Frontiers in Psychiatry

Background: Adverse childhood experiences (ACEs) have life-long detrimental effects on physical and mental health. ACE impacted young people are under-represented in research and cautious about seeking help. Practitioners fear re-traumatisation during assessment and treatment. Innovative approaches to care are needed. To respond, we co-designed a serious game called ACE of Hearts (AoH) focusing on promoting awareness of ACEs and providing guidance and support. This paper reports on a feasibility and acceptability study and a process evaluation of mechanisms. Method: A diverse cohort of young people aged between 12-24 years, reporting three or more ACEs, and living in different geographical areas of England were recruited through trusted partner organisations. Following informed consent, they were provided access to AoH for three months. Feasibility (rates of recruitment, uptake, engagement, retention, and follow-up), acceptability (affective attitude, burden, ethicality, intervention coherence, perceived effectiveness, self-efficacy), demographic and mental health outcome data were collected at baseline, 1- and 3-month follow-up. Process evaluation interviews at 3 months assessed mechanisms, acceptability and feasibility. Results: Of 40 eligible subjects, 36 completed the baseline assessment and 22 downloaded AoH. Twenty participants completed the questionnaires at 1 and 3 months (91% follow-up of those accessing AoH); 19 participants completed all assessments (86%). Many participants valued the central cosy den space and its customisation options. Engagement rates for 4 mini-games ranged from 32% to 73%. Most participants enjoyed playing AoH, found it easy to use, and felt it helped them reflect on their experiences. The process evaluation found positive views of the game design, style, and content along with perceived benefits through education, awareness, emotional connection, and considering help-seeking. Several improvements for access, relevance, acceptability, engagement and retention were recommended. No adverse events were reported. Conclusions: AoH was found to be feasible to use and acceptable and recommendations were given for improvements.

Large Language Models are increasingly used in consumer-facing mental health tools, many of which claim that prompt engineering alone can ensure safe therapeutic behavior. This study evaluates that assumption by testing 20 proprietary and open-source LLMs on high-risk psychiatric scenarios, using prompts grounded in behavioral therapy principles. Prompt engineering reduced some predictable risks, such as explicit endorsement of self-harm, but consistently failed in ambiguous or clinically nuanced situations. Models frequently validated harmful statements, colluded with hallucinations, minimized symptoms, or used stigmatizing language, including in the newest and largest models. These failures reflect structural limitations such as lack of memory, insufficient contextual reasoning, and training-related biases. Prompt engineering alone is therefore insufficient for safe AI-mediated psychotherapy; clinician-guided fine-tuning, integrated safety mechanisms, and system-level oversight will be required. This work provides early evidence motivating deeper clinician-led evaluation and safety-oriented model development.

Introduction: Autism spectrum disorder, or autism, is a common neurodevelopmental condition characterized by socio-communicative problems together with restrictive and repetitive behaviors. Typically, the latter is manifested as deficits in behavioral flexibility, i.e. changing routine behaviors to adapt to environmental changes. Despite noticeable difficulties with flexible behavior in autism, there is to date not adequate knowledge about the intricacies of such challenges and neurobiological processes that may subserve them. This study aims to investigate both cognitive and motor flexibility in autistic compared with neurotypical adults using a novel combination of detailed methods for brain imaging and behavioral investigations in relation to probabilistic reversal learning (PRL) paradigms. In addition, the experiences of autistic adults on flexible behavior in education and everyday activities will be explored. Methods and analysis: Differences in cognitive flexibility between autistic (n[≥]20) and neurotypical (n[≥]20) adults (18-35 years) will be investigated in terms of brain activations, measured by functional magnetic resonance imaging (fMRI), during two-choice PRL performance (cognitive task). In addition, group differences in microcirculation as measured by arterial spin labelling (ASL) will be evaluated. Group differences in motor flexibility will be investigated as expressed in movement planning and execution (spatio-temporal parameters), measured by a robotic manipulandum platform (KinArm End-Point Robot), during two-choice PRL performance (motor task). Semi-structured interviews will be conducted individually with autistic participants (n=15). Questions concern own experiences of cognitive and motor behavior, and strategies used to support flexibility in these behaviors. Data from this qualitative approach will be analyzed by thematic analysis. Ethics and dissemination: Ethical approval has been obtained from the Swedish Ethical Review Authority (ref:2025-07939-01) and the study will be conducted in accordance with the Declaration of Helsinki, the European Union General Data Protection Regulation (GDPR) and national guidelines for the storing of personal data. The different investigations included are well-established, non-invasive and safe. Study outcomes will be published in peer-reviewed international scientific journals (open access), presented at national and international conferences, and to any interested audience/stakeholders.

