Postpartum depression (PPD) affects approximately 17% of women globally and is predicted by prior mental illness and socioeconomic disadvantage. Emerging evidence suggests that negative bias in the cognitive processing of infant cues and heightened reactivity to infant distress during pregnancy contribute to PPD risk and may thus represent a treatment target. This randomized controlled trial examined whether prenatal affective cognitive training (PACT) reduces PPD risk by modulating negative biases in pregnant women at heightened risk. Of 280 women assessed, 147 at heightened risk were randomized to PACT or care as usual (CAU). PACT comprised five individualized 90-minute sessions over five weeks, combining computerized and virtual-reality tasks targeting negative cognitive bias, attentional bias, emotion regulation, and working memory in response to infant stimuli. Affective cognition was assessed at baseline and post-intervention; mood was rated at baseline, post-intervention, and weekly for six weeks after birth. Diagnostic interviews (Present State Examination) were conducted six months postpartum. The primary outcome was PPD incidence within six months postpartum; secondary outcomes included depressive symptoms over the first six weeks and changes in affective cognition. Follow-up data were available for 121 (83%) participants (PACT n = 54; CAU n = 67). PACT did not significantly reduce PPD overall (p = 0.18) or depressive symptoms (p = 0.31) but increased positive interpretation of infant emotions (p = 0.003). Exploratory analyses indicated lower PPD incidence among multiparous participants receiving PACT than CAU (5% vs. 35%; OR=0.10, 95% CI [0.01-0.92], p = 0.04) and additional parity-specific changes in affective cognition. These exploratory findings require replication in larger trials. Trial registration: The trial was registered at ClinicalTrials.gov with Trial number NCT06046456 on 21 September 2023 https://clinicaltrials.gov/study/NCT06046456.
In youth-i.e., adolescents and young adults-mental disorders represent a major burden on well-being and quality of life. This scientometric study aimed to map research trends concerning any mental disorder in youth. We searched the Web of Science Core Collection for studies published between 01/01/1985 and 28/08/2025, using keywords on mental disorders and youth. Co-citation reference networks were extracted. R-bibliometrix/Cite-Space were used. 65,684 studies were included, forming a well-structured co-reference network. A trend in the 1980s concerned the prevalence/outcomes of specific mental disorders such as depressive and anxiety disorders. In the 1990s/2000s, research trends concerned assessment tools, pharmacotherapy (e.g., antidepressants, antipsychotics), bipolar disorders and schizophrenia. In the 2010s, early intervention for psychosis was a dominant focus, influencing prevention in mental health. The 2010s also focused on ADHD, eating disorders, gaming disorder, autism, depression and suicide, fMRI, and transgender health, beyond gender dysphoria. In the 2020s, a recent trend has increasingly focused on global evidence on the age at onset and burden across mental disorders, access to care, network analysis, and COVID-19. Over time, research has broadened the focus from adolescence to youth across mental health problems. Both disorder-specific and transdiagnostic research trends were observed. Novel trends in 2023-2025 included substance use, lifestyle psychiatry, evidence synthesis and non-suicidal self-injury. Most studies were conducted in Western countries. Underrepresented research areas included the involvement of youth with lived/living experience in identifying meaningful outcomes, open science, implementation research, economic evaluations, machine-learning approaches, and social determinants of mental health.
