Social strain and support as mediators in the relationship between childhood abuse and adult depression

Journal of Affective Disorders · 2026-03-19

Executive Functioning as a Predictor of Digital Cognitive Behavioral Therapy Outcomes for Body Dysmorphic Disorder: A Machine Learning Approach

Cognitive Therapy and Research · 2026-01-20

Prior research presents mixed findings regarding whether executive functioning (EF) issues, such as cognitive flexibility and inhibition deficits, predict cognitive behavioral therapy (CBT) outcomes in body dysmorphic disorder (BDD). Elucidating how EF domains contribute to CBT response can contribute to our understanding of why some individuals benefit more than others and may inform personalized intervention strategies. We harnessed precision medicine methods to determine key inhibition and cognitive flexibility domains hypothesized to predict treatment outcomes for BDD. We predicted treatment outcomes (response, reliable change, and remission) following 12 weeks of coach-guided smartphone-based CBT for BDD, using baseline EF features. Specifically, we evaluated five inhibition features derived from the Stroop Color Word Task, eight cognitive flexibility features from the Wisconsin Card Sorting Test (WCST), and baseline symptom severity. The final models with the top five baseline EF predictors of post-CBT outcomes yielded good multivariate predictive performance (area under the receiver operating characteristic curve = 0.723–0.768) for all examined outcomes. Better conceptual learning efficiency (WCST learning-to-learn), better cognitive efficiency (fewer WCST total trials), and poorer cognitive proficiency (lower WCST number of total correct trials) predicted greater benefits from the app-based treatment. Other inhibition and cognitive flexibility measures were inconsistent or nonsignificant predictors. Results suggest the potential value of EF domain predictors for BDD treatment outcomes and warrant replication in larger samples.

Dynamic Systems Forecasting Project

OSF Preprints (OSF Preprints) · 2026-03-03

Dynamic Systems Forecasting Project

Open Science Framework · 2026-01-01

A Cross-Cultural Comparison of Individual Differences in Musical Reward

Music Perception An Interdisciplinary Journal · 2026-01-01

Music is an important source of pleasure, yet sensitivity to the rewards of musical experiences varies greatly across individuals. The Barcelona Music Reward Questionnaire (BMRQ) is a frequently used scale to assess individuals’ sensitivity to musical reward. While the BMRQ has been translated into seven languages to date, its five-factor structure has not yet been compared across cultures. Here we compared musical reward sensitivity and its relationship with music training in 1,333 Chinese participants and 1,433 United States (US) participants based on self-report survey data. Our findings showed that the Chinese group reported less tendency to dance with music, but more tendency to sing than the US group. Although both groups showed four clusters of items in the musical reward sensitivity network, network analysis revealed distinct motifs of Sensory-Motor facet in reward sensitivity between the groups: the US group showed a single Sensory-Motor cluster, whereas dance separated from music-induced spontaneous movement and vocalization in the Chinese group. We also found that music training was strongly related to Social Reward in the US group, whereas in the Chinese group it was primarily associated with Music Seeking and Sensory-Motor. These results underscore the importance of culture and music training in contributing to individual differences in musical reward sensitivity.

Mental health and emotional competencies: investigating interpersonal emotion regulation, emotional resilience, and self-efficacy in emotion regulation

Asia Pacific Journal of Counselling and Psychotherapy · 2025-07-03

Machine learning in the prediction of treatment response for emotional disorders: A systematic review and meta-analysis

Clinical Psychology Review · 2025-05-22 · 11 citations

BACKGROUND: Emotional disorders such as depression and anxiety affect millions globally and pose a significant burden on public health. Personalized treatment approaches using machine learning (ML) to predict treatment response could revolutionize treatment strategies. However, there is limited evidence as to whether ML is successful in predicting treatment outcomes. This meta-analysis aims to evaluate the accuracy of ML algorithms in predicting binary treatment response (responder vs. non-responder) to evidence-based psychotherapies, pharmacotherapies, and other treatments for emotional disorders, and to examine moderators of prediction accuracy. METHODS: Following PRISMA guidelines, a comprehensive literature search was conducted across PubMed and PsycINFO from January 1st, 2010 to March 27th, 2025. Studies were included if they used ML methods to predict treatment response in patients with emotional disorders. Data were extracted on sample size, type of treatment, predictors used, ML methods, and prediction accuracy. Meta-analytic techniques were used to synthesize findings and identify moderators of prediction accuracy. RESULTS: Out of 3816 non-duplicate records, 155 studies met inclusion criteria. The overall mean prediction accuracy was 0.76 (95 % CI: 0.74-0.78), and the mean area under the curve was 0.80 indicating good discrimination. The average sensitivity and specificity were 0.73 and 0.75, respectively. Moderator analyses indicated that studies using more robust cross-validation procedures exhibited higher prediction accuracy. Neuroimaging data as predictors were associated with higher accuracy compared to clinical and demographic data. Moreover, results indicated that studies with larger responder rates, as well as those that did not correct for imbalances in outcome rates, were associated with higher prediction accuracy. CONCLUSIONS: ML methods show promise in predicting treatment response for emotional disorders, with varying degrees of accuracy depending on the type of predictors used and the rigor of methodological procedures implemented. Future research should focus on improving methodological integrity and exploring the integration of multimodal data to enhance prediction accuracy.

