JAMA Psychiatry

Psychosis prevention relies on early detection of individuals at clinical high risk for psychosis (CHR-P) remains limited, constraining preventive care. The effectiveness of the CHR-P state is constrained, in part due to clinical assessments requiring specialist interpretation of narrative interviews, limiting scalability. Here, we evaluate whether large language models (LLMs; deep learning models trained on large text corpora to process and generate language) can extract clinically meaningful information from such interviews to support psychosis risk assessment. We assessed 11 open-weight LLMs on 678 PSYCHS interview transcripts from 373 participants (77.7% CHR-P). Models inferred CHR-P status and estimated severity and frequency across 15 symptom domains, benchmarked against researcher-rated scores. Larger models achieved the strongest classification performance (Llama-3.3-70B: accuracy = 0.80, sensitivity = 0.93, specificity = 0.58). LLM-generated symptom scores showed good correlations with researcher-rated scores (ICCsev = 0.74, ICCfreq = 0.75). Performance disparities were minimal across most demographic groups but varied across sites. Generated summaries were largely faithful to source transcripts, with low rates of clinically relevant confabulation (3%). Errors primarily reflected over-pathologisation of non-clinical experiences. While accuracy scaled with model size, smaller models achieved competitive performance with substantially lower computational cost. These findings demonstrate that open-weight LLMs can assess psychosis risk from clinical interview transcripts, supporting scalable, human-in-the-loop approaches to early detection.

BackgroundThe severity of positive psychotic symptoms largely defines emerging psychosis syndromes. However, depressive and negative symptoms are strongly psychologically and biologically interlinked. A transdiagnostic exploration of symptom severity across early illness syndromes could enhance the understanding of shared common factors and future trajectories of mental illness. We aimed to identify subgroups based on the severity of positive, negative, and depressive symptoms and assess relationships with: 1) premorbid functioning, 2) longitudinal illness course, 3) genetic risk, and 4) brain volume differences. MethodsWe analysed 749 participants from a multisite, naturalistic, longitudinal (18 months) cohort study of: clinical high risk for psychosis (n=147), recent onset psychosis (n=161), and healthy controls (n=286), and recent onset depression (n=155). Participants were stratified into subgroups based on severity of baseline positive, negative, and depression symptoms. Baseline and longitudinal differences between groups for clinical, functioning, and polygenic risk scores (schizophrenia, depression, cross-disorder) were assessed with ANOVAs and linear mixed models. Voxel-based morphometry was used to examine whole-brain grey matter volume differences. Discovery findings were replicated in a held-out sample (n=610). ResultsParticipants were stratified into no (n=241), mild (n=50), moderate (n=182), and severe symptom (n=254) subgroups. The mean (SD) age was 25.3 (6.0) and 344 (47.3%) were male. Symptom severity was associated with poorer premorbid functioning and illness trajectory, greater genetic risk, and lower brain volume. Findings were not confounded by the original study groups or symptoms and were largely replicated. Conclusions and relevanceTransdiagnostic symptom severity is linked to shared aetiologies, prognoses, and biological markers across diagnoses and illness stages. Such commonalities could guide therapeutic selection and future research aiming to detect unique contributions to specific psychopathologies.

Schizophrenia spectrum disorders (SSDs) are clinically and neurobiologically heterogeneous. Normative modeling addresses heterogeneity of structural brain alterations by focusing on individual-level deviations, but their clinical relevance in SSDs remains controversial. We mapped the relationship between individual gray matter volume (GMV) deviations and schizophrenia diagnosis and symptoms. Normative models of GMV were established using cross-sectional, T1-weighted magnetic resonance imaging data from a large, multi-site, healthy reference cohort (N = 7957). Deviations were derived for SSD patients (n = 379) and healthy controls (n =149). Patients showed a significantly more negative average deviation compared to controls and regional deviations predicted diagnostic status with adequate performance (AUC = 0.79). A more negative deviation was associated with higher symptom severity and lower cognitive functioning in SSD. Negative deviations were scattered across the brain, with the largest alterations in the salience network. Our findings strengthen the potential of normative modeling to disentangle the heterogeneous underpinnings of SSD and provide further evidence for individualized structural deviations, particularly in the salience network, as promising markers of illness severity in SSDs.

