later (primary timepoint), and 3-months later. The trial was prospectively registered.

Between 7 February 2022, and 16 August 2022, 242 participants were enrolled in the ultra-brief treatment (n = 85), standard-length treatment (n = 80), and waitlist control (n = 77). Participants were mostly women with an average age of 48.56 years. At 9-weeks post-baseline, participants in the ultra-brief treatment group reported significantly lower depression (between groups d = 0.41) and anxiety (d = 0.53) than the waitlist control. The ultra-brief treatment was non-inferior for anxiety at both 9-weeks and 3-months follow-up. Non-inferiority for depression was observed at 9-weeks.

The online ultra-brief treatment resulted in significant reductions in depression and anxiety that were non-inferior to a longer treatment course after 9-weeks. Remotely delivered ultra-brief treatments have the potential to provide accessible and effective care for those who cannot, or would prefer not to, access longer psychological interventions.

A large and accumulating body of evidence shows that loneliness is detrimental for various health and well-being outcomes. However, less is known about potentially modifiable factors that lead to decreased loneliness.

We used data from the Health and Retirement Study to prospectively evaluate a wide array of candidate predictors of subsequent loneliness. Importantly, we examined if changes in 69 physical-, behavioral-, and psychosocial-health factors (from t0;2006/2008 to t1;2010/2012) were associated with subsequent loneliness 4 years later (t2;2014/2016).

Adjusting for a large range of covariates, changes in certain health behaviors (e.g. increased physical activity), physical health factors (e.g. fewer functioning limitations), psychological factors (e.g. increased purpose in life, decreased depression), and social factors (e.g. greater number of close friends) were associated with less subsequent loneliness.

Our findings suggest that subjective ratings of physical and psychological health and perceived social environment (e.g. chronic pain, self-rated health, purpose in life, anxiety, neighborhood cohesion) are more strongly associated with subsequent loneliness. Yet, objective ratings (e.g. specific chronic health conditions, living status) show less evidence of associations with subsequent loneliness. The current study identified potentially modifiable predictors of subsequent loneliness that may be important targets for interventions aimed at reducing loneliness.

Individuals with bipolar disorder are commonly correctly diagnosed a decade after symptom onset. Machine learning techniques may aid in early recognition and reduce the disease burden. As both individuals at risk and those with a manifest disease display structural brain markers, structural magnetic resonance imaging may provide relevant classification features.

Following a pre-registered protocol, we trained linear support vector machine (SVM) to classify individuals according to their estimated risk for bipolar disorder using regional cortical thickness of help-seeking individuals from seven study sites (N = 276). We estimated the risk using three state-of-the-art assessment instruments (BPSS-P, BARS, EPIbipolar).

For BPSS-P, SVM achieved a fair performance of Cohen's κ of 0.235 (95% CI 0.11–0.361) and a balanced accuracy of 63.1% (95% CI 55.9–70.3) in the 10-fold cross-validation. In the leave-one-site-out cross-validation, the model performed with a Cohen's κ of 0.128 (95% CI −0.069 to 0.325) and a balanced accuracy of 56.2% (95% CI 44.6–67.8). BARS and EPIbipolar could not be predicted. In post hoc analyses, regional surface area, subcortical volumes as well as hyperparameter optimization did not improve the performance.

Individuals at risk for bipolar disorder, as assessed by BPSS-P, display brain structural alterations that can be detected using machine learning. The achieved performance is comparable to previous studies which attempted to classify patients with manifest disease and healthy controls. Unlike previous studies of bipolar risk, our multicenter design permitted a leave-one-site-out cross-validation. Whole-brain cortical thickness seems to be superior to other structural brain features.

We investigate if covariation between parental and child attention-deficit hyperactivity disorder (ADHD) behaviors can be explained by environmental and/or genetic transmission.