Executive function (EF) deficits are central features of schizophrenia and strongly influence long-term functional outcomes. Conventional cognitive assessments often lack ecological validity and cultural relevance. This study introduces the Luk Chup Augmented Reality (LCAR) tool a video guided, clay modeling task delivered through wearable AR that integrates culturally familiar activity with realtime neurophysiological monitoring. Thirty individuals diagnosed with schizophrenia (mean age = 38.9, SD. = 7.15 years) completed a series of modeling and memory tasks using LCAR while undergoing quantitative EEG (QEEG). Task duration and theta/beta power were analyzed across procedural and color shape memory phases. Memory phases took significantly longer to complete and were associated with decreased lateral prefrontal theta and increased frontal midline theta activity (Fz, Cz), indicating higher EF demand. A repeated-measures ANOVA revealed significant condition, site, and interaction effects on theta power. The LCAR tool shows promise as a culturally grounded, dual-mode assessment of EF in schizophrenia. It offers a novel integration of performance-based and neurophysiological metrics that may inform future interventions in psychiatric rehabilitation.

Importance: AI chatbots are increasingly used for mental health support, but little is known about how adults with lived experience of mental health condition use and perceive these tools. Objective: To characterize the use and perception of AI chatbots, including for mental health purposes, among adults connected to a large US mental health organization. Design: Cross-sectional online survey conducted from March to May 2026. Setting: Adults recruited through email newsletters from the National Alliance on Mental Illness (NAMI), the largest grassroots mental health organization in the US. Participants: Adults aged 18 years older with English proficiency. Affiliation with NAMI or a diagnosis of mental health disorder was not required. Results: Of 454 participants, 316 (69.6%) reported having used an AI chatbot. Use was more common among younger participants and those with a current mental health diagnosis. Among AI users, 133 (42.1%) reported using a chatbot for mental health purposes. Mental health-related use was typically brief and focused on information gathering and in-the-moment emotion regulation. Most users rated chatbots as helpful for their mental health. Among the 95 participants with a mental health provider, only 14 (14.7%) had openly discussed their AI use with their provider. Higher frequency of AI use was associated with greater odds of disclosure (OR, 1.67; 95% CI, 1.20-2.38; P = .003). Conclusion and Relevance: In this survey of adults connected to a large mental health organization, AI chatbots were widely used but engagement for mental health purposes was typically brief and focused. Most use occurred without clinician awareness, suggesting a need for proactive conversations about AI use in routine mental health care.

Introduction: Auditory verbal hallucinations (AVH) affect 60-80% of people with schizophrenia, yet existing assessment tools inadequately capture their phenomenological complexity. Aim: To determine whether individuals with schizophrenia spectrum disorders could accurately match auditory verbal hallucination loudness to an external audio track, and to explore participant perspectives on this approach. Methods: Eight participants with schizophrenia spectrum disorders and active AVHs rated loudness via a Likert scale and by adjusting a headphone audio track to match their experience. Structured interviews and thematic analysis captured participant viewpoints. Results: No significant correlation was found between audio tool and Likert scale scores. Seven of eight participants reported the audio tool provided greater precision in quantifying AVH loudness and better enabled them to convey their internal experience to others. Discussion: Audio-matching tools may offer meaningful advantages over traditional scales for quantifying AVH loudness, even where statistical convergence with existing measures is absent. Limitations: Small sample size; loudness alone cannot fully capture the qualitative experience of hearing voices. Implications: This tool shows promise for longitudinal tracking of AVH loudness. Recommendations: Further investigation of digital approaches to AVH assessment is warranted.

