JAMA Psychiatry

ObjectiveDespite recommendations that young people at clinical high risk (CHR) for psychosis receive stepped treatment, few programs have published details of their clinical models or outcomes. This study describes the preliminary effectiveness of a risk calculator-informed stepped care model used at the Yale PRIME Clinic, a specialized outpatient clinic for young people at CHR. MethodsSeventy-one individuals (ages 12-25) at CHR enrolled in Yales PRIME Clinic during the first four years of the treatment program. Participants completed clinical assessments at six timepoints over two years of treatment within a care model informed by an empirically grounded psychosis risk calculator. Linear mixed-effect models were fit to examine changes in clinical symptoms over time, and sensitivity analyses evaluated differences in clinical trajectories between completers and non-completers. ResultsIndividuals engaged in treatment demonstrated significant and sustained improvements in positive, negative, general, disorganized, and depressive symptoms. Improvements in positive symptoms emerged by 6 months and continued to improve across most subsequent timepoints (6, 12, and 24 months). Pattern mixture analyses suggested that clinical trajectories did not significantly differ between completers and non-completers, though non-completers possessed more heterogeneous trajectories. ConclusionsA stepped care model informed by individualized risk calculator scores was feasible for delivery in a specialized outpatient setting, and was associated with broad symptom improvement for young people at CHR. Further controlled studies with blinded raters are needed to further confirm the efficacy of stepped care models and isolate the active components of treatment. HighlightsO_LIParticipants at clinical high risk for psychosis experienced significant reductions in attenuated psychotic symptoms and improvements in mood while enrolled in a risk-calculator-informed stepped care treatment model. C_LIO_LIParticipants who disengaged from treatment did not have significantly different clinical trajectories than those who remained in care. C_LIO_LIThe results suggest preliminary evidence for the feasibility of implementing a risk-calculator-informed stepped care model. C_LI

BackgroundPoor patient retention and family engagement compromise the effectiveness of coordinated specialty care (CSC) for first-episode psychosis (FEP). This mixed methods study aimed to identify program-level characteristics (CSC fidelity and engagement strategies) associated with patient retention and family engagement in Massachusetts CSC programs. MethodsPrimary outcomes were rates of patient retention and family engagement ([&ge;]1 evidence-based family intervention session), based on CSC program census (October 2022 - September 2023). Quantitative analyses explored program characteristics (EPINET Program-Level Core Assessment Battery) and fidelity ratings (Massachusetts Psychosis Fidelity Scale) as predictors using t-tests or univariate linear regressions. Thematic analysis of program interviews compared patient and family engagement strategies employed by high versus low performing programs. ResultsAcross nine programs, mean patient retention was 86% (range: 58-97%) and family engagement was 40% (range: 12-100%). Higher fidelity to evidence-based services (e.g., individual therapy, family intervention, and supported education/employment) was significantly associated with both outcomes (p<.05; R2 range: .51-.72). Mixed-methods analysis showed that high performing programs used case management-related supports to meet service users practical needs. Factors associated with higher patient retention included having comprehensive intake assessments, provider visits during hospitalization, and periodic treatment reviews. Programs that conducted benefits counseling and proactively recommended family services as standard care had higher family engagement. ConclusionsHigher fidelity CSC programs had better patient retention and family engagement. Case management-related supports addressed treatment barriers. Strategies designed to strengthen therapeutic alliance and goal alignment may promote patient engagement, while family engagement may benefit from proactive recommendation of family intervention.

