Alcohol-related hospital admissions are rising in England, particularly among individuals aged 65 and over. While alcohol intake and nutritional status are important factors in health outcomes, they are often overlooked. Loneliness, also common in this age group, is associated with poorer health and increased mortality. (1) To explore the clinical and sociodemographic characteristics, and nutritional status of older adults with alcohol use disorder (AUD) admitted to hospital. (2) Explore participants’ experiences of alcohol use in later life, perceptions of health and wellbeing, social relationships, and views on support following hospital admission. Participants were recruited from a UK tertiary teaching hospital and identified as drinking at increased risk using routine AUDIT-C alcohol screening. Measures including 6CIT (Six-item Cognitive Impairment Test), Depression and anxiety (HADS), 24-hour Dietary Recall, Social network composition, De Jong Loneliness Scale, and Meaning in life questionnaire were measured at baseline and 6 months. Qualitative telephone interviews were conducted at 6 months (n = 7). Qualitative data were analysed using reflexive thematic analysis. Thirty older adults (mean age 71) were recruited. Over half (53

BACKGROUND AND AIMS:Substance use disorders are associated with an elevated risk of self-harm. Currently, clinical and structured assessment of self-harm risk typically relies on evidence from the general population samples. The aim of this study was to develop a risk model for self-harm that incorporates predictors specific to individuals with substance use disorders. METHODS:Using national registers, we identified a population-based cohort of 449 720 individuals with substance use disorders in Sweden between 2006 and 2020. We tested independence and strength of a range of socio-demographic and clinical factors, obtained through linkage of population-based registers, with a Cox proportional hazards model, and estimated the risk of self-harm. For the risk model, 361 120 individuals were allocated to the development sample and 88 600 to external validation based on different geographical regions. We assessed self-harm risk over five predetermined follow-up periods-within 7 days, 1 month, 3 months, 6 months and 12 months-following a healthcare contact for substance use disorders. RESULTS:In the development sample, self-harm rates ranged from 0.6% to 3.5%, and in the validation sample from 0.5% to 3.6%. Ten risk factors were retained in the final risk model. Strongest associations with subsequent self-harm were for clinical factors: previous self-harm [hazard ratio (HR) = 3.17, 95% confidence interval (CI) = 3.08-3.26] and comorbidity of mental disorders (HR = 2.63, 95% CI = 2.50-2.72). Recent psychotropic medication use, including antidepressant (HR = 1.29, 95% CI = 1.23-1.38) and antipsychotic treatments (HR = 1.34, 95% CI = 1.24-1.44), was associated with increased risk, even after adjusting for psychiatric comorbidity, likely reflecting greater clinical severity and complexity. Across follow-up periods, performance was good in terms of discrimination, with area under the curve (AUCs) ranging from 0.73 (95% CI = 0.71-0.76) to 0.79 (95% CI = 0.78-0.80). In relation to calibration, expected-to-observed risk ratios were 1.00 to 1.04 and Brier scores 0.01 to 0.04 across follow-up periods. We used the model to generate a simple web-based risk calculator [Oxford Self-hArM after substance use disorders (OxSAMS)]. CONCLUSIONS:Modifiable clinical factors appear to have the strongest associations with increased risk of self-harm in people with substance use disorders. Structured tools, taking account of the different strengths of those factors, could inform clinical decision-making and provide a baseline assessment for training and research.