We employed a large children-of-twins-and-siblings sample (N = 22 276 parents and 11 566 8-year-old children) of the Norwegian Mother, Father and Child Cohort Study. This enabled us to disentangle intergenerational influences via parental genes and parental behaviors (i.e. genetic and environmental transmission, respectively). Fathers reported on their own symptoms and mothers on their own and their child's symptoms.

Child ADHD behaviors correlated with their mother's (0.24) and father's (0.10) ADHD behaviors. These correlations were largely due to additive genetic transmission. Variation in children's ADHD behaviors was explained by genetic factors active in both generations (11%) and genetic factors specific to the children (46%), giving a total heritability of 57%. There were small effects of parental ADHD behaviors (2% environmental transmission) and gene–environment correlation (3%). The remaining variability in ADHD behaviors was due to individual-specific environmental factors.

The intergenerational resemblance of ADHD behaviors is primarily due to genetic transmission, with little evidence for parental ADHD behaviors causing children's ADHD behaviors. This contradicts theories proposing environmental explanations of intergenerational transmission of ADHD, such as parenting theories or psychological life-history theory.

Laboratory paradigms are widely used to study fear learning in posttraumatic stress disorder (PTSD). Recent basic science models demonstrate that, during fear learning, patterns of activity in large neuronal ensembles for the conditioned stimuli (CS) begin to reinstate neural activity patterns for the unconditioned stimuli (US), suggesting a direct way of quantifying fear memory strength for the CS. Here, we translate this concept to human neuroimaging and test the impact of post-learning dopaminergic neurotransmission on fear memory strength during fear acquisition, extinction, and recall among women with PTSD in a re-analysis of previously reported data.

Participants (N = 79) completed a context-dependent fear acquisition and extinction task on day 1 and extinction recall tests 24 h later. We decoded activity patterns in large-scale functional networks for the US, then applied this decoder to activity patterns toward the CS on day 1 and day 2.

US decoder output for the CS+ increased during acquisition and decreased during extinction in networks traditionally implicated in human fear learning. The strength of US neural reactivation also predicted individuals skin conductance responses. Participants randomized to receive L-DOPA (n = 43) following extinction on day 1 demonstrated less US neural reactivation on day 2 relative to the placebo group (n = 28).

These results support neural reactivation as a measure of memory strength between competing memories of threat and safety and further demonstrate the role of dopaminergic neurotransmission in the consolidation of fear extinction memories.

Suicide attempts are a moderately heritable trait, and genetic correlations with psychiatric and related intermediate phenotypes have been reported. However, as several mental disorders as well as major depressive disorder (MDD) are strongly associated with suicide attempts, these genetic correlations could be mediated by psychiatric disorders. Here, we investigated genetic correlations of suicide attempts with psychiatric and related intermediate phenotypes, with and without adjusting for mental disorders.

To investigate the genetic correlations, we utilized large-scale genome-wide association study summary statistics for suicide attempts (with and without adjusting for mental disorders), nine psychiatric disorders, and 15 intermediate phenotypes.

Without adjusting for mental disorders, suicide attempts had significant positive genetic correlations with risks of attention-deficit/hyperactivity disorder, schizophrenia, bipolar disorder, MDD, anxiety disorders and posttraumatic stress disorder; higher risk tolerance; earlier age at first sexual intercourse, at first birth and at menopause; higher parity; lower childhood IQ, educational attainment and cognitive ability; and lower smoking cessation. After adjusting for mental disorders, suicide attempts had significant positive genetic correlations with the risk of MDD; earlier age at first sexual intercourse, at first birth and at menopause; and lower educational attainment. After adjusting for mental disorders, most of the genetic correlations with psychiatric disorders were decreased, while several genetic correlations with intermediate phenotypes were increased.

These findings highlight the importance of considering mental disorders in the analysis of genetic correlations related to suicide attempts and suggest that susceptibility to MDD, reproductive behaviors, and lower educational levels share a genetic basis with suicide attempts after adjusting for mental disorders.