ObjectiveWe examined the clinical utility of resting state electroencephalography (rsEEG) by evaluating its temporal stability, discriminant validity for B-SNIP psychosis Biotypes, and suitability as a treatment target for brain stimulation. MethodsWe collected 5 minutes of eyes-open rsEEG from 1401 participants with psychosis and 750 healthy persons. A subset of participants was re-tested after 6 months and 12 months (N=109). In a pilot target engagement study (n=5) we collected rsEEG before and after 2 high-definition transcranial direct current stimulation (HD-tDCS) interventions targeting the left dorsolateral prefrontal cortex (dlPFC) and temporoparietal junction (TPJ). Data were reduced with principal component analyses to delta/theta, alpha, beta, and gamma frequency bands, and compared between groups and timepoints. ResultsrsEEG frequency bands displayed good-to-excellent stability and significantly distinguished psychosis Biotypes with large effect sizes. Compared to healthy, Biotype-1 had low activity (average ES=-.58), Biotype-2 had high activity (ES=1.07), and Biotype-3 had slightly elevated activity (ES=.33). There were no rsEEG differences between DSM psychosis groups. After anodal dlPFC stimulation, alpha and gamma power slightly increased while positive symptoms and verbal fluency improved. After cathodal TPJ stimulation, delta/theta power slightly increased while psychoticism and digit sequencing improved. ConclusionsResting state brain activity is a trait-like marker that differentiates B-SNIP psychosis Biotypes, suggesting differing underlying neurophysiology. The pilot intervention supports the feasibility of targeting this underlying neurophysiology with HD-tDCS. Integrating rsEEG in diagnostic procedures and stratified intervention selection may be beneficial for psychosis patients.

Background and HypothesisAbnormal default mode network (DMN) connectivity was observed in both tobacco use and psychotic spectrum disorders, but it remains unknown how psychosis impacts the relationship between connectivity and tobacco use. Interventions targeting the left lateral parietal DMN node (LLPDMN) have modulated DMN connectivity and nicotine craving in psychosis. We aimed to investigate relationships between DMN connectivity, psychotic illness, and tobacco use. Study Design336 participants (psychosis: n=161, control: n=175) reported their tobacco use history and underwent resting-state functional magnetic resonance imaging. We calculated connectivity within DMN and salience network (SN), between DMN-SN, and from LLPDMN to other DMN and SN nodes. Logistic and LASSO regression with bootstrapping were performed to investigate diagnosis-by-connectivity interactions on lifetime tobacco use. Exploratory brainwide analysis was conducted by regressing brainwide connectivity to LLPDMN against daily cigarette use. Study ResultsWe observed a significant diagnosis-by-DMN connectivity interaction for lifetime tobacco use (p=0.0281, coefficient=0.457, OR=1.579, 95% CI=[1.063, 2.411]); in the psychosis group, higher DMN connectivity was associated with higher odds of lifetime tobacco use. LASSO regression yielded four predictors of lifetime tobacco use: age, diagnosis, LLPDMN connectivity to a prefrontal SN node, and the interaction between diagnosis and LLPDMN connectivity to a right parietal DMN node. Brainwide analysis identified bilateral somatomotor clusters where higher connectivity to LLPDMN correlated with higher daily cigarette use (voxel-wise p<0.001, cluster p<0.05). ConclusionsPsychosis diagnosis modified relationship between DMN connectivity and tobacco use. Modulating DMN connectivity may provide a psychosis-specific treatment target for tobacco dependence.

BackgroundForensic psychiatric services are expanding in many countries, and discharging patients from secure hospitals relies on accurate estimates of risk of adverse outcomes. Novel evidence-based tools for estimating one key risk, violent reoffending, have been developed in recent years. We aimed to externally validate one new tool, FoVOx, in forensic psychiatric patients sentenced to treatment, and to develop an updated model (FoVOx2), incorporating additional clinical predictors. MethodsUsing Swedish national registers, we conducted a temporal external validation of FoVOx by examining 767 patients discharged between 2014 and 2023. For the FoVOx2 cohort, 906 patients discharged between 2008 and 2023 were followed up, and additional predictors tested. The outcome was violent reconviction within 12 or 24 months. Model performance was evaluated using Harrells C-index, time-dependent AUCs, calibration, and classification metrics at predefined thresholds. ResultsIn temporal validation, FoVOx showed moderate discrimination (AUCs 0.69 and 0.71; C-index = 0.69) and acceptable overall accuracy (Brier <0.11). Calibration was generally good, with mild overestimation at the highest predicted risks (>20%) at 12 months and slight underprediction at 24 months. The updated FoVOx2 model newly incorporated clozapine treatment and additional diagnostic categories. It was associated with improved performance (AUCs 0.77; optimism-corrected C-index = 0.72; Brier 0.06 and 0.09) and achieved good calibration (intercept {approx} 0; slopes 1.03 and 1.05). ConclusionsUpdating risk assessment tools with additional clinical factors can lead to incremental improvement in model performance. Implementing tools should consider clinical utility and impact as next steps.