The STRATIFY (Brain Network-Based Stratification of Reinforcement-Related Disorders) and ESTRA (Eating Disorders Stratification) studies were established as harmonised “sibling” cohorts to develop a mechanistically informed framework for stratifying psychiatric disorders. Here, we describe the study design, methodology, and cohort characteristics. Both studies investigate how network properties of brain structure and function, together with biological markers derived from blood-based genomics, epigenetics, and proteomics, relate to reinforcement-related behaviours that cut across major depressive disorder, alcohol use disorder, psychosis, and eating disorders. A further objective is to identify discriminative multimodal features that predict disease onset, symptom course, and functional outcomes, thereby supporting the development of targeted interventions. STRATIFY and ESTRA recruited 674 patients and 70 healthy controls aged 18–30 years (76% females), supplemented by 199 age- and sex-matched healthy controls from the population-based IMAGEN cohort assessed at the same sites using harmonised protocols. Multimodal assessment included structured clinical interviews, self-report measures, cognitive testing, biosamples for molecular analyses, and multimodal MRI (structural, diffusion, resting-state, and task-based fMRI). ESTRA participants additionally completed longitudinal follow-up, and all cohorts were assessed during the COVID-19 pandemic. STRATIFY and ESTRA together constitute a large-scale, open-science resource integrating multimodal brain, behavioural, and biological data across transdiagnostic patient cohorts in early adulthood. The anonymised dataset is available to the research community through managed access, supporting international collaboration and accelerating the development of mechanistically informed classification systems and predictive tools in psychiatry.

Childhood trauma (CT) is associated with cognitive impairment across major psychiatric disorders. We tested a novel transdiagnostic hypothesis that atypical connectivity of the default mode network (DMN) mediates the association between childhood trauma (CT) and cognitive impairment. The sample of 1851 individuals aged 18-25 included 433 patients with depression, eating disorders, alcohol use disorder, psychosis and ADHD) and were recruited as part of the ESTRA/STRATIFY/IMAGEN studies. CT was measured using the Childhood Trauma Questionnaire (CTQ). The CANTAB spatial working memory task was administered to assess cognition. Four a priori seeds of the default mode network (DMN) were measured during face processing, namely the medial prefrontal cortex (PFC), right lateral parietal (LP), left lateral parietal (LP) and posterior cingulate cortex (PCC), according to the Harvard-Oxford Cortical and Subcortical Atlas (http://www.cma.mgh.harvard.edu/fsl_atlas.html) as implemented in CONN. Patients had significantly reduced DMN connectivity between the four chosen DMN seeds and the rest of the brain. Reduced DMN connectivity mediated the association between higher CT and worse cognitive performance. Our findings are transdiagnostic in nature with stronger effects in some regions observed in depression, and suggest one transdiagnostic cortical network via which CT's effects on cognition are transmitted.

Linking synaptic-level perturbations to distributed brain-network dynamics remains a central challenge for understanding and treating mental illness. Although recent whole-brain models can reproduce individual brain activity patterns, they largely function as descriptive simulators rather than mechanistic, intervention-capable systems. Here we present an intervention-capable digital twin of the human brain, integrating individual neuroanatomy and task-evoked dynamics within a neuronal-scale framework. Individualised digital twin brains recapitulate a participant-specific compact cortico-subcortical network phenotype that captures transdiagnostic psychopathology across population and clinical cohorts. In silico modulation of excitatory and inhibitory synaptic conductance produces bidirectional, heterogeneous network responses across individuals. Population-scale simulations stratify individuals and predict longitudinal symptom trajectories from DTB-derived response profiles. Independent pharmacological functional MRI data further validate the predicted baseline-dependent network responses in vivo. Together, these findings establish digital brain models as experimental platforms for mechanistic perturbation, behavioural prediction and stratification, providing a foundation for precision neuroscience and psychiatry.