Non-suicidal self-injury (NSSI) among adolescents is a prevalent mental health problem and an important indicator of potential suicide risk. Early objective identification and neural mechanism analysis are therefore crucial for clinical screening and intervention. Traditional assessments mainly rely on self-report scales and clinical interviews, which are vulnerable to subjective bias, clinical experience, and missed diagnosis. Electroencephalography (EEG), with its non-invasive, low-cost, and high-temporal-resolution characteristics, provides a promising physiological basis for identifying NSSI-related neural abnormalities. However, EEG-based intelligent recognition of adolescent NSSI remains limited, and existing studies often emphasize classification performance while lacking systematic neurophysiological interpretation. To address these issues, this study proposes CGA-NSSI, a lightweight deep learning framework for adolescent NSSI recognition. The model integrates a one-dimensional convolutional neural network, bidirectional gated recurrent unit, and multi-head self-attention mechanism to extract local spatiotemporal EEG features, model long-range temporal dependencies, and focus on key pathology-related time segments and channels. A standardized preprocessing pipeline, together with Mixup augmentation and Focal Loss, is further used to alleviate sample imbalance and improve robustness in small clinical EEG datasets. Experiments on a real-world adolescent clinical EEG dataset show that CGA-NSSI can effectively identify NSSI-related EEG patterns under imbalanced sample conditions. Interpretability and functional connectivity analyses further reveal prefrontal-centered cross-regional network reorganization, excessive static functional coupling, reduced dynamic connectivity fluctuations, and increased abnormal state occupancy. These findings suggest that CGA-NSSI not only improves objective NSSI recognition but also provides neurophysiological evidence for understanding adolescent self-injury.

Background: Major depressive disorder is a severe, recurrent and disabling condition. Although diagnosis and clinical monitoring are based on medical interviews and validated rating scales, speech and discourse analysis may provide complementary digital biomarkers reflecting depressive severity and clinical evolution. However, current evidence remains limited by methodological heterogeneity, predominantly cross-sectional designs, limited longitudinal data and underrepresentation of non-English-speaking clinical populations. Objective: The aim of the VOICE-DEP study is to develop and formalize a standardized, reproducible and clinically grounded protocol for the multimodal analysis of voice and discourse during medical interviews as a tool to support the diagnosis of depressive disorder and to assess whether speech-derived biomarkers change over time in parallel with clinical severity measures. Methods: VOICE-DEP is an observational, prospective, longitudinal pilot study of patients with major depressive disorder with a healthy control group, conducted in a hospital-based clinical setting in Spain. The study will include 25 adult patients with moderate or severe unipolar depression, with or without psychotic symptoms, and 50 healthy controls without a personal history of psychiatric disorders. Patients will be assessed at five time points: baseline (V0) and four monthly follow-up visits at 30, 60, 90 and 120 days. Healthy controls will be assessed once at baseline. The planned dataset comprises 175 voice recordings: 125 from patients and 50 from controls. At each assessment, the Montgomery-Asberg Depression Rating Scale related part of the medical interview, lasting approximately 10-30 minutes and including an initial free-speech segment, will be recorded using a standardized audio protocol. Acoustic, paralinguistic and linguistic features will be extracted and analyzed in relation to clinician-rated severity measures and self-reported symptoms. Ethics: This protocol has been reviewed and approved by the local Research Ethics Committee, which complies with the international standards of GCP CPMP/ICH/135/95 (Comunidad Foral de Navarra Research Ethics Committee; reference code: 2026.110). Written informed consent will be obtained from all participants before any study procedure. Voice recordings and clinical data will be pseudonymized, stored securely and processed in accordance with applicable Spanish and European data protection regulations. Expected outcomes: This protocol is expected to generate a clinically grounded Spanish-language longitudinal speech corpus and a transparent analytical framework for evaluating voice- and discourse-derived biomarkers as complementary tools for depression assessment and monitoring

