Frontiers in Psychiatry

Internet gaming disorder (IGD) presents a significant public health challenge, yet its complex biopsychosocial mechanisms and dynamic risk trajectories remain poorly understood due to a scarcity of comprehensive longitudinal and multimodal cohorts. To address this critical gap, we established the Chinese College Student Gamers Cohort (CCSGC), a prospective, multimodal longitudinal study of 793 first-year undergraduates primarily playing Honor of Kings from 2022 Sept. The CCSGC integrates semi-annual psychosocial questionnaires, annual neuroimaging (EEG/fMRI), and biospecimen collection over multiple years. Baseline data revealed individuals with IGD (n=211) exhibited significantly higher gaming craving, psychological distress (depression, anxiety), impulsivity, and maladaptive motivational features compared to non-IGD gamers (regular players (RP) n=400; casual players (CP) n=182). Longitudinal analyses across four waves indicated bidirectional temporal associations between IGD severity and mental symptoms, and a stabilization of IGD incidence after an initial decrease. Furthermore, specific neurophysiological (e.g., N400 amplitude to game cues) and neuroimaging (e.g., superior parietal activation) markers were identified that correlated with IGD severity and predicted one-year outcomes in gaming disorder or social functioning. The CCSGC provides an invaluable resource for dissecting the heterogeneity, comorbidity, and intricate biopsychosocial mechanisms of IGD, holding significant potential to advance risk prediction, early identification, and targeted intervention strategies.

Somatic symptoms, such as bodily pain, fatigue, or signs of bodily dissociation, are frequent in the general population, impair mental wellbeing, and form early signs of developing mental disorders, such as depression. Managing somatic symptoms effectively in daily life is a crucial step towards establishing early intervention strategies that prevent the occurrence of mental disorders. Yet, somatic symptoms that occur in daily life have received little scientific attention so far. Here, we ask if mentalizing abilities, specifically the ability to reflect on ones own or others emotion, cognitive, or bodily states, explain somatic symptom burden in daily life. Reflective functioning was assessed in N = 96 healthy individuals via a standardized questionnaire, RFQ-8, in addition to a novel questionnaire focusing on the ability to understand ones own and others bodily reactions, BRFQ-9. Subsequently, over the period of 8 weeks, somatic symptoms were sampled in daily life via a novel Mobile Application that combines standardized questionnaire items of the FFSS, SCL-90, SDQ and SSD-12 with an interactive 3D avatar. 91.7% of participants reported somatic symptoms in the assessment period, and BRFQ scores show a significant negative relationship to overall somatic symptom burden. Such a relationship could not be evidenced for RFQ scores. Body reflective functioning abilities are also a significantly stronger predictor of somatic symptoms and explain more variance than standard reflective functioning abilities. This study introduces a new mobile Application that monitors somatic symptoms in daily life and suggests that body reflective functioning is a novel target for prevention and early intervention techniques with the aim to reduce the negative influence of aberrant bodily feelings on daily life.

The goal of this work was to leverage a large corpus of text based psychotherapy data to create novel machine learning algorithms that can identify suicide risk in asynchronous text therapy. Advances in the field of natural language processing and machine learning have allowed us to include novel data sources as well as use encoding models that can represent context. Our models utilize advanced natural language processing techniques, including fine-tuned transformer models like RoBERTa, to classify risk. Subsequent model versions incorporated non-text data such as demographic features and census-derived social determinants of health to improve equitable and culturally responsive risk assessment, as well as multiclass models that can identify tiered levels of risk. All new models demonstrated significant improvements over our previous model. Our final version, a multiclass model, provides a tiered system that classifies risk as "no risk," "moderate," or "severe" (weighted F1 of 0.85). This tiered approach enhances clinical utility by allowing providers to quickly prioritize the most urgent cases, ensuring a more accurate and timely intervention for clients in need.

ObjectiveTo analyze the structure of mental disorders in children in the outpatient practice of a specialized mental health center for optimization of care organization for this patient category. MethodsA retrospective analysis of medical records of 23 children (out of 44 patients) at the Insight Mental Health Center (Dushanbe, Tajikistan) was conducted for the period from December 9, 2025, to January 8, 2026. Diagnosis was performed according to ICD-10 criteria using standardized instruments: M-CHAT-R, ADOS-2, and ADI-R for autism spectrum disorder (ASD); SNAP-IV for attention deficit hyperactivity disorder (ADHD); CGI; and pediatric versions of PHQ and GAD. ResultsChildren accounted for 52% of all patients. Primary school-age children (7-12 years) predominated at 43.5%. Disorders of psychological development (F80-F89) dominated the nosological structure at 82.6%, with ASD comprising 56.5%. ADHD was diagnosed in 30.4% of cases. Comorbidity was registered in 47.7% of patients. ConclusionThe structure of pediatric psychiatric pathology is characterized by a predominance of developmental disorders and high comorbidity levels, justifying the need for a multidisciplinary approach.