BackgroundAntipsychotic medications are recommended for managing schizophrenia spectrum disorders, yet their long-term effects on functional recovery remain unclear. Existing evidence is conflicting, often derived from between-subject comparisons vulnerable to confounding by indication. MethodsWe conducted a nationwide register-based cohort study of 65,630 individuals with incident schizophrenia spectrum disorders in Denmark (1998-2023). We modelled antipsychotic exposure against productive engagement (employment or education). We employed two distinct analytical approaches to separate causal from prognostic associations: (1) Within-subject stratified Cox models with time-varying covariates, where patients served as their own controls to eliminate time-invariant confounding; and (2) Fine-Gray competing risks models using a between-subject design with baseline exposure, accounting for mortality and emigration. FindingsOver 26.9 million person-weeks, the overall productive engagement rate was 48.2%. Integration of hospital pharmacy data revealed a 6.1% exposure misclassification bias in previous studies relying solely on community records. The primary within-subject analysis revealed significant temporal heterogeneity: medication use was associated with reduced engagement rates in the acute (0-2 years: HR 0.908) and consolidation phases (2-5 years: HR 0.946), but reversed to a positive association in the maintenance phase (5+ years: HR 1.019). In contrast, the between-subject Fine-Gray model yielded a null result (SHR 1.002, 95% CI 0.988-1.015), failing to detect these phase-specific dynamics. InterpretationWithin-subject modelling reveals that antipsychotic treatment involves a functional trade-off: it is associated with a transient reduction in engagement rates during the early consolidation phase (2-5 years), followed by stabilisation and potential benefit in the maintenance phase (5+ years). The null result in standard between-subject (Fine-Gray) analysis likely reflects residual confounding by indication and exposure misclassification, highlighting the necessity of within-person designs to unmask the true stage-specific impact of pharmacotherapy on vocational recovery. FundingNone directly for this study. Danmarks Nationalbank funded the research visit that facilitated this collaboration.

BackgroundIn 2020, Oregon became the first U.S. state to establish a regulated framework for adults to access psilocybin services using naturally-derived mushroom products. No studies have examined mental health outcomes among individuals receiving psilocybin in this context. AimsTo evaluate changes in self-reported symptoms of depression, anxiety, and well-being 30-days post-psilocybin session under the Oregon state-regulated model , and document session-related adverse events and doses consumed. MethodsThis was a naturalistic study (March 2024-April 2025) among adults [&ge;]21 years participating in a legal psilocybin services program. Online surveys were completed pre-session, 1-day, and 30-days post-session. Primary outcomes were change in depression, anxiety, and well-being symptoms pre-session to 30-days post-session evaluated using linear mixed-effects models (random effect: timepoint; fixed effects: sex, concurrent psychiatric medication use, age, session dose [total psilocybin equivalents, TPE, mg: psilocybin mg + 1.39 * psilocin mg]). Adverse events (e.g., hallucinogen persisting perception disorder [HPPD]) were assessed at 1-day and 30-days post-session. ResultsParticipants (n=88; median age 43 years; 52% male) were predominantly Oregon residents (53.4%), psychedelic-experienced (64.8%), and concurrently using psychiatric medication (46.6%). All outcomes improved significantly at 30-days post-session (p<0.001), including in sensitivity analyses stratified by concurrent psychiatric medication usage (p<0.001 all outcomes, both groups). Two participants (2.3%) reported symptoms consistent with HPPD at 1-day post-session, but none at 30-days. Mean dose was 27.8 mg (SD 8.2) TPE. ConclusionsPsilocybin sessions delivered under the Oregon regulatory model were associated with clinically meaningful improvements in depression, anxiety, and well-being 30-days post-session, supporting therapeutic effectiveness of legal psilocybin services.