Recent evidence suggests that psychosis involves glutamatergic dysfunction and altered activity/connectivity within corticolimbic circuitry. While altered relationships between corticolimbic glutamatergic metabolite levels and resting-state functional connectivity (FC) have been described in schizophrenia and first-episode psychosis (FEP), whether these disruptions are also present prior to psychosis onset remains unclear. We measured Glx (glutamate + glutamine) levels in the anterior cingulate cortex (ACC) and hippocampus with magnetic resonance spectroscopy (MRS), and resting-state FC between corticolimbic regions of interest (ACC, hippocampus, amygdala and nucleus accumbens (NAc)) in antipsychotic-naive participants at clinical high-risk for psychosis (CHR-P, n=22), compared to healthy controls (HC, n=23) and FEP participants (n=10). Primary analyses compared corticolimbic Glx-FC interactions between CHR-P and HC groups. FEP individuals were included in secondary Glx comparisons but were excluded from FC analyses due to insufficient sample size after quality control. There was a significant interaction between group and ACC Glx for FC between the NAc and the bilateral amygdala and hippocampus (p-FDR=0.021), which was driven by a significant negative association in the CHR-P group (p-FDR=0.005). Complementary seed-to-whole-brain analyses revealed additional negative associations between ACC Glx and FC with the left middle temporal gyrus, and between hippocampal Glx and FC with the parahippocampal and temporal fusiform cortices in CHR-P individuals, which were absent in HC. FEP showed higher Glx than HC across both regions (p=0.015), but there were no significant Glx differences between CHR-P and HC. These data suggest that increased risk for psychosis is associated with altered relationships between corticolimbic connectivity and glutamatergic function.

ImportanceBlood neurofilament light chain (NfL) and glial fibrillary acidic protein (GFAP) are entering clinical use in neurology as markers of neuroaxonal and astrocytic injury, but their utility in psychiatry is unclear. ObjectiveTo determine whether psychiatric diagnoses are associated with altered plasma NfL and GFAP levels. Design, Setting, and ParticipantsThis population-based study examined plasma NfL and GFAP among 47,495 participants from the UK Biobank (54.0% female; 93.5% White; mean [SD] age 56.8 [8.2] years) who provided blood samples and sociodemographic and clinical data between 2006 and 2010. Normative modeling was applied to assess associations between 7 lifetime psychiatric diagnostic categories and deviations from expected NfL and GFAP levels, while accounting for neurological diagnoses, cardiometabolic burden, and substance use. Data were analyzed between July 2025 and March 2026. Main Outcomes and MeasuresDeviations in plasma NfL and GFAP levels from normative predictions. ResultsRelative to the reference population, plasma NfL levels were higher among individuals with bipolar disorder (d=0.20; 95% CI, 0.03-0.37; p=0.03), recurrent depressive disorder (d=0.23; 95% CI, 0.07-0.38; p=0.009), and depressive episodes (d=0.06; 95% CI, 0.02-0.10; p=0.01), lower among individuals with anxiety disorders (d=-0.07; 95% CI, -0.12 to -0.02; p=0.008), but did not differ in schizophrenia spectrum, stress-related, or other psychiatric disorders. Plasma GFAP levels were not elevated in any psychiatric disorders. Variability in NfL levels was greater among individuals with schizophrenia spectrum disorders (variance ratio [VR]=1.30; p=0.005), depressive episodes (VR=1.06; p=0.006), and anxiety disorders (VR=1.08; p=0.005). Variability in GFAP levels was increased only in anxiety disorders (VR=1.08; p=0.01). Plasma NfL levels exceeding percentile-based normative thresholds were more common among individuals with schizophrenia spectrum disorders, bipolar disorder, recurrent depressive disorder, and depressive episodes. Neurological diagnoses, cardiometabolic burden, and substance use were associated with plasma NfL and GFAP levels. Conclusions and RelevanceThis study provides population-level evidence of plasma NfL elevation in bipolar and depressive disorders and increased variability in schizophrenia spectrum, bipolar and depressive disorders, supporting its potential as a biomarker in psychiatry and informing its ongoing neurological applications. Plasma GFAP levels, in contrast, were largely unaltered across psychiatric disorders. Key PointsO_ST_ABSQuestionC_ST_ABSAre plasma neurofilament light chain (NfL) and glial fibrillary acidic protein (GFAP) levels altered in psychiatric disorders? FindingsIn this cohort study including 47,495 individuals, normative modeling revealed that plasma NfL levels were elevated in bipolar and depressive disorders, whereas plasma GFAP levels were not elevated in any psychiatric disorder. Plasma NfL levels also showed higher variability in schizophrenia spectrum, bipolar, and depressive disorders. MeaningPlasma NfL shows distinct alterations in schizophrenia spectrum and affective disorders, supporting its further investigation as a biomarker in clinical psychiatry and highlighting the need to consider psychiatric comorbidity in neurological applications.