BackgroundSubstance use disorders (SUD) are associated with a high global burden of disease, with 5.4% of all disability-adjusted life years lost due to alcohol and illicit drugs. Highly prevalent multimorbidity includes polysubstance use, mental health conditions, and other non-communicable and infectious diseases. Where traditional treatments are insufficient alone, music therapy (MT) is highly engaging and improves motivation and reduces craving; however, its long-term effects are unknown. The present study aims to examine long-term effects of active music groups (AMG) and music listening groups (MLG) versus treatment as usual (TAU) on addiction severity, recovery, and other outcomes in people with SUD Immediate and short-term effects, as well as mechanisms of these interventions, will also be examined. MethodsIn individuals with SUD across a wide range of age, gender, socioeconomic, and cultural backgrounds, a parallel 3-arm assessor-blinded pragmatic multinational randomised controlled trial (RCT) with embedded exploratory trials and mechanistic studies will determine long-term effects of AMG and MLG versus TAU on addiction severity (primary endpoint: 1 year), recovery, and other outcomes. Embedded trials will examine immediate effects of AMG or MLG combined with individual components of TAU combined to determine the best combinations of interventions. Experimental studies will examine mechanisms using cognitive testing and brain imaging. With 600 participants in 7 countries randomised, the trial will have 80% power on the primary outcome. Patient representatives, health technology assessment (HTA) bodies, and interventionists have been involved from conception and will ensure feasibility and applicability of the intervention across Europe. DiscussionThis document describes the FALCO RCT, the main part of the FALCO project, which aims to reduce disease burden through innovative, effective, and affordable treatment, and will strengthen research and innovation expertise. Recommendations from FALCO will inform intervention delivery across Europe and beyond, leading to increased safety, effectiveness, and cost-effectiveness, and improved quality of life for individuals with SUD. Stakeholders will be involved in communicating findings across all European countries and regions and ensuring that findings are effectively implemented. Trial registrationClinicalTrials.gov, NCT07028983, registered 11th of June 2025. https://clinicaltrials.gov/study/NCT07028983

BackgroundCo-occurring substance use and mental health disorders (COD) represent a growing public health concern, yet healthcare utilization studies with a large sample size remain limited. This study examined healthcare utilization patterns and sociodemographic correlates among COD adults using data from the All of Us Research Program (2018-2023). MethodsElectronic health record data were analyzed for adults aged [≥]18 years with confirmed diagnoses of substance use and mental health disorders recorded on at least two occasions. Healthcare services were identified using the standardized Current Procedural Terminology and Healthcare Common Procedure Coding System codes and categorized into counseling and therapy, medication/somatic services, online or telehealth care, and other supportive modalities. Multivariable logistic regression was employed to assess sociodemographic and structural correlates of healthcare utilization. ResultsAmong 19,423 adults with COD, 57.1% received healthcare. Counseling and therapy accounted for the largest share of encounters, while online services surged in 2020 during the COVID-19 pandemic. Healthcare utilization was higher among older adults ([≥]65 years: aOR=1.52, 95%CI:1.29-1.78), males (aOR=1.19, 95%CI:1.12-1.26), individuals with disabilities (aOR=1.46, 95%CI:1.36-1.56), and those with employer-sponsored (aOR=1.22, 95%CI:1.10-1.36) or other private insurance (aOR=2.15, 95%CI:1.97-2.34). The level of healthcare utilization was lower among participants with lower income ([≤]$25,000: aOR=0.75, 95%CI:0.69-0.81) or Medicaid coverage (aOR=0.83, 95%CI:0.77-0.89). ConclusionsDespite high clinical need, healthcare utilization among adults with COD remains suboptimal and is shaped by structural inequities across income and insurance lines. Findings highlight the need to expand integrated healthcare services, strengthen Medicaid coverage, and sustain telehealth infrastructure to promote equitable, long-term engagement in care. Highlights{o} Individuals with co-occurring disorders continue to face low healthcare utilization. {o} Counseling and therapy were the major mode of care, while telehealth peaked during COVID-19. {o} Lower income and Medicaid coverage were tied to lower healthcare utilization. {o} Older adults and people with disabilities were more likely to use healthcare services. {o} Findings highlight the needs to expand integrated, equitable behavioral care.

