Anxiety disorders in children and young people are common and impairing. Cognitive behavioural therapy (CBT) is the recommended first-line treatment and the past two decades have seen numerous digital CBT interventions emerge. Digital CBT has the potential to improve access, reduce waiting times and scale services. However, guidance on how digital CBT should be integrated into routine care pathways remains limited. This article summarises current clinical guidelines for children and young people with anxiety disorders from different parts of the world, with a specific focus on recommendations for digital CBT and its integration into broader care models. Finally, it discusses the role of digital CBT in stepped-care and stratified-care approaches, including ongoing research integrating and sequencing digital CBT differently.

Coronavirus disease 2019 (COVID-19) is a worldwide pandemic that causes anxiety. Research on possible risk factors for anxiety during the COVID-19 pandemic is scarce. In this study, we examined 54,890 participants' web-based self-reported data from the Korea Youth Risk Behavior Web-based Survey in 2020. The Generalized Anxiety Disorder-7 questionnaire was used to measure anxiety levels. The prevalence of anxiety (Generalized Anxiety Disorder-7 score ≥ 10) was 11.2%. Anxiety in adolescents during the COVID-19 pandemic was significantly associated with female gender (odds ratio [OR] = 1.29, 95% confidence interval [CI]: 1.02-1.09), low family affluence scale (OR = 1.17, 95% CI: 1.03-1.34), economic aid (OR = 1.11, 95% CI: 1.01-1.23), financial difficulties caused by COVID-19 (OR = 1.19, 95% CI: 1.11-1.26), substance use (OR = 1.38, 95% CI: 1.05-1.37), asthma (OR = 1.21, 95% CI: 1.08-1.37), allergic rhinitis (OR = 1.08, 95% CI: 1.01-1.15), atopic dermatitis (OR = 1.09, 95% CI: 1.01-1.17), severe stress (OR = 10.01, 95% CI: 8.40-12.03), perceived unhealthy status (OR = 1.67, 95% CI: 1.52-1.82), perceived unhappiness (OR = 2.30, 95% CI: 2.12-3.11), internet addiction (OR = 1.97, 95% CI: 1.85-2.10), school violence experience (OR = 1.59, 95% CI: 1.29-1.97), depression (OR = 2.78, 95% CI: 2.60-2.97), suicidal ideation (OR = 1.94, 95% CI: 1.79-2.11), suicidal plan (OR = 1.48, 95% CI: 1.30-1.69), and suicidal attempt (OR = 1.23, 95% CI: 1.04-1.45). These results highlight the risk factors for anxiety and offer a straightforward method for identifying teenagers who are more likely to experience anxiety during the COVID-19 pandemic.

Loneliness is associated with altered social cognition and decision-making, yet the processes through which it shapes behavior in uncertain social contexts remain poorly understood. In particular, little is known about how loneliness relates to metacognitive evaluations of decision reliability. The present study examined whether loneliness is associated with differences in decision confidence and its calibration to uncertainty during social decision-making. Data were collected from two independent general population samples (N = 6,517) using a vignette-based task in which participants decided to engage in or avoid social interactions and rated their confidence. Across both samples, higher loneliness was associated with lower overall decision confidence. Critically, loneliness moderated the relationship between decision difficulty and confidence such that individuals reporting higher loneliness showed reduced sensitivity of confidence to increasing difficulty, indicating diminished calibration to uncertainty across diverse social decision contexts. In contrast, loneliness was not associated with differential confidence between engagement and avoidance decisions. These findings were robust after adjusting for demographic variables and symptoms of depression and anxiety. The results suggest that loneliness is linked to altered metacognitive evaluation rather than biases in decision outcomes, highlighting impaired confidence calibration as a potential mechanism underlying persistent loneliness and maladaptive social functioning.

Sex differences in the prevalence, age of onset of symptoms, and symptom presentation have been widely described in autism spectrum disorders, yet the mechanisms underlying these sex differences remain poorly characterized. Exposing rats prenatally to valproic acid (VPA) is widely used as a model to study autism-like phenotypes. Using this model, sex-specific neuroanatomical and functional characteristics were examined in adolescent rats. We found sex differences in cortical pyramidal neuron complexity, spine density, and morphology in VPA-prenatally exposed adolescent rats. The expression of autism has been linked to various processes such as inflammation, cell signalling, endothelial function, and myelination. Sex-specific analysis of cortical gene expression showed that male VPA-prenatally exposed rats had elevated expression of genes related to neuropeptide signaling (Pdyn, Adora2a, Drd2). Female VPA-prenatally exposed rats had lower expression of genes related to vascular permeability (Angptl4, Cldn5) and inflammation (Cyr61, Usp18). Dimethyl fumarate, an activator of nuclear factor (erythroid-derived 2)-like 2 (Nrf2) and clinically approved to treat multiple sclerosis, alleviated VPA-induced behavioural alterations in object recognition, anxiety-like behaviour, and social behaviour in female VPA-exposed rats. In addition, dimethyl fumarate corrected the associated VPA-induced changes in neuronal oscillatory power and cross-frequency coupling and suppressed microglial activity, with the latter occurring selectively in female animals. Together, these findings indicate sex as an essential consideration in the development of novel pharmacotherapies for autism. Dimethyl fumarate had therapeutic properties in VPA-prenatally exposed female rats, however further studies examining its therapeutic efficacy in other models of autism are required.