ImportanceLarge language models (LLMs) have demonstrated diagnostic potential in several medical specialties, but their application to psychiatry - where diagnosis relies heavily on clinical judgment, narrative interpretation, and reasoning under uncertainty - remains insufficiently evaluated. ObjectiveTo evaluate diagnostic accuracy and clinician-judged reasoning quality of multiple large language models using psychiatric case vignettes. DesignMixed-methods evaluation study of diagnostic accuracy across four LLMs using 196 psychiatric case vignettes (135 published and 61 novel). Clinical reasoning quality was evaluated on a randomly selected subset of 30 vignettes using structured clinician ratings along two reasoning dimensions. The highest-performing model was illustratively compared with psychiatry trainees on the same subset. Diagnostic correctness for the full vignette set was assessed by a separate adjudicator LLM. SettingPublicly available model interfaces, December 2025. ParticipantsFive board-certified psychiatrists evaluated model-generated clinical reasoning. Two psychiatry residents served as the illustrative human comparison. Main Outcomes and MeasuresDiagnostic accuracy and clinician-rated clinical reasoning quality. Diagnostic accuracy was assessed using top-1 accuracy, top-5 accuracy, recall@5, and mean reciprocal rank based on ranked lists of five differential diagnoses per vignette. Clinical reasoning quality was assessed using two 5-point Likert scales adapted from the American Council of Graduate Medical Education Psychiatry Residency Milestones, evaluating data extraction and diagnostic reasoning. ResultsAcross 196 psychiatric case vignettes, Claude Opus 4.5 (Anthropic) achieved the highest diagnostic accuracy (top-1 accuracy, 0.638; top-5 accuracy, 0.801; recall@5, 0.731; mean reciprocal rank, 0.710) and clinician-rated reasoning scores. Higher clinician-rated diagnostic reasoning quality was strongly associated with diagnostic correctness in mixed-effects logistic regression analyses ({beta} = 1.80; p < 0.001), corresponding to an approximately six-fold increase in odds of a correct diagnosis per 1-point increase in reasoning score. In an illustrative comparison, diagnostic accuracy of Claude Opus 4.5 fell within the range observed for psychiatry trainees. Conclusions and RelevanceLLMs demonstrated high diagnostic accuracy and generated clinical reasoning that clinicians judged to be largely coherent and safe. Diagnostic reasoning quality was more strongly associated with diagnostic correctness than data extraction quality, underscoring the importance of evaluating reasoning alongside accuracy when assessing LLMs for clinical decision support in psychiatry. Key PointsO_ST_ABSQuestionC_ST_ABSCan multiple large language models accurately diagnose psychiatric conditions and generate diagnostic reasoning that clinicians judge as coherent, safe, and clinically meaningful? FindingsAcross 196 psychiatric case vignettes, four large language models demonstrated high diagnostic accuracy. In a clinician-evaluated subset of 30 vignettes, model diagnostic accuracy fell within the range observed for psychiatry residents. Clinicians judged model-generated diagnostic reasoning to be largely coherent and safe. Higher clinician-rated reasoning quality was strongly associated with diagnostic correctness, independent of data extraction quality. MeaningEvaluating diagnostic reasoning, in addition to accuracy, may be important when assessing large language models for potential clinical decision support in psychiatry.

Working memory (WM) impairments have been reported in different stages of psychosis but whether their neural correlates are shared or stage-specific is unknown. This meta-analysis examined WM-related brain activity across psychosis stages: familial and clinical high-risk for psychosis (at-risk stage), first-episode psychosis (early psychosis stage), and chronic schizophrenia (chronic psychosis stage). PubMed, Ovid, and Web of Science were searched up to July 2025 for functional magnetic resonance imaging (fMRI) studies comparing individuals in each stage and healthy controls during WM. Seed-based d-mapping assessed WM-related fMRI correlates at each stage. Significance was set at family-wise error-corrected p<.05. Forty-two studies were included: 7 in the at-risk stage, 5 in the early psychosis stage, and 30 in the chronic psychosis stage. In chronic psychosis, higher activation relative to controls was observed in the medial prefrontal cortex, rostral anterior cingulate, right insula and superior temporal gyrus, posterior cingulate cortex, left superior temporal and supramarginal gyri. Lower activation in chronic psychosis vs controls was found in the cerebellum, bilateral precuneus, middle temporal gyrus, and thalamus. The early psychosis stage was characterised by lower activation compared to controls in the dorsal anterior cingulate, bilateral caudate nuclei, and inferior frontal gyrus. No significant clusters emerged in the at-risk stage, or across stages. In combined early and chronic psychosis analyses, anterior cingulate cortex activation was positively associated with both antipsychotic dose and illness duration. These findings indicate that disruptions in WM circuitry may evolve after illness onset and may represent a potential biomarker of psychosis staging. HighlightsO_LIThis study revealed distinct brain activity patterns in early and chronic psychosis stages. C_LIO_LIAlterations in brain activity were more widespread in the chronic stage of psychosis. C_LIO_LIAntipsychotic dose and illness duration predicted anterior cingulate cortex activation. C_LIO_LIDistinct neural correlates of working memory may reflect illness progression. C_LI