The rapid growth of tele-counseling and the use of lay counselors in high-volume, low-resource mental health services has created a need for scalable tools for early detection and triage. Effective personalization now requires stratifying individuals by dominant symptom profiles, such as appetite, agency, anxiety, and sleep disturbances. Depression symptoms vary widely, even among those with similar scores, reflecting distinct psychophysiological and cognitive-affective patterns. In tele-mental-health settings, where contextual cues are limited, multimodal behavioral signals from natural interactions can complement traditional assessments. Using synchronized audio, video, and text data from the EDAIC dataset (N=275), we propose a multimodal learning framework to classify five clinically validated outcomes: Depression, Appetite disturbance, Agency impairment, Anxiety, and Sleep problems. We developed a comprehensive multimodal machine-learning pipeline, incorporating automated dataset construction, modality-specific feature extraction (acoustic, facial action unit, linguistic), and supervised learning with cross-validation. Labels were derived from validated scoring rules to ensure clinical relevance. Sentiment analysis revealed lower sentiment scores in participants with high Depression, Anxiety, or Agency scores, but no significant differences in Appetite or Sleep severity. Model performance was assessed across three scenarios: text (transcripts), phone calls (audio + transcript), and video calls (audio + video + transcript). Temporal models (CNN+BiLSTM) achieved over 65% accuracy across modalities, while a fine-tuned temporal model for depression detection using video calls reached an accuracy of 81% with an f1-score of 0.79, demonstrating that our approach performs on par with state-of-the-art methods. XGBoost excelled in phone and video calls, while Ridge classifiers performed best for text-based inputs. SHAPley analysis identified key audio and video features for detecting Depression and other symptoms. A translational avatar-based interface validated system operability, demonstrating the potential for scalable, objective mental-health assessment in tele-counseling.

PurposeTo address high levels of depression and anxiety among college students, innovative, feasible, and effective treatment approaches with high potential for dissemination in university counseling centers are needed. This pilot study aimed to develop a toolkit and training intervention to support implementation of nature-based physical activity into group therapy in a university counseling center, and to evaluate the feasibility, acceptability, and preliminary effectiveness of the intervention from the perspective of both therapists and participating clients. MethodsPhysical activity researchers and staff therapists collaborated to develop an 8-week therapy group, with each 90-minute weekly session incorporating discussions of cognitive behavioral strategies for managing anxiety and 30 minutes of moderate-intensity outdoor physical activity. Measures included staff surveys completed pre/post training, standard client assessments (Group Session Rating Scale and Counseling Center Assessment of Psychological Symptoms), and a group facilitator interview. ResultsIn Spring 2025, six students enrolled in the inaugural group. All students completed the group, demonstrated high satisfaction (M=8.78/10 across all sessions), and reductions in depression (d=0.96) and social anxiety (d=0.82). Staff confidence to discuss and recommend nature-based physical activity increased from 7.05 (pre-training) to 8.48 (follow-up). Group therapy facilitators reported high enjoyment and desire to continue offering the group. ConclusionThis study highlights an innovative intervention with promise for translation across university counseling center contexts. The toolkit and training intervention developed for this study could provide a blueprint for other university counseling centers to offer similar therapy groups and expand the integration of nature-based physical activity into mental health services. Keywords: anxiety, college students, group therapy, physical activity, nature

