Tag: News Approved

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Reducing Intrusive Trauma Memories Using a Brief Mental Imagery Competing Task Intervention: Case Series of Trauma-Exposed Women in Iceland

Background: There is a need for scalable and simple interventions for trauma-exposed people. In this case series, we built on our previous case study and case series findings and further explored the use and potential effectiveness of a brief novel intervention to reduce the number of past intrusive memories of trauma. The imagery competing task intervention consists of a memory reminder and the visuospatial task Tetris played with mental rotation, targeting 1 intrusive memory at a time. Here, we test remote delivery of the intervention, including guidance from researchers without specialist mental health training, in a sample of women in Iceland with current intrusive memories from trauma. Objective: In a case series of trauma-exposed women, we aimed to explore whether this brief novel intervention reduces the number of established intrusive memories (primary outcome) and improves general functioning and symptom reduction in posttraumatic stress, depression, and anxiety (secondary outcomes). The acceptability of the intervention along with adaptations, that is, delivery by psychology students without specialist mental health training and digital delivery, was explored. Methods: Participants (N=8) monitored the number of intrusive memories from an index trauma (occurring 3‐16 years previously) in a daily diary at baseline, during the intervention, and postintervention at 1-month and 3-month follow-ups. The intervention was delivered digitally with guidance from clinical psychologists or psychology students. A repeated AB design was used (“A”: preintervention baseline, “B”: intervention phase). Intrusions were targeted one by one, creating repetitions of an AB design (ie, length of baseline “A” and intervention “B” varied for each memory). Results: The number of intrusive memories reduced for all participants from the baseline phase compared with the intervention phase, although the reduction was minimal for 2 participants (6.3%‐93%). The number of intrusive memories continued to reduce for 6 out of 8 participants (58%‐100% reduction at 1-month follow-up; 72%‐100% reduction at 3-month follow-up). Symptoms of posttraumatic stress, depression, and anxiety were reduced for most participants postintervention and continued to decrease during the follow-up periods. Functioning was improved for 7 of the 8 participants from baseline to postintervention and continued to improve at the follow-up assessments for 3 participants. The intervention delivered digitally and partly by students was perceived to be an acceptable way to reduce the frequency of intrusive memories by all participants (mean rating 9.5 out of 10). Conclusions: Data from this case series of traumatized women provide preliminary evidence for the effectiveness of this novel brief intervention in reducing intrusive memories of trauma occurring several years ago and in improving functioning and reducing core symptom burden. This study will inform a randomized controlled trial of this novel intervention, which may have considerable implications for large-scale clinical management of traumatized populations. Trial Registration: ClinicalTrials.gov NCT04209283; https://clinicaltrials.gov/study/NCT04209283 International Registered Report Identifier (IRRID): RR2-10.2196/29873
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Large Language Models and Their Applications in Mental Health: Scoping Review

Background: Large language models (LLMs) are poised to transform mental health care, offering advanced capabilities in diagnosis, prognosis, and decision support. Since their inception, numerous mental health-focused LLMs have emerged in the scientific literature, reflecting the growing interest in leveraging these models across various clinical applications. With a broad range of models available, diverse optimization strategies, and multiple use cases, reviewing the current landscape is critical to understanding where future impact lies. Objective: This study aimed to conduct a scoping review investigating the use of LLMs in mental health across diagnostic, prognostic, and decision support tasks. Methods: We screened 3121 papers from PubMed, Scopus, and Web of Science for studies published between January 2023 and October 2025, using terms related to LLM and mental health. After removing duplicates, 2 reviewers (MCL and WWBG) independently screened the studies, with a third (JJK) to resolve conflicting opinions. We extracted and synthesized information on the models, use cases, datasets, and adaptation methods from selected papers. Results: In total, 41 papers were selected. Many studies included evaluations on OpenAI’s GPT series applications: GPT-4 (24 studies, 58.5%) and GPT-3.5 (16 studies, 39%). Others included Bidirectional Encoder Representations from Transformers-derived models (9 studies, 22%), LLaMA (8 studies, 19.5%), and RoBERTa-derived models (6 studies, 14.6%). While all studies initially applied out-of-the-box LLMs, several adapted them through few-shot learning or fine-tuning to better align with specific research goals. The most common use case was in diagnostics (31 studies, 75.6%), while the most common target condition was depression (11 studies, 26.8%). While many studies reported superior performance of LLMs, only a minority of studies (13 studies, 31.7%) validated LLM performance against clinician assessments using real patient data, with the majority relying on proxy outcomes such as clinical vignettes, examination questions, or social media posts. Conclusions: Despite rapid growth and diversity of LLM applications in mental health, the field remains nascent and exploratory. Future developments must emphasize consistent model adaptation procedures to ensure safety and clinical workflow alignment. Models must also be evaluated on robust evaluation criteria by using standardized protocols and real clinical outcome measures.
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<![CDATA[Study ties higher schizophrenia rates in Black Americans to neighborhood vulnerability, spotlighting faster early-psychosis care and social supports.]]>
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A hierarchical machine learning model for predicting self-harm and suicidal behaviour in hospitalised patients with schizophrenia using clinical history and nursing observations