Background: In England, alcohol-related hospital admissions exceed 1million per annum. Alcohol care teams (ACT) have evolved in response to this, yet limited generalisable evidence exists about their effectiveness. This study will evaluate the clinical and cost effectiveness of ACT targeting adults with alcohol dependence admitted to NHS Hospitals. Methods: This prospective pragmatic quasi-experimental study will evaluate the effectiveness and cost-effectiveness of ACTs by assessing patient outcomes recruited from three hospitals in England with optimised ACT (oACT) compared to a cohort of participants recruited from similar hospitals with no, or minimal alcohol support (NoACT). N=545 adults (>=18 years) with alcohol dependence admitted (N=245 from three oACT hospitals and N=300 from three NoACT sites) will be recruited. To draw causal inferences of the relative effect of oACTs, a counterfactual control group will be derived, using propensity score matching. We anticipate 70% of participants will be followed up at 6-month (N=175 oACT group; N=210 NoACT (control) group), which allows for a potential unmatched pool in the control group of 20%. The primary outcome measure is total alcohol consumption in the 28 days prior to the 6-month follow-up measured in units of alcohol derived using Timeline Follow Back 28 (TLFB-28). Secondary outcomes include quantity and frequency of substance use in the 28-day period prior to the 6-month assessment, changes in alcohol risk and consequences assessed by Alcohol Use Disorder Identification Test (AUDIT), Severity of Alcohol Dependence Questionnaire (SADQ) and Alcohol Problems Questionnaire (APQ) collected at baseline and at 6-months. Mental health and well-being will be measured at 6-month follow-up using the short Warwick-Edinburgh Mental Well-Being Scale (SWEMWBS), Personal Health Questionnaire-9 items (PHQ-9) and Generalised Anxiety Disorder assessment (GAD-7). Consent will be requested to access individual health records to calculate the Charlson Comorbidity Index (CCI) at baseline. Economic outcomes will be assessed using the Client Service Receipt Inventory (CSRI) and the cost-effectiveness of oACTs, calculated as the cost per Quality-Adjusted Life Year (QALY) gained compared to control. QALYs will be derived from the EuroQol-5D-5L data. Discussion: Our findings will provide evidence to patients, clinicians, policy makers and commissioners about the best use of NHS funds. Trial registration: ISRCTN10723141. Registration data 1 November 2023

Early detection and prevention of psychiatric disorders, particularly depression, remain as major global health challenges, yet reliable tools for identifying individuals before symptom onset are lacking. Here, we combine functional neuroimaging with computational modeling to identify a mechanistic biomarker of depression risk. In a population-based adolescent cohort (IMAGEN, N = 1332), we found that weakened neural representations of emotional signals were linked to depressive symptoms. Perturbation experiments in a brain-aligned deep learning model showed that this deficit reflects overregularized emotion perception, producing a negative perceptual bias. A neurocomputational signature of this mechanism predicted depression symptom onset up to 4 years later at the IMAGEN follow-up (N = 725), was associated with both a genetic-risk variant and polygenic risk for depression, and improved depression classification in a patient cohort (STRATIFY, N = 411). These findings suggest a possible mechanism linking genetic vulnerability to altered emotion perception and future depression, and propose a predictive computational marker with potential for early detection and prevention.