Spending time in locations outside the home and workplace (termed "third places"), has been linked to better mental health. However, studies to date have typically been cross-sectional, based on self-reported location data and employed small sample sizes, limiting their ability to assess the presence and nature of the association between third places and mental health. To overcome these limitations, we collected 18,795 person-days of objective SensorKit location data passively from a national cohort of 410 first-year medical residents across the United States, to assess visits to third places and their associations with mood and depression over the course of one year. On average, participants visited 3.3 unique locations per day (SD=1.7) and spent 17.9% of their time at third places (SD = 26.5%). Within individuals, both a higher percentage of time spent at third places (B=0.013 [per 10% increase], p<0.001) and a greater number of unique locations visited (B=0.032, p<0.001) were associated with better mood later that same day, independent of the time spent at work. These associations were partially mediated by step counts and outdoor light exposure jointly (19.2% and 27.6%). Reverse-direction associations were observed, with better mood on one day predicting both more time spent at third places (B=0.052, p<0.001) and more unique locations visited (B=0.032, p<0.001) the following day. Between subjects, depressed subjects spent less percentage of time at third places (12.3% vs. 21.2%, t=-3.7, p<0.001) and visited fewer unique places per day (2.9 vs. 3.4, t = -3.8, p<0.001) compared to non-depressed subjects. These findings demonstrate the relationship between visiting third places and well-being, and suggest that interventions and policies aimed at encouraging third places visits have the potential to improve mental health.

Schizophrenia is a serious mental disorder characterized by enhanced sensory-perceptual alterations. We investigated face identity recognition in people with schizophrenia with the Facial Identity Recognition Structured Task (FIRST) develop at our laboratory. This was created with natural interference features (beard, makeup and mask). This task consists in six block-trails of six images for identity recognition. Forty three adult volunteers divided into two groups: a Health Control (HC) and a group of hospitalized patients with Schizophrenia (SchG) participated in the study. We measured the total number of correct answers as well as the average reaction time for each block. We observed significant losses in recognition of identity faces with interferences such as make up, beard and facial-mask.

BackgroundThe population receptive field (pRF) in vision reflects the functional receptive field arising from millions of overlapping single receptive fields across visual areas and eccentricities. pRFs are typically estimated with fMRI to gain insight into visual processing. Alternative methods of pRF estimation, such as using optical illusions, have been explored only sparingly. In this study, we explore the rotating tilted lines illusion (RTLI), in which a circle formed by tilted lines appears to rotate as it expands or contracts in the visual field (e.g., from moving the head back and forth). New MethodWe propose a novel set of computer-generated animations of the RTLI that measure the visual and temporal characteristics of the illusory rotation, enabling quantitative estimation of the spatial extent and temporal dynamics of the pRF. ResultsWe derived pRF size estimates consistent with those estimated from fMRI and electrophysiological methods. We then projected changes in RTLI percept trends according to abnormalities in visual processing in autism spectrum disorder (ASD), schizophrenia (SZ), aging, and Alzheimers disease (AD). Comparison with existing methodsCompared to fMRI and electrophysiology, RTLI-based pRF estimation is accessible, low-cost, and feasible at home or during inpatient visits without specialized equipment. ConclusionsWe show that our novel method can approximate pRFs, which in turn can be potentially applied for early detection, probing the progress, and treatment screening in AD, SZ and ASD.

BackgroundExercise therapy reduces depressive and anxiety symptoms, but its neural mechanisms are not fully understood. We examined whether and how running therapy reorganizes dynamic brain functional connectivity in affective disorders. MethodsAt baseline, resting-state fMRI was collected from 66 healthy controls and 50 individuals with affective disorders. Co-activation patterns analyses (CAPs) identified recurring whole-brain network states characterized by spatial patterns of regional co-activation/codeactivation patterns and their temporal occurrence rates. We compared CAPs between groups at baseline. Participants with affective disorders then received 16 weeks of running therapy or antidepressant treatment. We examined: (1) treatment-induced changes in brain CAPs and clinical symptoms, (2) brain-symptom associations at baseline versus post-treatment, and (3) associations between network reorganization and symptom improvement. ResultsAt baseline, individuals with affective disorders showed fewer occurrences of the visual-somatomotor-subcortical network state (VS-SCCAP) than controls (F=5.4, P=0.02, {superscript 2}=0.04). Running therapy significantly altered the temporal dynamics of two brain systems: the default mode (DMCAP: {beta} = -0.88, P = 0.006, d =- 0.88) and VS-SCCAP ({beta} = 0.87, P = 0.006, d = 0.85). These reorganizations were accompanied by significant improvements in depressive and anxiety symptoms (IDS: {beta} = -1.23, P < 0.001, d = -1.15; BAI: {beta} = - 0.98, P = 0.008, d = -0.93). DMCAP-symptom coupling changed significantly from baseline to post-treatment ({Delta}RHO=-0.48, Z{approx}-2.0, P<0.05). ConclusionsRunning therapy altered dynamic brain networks in association with clinical symptom improvement. These findings provide neurobiological evidence for exercise-induced therapeutic effects through transient brain-state reorganization, demonstrating the utility of dynamic connectivity approaches for characterizing neural mechanisms in affective disorders.