Psychosis as a symptom manifests in schizophenia and bipolar disorder, two highly heterogeneous psychiatric illnesses with overlapping clinical manifestations. Resting-state functional Magnetic Resonance Imaging (rsfMRI), represents a promising tool for identifying objective biomarkers of functional brain alterations to aid differential diagnosis. In this work, we comparatively evaluate multiple rs-fMRI representations for differentiating schizophrenia and bipolar disorder using intrinsic connectivity network (ICN) temporal profiles and several functional network connectivity (FNC) approaches, including static, dynamic, and high-order connectivity analyses. The study was conducted on a cohort of 371 subjects with psychosis, while evaluation was performed using a separate held-out cohort of 315 subjects. We investigated convolutional neural network architectures applied to ICN temporal profiles, spectrograms, and scalograms, alongside classical machine learning models trained on connectivity-derived features. Across the evaluated approaches, ICN temporal profiles provided the most consistent discriminative performance, with a 1D convolutional neural network achieving the strongest overall results under the benchmark protocol. Among connectivity-based methods, static functional connectivity generally outperformed dynamic and high-order representations, suggesting that increased representational complexity did not necessarily translate into improved generalization. Although the obtained classification performance remained modest, the results highlight the challenges of robust psychosis differentiation using rs-fMRI while emphasizing the relative stability of low-order connectivity representations and temporal ICN features. These findings contribute to ongoing efforts toward reproducible and interpretable neuroimaging biomarkers for psychiatric disorders.

The use of meditation as a tool to improve human wellbeing is receiving considerable scientific interest. However, most existing research has focused on concentration-based practices. One powerful alternative is jhana meditation, which leads to states characterised by self-reinforcing bliss, potentially useful for a variety of clinical and scientific domains. However, our understanding of these states is limited by small amounts of data and poor access to experts. To enable new insights, here we release the largest to date and first open-access dataset of electroencephalographic and physiological recordings in expert Jhana meditators. This includes 100+ hours of data in N=26 subjects across three retreats, alongside a detailed description and example code illustrating analysis of the data. This open dataset release can enable wider collaboration and has the potential to move us closer to an understanding of endogenously generated altered states of consciousness.

Introduction: In 2020, Oregon became the first US state to legalize state-regulated psilocybin services. This study aims to examine: 1) the clinical and demographic characteristics, 2) psilocybin use motivations, and 3) differences in preparedness among patients seeking care in a Oregon- based pilot consult service specializing in psilocybin risk reduction. Methods: This retrospective chart review abstracted sociodemographics, trauma history, and medical and psychiatric risks of patients (November 2023 - September 2025). The Psychedelic Preparedness Scale (PPS), a validated self-report questionnaire, measured preparedness. Two sample t-tests examined associations of PPS scores by insurance, consult motivations, and prior psychedelic use. Results: Patients (N=29) had a mean age of 47.14 years (SD=15.9), were majority female (55.2%); White (82.8%); and privately insured (62.1%). Patients mostly sought psilocybin to address only a psychiatric concern (75.9%); 27.6% anticipated naturalistic (non-state regulated) use. Most patients were deemed low risk for adverse events. Prevalence of prior challenging psychedelic experiences (CPE) was 17.2%; 58.6% reported lifetime psilocybin use. 86.2% endorsed >1 form of lifetime trauma. Of PPS completers (N=23, 79%), mean score was 91.3 (SD = 23.99). Scores did not significantly differ by insurance; consultation motivation; CPE; prior psilocybin or psychedelic use. Conclusion: Patients utilizing a novel consultation service demonstrate a high prevalence of trauma, prior psilocybin use, and baseline preparedness. While preliminary, this is among the first descriptions of patients seeking medical and psychiatric consultation when considering psilocybin and highlight the potential role of healthcare systems in providing evidence-based patient education and risk reduction as interest in psychedelics grows.