Importance:Psychiatric diagnoses are not defined by neurobiological measures hindering the development of therapies targeting mechanisms underlying mental illness. Research confined to diagnostic boundaries yields heterogeneous biological results, whereas transdiagnostic studies often investigate individual symptoms in isolation. Objective:To develop a framework that groups clinical symptoms compatible with ICD-10 and DSM-5 according to their covariation and shared brain mechanisms. Design, Setting, and Participants:This diagnostic study was conducted in 2 samples, the population-based Reinforcement-Related Behaviour in Normal Brain Function and Psychopathology (IMAGEN) cohort (longitudinal assessments at 14, 19, and 23 years; study duration from March 2010 to the present) and the cross-diagnostic Brain Network Based Stratification of Mental Illness (STRATIFY)/Earlier Detection and Stratification of Eating Disorders and Comorbid Mental Illnesses (ESTRA) samples (study duration from October 2016 to September 2023). The samples are from 8 clinical research hospitals in Germany, the UK, France, and Ireland. For the population-based IMAGEN study, 794 of 1253 23-year-old participants had complete assessments including complete clinical assessments and neuroimaging data across all time points. For the cross-diagnostic STRATIFY/ESTRA samples, 209 of 485 participants aged 18 to 26 years had complete clinical and neuroimaging data. The sample included healthy control individuals and patients with alcohol use disorder, major depressive disorder, anorexia nervosa, and bulimia nervosa. Exposures:Sparse generalized canonical correlation analysis was used to integrate diverse data from clinical symptoms and 7 brain imaging modalities. Main Outcomes and Measures:The prediction of symptom features was the main outcome. The model was developed in the training set from the IMAGEN Study at age 23 years (70%), then applied in the remaining holdout test sample (30%), the independent STRATIFY/ESTRA patient sample, and longitudinally in the IMAGEN set. Results:In total, 1003 participants were included (425 male and 578 female; mean [SD] age, 22.1 [1.5] years). The reassembly of existing ICD-10 and DSM-5 symptoms revealed 6 cross-diagnostic psychopathology scores. They were consistently associated with multimodal neuroimaging components: excitability and impulsivity (training set: r, 0.26; 95% CI, 0.18-0.33; test set: r, 0.22; 95% CI, 0.10-0.35; STRATIFY/ESTRA set: r, 0.19; 95% CI, 0.07-0.31), depressive mood and distress (training: r, 0.30; 95% CI, 0.20-0.38; test: r, 0.22; 95% CI, 0.09-0.35; STRATIFY/ESTRA: r, 0.19; 95% CI, 0.04-0.33), emotional and behavioral dysregulation (training: r, 0.40; 95% CI, 0.31-0.48; test: r, 0.17; 95% CI, 0.14-0.36; STRATIFY/ESTRA: r, 0.19; 95% CI, 0.06-0.30), stress pathology (training: r, 0.32; 95% CI, 0.19-0.43; test: r, 0.14; 95% CI, 0.05-0.23; STRATIFY/ESTRA: r, 0.12; 95% CI, 0.01-0.22), eating pathology (training: r, 0.34; 95% CI, 0.25-0.42; test: r, 0.26; 95% CI, 0.15-0.37; STRATIFY/ESTRA: r, 0.15; 95% CI, 0.12-0.34), and social fear and avoidance symptoms (training: r, 0.31; 95% CI, 0.25-0.42; test: r, 0.18; 95% CI, 0.15-0.35; STRATIFY/ESTRA: r, 0.12; 95% CI, 0.12-0.33). Conclusion and Relevance:In this study, the identification of symptom groups of mental illness robustly defined by precisely characterized brain mechanisms enabled the characterization of dimensions of psychopathology based on quantifiable neurobiological measures.

AIMS:This study aimed to identify (i) the number of alcohol care teams (ACTs) in England, (ii) the characteristics of patients supported by ACTs, and (iii) the service structure and care components offered by ACTs. METHODS:All acute hospitals (i.e. those providing short-term high-dependency medical care) in England were approached to complete a survey of alcohol care provision. Surveys were completed through researcher-guided interviews by staff familiar with the hospital's alcohol provision. It featured questions on service structure, patient characteristics, service functions, and policies. Data collection took place between May and October 2023. RESULTS:Of 170 hospitals approached, 122 completed a survey and 80 reported having an ACT. Most ACT patients were male (mean 64.1%; 95% confidence interval (CI) 61.8-66.4), white (mean 79.2%; 95% CI 75.1-83.4), aged 45-54 (mean 27.8%; 95% CI 25.0-30.5), and experiencing severe alcohol dependence (mean 66.2%; 95% CI 36.8-95.7). Most services had a clinical lead but only 58% funded this role. Fifty-nine percent of services operated 7 days per week. Most services reported identification and brief advice, though it was rarely systematized. Nearly all supported medically assisted alcohol withdrawal, though a quarter of patients did not complete medically assisted alcohol withdrawal before discharge. CONCLUSIONS:ACT numbers increased significantly between 2019 and 2024. They offer a clinical service to highly vulnerable and complex patients. There is significant variation in ACT operational models, training, and leadership which will impact the effectiveness of identification strategies and management of patients with comorbid alcohol use disorder within acute medical settings.