ObjectiveThis study aimed to develop and evaluate a two-layered machine learning framework that combines admission clinical information with longitudinal nursing observations to identify schizophrenia inpatients at high risk of self-harm or suicidal acts.MethodsWe retrospectively reviewed the records of 477 patients with schizophrenia hospitalised in Liaoning Province between July 2021 and July 2024. According to whether at least one self-injurious or suicidal episode was documented during the index admission, 159 individuals were assigned to a high-risk group and 318 to a non-high-risk group. At admission, 18 baseline variables (including age, sex, history of self-harm, hopelessness/depression, and educational attainment) were extracted from electronic medical records, and 39 nurse-rated behavioural items were scored weekly using the Psychiatric Patient Nursing Observation Scale. Static and dynamic feature sets were used to train six classifiers [regularized logistic regression (LR), support vector machine (SVM), extreme gradient boosting, random forest, multi-layer perceptron, and K-nearest neighbours]. The best static model (regularized LR) and the best dynamic model (SVM) were combined through probability-level weighted fusion to generate a hierarchical risk score.ResultsMultivariable analysis of admission features showed that previous self-harm [odds ratio (OR) = 4.323], hopelessness/depression (OR = 3.090), younger age (OR = 0.938), and higher educational level (OR = 1.357) were independent predictors of self-harm/suicidal behaviour. Among dynamic indicators, negative self-evaluation (OR = 2.303), self-reported depression (OR = 1.812), insomnia (OR = 1.768), talking to oneself (OR = 1.733), crying (OR = 1.700), and reduced conversation with others (OR = 1.422) remained significant. The optimised static LR model achieved an area under the curve (AUC) of 0.7564, and the dynamic SVM model reached an AUC of 0.8531. Their fusion further improved performance (AUC = 0.9048; sensitivity 0.8542; specificity 0.7789; accuracy 0.8042). This hierarchical model outperformed the best flat combined-feature model (SVM; AUC = 0.9022) in sensitivity (0.8542 vs. 0.6667), indicating a more clinically appropriate detection of high-risk patients.ConclusionA hierarchical machine learning approach that integrates baseline clinical history with repeated nursing assessments can effectively flag schizophrenia inpatients at high risk for self-harm and suicidal behaviour, supporting timely and individualised preventive strategies in psychiatric wards.
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Context-dependent interaction between oxytocin gene polymorphisms and alcohol dependence in modulating negative emotions during acute alcohol withdrawal in adult males