Background Negative symptoms in schizophrenia spectrum disorders (SSD) remain insufficiently treated and require novel therapeutic approaches. Oxytocin may improve negative symptoms, although its effects appear highly context-dependent according to the social salience hypothesis. We conducted a randomized, triple-blind, placebo-controlled pilot study combining intranasal oxytocin with mindfulness-based group therapy (MBGT), hypothesizing that the positive social context of MBGT would enhance oxytocin-related effects. Methods 47 participants with SSD (34% female) were randomized to receive either 24 IU oxytocin (MBGT+OXT; n = 26) or placebo (MBGT+PLA; n = 21) before four MBGT sessions. Primary outcome was negative symptoms assessed with the Positive and Negative Syndrome Scale negative subscale (PANSS-N) at post-intervention and 4-week follow-up. Secondary outcomes included the Brief Negative Symptom Scale (BNSS), Self-Evaluation of Negative Symptoms Scale (SNS), and additional clinical measures. Linear mixed models estimated within- and between-group effects. Results Overall dropout rate was 14.89%, with one dropout potentially treatment-related. Blinding was successful. Participants completed 95.63% of sessions. Only the MBGT+OXT group showed significant improvements in PANSS-N from baseline to post-intervention (d = -0.74) and follow-up (d = -0.77), with a small between-group effect at follow-up (d = 0.39). BNSS total improved significantly only in the MBGT+OXT group from baseline to post-intervention (d = -0.88) and follow-up (d = -0.91), with between-group effects favoring MBGT+OXT at follow-up (d = -0.38). No serious adverse events occurred. Conclusions These findings suggest, oxytocin combined with MBGT may improve negative symptoms in SSD and support further large-scale trials. Clinical Trials Registration: https://clinicaltrials.gov/study/NCT06136390, Registration number: NCT06136390

Abstract Background Despite a rising global psychiatric burden, a treatment gap persists where the majority of symptomatic individuals remain unmedicated. Traditional epidemiological analyses treat this untreated population as a single, uniform block, obscuring specific barriers to care. This study uses an unsupervised machine learning pipeline to identify distinct socio-behavioural and biological sub-populations within the untreated cohort to guide targeted public health interventions. Methods Data were pooled from the 2015-2018 National Health and Nutrition Examination Survey (NHANES) cycles (N=11,848 total adult respondents). A symptomatic cohort of 3,075 individuals experiencing daily or weekly anxiety or depression symptoms was isolated, excluding severe liver pathology outliers ("GGT"[≥]80" U/L" ). A 22-feature matrix combining continuous clinical biomarkers (systolic blood pressure, waist circumference, HbA1c) and categorical social variables was projected using Factor Analysis of Mixed Data (FAMD). Latent sub-populations were identified via Gaussian Mixture Modelling (GMM), optimized by the Bayesian Information Criterion (BIC). Results The broad baseline population revealed a substantial mental health burden, with 30.4% reporting active psychiatric symptoms, of whom 71.6% were entirely unmedicated. The GMM pipeline successfully isolated three distinct sub-populations (k=3) separated by age, clinical strain, and treatment rates: Cluster 0 (Mature Adults, mean age 55.03): high psychiatric severity (34.1% severe untreated), central obesity, and hypertensive strain (135.82 mmHg), with 64.1% untreated despite frequent primary care contact; Cluster 1 (Working Professionals, mean age 38.38): highly educated, female-dominated (70.5%), with 77.7% untreated driven by moderate distress; Cluster 2 (Emerging Youth, mean age 18.49): a highly vulnerable late-adolescent group with a staggering 90.2% untreated rate. Conclusion The unmedicated symptomatic population is highly diverse and segmented by life stage. These profiles show that the treatment gap is driven by age-specific barriers, specifically workforce-age symptom masking and late-adolescent developmental transitions. Closing this deficit requires shifting from uniform public health approaches toward targeted interventions, such as digital peer support networks for youth and integrated primary care screenings for older adults.