Mounting evidence suggests hierarchical psychopathology factors underlying psychiatric comorbidity. However, the exact neurobiological characterizations of these multilevel factors remain elusive. In this study, leveraging the brain-behavior predictive framework with a 10-year longitudinal imaging-genetic cohort (IMAGEN, ages 14, 19 and 23, N = 1,750), we constructed two neural factors underlying externalizing and internalizing symptoms, which were reproducible across six clinical and population-based datasets (ABCD, STRATIFY/ESTRA, ABIDE II, ADHD-200 and XiNan, from age 10 to age 36, N = 3,765). These two neural factors exhibit distinct neural configurations: hyperconnectivity in impulsivity-related circuits for the externalizing symptoms and hypoconnectivity in goal-directed circuits for the internalizing symptoms. Both factors also differ in their cognitive-behavior relevance, genetic substrates and developmental profiles. Together with previous studies, these findings propose a hierarchical neurocognitive spectral model of comorbid mental illnesses from preadolescence to adulthood: a general neuropsychopathological (NP) factor (manifested as inefficient executive control) and two stratified factors for externalizing (deficient inhibition control) and internalizing (impaired goal-directed function) symptoms, respectively. These holistic insights are crucial for the development of stratified therapeutic interventions for mental disorders.

Task-fMRI analyses typically focus on localized activation contrasts between stimuli, neglecting the brain’s dynamic hierarchy. We introduce Brain Diffusion Transformer (Brain-DiT), a deep generative model capturing recurrent processing underlying individualized neurocognitive state transitions via functional networks. Without prior assumptions, Brain-DiT identifies canonical cognitive regions in the brain and reveals replicable subgroups with distinct neural circuits in large cohorts, offering critical clinical insights overlooked by traditional methods: individuals exhibiting negative emotion bias, linked to language-related regions, had a 12-fold higher likelihood of major depression, and those with maladaptive inhibition strategies, associated with overactive medial frontal regions, showed a 9-fold increased risk of alcohol abuse. By bridging cognitive theory and psychiatric applications, Brain-DiT provides a unified analytical paradigm, paving the way for operational personalized medicine in psychiatry. ### Competing Interest Statement Dr Banaschewski served in an advisory or consultancy role for AGB Pharma, eye level, Infectopharm, Medice, Neurim Pharmaceuticals, Oberberg GmbH and Takeda. He received conference support or speaker fee by Janssen-Cilag, Medice and Takeda. He received royalities from Hogrefe, Kohlhammer, CIP Medien, Oxford University Press; the present work is unrelated to these relationships. Dr Barker has received honoraria from General Electric Healthcare for teaching on scanner programming courses. Dr Poustka served in an advisory or consultancy role for Roche and Viforpharm and received speaker fee by Shire. She received royalties from Hogrefe, Kohlhammer and Schattauer. Gareth J. Barker received honoraria for teaching from GE Healthcare. Lei Peng is the Co-founder & CEO of Neuroxess and holds shares in the company. However, the research presented in this manuscript was conducted solely in the capacity of Lei Peng as a PhD student at Fudan University, without the use of any company funds. Additionally, the research findings do not have any direct or indirect commercial interests or intellectual property conflicts with Neuroxess or its products. The study design, data collection, analysis, and conclusions were entirely independent of any corporate influence. Lei Peng has disclosed this information to ensure transparency and to confirm that there are no competing interests that could potentially influence the integrity of this research. The present work is unrelated to the above grants and relationships. The other authors report no biomedical financial interests or potential conflicts of interest.

Background Alcohol consumption is a potentially modifiable risk factor for breast cancer (BC). Reducing alcohol consumption within the daily amount at low-risk for alcohol-related consequences (daily alcohol threshold) may contribute to preventing BC new cases. However, most women are unaware of risk factors for BC, the daily alcohol threshold, and how to measure alcohol use. We aimed at investigating the efficacy of accessing an interactive website in increasing the knowledge that alcohol is a BC risk factor. Methods We conducted a randomized controlled trial among women waiting for mammography. Women completed a questionnaire to investigate their knowledge before and after accessing an interactive (intervention group) and non-interactive (control group) website. Results We recruited 671 women, randomized 329 (49.0%) and 342 (51.0%) to the intervention and control groups, respectively. At baseline, most women were not aware of most modifiable BC risk factors. Accessing either website significantly increased the percentage of women who acquired the knowledge on BC risk factors, with the interactive website achieving better results: 82% and 69% of women acquired the knowledge that alcohol is a risk factor for BC in the intervention and control groups, respectively (p<0.001). Among women with lower levels of education, the probability of acquiring this knowledge was higher in the intervention group than control group. Conclusion Our results show that accessing an interactive website may increase the percentage of women who acquire the knowledge that alcohol is a BC risk factor especially among women of lower levels of education.