BrainEffeX: A Web App for Exploring fMRI Effect Sizes

2025-05-01

Effect size estimation is crucial for power analyses and experimental design, but posesunique challenges in fMRI research due to the complexity of the data and analysistechniques. Here, we introduce an interactive web application for exploring fMRI effect maps(neuroprismlab.shinyapps.io/BrainEffeX). We utilized large fMRI datasets to obtain precisevoxel-wise and multivariate effect size estimates from “typical” fMRI study designs:brain-behavior correlation, task vs. rest, and between-group analyses of functionalconnectivity and task-based activation maps. The app is intentionally designed as a growingresource, and we welcome contributions of large (n > 500) datasets.

Digital positive affect intervention (PAI) versus self-monitoring placebo in the treatment of anxiety and depression: a two-arm randomized controlled trial (RCT)

BMC Psychiatry · 2025-10-10 · 1 citations

Anxiety and depressive disorders are highly prevalent, common mental disorders globally, but access to efficacious, high-reach treatment remains scarce. Although digital cognitive-behavioral therapies (CBTs) demonstrate efficacy and scalability, they remain limited in enhancing positive affect (PA) and reward processing, which are core deficits underlying anhedonia, a key transdiagnostic symptom. This randomized controlled trial (RCT) evaluates a locally tailored, guidance-on-demand, low-intensity Digital Positive Affect Intervention (PAI) developed to improve positive valence systems while alleviating symptoms in adults and university students in Singapore. This single-blind, two-arm, pragmatic RCT will recruit 1,200 community-dwelling adults with mild-to-moderate symptoms of anxiety or depression. Participants will be randomly assigned in a 1:1 allocation to either the Digital PAI or the Self-Monitoring Placebo as the placebo control across six weeks. Digital PAI comprises six weekly 30-minute self-guided online sessions coupled with thrice-daily ecological momentary interventions (EMIs) focusing on PA regulation. The Self-Monitoring Placebo offers non-therapeutic mood-tracking instructions three times daily. The same ecological momentary assessments (EMAs) are administered in both groups after each EMI prompt. Clinical outcome assessments are administered at baseline, mid-treatment (Week 3), post-treatment (Week 6), and 3-month, 6-month, and 12-month follow-ups. Primary outcomes are changes in anxiety and depression severity (Generalized Anxiety Disorder-7 [GAD-7] and Patient Health Questionnaire-9 [PHQ-9]). Secondary outcomes comprise anhedonia, emotion regulation, reward processing, and sleep quality. Randomly selected subsamples will provide wearable data. Analyses will harness multilevel modeling, generalized estimating equations, causal mediation, structural equation modeling, and precision medicine methods to evaluate treatment efficacy, change mechanisms, and moderators. This RCT examines the proximal (immediate) and distal (long-term) efficacy of a locally tailored PAI, aligned with cultural values and context, that combine PA improvement strategies with scalable digital delivery to integrate skills into daily routines and settings. It fills essential knowledge gaps in digital mental health research by addressing the positive valence system and incorporating prospective EMA and wearable assessments in real-time. If successful, the Digital PAI may inform stepped-care and stratified care models as well as AI-triaging approaches. These efforts may contribute to more extensive implementation of data-driven, patient-centered care in culturally diverse, resource-limited contexts. ClinicalTrials.gov ID (NCT06978257) April 15, 2025 https://clinicaltrials.gov/study/NCT06978257 .

BrainEffeX: A Web App for Exploring fMRI Effect Sizes

2025-05-01

Effect size estimation is crucial for power analyses and experimental design, but posesunique challenges in fMRI research due to the complexity of the data and analysistechniques. Here, we introduce an interactive web application for exploring fMRI effect maps(neuroprismlab.shinyapps.io/BrainEffeX). We utilized large fMRI datasets to obtain precisevoxel-wise and multivariate effect size estimates from “typical” fMRI study designs:brain-behavior correlation, task vs. rest, and between-group analyses of functionalconnectivity and task-based activation maps. The app is intentionally designed as a growingresource, and we welcome contributions of large (n > 500) datasets.