Understanding factors that predict the course of schizophrenia remains essential for improving long-term clinical management. Rate and severity of symptom exacerbations vary widely across individuals, and although prior studies have examined potential predictors, findings have been inconsistent and often limited by small samples, infrequent assessments, and non-standardized measures. Using data from phase 1 of the Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE), which includes a large cohort with monthly standardized evaluations, this study investigated whether baseline negative symptom severity predicts risk of symptom exacerbation over time. Participants were 1139 adults aged 18-65 years meeting DSM-IV criteria for schizophrenia. Symptoms worsening or exacerbation was defined as a [&ge;]12-point increase from baseline on the PANSS total score. Cox regression survival models examined the association between baseline PANSS negative symptom tertiles and time to exacerbation, adjusting for age, sex, PANSS positive and general psychopathology subscales, and CGI-Severity. Overall, 25.5% of participants experienced exacerbation over a 18-month period of follow-up. Survival curves demonstrated significant separation across negative symptom tertiles (p=0.047), with higher baseline negative symptoms associated with longer time to exacerbation. Compared with the lowest tertile, medium and high negative symptom groups showed reduced exacerbation risk (HR=0.73 and HR=0.69, respectively; both p=0.03). Findings indicate that greater baseline negative symptom severity is associated with a lower likelihood of short-term symptom worsening, suggesting a relatively stable illness course among individuals with more severe negative symptoms. These results have implications for prognosis and treatment planning, while underscoring the persistent functional burden imposed by negative symptoms despite lower exacerbation risk.

Psychiatric disorders are fundamentally challenged by symptom heterogeneity, high comorbidity, and the absence of objective biomarkers, which together result in substantial variability in clinical assessment and treatment selection. Patient-generated language captures rich information about subjective experience and symptom severity, which can be systematically encoded and analyzed using computational models, making it a scalable signal for psychiatric assessment. We compare two approaches: (i) a domain-specialized transformer fine-tuned on clinical language, based on the Bio-ClinicalBERT encoder architecture, and (ii) a large-scale instruction-tuned generalist encoder (Instructor-XL) used as a frozen feature extractor with a shallow classification head. A corpus of N = 151,228 de-identified texts was compiled from five public sources, covering four psychiatric phenotypes: anxiety, depression, schizophrenia, and suicidal intention. Models were evaluated using stratified 10-fold cross-validation with cost-sensitive training, prioritizing imbalance-aware metrics, including Macro-F1 and Matthews Correlation Coefficient (MCC), over accuracy. Bio-ClinicalBERT achieved superior overall performance (Macro-F1 = 0.78, MCC = 0.6752), indicating more reliable separation of diagnostically overlapping affective categories. In contrast, Instructor-XL achieved its highest class-specific performance for schizophrenia (F1 = 0.798). Explainability analyses suggest that the domain-specialized model places greater weight on clinically relevant terms, whereas the generalist model relies on a broader set of lexical features.

Background At present, there are no approved pharmacological treatments for people at clinical high risk for psychosis (CHR-P). We sought to assess the acceptability of cannabidiol (CBD): a promising candidate treatment for this population. Methods CHR-P individuals completed a survey which assessed their views on the acceptability of CBD, its expected effectiveness and side effects, and on formulation preferences. Results The sample comprised 55 CHR-P individuals (24.3 years and 69% female). Most (91%) were familiar with CBD, and had previously used cannabis (64%), and around half (42%) had tried over-the-counter CBD. 75% were willing to take CBD as an intervention for mental health problems. Most participants anticipated fewer side effects with CBD than with existing medications, and preferred tablet or capsule formulations over liquids. Discussion CBD is perceived as a highly acceptable treatment among CHR-P individuals.