Objective Cognitive behavioral therapy (CBT) is an effective treatment for obsessive-compulsive disorder (OCD), yet it remains difficult to predict who will respond. This study investigates associations between neural activity during inhibitory control tasks and CBT outcomes, and its value as a predictive marker of CBT outcome. Methods Using fMRI data from individuals performing an inhibitory control task across five ENIGMA-OCD samples (n=130, age range=8-57, 54% female), mass-univariate associations were analyzed between response inhibition and error processing activity and three CBT outcomes: response, remission, and change in symptom severity. Random forest classifier and support vector machine models using leave-one-sample-out and 5-fold cross-validation were used to predict CBT response and remission from fMRI activity and clinical data. Results Remission after CBT was associated with weaker activity in default mode regions during response inhibition and in the right supramarginal gyrus during error processing. Greater symptom reduction was linked to weaker pre-treatment activity across frontoparietal, dorsal attention, visual, and subcortical regions during response inhibition, and stronger default mode activity during error processing. These group-level effects were partially confirmed with machine learning, which predicted CBT response using inhibitory and error-related activity with modest AUC values of 0.66-0.67. Prediction of CBT remission or using other data types did not exceed chance-level. Conclusion Wide-spread weaker activity during response inhibition and stronger activity in default mode regions during error processing pre-treatment appear beneficial for CBT. Despite modestly predictive AUC-values, these findings cannot yet be translated into clinically relevant individual markers of CBT outcome.
Abstract Background and Hypothesis Predicting psychosis conversion in clinical high-risk for psychosis (CHR-P) individuals is limited by unaccounted cognitive and demographic confounding, which obscures the true prognostic value of clinical and functional markers. Study Design A total of 920 CHR-P individuals from the Shanghai At Risk for Psychosis-extended program were followed for 3 years. Propensity score matching (PSM) balanced 5 demographic variables (age, gender, education, paternal/maternal education) and 6 core cognitive domains. Group comparisons (CHR converters [CHR-C] vs. non-converters [CHR-NC]) and multivariate logistic regression were conducted in the entire and PSM cohorts. Receiver Operating Characteristic curves quantified predictive accuracy. Study Results After PSM, demographic and cognitive disparities were eliminated, while clinical distinctions (symptoms, Global Assessment of Functioning [GAF] drop rate) became prominent (all P < .05). The PSM-adjusted model identified bizarre thinking, lower unusual thought content, emotional experience disturbances, and GAF-related indicators as independent predictors, with higher discriminative ability (Area Under the ROC Curve [AUC] = 0.808) than the unadjusted model (AUC = 0.685). The GAF drop rate alone achieved 73% predictive accuracy post-PSM, versus weak performance in the entire cohort (AUC = 0.589). Conclusions Systematic control of cognitive and demographic confounding via PSM clarifies the true prognostic value of clinical and functional markers for CHR-P conversion. The findings provide actionable tools for risk stratification and highlight the critical need to account for cognitive function in early psychosis prediction research.
Abstract Background and Hypothesis The longitudinal course of cognitive functioning in schizophrenia is debated. Previous longitudinal studies have reported selective attrition. We aimed to analyze cognitive trajectories over 13 years among patients with schizophrenia spectrum disorders (SSDs) in comparison with controls, while accounting for dropout bias, and explored baseline predictors of cognitive outcome. Study Design Cognition was assessed at study start, 5-year, and 13-year follow-up in 108 participants with schizophrenia spectrum disorder and 85 controls of similar age. Linear Mixed Effects models were used to model trajectories over time, with inverse probability weights to account for non-random dropout in patients. Predictors of cognitive function at 13 years were explored using linear regression. Study Results Retention was 51% for patients and 65% for controls. At study start, patients who dropped out performed worse on most cognitive tasks and were significantly older (4.5 years) than completers. In primary inverse probability of attrition weight–weighted analyses, patients scored lower than controls across nearly all cognitive domains (cognitive composite: β = −0.91 SD [95% CI −1.42, −0.39]) but their trajectories did not significantly differ from those of controls. Higher age predicted worse cognitive function. Illness duration was an independent negative predictor in patients. Study start cognition predicted 13-year follow-up cognition in patients. Conclusions Although cognitive performance of patients was lower than that of controls at study start, their change in performance over 13 years did not differ from the controls. Patients who dropped out were more cognitively impaired, highlighting risk of attrition bias. The robustness of findings across weighted and unweighted analyses strengthens confidence in the observed trajectories.