Seasonal differences in young people's mental health may be concentrated in specific symptoms. This population-based repeated cross-sectional study examined winter-non-winter differences in depressive affect, worry-related sleep disturbance and low energy among people aged 16-24 years in the United Kingdom. Understanding Society Calendar Year Datasets were analysed for 2022-2023, with extensions covering 2020-2023. Winter was defined as an interview data in December, January or February. The primary outcome was the mean of three equally weighted standardised symptom components. Standardised Pearson alpha was 0.717 and ordinal alpha was 0.737, supporting use of the index as a shared descriptive measure alongside component-level analysis. Targeted maximum likelihood estimation with Super Learner estimated covariate-standardised mean contrasts. Survey weights informed point estimation and respondent clustering informed variance estimation. Full survey-design regression and inverse-probability-of-complete-case weighting provided complementary checks. The main analysis included 5,949 person-interview observations from 4,167 young people. The winter-non-winter difference was 0.081 index points, with a 95% confidence interval from 0.029 to 0.132. On the original scales, low energy differed by 0.119 points on a 0-4 scale and depressive affect by 0.080 points on a 0-4 scale. Worry-related sleep disturbance differed by 0.044 points on a 0-3 scale, with greater uncertainty. Estimates were similar across analytical approaches and calendar periods, while winter remained at the upper end of the adjusted four-season profile. This period is for closer youth mental health monitoring and for making existing low-intensity support more visible and accessible.

Behavioral tests in mice and other animals are widely used to study anxiety and to evaluate the efficacy of anxiolytic compounds. However, the validity of behavioral tests used to assess anxiety-like behavior has been questioned because their results are often inconsistent and not readily replicated across studies. We developed a simple falling-trap test to examine whether the number of falls could serve as an index of anxiety-like behavior. The test is designed to produce falls from an elevated path, based on the hypothesis that less-anxious mice would be more prone to fall. Male C57BL/6N mice were given vehicle or chlordiazepoxide (CDP), and their behavior was evaluated using both the novel test and the elevated plus maze (EPM). The outcomes were the number of falls in the falling-trap test and open-arm time in the EPM. In the falling-trap test, CDP-treated mice did not show a statistically significant increase in the number of falls compared with control mice. In contrast, CDP significantly increased open-arm time in the EPM. Thus, under the present experimental conditions, these findings did not support the hypothesis that the number of falls in the falling-trap test could serve as a measure of anxiety-like behavior. Further studies are needed to determine the conditions under which this assay could be useful and to establish its reliability and validity.

Bio-psycho-social models of psychopathology have been long proposed. Despite this, few details have been identified in part due to the poor match between clinical phenomenology and diagnostic categories (e.g., overlapping symptoms and comorbidity). In the present paper, the author reviews the evidence for three factors thought to affect vulnerability across diagnoses: mesocorticolimbic function, early life adversity, and early propensities to high emotional and behavioral reactivity. All three factors now have compelling support but their effects on clinical outcomes are small to moderate in isolation. In comparison, our preliminary studies suggest that combining these factors yields a powerful model that predicts liability to all common psychiatric disorders. Narrower mental health problems are identified when the model incorporates high brain reactivity to disorder-specific stimuli. Since the combination of these factors accounts for more variance than the sum of their parts, this novel, hierarchical model might explain how the structure of psychopathology arises.

Complex disorders such as depression and alcohol use involve numerous genetic variants, and implicated loci continue to grow with sample size. This proliferation hampers interpretability, as the mechanisms by which so many variants jointly contribute to pathophysiology remain unclear. In contrast, classical Mendelian diseases arise from a single causal locus and are easier to interpret. We thus introduce Mendelianization-an algorithm distinct from Mendelian randomization-that learns weighted combinations of outcomes so that each aggregated phenotype concentrates association at one locus. We prove that this locus is causal under four structural assumptions natural to genetic data. The method handles partial sample overlap, provides calibrated hypothesis tests, maps coefficients to interpretable scales, and quantifies the degree of Mendelianism using summary z -statistics alone. In experiments, Mendelianization enhances statistical power to detect Mendelian symptom profiles even in heterogeneous disorders like major depression, generalized anxiety, and alcohol use disorder. An R implementation is available at github.com/ericstrobl/Mendelianization.