ObjectiveThe importance of multiple gene-environment interaction (G × E) has been highlighted in understanding the etiology of negative emotions. This study examines the impact of oxytocin (OXT) polymorphisms (rs2740210, rs6133010, and rs2740209) in combination with alcohol dependence on anxiety and depression symptoms during acute alcohol withdrawal under different social and environmental contexts.MethodA total of 414 Chinese Han male adults undergoing acute alcohol withdrawal were recruited. Participants provided blood samples for genotyping, self-reported measures of depression and anxiety, assessments of alcohol dependence severity, and demographic information regarding social and environmental contexts.ResultsResults revealed a positive correlation between severity of alcohol dependence and symptoms of depression and anxiety, while oxytocin polymorphism did not have a direct effect on depressive and anxiety symptoms. A significant interaction between OXT polymorphism (rs2740210 and rs2740209) and alcohol dependence in relation to anxiety symptoms solely among adults living with family and/or those who were married was observed. Further analyses indicate that the GG and CC genotypes are risk genotypes, while the T allele (rs2740210) and G allele (rs2740209) are non-risk alleles in the interaction between OXT genotypes (rs2740210, rs2740209) and alcohol dependence on anxiety among the aforementioned participants.ConclusionsThese findings provide evidence for distinct G × E interaction effects on anxiety and depression symptoms during acute alcohol withdrawal, supporting the weak diathesis-stress model. Furthermore, the study highlights the importance of considering environmental factors when investigating the role of oxytocin as a biological substrate underlying social bonding and the regulation of negative emotions.
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Validation of a criterion-based screening and triage pathway for adult ADHD: a prospective observational study of safety and operational efficiency

BackgroundThe increasing demand for adult attention-deficit hyperactivity disorder (ADHD) assessments has required the development of efficient triage pathways. This study provides a formal assessment of a criterion-based screening model designed to prioritise patient safety and operational efficiency within a National Health Service (NHS) specialist secondary care setting.MethodsA prospective observational validation design was employed, involving 49 consecutive adults referred for ADHD assessment none of whom had a previous ADHD diagnosis. The Comprehensive ADHD Screening Questionnaire (CASQ), a clinician-administered instrument based on DSM-5 criteria, was utilised by four trained Physician Assistants. To ensure an assessment of triage safety, a universal assessment model was adopted: all participants received a blinded, gold-standard diagnostic assessment (NICE-compliant) regardless of the initial triage recommendation thereby eliminating verification bias. The primary outcome measure was the Number Needed to Harm (NNH), defined as the number of people screened before a single false-negative result occurs.ResultsOf the 48 participants who completed the diagnostic process, six (12.5%) received an ADHD diagnosis. The triage pathway correctly identified all six cases, resulting in a sensitivity of 100.0% (95% CI: 61.0%–100.0%) and an infinite NNH. Specificity was 45.2% (95% CI: 31.2%–59.9%), with a positive predictive value of 20.7%. The pathway permitted 39.6% (n = 19) of referrals to be triaged to alternative pathways rather than full ADHD assessment, potentially saving significant specialist clinician time. Exploratory analyses indicated that score magnitude did not reliably distinguish between true and false positives within the group triaged as appropriate for further assessment.ConclusionsThese preliminary findings suggest that criterion-based screening conducted by appropriately trained non-specialist clinicians can achieve high levels of safety whilst improving service efficiency. The findings support the feasibility of task-shifting models in adult ADHD services, provided that triage thresholds are calibrated to prioritise sensitivity. These results require replication in adequately powered multi-site studies before firm conclusions regarding pathway safety can be drawn. Further research is required to establish inter-rater reliability and cost-effectiveness across diverse clinical settings.
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Protracted encephalopathy and subacute combined degeneration associated with chronic nitrous oxide use: a case report

Nitrous oxide is a dissociative hallucinogen that is increasingly used recreationally, in part due to its widespread availability. Its use is known to cause subacute combined degeneration via inactivation of vitamin B12; it may also result in acute delirium and chronic progressive encephalopathy. Though current practice guidelines call for treatment of neurological sequelae of nitrous oxide use with vitamin B12 supplementation, a paucity of long-term outcome data limits our ability to guide extended courses of treatment. In this report, we discuss a case of protracted encephalopathy associated with nitrous oxide use. We track the response to vitamin B12 supplementation in the hospital setting using the Mini-Mental Status Exam to assess the severity and improvement of cognitive impairment. We also review the patient’s comorbid medical and psychiatric conditions, which complicate diagnosis and treatment planning in this patient population.
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Parsing autism spectrum heterogeneity through fMRI

Nature Neuroscience, Published online: 15 May 2026; doi:10.1038/s41593-026-02269-1

Autism is remarkably heterogeneous, posing a long-standing challenge for linking genetics to brain dynamics. A cross-species study identifies two principal dysconnectivity signatures across 20 mouse models of autism risk, each associated with distinct molecular pathways, and shows analogous connectivity patterns in autistic humans. These results establish a translational framework for biologically grounded fMRI phenotyping.