IntroductionResearch has shown that social and physical stressors of early-life adversity (ELA) can negatively affect long-term health trajectories. Despite differences in types of ELA exposure, previous studies have identified common health-related outcomes in adults who had experienced less favourable conditions during developmentally sensitive periods. This meta-analysis investigates the potential role of DNA methylation in mediating these adverse health trajectories by identifying common biological signatures across cohorts with distinct adversity exposures and environmental backgrounds. Materials and MethodsDNA methylation data from previously published studies was used to perform a meta-analysis on 227 individuals across three cohorts. These include the EpiPath cohort consisting of adults who were exposed early institutional care, ImmunoTwin cohort consisting of adversity discordant monozygotic twin pairs and lastly a cohort of young children exposed to early institutional care. ResultsDNA methylation analysis across the three cohorts revealed differential methylation at CpG loci associated with 15 genes common to all cohorts. These genes are involved in neuronal development, chromatin remodeling and metabolism. Pathway enrichment analysis of the combined dataset showed potential associations with oxytocin signalling, regulation of nervous system development, and calcium signalling in relation to the later-life phenotype of the adversity exposed individuals. In addition, a poly-epigenetic score was developed by identifying a subset of 200 differentially methylated CpG sites through PLS-DA analysis with the combined beta matrix of these cohorts. ConclusionThis study highlights the long-term impact of adversity by identifying common DNA methylation signatures of negative life experiences across three cohorts. The development of a poly-epigenetic score represents the first steps towards identifying group differences by combining weighted methylation values for CpG sites of interest. This method illustrates the potential to track changes in individuals across long-term studies that may benefit research in lifelong healthoutcomes.

Introduction. Current best practice is for primary care physicians (PCPs) to screen patients for problematic substance use at checkups. However, this practice is not routine, is done in an unstandardized manner, and contributes to the overburdening of PCPs. Screening practices also target current, potentially problematic use behaviors, thus limiting their capacity to help patients prevent problems before they start. Recent scientific advances in identifying people at high risk for substance use problems as a means of facilitating prevention efforts have not yet been integrated into medical practice. To address these issues, our research team developed a freestanding platform called the Comprehensive Addiction Risk Evaluation System (CARES). CARES provides personalized information about genetic and behavioral/environmental risk for substance use disorder (SUD) and connects individuals to resources based on their risk profile. The present study evaluated the potential for adoption and implementation of CARES within a health care system through qualitative interviews with key stakeholders. Methods. Semi-structured interviews were developed using the Consolidated Framework for Implementation Research (CFIR) and conducted with N=15 interviewees. Transcripts were analyzed using rapid qualitative analysis. Results. Key themes included perceived need for new SUD screening tools, current SUD screening procedures and their pros/cons, openness to new ideas and clinical tools, fit of CARES with organizational goals and priorities, considerations for use of CARES with adolescent populations, anticipated patient response to CARES, barriers to implementation and uptake of CARES, changes required for implementation, and possibility for medical record integration. Interviewees generally expressed need for new screening tools and openness to using new tools, but expressed concern that existing provider burden, lack of SUD knowledge, and discomfort/stigma could stymie efforts to implement CARES. Conclusions. There is a clear need for a low-burden, easy-to-use tool for substance use screening. CARES appears to be an acceptable and feasible approach to fill this gap. These findings will be used to inform pilot implementation of CARES in a clinical care setting.

ObjectiveTo characterize the familial patterns of misophonia and other commonly co-occurring neuropsychiatric conditions. MethodsWe examined cross-sectional survey responses from 101 probands with misophonia and their biological parents enrolled in a genetics study. ResultsProbands had a mean age of 24.6 {+/-} 11.6 years (8-64 years), were predominantly female (88%), and had high rates of co-occurring neuropsychiatric conditions, including anxiety (70%), depression (38%), ADHD (31%), and OCD (25%). Among probands, 39% had a first-degree relative with misophonia, and 48% had at least one any-degree relative with misophonia. In addition, many probands had at least one first-degree relative with anxiety (65%), depression (57%), ADHD (40%), OCD (20%), and autism (13%). Comparing rates of neuropsychiatric conditions reported by parents, mothers had significantly higher rates of misophonia (29% maternal vs. 9% paternal, p = 0.001) and anxiety (44% maternal vs. 26% paternal, p = 0.02) than fathers. ConclusionThese findings provide new insight into the familial patterns of misophonia and co-occurring neuropsychiatric conditions. Future research on underlying genetic and environmental factors is needed to shed light on the observed shared predispositions for misophonia and other neuropsychiatric conditions in families.