Background Alcohol use in autism spectrum disorder (ASD) is under-researched. Previous reviews have explored substance use as a whole, but this neglects individual characteristics unique to different substances. Alcohol use in non-clinical samples is associated with diverse responses. To advance practice and policy, an improved understanding of alcohol use among people with ASD is crucial to meet individual needs. Aims This was a narrative systematic review of the current literature on the association between alcohol use and ASD, focusing on aetiology (biological, psychological, social and environmental risk factors) and implications (consequences and protective factors) of alcohol use in autistic populations who utilise clinical services. We sought to identify priority research questions and offer policy and practice recommendations. Method PROSPERO Registration: CRD42023430291. The search was conducted across five databases: CINAHL, EMBASE, MEDLINE, PsychINFO and Global Health. Included studies explored alcohol use and ASD within clinical samples. Results A total of 22 studies was included in the final review. The pooled prevalence of alcohol use disorder in ASD was 1.6% and 16.1% in large population registers and clinical settings, respectively. Four components were identified as possible aetiological risk factors: age, co-occurring conditions, gender and genetics. We identified ten implications for co-occurring alcohol use disorder in ASD, summarised as a concept map. Conclusion Emerging trends in the literature suggest direction and principles for research and practice. Future studies should use a standardised methodological approach, including psychometrically validated instruments and representative samples, to inform policy and improve the experience for autistic populations with co-occurring alcohol use.

The limited availability and high cost of 7 Tesla (7T) structural MRI hinder its widespread application despite its superior imaging quality. This study introduces a High Frequency-Generative Adversarial Network (HF-GAN) to predict three-dimensional 7T-equivalent (P7T) images from standard 3T structural MRI scans, offering a cost-effective alternative. HF-GAN was trained on paired 3T and 7T MRI data and validated on external datasets, including STRATIFY/ESTRA (N=671) and ADNI2 (N=643), covering psychiatric and neurodegenerative disorders. Results indicate that P7T images generally exhibit enhanced contrast and better preservation of fine structural details compared to 3T, with improved sensitivity in detecting disease-related differences in key brain regions such as the thalamus, caudate, putamen, and frontal cortical areas. The partial η^2 values revealed that P7T explained a higher proportion of variance compared to 3T in most comparisons, highlighting its improved sensitivity to disease-related structural changes. These findings demonstrate that HF-GAN effectively enhances 3T MRI data quality, providing a scalable solution for research and clinical applications in neurodegenerative and psychiatric disorders. Additional validations in brain and other organ systems are warranted to further advance clinical translation. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This study was funded by the following sources: the European Union-funded Horizon Europe project environMENTAL (101057429) and co-funding by UK Research and Innovation under the UK Governments Horizon Europe funding guarantee (10041392 and 10038599) and the the National Key R&D Program of Ministry of Science and Technology of China (MOST 2023YFE0199700); the Horizon 2020-funded European Research Council Advanced Grant STRATIFY (Brain network based stratification of reinforcement-related disorders; 695313); the German Research Foundation (COPE; 675346); the Medical Research Council and Medical Research Foundation (grants MR/R00465X/1 and MRF-058-0004-RG-DESRI: ESTRA: Neurobiological underpinning of eating disorders: integrative biopsychosocial longitudinal analyses in adolescents; MR/S020306/1 and MRF-058-0009-RG-DESR-C0759: Establishing causal relationships between biopsychosocial predictors and correlates of eating disorders and their mediation by neural pathways) and the National Institute for Health and Research (NIHR) Biomedical Research Centre (BRC) and Maudsley NHS Foundation Trust (SLaM)；Medical Research Council (grant MR/W002418/1: Eating Disorders: Delineating illness and recovery trajectories to inform personalized prevention and early intervention in young people (EDIFY)); ERC Consolidator Grant (MENTALPRECISION 101001118); Medical Research Foundation (MRF-058-0014-F-ZHAN-C0866)；National Key R&D Program of China (No. 2023YFF1204804), National Natural Science Foundation of China (No. 82271956, No. 62331021), Shanghai Municipal Science and Technology Explorer Project (No. 23TS1400500). ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: 1. Shanghai Public Health Clinical Centre Ethics Committee gave ethical approval for this work I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes All data produced in the present study are available upon reasonable request to the authors