Background: In the age of TikTok, YouTube, and Netflix, video streaming (VS) is highly popular among adolescents. Yet, risky to addiction-like viewing patterns (i.e., problematic (P)VS) may adversely affect well-being. Prevalence estimates based on established criteria and etiological understanding of this phenomenon remain scarce. It is associated with de-pression, a major issue within the youth mental health crisis. However, causality remains unclear. This study investigated prevalence trends of adolescent PVS and its temporal rela-tionship with depression. Methods: Population-based data were drawn from four annual waves (2022-2025) of a rep-resentative online survey among 3,477 German adolescents (aged 10-17 years). Weighted annual PVS prevalence estimates were calculated based on standardized measures applying ICD-11 criteria of behavioural addictions distinguishing pathological from hazardous behav-ioural patterns. A cross-lagged panel analysis examined the reciprocal relationship between PVS and depression over four years. Results: Prevalence of pathological VS ranged between 2 to 4% across waves. Hazardous VS prevalence was 13-14% from 2022 to 2024, before increasing to 25% in 2025. Up to 81% of adolescents with pathological VS (21% with hazardous VS) showed clinically relevant symptoms of depression, versus 8-9% of non-affected adolescents. Depression significantly predicted PVS in two of three lags ({beta}W1-W2=0.233, {beta}W2-W3=0.155), but not vice versa. Conclusions: Prevalence rates and their divergent associations with depression support dis-tinguishing hazardous from pathological VS and underline the clinical relevance of PVS. Depression preceded PVS, pointing to the role of maladaptive coping. This has direct impli-cations for effective intervention measures. Future research should clarify the mechanisms underlying this relationship.

IMPORTANCE With a growing global user base of 3.5 billion and users spending nearly as much time gaming as on social media, video gaming's effects on mental well-being have attracted scholarly and public interest. Despite the WHO's inclusion of gaming disorder in ICD-11 and government-mandated restrictions in multiple countries, the causal evidence supporting such policies remains limited. OBJECTIVE To investigate the causal effect of video gaming on mental well-being in the post-COVID period. DESIGN A natural experiment of game console lottery was used to identify the causal effect of video gaming on mental well-being. The intention-to-treat effect was estimated using multivariate regression and propensity score matching. Causal effects of game engagement were estimated using the instrumental variable method (two-stage least squares) and a causal machine learning algorithm, instrumental forest. SETTING Online surveys were conducted between September 2022 and March 2023, covering all 47 prefectures in Japan. PARTICIPANTS A total of 71,435 participants aged 10-69 answered the surveys. 6,911 individuals participated in the natural experiment. EXPOSURES Video game engagement, including video game console ownership, use of the console in the last 30 days, and video gaming duration. MAIN OUTCOMES AND MEASURES Psychological distress and life satisfaction. RESULTS The intention-to-treat effects of winning game console lotteries on mental well-being were positive (0.1 SD). Game console ownership improved mental well-being by 0.1-0.2 SD, and past-month play improved it by 0.2-0.3 SD. An extra hour of daily video game play led to 0.3-0.5 SD improvements in mental well-being. CONCLUSIONS AND RELEVANCE This study found that video gaming had a positive effect on mental well-being in the post-COVID period. The consistency of the effect size with that of a related COVID-period study adds robustness to our findings. Our findings add to the growing evidence that digital media screen time has diverse effects on well-being and support public health policies that recognize the potential mental well-being benefits of appropriate levels of video gaming.