Polygenic risk scores (PRS) have shown increasing utility for risk stratification across complex diseases, but for psychiatric disorders such as bipolar disorder (BD), current PRS explain only a fraction of disorder liability (~1-9%), with predictive performance further diminished in non-European populations and real-world clinical cohorts. To explore the potential of integrating social and environmental risk factors alongside genetic liability to improve risk prediction, we evaluated the relationship between a PRS for BD (PRSBD) and six social risk measures - perceived stress, discrimination in medical settings, neighborhood social cohesion, perceived neighborhood disorder, cost-related medication nonadherence, and adverse childhood experiences - to BD case status in 115,275 participants (7,000 cases; 108,275 controls) from the All of Us Research Program. PRSBD was associated with BD case status across ancestry groups, though liability-scale variance explained was attenuated relative to what has been reported for curated research cohorts (R2 = 1.86% in European, 0.60% in African, 1.65% in Latino/Admixed American ancestries). Each social risk factor tested exhibited a larger effect size than PRSBD, with perceived stress (OR = 2.05 per SD) and adverse childhood experiences (OR = 2.68 for [≥]4 ACEs) demonstrating the strongest associations. Individuals in the lowest genetic risk decile with high social burden exhibited BD prevalence comparable to or exceeding those in the highest genetic risk decile with low social burden. These findings demonstrate the substantial explanatory power of social risk factors and support the development of integrated genetic-social risk frameworks for more accurate and equitable psychiatric risk prediction.

Severe mental disorders (SMDs), including bipolar disorder, schizophrenia, and major depressive disorder, are highly complex conditions associated with a substantial clinical burden and an increased suicide risk. Here, we present the Madrid Manic Cohort (MadManic), a large-scale initiative from Spain designed to integrate genomic, multi-omics, clinical, and digital phenotyping data to investigate the biological basis and clinical heterogeneity of SMDs. The cohort is still expanding and currently includes over 4,400 participants (~2,300 psychiatric patients and ~2,100 controls) and >11,000 biospecimens. Genotyping, transcriptomic and epigenetic data are available for different subsets of the cohort. By establishing the MadManic cohort we aim to integrate molecular data with detailed clinical and longitudinal digital information, allowing a more precise characterization of patient subgroups based on biological and phenotypic profiles. The MadManic cohort is well positioned to contribute to major international efforts in psychiatric genetics by enhancing the representation of Southern European populations, and advancing the identification of genetic risk, clinical predictors, and pharmacogenomic markers of treatment response. This cohort represents a valuable resource for advancing precision psychiatry, with the potential to improve risk prediction and guide personalized interventions in SMDs.