ObjectiveIn response to a clinical observation of an Anorexia Nervosa (AN) patient with family history of Parkinsons Disease (FHoPD), and evidence of similarities in dopamine function, personality, anxiety and weight loss symptoms between AN and PD, we completed a pilot study to estimate FHoPD in families of those with eating disorders (EDs). MethodWe ascertained FHoPD among ED patients and community participants, and estimated relative risks (RRs) for AN, Bulimia Nervosa (BN) and Binge Eating Disorder (BED). ResultsWe observed increased FHoPD among patients and community participants (N=482) meeting criteria for ED diagnoses compared to community participants (N=394) without an ED diagnosis. For AN, FHoPD prevalence was 6.6% vs 3.4%, {chi}2=4.638, p=.031, RR=1.935, 95%CI=1.012-3.768. For BN, FHoPD prevalence was 7.4% vs 3.4%, {chi}2=4.941, p=.026, RR=2.169, 95%CI=1.023-4.620. For BED, FHoPD prevalence was 13.3% vs 3.4%, {chi}2=6.953, p=.008, RR=3.886, 95%CI=1.108-11.524. ConclusionsEDs are associated with an elevated FHoPD. Translational analyses leveraging disorder-specific research resources may benefit our understanding of the genetics and neuroscience of both disorders. HighlightsO_LIAN and PD share premorbid anxiety, harm avoidance and dopaminergic dysfunction. C_LIO_LIFHoPD RRs are two-fold for AN and Bulimia Nervosa (BN) and four-fold for Binge Eating Disorder (BED) in a sample of treatment seeking and community participants. C_LIO_LIED and PD familiality, and advances in PD and ED genetics and neuroscience research provide opportunities for cross-disorder research. C_LI

ObjectivesTrauma-focused cognitive behavioural therapy (TF-CBT) is the established first-line treatment for paediatric posttraumatic stress disorder (PTSD), but access to evidence-based care remains limited. This study aimed to evaluate the feasibility and acceptability of a therapist-guided, 12-week, internet-delivered TF-CBT (iTF-CBT) programme for adolescents with PTSD, and to explore preliminary changes in PTSD symptoms. DesignSingle-group feasibility trial. SettingSave the Children, Sweden. ParticipantsTwenty-two adolescents (13-17 years, 82% female) with primary PTSD. InterventionsA 12-week, therapist-guided, internet-delivered TF-CBT comprising eight modules and parallel caregiver modules with joint child-caregiver activities. OutcomesFeasibility measures included recruitment pace, participant retention, treatment adherence (module completion), and therapist time. Acceptability was evaluated through satisfaction, credibility, negative effects, and reported adverse events. Preliminary treatment effects were evaluated as within-group changes in PTSD severity using independent evaluator-rated Clinician-Administered PTSD Scale (CAPS-CA-5) and the self-reported Child and Adolescent Trauma Screen 2 (CATS-2). Assessments occurred at baseline, during treatment, post-treatment, and at 1-month follow-up (primary endpoint). ResultsRecruitment was completed after seven months of active enrolment. Retention and adherence were high, satisfaction and credibility ratings were favourable, and no intervention-related serious adverse events occurred. Clinically meaningful within-group improvements were observed at the primary endpoint, with large reductions on CAPS-CA-5 (Cohens d = 1.27) and CATS-2 (Cohens d = 1.51). ConclusionsTherapist-guided iTF-CBT for adolescents with PTSD was safe, feasible, acceptable, and associated with clinically meaningful symptom improvements. These findings support further evaluation in larger, controlled trials to determine efficacy, cost-effectiveness, and long-term outcomes. Trial registrationClinicalTrials.gov NCT06185244. Article SummaryO_ST_ABSStrengths and limitations of this studyC_ST_ABSO_LIFirst internet-delivered TF-CBT trial for young people with PTSD C_LIO_LIUse of clinician-rated PTSD symptoms (CAPS-CA-5) in combination with validated self-report measures. C_LIO_LIThe intervention was developed in close collaboration with clinicians, alongside contributions from young people. C_LIO_LIAbsence of a control group. C_LI