Autistic psychiatrists occupy a paradoxical position: trained to recognise and assess autism in others, yet navigating a professional culture in which their own autistic identity remains largely concealed. Despite growing visibility of autistic clinicians, the barriers autistic psychiatrists face to formal diagnosis and professional disclosure remain unexplored. This study used interpretive phenomenological analysis to examine the experiences of seven autistic psychiatrists in relation to diagnosis and disclosure. Data were generated through in-depth interviews and Retzinger's framework for identifying shame in discourse was applied as an analytical tool within the interpretive process. Shame emerged as the overarching theme across the dataset, operating through four group experiential themes. Its origins lay in childhood experiences of difference and perceived defectiveness, transmitted through family, peers, and the broader social environment. In professional life, shame was sustained and amplified by colleagues' misconceptions about autism, anticipated loss of credibility, and the deficit-based diagnostic criteria - which rendered self-recognition difficult and made formal diagnosis a perceived professional liability. Critically, shame did not only create barriers: it functioned as an override mechanism, rendering the known benefits of disclosure - to participants themselves, to colleagues, and to patients - insufficient to translate into action. This override function was not explained by fear of discrimination or rational career protection alone; it reflected shame's operation as an internal prohibition, dissociated from its original social source and persisting even where stigma had been intellectually processed and rejected. These findings reposition shame not as one barrier among many but as the organising force through which all barriers operate. Interventions aimed at increasing disclosure by raising awareness of its benefits misread the operative mechanism. Creating conditions in which autistic psychiatrists can make decisions about their identities freely requires naming and addressing shame - in research, in clinical training, and in the culture of psychiatry.

Background: Post-traumatic stress disorder (PTSD) remains highly prevalent affecting ~23% of World Trade Center (WTC) responders more than two decades after 9/11. While MRI studies have identified neural differences associated with PTSD, these findings have not translated into improved treatment. We introduce a novel multimodal MRI approach, DAta-driven Network Connectivity Estimate (DANCE), integrating structural and functional magnetic resonance imaging (MRI) to better capture PTSD mechanisms and inform biomarkers. Methods: In 96 WTC responders , including 45 with current WTC-related PTSD and 51 without PTSD. We applied graph theory to resting-state functional MRI to identify functional hubs via eigenvector centrality and identified divergence between groups using partial least squares discriminant analysis (PLS-DA). From diffusion MRI, we reconstructed five anatomical tracts (i.e., streamlines) in the temporal lobes. Using DANCE, we quantified the differential distribution of streamlines of the reconstructed tracts connecting the functional hubs. We then tested whether WTC exposure duration moderated associations between PTSD and DANCE indices. Results: Responders with PTSD showed altered centrality in nine functional hubs (AUC=0.75 (0.651-0.847)) including bilateral anterior inferior temporal gyrus, right superior parietal lobule, right anterior parahippocampal gyrus, right anterior/posterior superior temporal gyrus (STG), right caudate nucleus, left amygdala and brainstem. Connectivity differences emerged in four tracts: hippocampus, parahippocampus, inferior and superior temporal gyri (STG). DANCE differed in the inferior fronto-occipital fasciculus (IFOF), medial (IFLmed) and lateral (IFLlat) components of the inferior longitudinal fasciculus and in the middle longitudinal fascicle (MdLF). WTC exposure duration significantly moderated the association between PTSD and DANCE values in the IFLmed, right posterior STG (p= 0.035). Conclusion: Our novel DANCE approach revealed converging functional and anatomical connectivity alterations uniquely associated with PTSD in WTC responders and offers compelling evidence for distinct neurobiological signatures of the disorder. These findings significantly advance our understanding of PTSD pathophysiology and highlight potential biomarkers for diagnosis and targeted intervention.