Mental health related problems in adolescents are not always properly evaluated because of incomplete evaluation methods that do not combine biological, behavioral, and demographic details. Therefore, our study proposes a twin-aware multimodal deep learning framework applied to the QTAB dataset for early prediction of adolescent anxiety disorders. We employ a 3D convolutional neural network for neuroimaging data and prototype-based learning modules with residual encoders for behavioral and phenotypic data. Each modality-specific encoder learns compact representations optimized for class-imbalanced prediction through multi-loss objective functions. Calibrated probability outputs from the three modules are combined via optimized weighted late fusion. The framework achieves an AUC of 0.8935 (95% CI: 0.792-0.969), representing an absolute gain of 11 percentage points over the best unimodal baseline (questionnaire: AUC = 0.7766), with a sensitivity of 85.7% and a specificity of 87.3%. Pairwise statistical testing indicated that the classification patterns of the fusion model differ significantly from the questionnaire-only baseline (McNemar p = 0.0008), though AUC differences did not reach statistical significance at this sample size (DeLong p > 0.05). The best fusion weights were 23% MRI, 63% questionnaire, and 14% phenotypic, highlighting the dominant role of behavioral data. These results demonstrate that calibrated late fusion of multimodal predictions provides robust performance for early adolescent anxiety screening in twin cohorts with family-aware evaluation protocols.

BackgroundAdolescent major depressive disorder (AMDD) is a prevalent and heterogeneous psychiatric condition that emerges during a critical period of brain development. Neuroimaging-based biomarkers derived from resting-state functional magnetic resonance imaging (rs-fMRI) hold promise for objective diagnosis; however, pronounced inter-individual variability and limited sample sizes pose major challenges for robust model development. MethodsWe propose a memory-augmented Meta-Graph Convolutional Network (BrainMetaGCN) to classify AMDD using rs-fMRI functional connectivity. Individual functional connectivity matrices were constructed by parcellating rs-fMRI time series into cortical regions of interest and computing pairwise correlations. A meta-graph generator dynamically learned subject-specific graph structures, which were processed by lightweight graph convolutional layers. A memory neural network was incorporated to encode population-level prototypical connectivity patterns and generate individualized representations via attention-based retrieval. Model performance was evaluated across multiple independent datasets and compared with state-of-the-art deep learning approaches. Additionally, network interpretability was examined through cortical hierarchy analysis and functional enrichment of discriminative network components. ResultsThe proposed BrainMetaGCN consistently outperformed baseline models, including convolutional and transformer-based approaches, achieving higher accuracy, area under the receiver operating characteristic curve, sensitivity, and specificity. Memory-module-derived functional networks exhibited clear modular organization and showed a significant positive correlation with cortical functional hierarchy, supporting their neurobiological validity. Functional enrichment analyses implicated synaptic transmission, axon guidance, receptor tyrosine kinase signaling, and immune-related pathways, suggesting neurodevelopmental and neuroimmune mechanisms underlying AMDD. Ablation analyses confirmed that memory augmentation and dynamic meta-graph construction were critical for robust performance under small-sample conditions. ConclusionsThis study introduces a robust and interpretable memory-augmented graph learning framework for AMDD classification. By effectively balancing individual specificity and population-level generalization, BrainMetaGCN advances neuroimaging-based precision diagnosis and provides new insights into the neural and biological mechanisms of adolescent depression.