Psychotic disorders are increasingly recognized as the extreme end of a progressive psychopathology continuum, with less advanced stages including the asymptomatic familial high-risk state (FHR), the help-seeking clinical high-risk state (CHR), and first episode psychosis (FEP). However, we lack a comprehensive study of clinical, cognitive, functional, and neuroanatomical markers across all three early stages of psychosis, limiting our understanding of how the multimodal phenotypes which define psychotic disorders emerge in the broader course of psychopathology. We leveraged a sample of 70 FEP, 40 CHR, 43 FHR, and 41 healthy participants recruited from the same clinical and sociodemographic setting - the first such dataset to be described in the literature. Several markers were elevated in CHR but did not worsen in FEP, including depression/anxiety and difficulties functioning, while FEP was uniquely defined by cognitive impairments and cortical thickness reductions characteristic of those seen in schizophrenia. Across the sample, the dominant axis of joint brain-behaviour variability captured a relationship between reduced cortical thickness and lower cognitive performance, a pattern which was equally established in both CHR and FEP. Initial longitudinal data revealed that depressive and negative symptoms best predicted lower functioning at 6-month follow-up, regardless of group status. Together, our analysis suggests that affective and functional disturbances emerge in earlier stages of psychosis, while cognitive and anatomical abnormalities characterize more advanced ones - though the overlap we observed across groups demonstrates that clinically relevant phenotypes can cut across group boundaries, requiring personalized care to manage.

PurposeThe COVID-19 pandemic coincided with worsening U.S. drug overdose mortality and widening racial and ethnic disparities. We estimated race/ethnicity-specific excess overdose deaths at the state level during 2020-2023 and examined how these burdens scale with population size. MethodsWe analyzed annual overdose deaths from 2014-2023 for five racial/ethnic groups in 47 states and the District of Columbia. Pre-pandemic trends (2014-2019) were fit using a generalized growth model, and deaths exceeding projections in 2020-2023 were classified as excess deaths. Scaling was evaluated by regressing excess and total deaths on 2020 state population in log-log models and relating excess-death rates to social and behavioral indicators. ResultsWhite populations experienced the largest absolute excess, peaking in 2021. Black populations showed minimal excess in 2020 but rose sharply in 2021-2022, nearly matching White deaths by 2023; Hispanic excesses were intermediate. Scaling analyses showed sublinear growth of Black deaths with population, while White and Hispanic deaths scaled approximately proportionally. Within-state rank tests indicated higher excess-death rates for Black than for White or Hispanic populations. ConclusionsExcess-mortality and scaling analyses reveal heterogeneous and inequitable overdose burdens across racial and ethnic groups during the pandemic and can inform equity-focused surveillance and prevention efforts.

BackgroundScientific research remains disproportionately grounded in data from high-income countries (HICs), yet efforts to map the distribution of neuroimaging findings by income levels remain limited. MethodsUsing data from the ENIGMA Consortium, we conducted a systematic quantitative synthesis of 83 publications across nine psychiatric and neurological conditions, analyzing T1-weighted structural MRI data from 16,086 cases in 27 countries. Representation was mapped using World Bank income classifications and World Health Organization (WHO) regions. ResultsHICs contributed 90.5% of cases; upper-middle-income countries 7.9%; lower-middle-income countries 1.6%; and low-income countries none. Geographically, 85% of cases originated from North America and Europe, while Africa, South-East Asia, and the Eastern Mediterranean were underrepresented. Supplemental analyses of other datasets (Brain Growth Charts; fMRI meta-analysis) revealed similar disparities. ConclusionsEquitable neuroimaging science is critical to inform practice and policy decision-making that is context specific. This requires targeted investment in infrastructure, data sharing, and participation from underrepresented regions.

Background and HypothesisClozapine is the only antipsychotic with protective effects against suicide in schizophrenia (SCZ). Newer third-generation antipsychotics (TGA) have better tolerability and modulate serotonin, dopamine, and N-methyl-d-aspartate neurotransmission pathways implicated in suicide. We aimed to investigate the effects of TGAs on suicide in SCZ. MethodsWe searched seven databases up to December 2023 for SCZ studies that reported suicide data. The primary outcome was suicide deaths and attempts; suicidal ideation was added as a secondary outcome. Random effects meta-analyses quantified suicide risk in randomized controlled trials (RCT) while single proportion meta-analyses assessed longitudinal suicide risk in open label extension trials (OLE). For RCTs, sensitivity analyses were conducted and subgroup analyses explored the impact of dose, drug type, and comparator arm. Study ResultsTwenty articles were included; thirteen excluded higher suicide risk participants. Compared to placebo control, TGAs did not significantly change the risk of primary [RR = 0.65, p = 0.38] or secondary [RR = 0.63, p = 0.15] suicide outcomes. Subgroup and sensitivity analyses were not statistically significant. For OLEs, there was a significant increase in the incidence of primary [Ip = 0.004, p = 0.048] and secondary [Ip = 0.024, p = 0.0013] suicide outcomes, but there was marked study heterogeneity. ConclusionThere is no current trial evidence to show that TGAs significantly impact suicide outcomes in SCZ. The signal from OLEs should be interpreted cautiously due to heterogeneity and requires replication. An effective clozapine alternative is needed for suicide prevention in SCZ.