There is a growing literature exploring the placebo response within specific mental disorders, but no overarching quantitative synthesis of this research has analyzed evidence across mental disorders. We carried out an umbrella review of meta-analyses of randomized controlled trials (RCTs) of biological treatments (pharmacotherapy or neurostimulation) for mental disorders. We explored whether placebo effect size differs across distinct disorders, and the correlates of increased placebo effects. Based on a pre-registered protocol, we searched Medline, PsycInfo, EMBASE, and Web of Knowledge up to 23.10.2022 for systematic reviews and/or meta-analyses reporting placebo effect sizes in psychopharmacological or neurostimulation RCTs. Twenty meta-analyses, summarising 1,691 RCTs involving 261,730 patients, were included. Placebo effect size varied, and was large in alcohol use disorder (g = 0.90, 95% CI [0.70, 1.09]), depression (g = 1.10, 95% CI [1.06, 1.15]), restless legs syndrome (g = 1.41, 95% CI [1.25, 1.56]), and generalized anxiety disorder (d = 1.85, 95% CI [1.61, 2.09]). Placebo effect size was small-to-medium in obsessive-compulsive disorder (d = 0.32, 95% CI [0.22, 0.41]), primary insomnia (g = 0.35, 95% CI [0.28, 0.42]), and schizophrenia spectrum disorders (standardized mean change = 0.33, 95% CI [0.22, 0.44]). Correlates of larger placebo response in multiple mental disorders included later publication year (opposite finding for ADHD), younger age, more trial sites, larger sample size, increased baseline severity, and larger active treatment effect size. Most (18 of 20) meta-analyses were judged ‘low’ quality as per AMSTAR-2. Placebo effect sizes varied substantially across mental disorders. Future research should explore the sources of this variation. We identified important gaps in the literature, with no eligible systematic reviews/meta-analyses of placebo response in stress-related disorders, eating disorders, behavioural addictions, or bipolar mania.

Current psychiatric diagnoses are not defined by neurobiological measures which hinders the development of therapies targeting mechanisms underlying mental illness 1,2 . Research confined to diagnostic boundaries yields heterogeneous biological results, whereas transdiagnostic studies often investigate individual symptoms in isolation. There is currently no paradigm available to comprehensively investigate the relationship between different clinical symptoms, individual disorders, and the underlying neurobiological mechanisms. Here, we propose a framework that groups clinical symptoms derived from ICD-10/DSM-V according to shared brain mechanisms defined by brain structure, function, and connectivity. The reassembly of existing ICD-10/DSM-5 symptoms reveal six cross-diagnostic psychopathology scores related to mania symptoms, depressive symptoms, anxiety symptoms, stress symptoms, eating pathology, and fear symptoms. They were consistently associated with multimodal neuroimaging components in the training sample of young adults aged 23, the independent test sample aged 23, participants aged 14 and 19 years, and in psychiatric patients. The identification of symptom groups of mental illness robustly defined by precisely characterized brain mechanisms enables the development of a psychiatric nosology based upon quantifiable neurobiological measures. As the identified symptom groups align well with existing diagnostic categories, our framework is directly applicable to clinical research and patient care.