Social disconnection (i.e., loneliness and social isolation) poses a severe risk to health and is particularly prevalent among older adults. Qualitative and behavioural evidence suggests that intergenerational social interactions can mitigate social disconnection by offering opportunities for adults to form new social connections, yet the neurophysiological and kinematic processes underpinning relationship formation remain poorly understood. This article presents and validates the InterGenSynchrony Dataset -- a longitudinal, multimodal hyperscanning dataset acquired during a 6-session program, where 30 same generation and 31 intergenerational dyads met as strangers and became acquainted through creative drawing. The dataset includes dyads' concurrent recordings of brain activity (i.e., fNIRS hyperscanning), motion capture, self-report measures including loneliness, collaborative behavioural scores, drawings (artefacts of collaboration), subjective experiences in text and interview forms. This dataset is optimised for multimodal investigations into the neural, physiological, behavioural, kinematic, and subjective aspects of relationship formation within and between generations in a real-world setting.

Social media has an estimated global user base of 3.5 billion. Adolescents, constituting over 90% of this group, and thereby university students. are extensively involved in social media use, emphasizing its significance in their daily lives. Excessive use of social media poses risks such as depression, affecting academic performance. The objective of this study was to determine the association between social media use and depression among university students in Urban Northern Tanzania. This was a cross-sectional study that involved university students from three universities. Data was collected through an online close-ended questionnaire. Statistical analysis included the use of frequencies, percentages, chi-square and bivariate logistic regression at 95% confidence intervals (CIs) and significance at p value <0.05. A total of 384 participants were enrolled in the study. 36.7% reported that they used social media for 1 to 3 hours daily. 58.1% of all participants reported high frequency of social media use for social purposes. 29.2% of university students who participated in the study were screened to have depression. The prevalence of depression increased with longer daily duration of social media use. Those using social media for more than 5 hours daily, had 3.049 times higher odds of being depressed. Moderate frequency of social media use for social purposes was linked to reduced depression. Daily duration of social media was significantly associated with depression levels among university students. Significant associations were also found between moderate use of social media for socializing, with lower risk of depression. We recommend that universities develop targeted mental health interventions and promote balanced, purposeful social media use among students.

Abstract Introduction: A clinician's initial assessment during the mental status examination (MSE) places substantial weight on a patient's general appearance, grooming, and hygiene. However, the logistical difficulty of producing simulated or standardized patient (SP) videos that systematically manipulate these characteristics limits the development of clinical AI tools and training curricula. This pilot study investigates the technical feasibility of using a video-generation diffusion model to re-animate modified reference images onto driving videos, enabling the creation of diverse patient presentations without the need for repeated filming. Methods: Utilizing an established publicly available dataset, we extracted reference images of three SPs and applied a text-to-image AI model to generate five appearance conditions: the unmodified baseline and four escalating hygiene-deterioration levels: mild, moderate, marked, and severe. We then used the Wan2.2-Animate-14B animate video generation AI model to re-animate these modified portraits onto the original driving footage. This factorial design varied several model parameters including; pose retargeting, classifier-free guidance scales, and generation modes, resulting in 180 unique videos. Quality was measured through Frechet Video Distance (FVD) for distributional fidelity and a physics-aware assessment performed by a multimodal large language model to evaluate physical plausibility. Results: Our analysis yielded two primary observations. First, compositing through replacement-mode achieved significantly higher temporal fidelity than animation-mode (mean FVD 8.6 vs. 19.4; Cohen's d = 1.84). Second, while distributional fidelity showed a monotonic decline as hygiene perturbation increased (Spearman rho = 0.48, p < 0.001), physics-aware scores did not follow a similar trend. This pattern is consistent with fine-motor artifacts arising from model-level generative constraints rather than from the severity of the appearance modification alone. Conclusions: These findings demonstrate that generating appearance-modulated clinical video libraries is technically achievable. Nevertheless, the persistence of fine-motor artifacts underscores the necessity of expert human oversight before these materials can be safely deployed in educational and translational settings. Keywords: Generative artificial intelligence; Standardized patients; Video diffusion models; Psychiatric simulation; Mental status examination; AI-generated video; Medical education; Digital psychiatry