Depression is a common and clinically significant mental health condition among university students, particularly those experiencing academic failure and course repetition, and is associated with adverse effects on cognitive functioning, emotional regulation, and academic performance. This study evaluated the efficacy of an internet-based cognitive behavioural therapy (iCBT) intervention, MoodGYM, in reducing depressive symptoms among repeating undergraduate students at the University of Zambia Ridgeway Campus. A quasi-experimental quantitative study design was employed. Seventy-five repeating undergraduate students with depressive symptoms were enrolled, with 33 assigned to the MoodGYM intervention group and 42 to a control group. Depressive symptom severity was assessed using the Beck Depression Inventory (BDI) at baseline and after an eight-week intervention period. Statistical analyses included within-group and between-group comparisons, difference-in-differences estimation, and fixed-effects regression modelling. At baseline, participants exhibited predominantly moderate to severe depressive symptoms, with no statistically significant differences between the intervention and control groups. Following the eight-week intervention, the MoodGYM group demonstrated a statistically and clinically significant reduction in depressive symptoms, with median BDI scores decreasing from 22 to 16 (p < 0.001), representing a large effect size (Cohens d = 1.02). In contrast, the control group showed persistence or worsening of depressive symptoms over the same period. Difference-in-differences analysis confirmed a robust intervention effect, with an approximately 10-point greater reduction in depression scores among MoodGYM participants compared with controls (p < 0.001). These findings indicate that MoodGYM is an effective internet-based intervention for reducing depressive symptoms among repeating undergraduate students and offers a feasible and scalable approach to addressing student mental health needs in low-resource university settings.

Normative Modelling ( brain growth charting) is now a well-established method for computational psychiatry and involves charting centiles of variation across a population in terms of mappings between biology and behavior, providing statistical inferences at the level of the individual. These models have helped the field to move away from case-control analysis toward individual-level analysis. Correspondingly, normative modelling has now been applied to chart brain development and ageing in many populations and has been used to quantify individual deviations across various neurological and psychiatric conditions. This has been supported by large-scale models that are openly accessible for diverse brain imaging modalities. As normative modelling continues to grow, several recent methodological developments, such as non-Gaussian models, longitudinal models, and federated learning, have been implemented in different software tools, including the Predictive Clinical Neuroscience toolkit (PCNtoolkit). In this protocol update, we provide: (i) a revised overview of this methodological landscape; (ii) an update to our 2022 standardised analytical protocol for normative modelling of neuroimaging data, including options for federated and longitudinal normative models; (iii) practical guidance suited to both novice and experienced practitioners supported by open-source code examples implemented in the refactored version of PCNtoolkit; and (iv) updated models for cortical thickness, volumetric data, diffusion-weighted imaging and longitudinal data for use by the community.

BackgroundCognitive deficits in anxiety disorders (ADs) contribute to clinical and socio-occupational dysfunction, necessitating targeted interventions. NoveltyIntegrated Cognitive Control Training (ICCT), a novel intervention, has demonstrated benefits in other disorders, however, remains unexplored in ADs. With its process-specific training and multi-pronged exercises for stimulation, metacognitive training and generalization, it has potential for enhancing cognitive functions in ADs. ObjectivesThis paper describes the study protocol for a multi-site randomized controlled trial (RCT) to test efficacy of ICCT in individuals with ADs. MethodsAdults diagnosed with ADs (n=100) will be recruited across two sites. Following baseline assessments, they will be randomized to either ICCT (8 weekly sessions) or Treatment As Usual (TAU). ICCT will be delivered through once-weekly therapist-guided, and smartphone app-based ( Cogtrain) homework (20-30 mins, 4-6 times per week). Multimodal assessments will be carried out at baseline, mid-intervention (4 weeks), post-intervention (8 weeks) and follow-up (20-24 weeks). The primary measure comprises Hamilton Anxiety Rating Scale, with secondary measures of Work and Social Adjustment Scale (socio-occupational functioning), neuropsychological tests (attention, memory and executive functions) and functional Magnetic Resonance Imaging of the cognitive control circuits. Intervention feasibility and acceptance metrics (response rate, intervention relevance) will also be recorded. Quality assurance and ethical procedures will be documented. Expected outcomeThe ICCT is expected to enhance cognitive functioning in adults with ADs, in addition to symptom reduction, changes in underlying neural circuits of cognitive control and improve overall functioning. Digital delivery through a smartphone app may provide a cost-effective and scalable intervention, useful in resource-constrained settings. Key MessagesThis multi-site randomized controlled trial evaluates a novel, smartphone-delivered Integrated Cognitive Control Training (ICCT) program for adults with anxiety disorders, targeting core cognitive deficits that contribute to functional impairment. By combining therapist-guided sessions with app-based training and multimodal assessments, the study examines both clinical and neural outcomes. Findings are expected to inform the scalability and feasibility of process-based digital cognitive interventions for anxiety disorders, particularly in resource-limited settings. Protocol RegistrationTrial registry name: Clinical Trial Registry of India URL: https://ctri.nic.in/Clinicaltrials/************** Registration number: CTRI/202*/**/******