Auditory-verbal hallucinations (AVHs) are among the most disabling symptoms of schizophrenia and often persist despite the use of adequate antipsychotic treatment. Conventional low-frequency repetitive transcranial magnetic stimulation (rTMS) targeting the T3P3 scalp site has demonstrated limited efficacy, likely due to interindividual variability in AVH-related brain networks. In this multicenter, randomized, double-blind phase 3 trial, 70 patients with drug-resistant AVHs received active 1-Hz rTMS targeted either with an individualized fMRI-based symptom-capture procedure or by using conventional T3P3 localization. fMRI-guided rTMS yielded a greater reduction in Auditory Hallucination Rating Scale (AHRS) scores at one month (mean difference, -5.43; 95% CI, -8.92 to -1.94), and the effects were sustained at three and six months. The number-needed-to-treat for neuroguided rTMS was 3.5. Clinical response was associated with greater E-field overlap with AVH-related networks. These findings demonstrate that fMRI-guided neuronavigation increases rTMS efficacy, thus supporting its use to optimize the treatment of drug-resistant AVHs in schizophrenia.

BackgroundMany people with depression do not respond well to the first antidepressant prescribed. Treatment Resistant Depression (TRD) refers to depression which does not respond to multiple subsequent antidepressant treatments. Identifying TRD in routinely-collected health records is challenging due to limited response-related data. Previous studies have used definitions based on the number of antidepressant switches observed. However, these do not account for other features clinically indicative of treatment resistance, such as augmentation of antidepressants with lithium or antipsychotics and switches between antidepressant classes. This study examined definitions of TRD and their impact on the resulting sample across three cohorts. MethodsAcross the DataLoch, UK Biobank, and Generation Scotland cohorts, we identified cases of depression from primary and secondary care record codes and extracted antidepressant treatment patterns from dispensing/prescribing records (N = 51,283, N = 10,556, and N = 649 respectively). We examined 9 TRD definitions that varied by: the number of switches required (1+, 2+ or 3+ switches), augmentation and between-class switches. We contrasted sample size and characteristics between definitions, and examined factors associated with inclusion versus a reference definition of 2+ switches. ResultsThe reference TRD definition included 10% of depression cases in the routine data collection, but substantially fewer cases (4%) in consented cohorts. More inclusive definitions that required fewer switches or included a between-class switch classified more individuals as TRD, but resulted in a proportionally older, more deprived sample with fewer depression-related health record codes, older age of depression onset, lower symptom severity, and greater use of first-line antidepressants. Requiring more switches (3+ switches) classified fewer individuals as TRD, but resulted in a proportionally younger sample, with more depression-related health record codes, younger age of depression onset, and greater use of antidepressants associated with later in the treatment line (e.g., Tricyclics). Definitions including augmentations resulted in a small increase in sample size without notable change in sample characteristics. ConclusionsTRD is underrepresented in consented cohort studies. A definition of TRD that includes 2+ antidepressant switches or augmented antidepressant treatment as indicators balances sample size with depression severity, while incorporating features from real-world treatment journeys. Key summaryO_LITreatment Resistant Depression (TRD) is often identified in health records using number of switches in antidepressant treatment, but this misses other important indicators of treatment resistance C_LIO_LIWe examined 9 TRD definitions across 3 cohorts, varying in the number of antidepressant switches, the inclusion of augmented treatment, and the inclusion of between-class switches C_LIO_LIA TRD definition that included 2+ switches or augmented treatment balanced sample size and severity C_LI