Background: Differences in age of psychosis onset (AOO) in schizophrenia (SCZ) are associated with different illness trajectories. Determining whether AOO differences can be explained by genome-wide or pathway-partitioned polygenic risk for SCZ (SCZ-PRS) may elucidate mechanisms underlying clinical variability. This study examined relationships between AOO, genome-wide SCZ-PRS, and pathway-partitioned SCZ-PRS in a harmonized, multi-ancestry North American dataset (SCZ-NA) and in UK Biobank (SCZ-UKBB). Methods: For each cohort, we computed one genome-wide SCZ-PRS and 18 mutually-exclusive pathway-based PRS derived from previous published and validated neurodevelopmental gene-sets. We evaluated 13 SNP-to-gene mapping strategies, including comparing non-coding SNP-to-gene mappings informed by functional annotations versus distance-based windows. SCZ case-control prediction and AOO associations were tested using logistic and linear mixed models, respectively, controlling for sex, ancestry principal components, and genetic relatedness. Results: Genome-wide SCZ-PRS robustly predicted SCZ case-control status in both cohorts but not AOO. In contrast, pathway-based analyses identified AOO associations for a fetal angiogenesis and a postnatal synaptic signaling and plasticity gene-set across both cohorts (p < .05), alongside nominal cohort-specific associations in other gene-sets. Associations depended on SNP-to-gene mapping definitions; experimentally informed strategies, particularly those incorporating brain expression Quantitative Trait Locus (eQTL) annotations performed best. Conclusion: Findings suggest that neurovascular and postnatal synaptic signaling and refinement mechanisms contribute to AOO variation in SCZ, and that pathway-informed PRS, especially with brain-specific non-coding SNP-to-gene mappings, can help identify mechanisms contributing to variability in AOO. Replication in larger, prospectively phenotyped cohorts with harmonized AOO definitions will further clarify genetic mechanisms underlying clinical variability in SCZ.

ImportanceBlood-based biomarkers hold promise for psychiatric diagnosis and prognosis, yet clinical translation is constrained by poor reproducibility. Psychiatric biomarker studies are typically small, and demographic, behavioral, and temporal covariates often go undetected or cannot be adequately modeled. This may lead to residual confounding and unstable associations. ObservationsLeveraging UK Biobank data (N=~500,000), we systematically quantified how technical, demographic, behavioral, and temporal covariates influence 29 blood biomarkers commonly measured in research studies in psychiatry. Variance analyses showed substantial differences across biomarkers. Technical factors explained 1-6% and demographic factors explained 5-15% of the variance, with pronounced age-by-sex interactions for lipids and sex hormones. Behavioral covariates, particularly body mass index (BMI) and smoking, strongly influenced inflammatory markers. Temporal factors introduced systematic confounding. Chronotype was associated with blood collection time, multiple biomarkers exhibited marked diurnal rhythms (including testosterone, triglycerides, and immune markers), and inflammatory markers showed seasonal peaks in winter. In association analysis of biomarkers with major depression, bipolar disorder and schizophrenia, covariate adjustments attenuated or eliminated a substantial proportion of the biomarker-disorder associations, with BMI emerging as the dominant confounder. These findings demonstrate that such confounding structures exist and can be characterized in large cohorts, though specific biomarker-disorder relationships require validation in clinical samples. Conclusions and RelevancePoor reproducibility of biomarkers may not only stem from insufficient biological signal but also from inconsistent handling of confounders. We propose a systematic framework distinguishing technical factors (to be removed), demographic factors (addressed through adjustment or stratification), temporal factors (ideally controlled at design stages), and behavioral factors (requiring explicit causal reasoning). Associations robust to multiple adjustment strategies should be prioritized for clinical biomarker development. Standardized collection protocols, comprehensive covariate measurement, and transparent reporting across models are essential to improve reproducibility and identify biomarkers that reflect genuine illness-related pathophysiology.

Background Cognitive impairment is a core feature of schizophrenia and a major determinant of functional disability. Executive deficits affect approximately 85% of patients and are associated with reduced activity in the prefrontal cortex (hypofrontality). Current pharmacological treatments show limited efficacy in improving cognition, highlighting the need for alternative therapeutic approaches. Combining non-invasive brain stimulation with cognitive remediation may enhance neuroplasticity and improve cognitive outcomes. Methods This prospective, randomized, double-blind, sham-controlled, parallel-group superiority clinical trial. A total of 120 adults aged 18-65 years with clinically stable schizophrenia diagnosed according to DSM-5 criteria will be enrolled at a single clinical center. Participants will be randomly assigned in a 1:1 ratio to receive either active transcranial direct current stimulation (tDCS) targeting the dorsolateral prefrontal cortex followed by cognitive remediation therapy (CRT) using the RehaCom system, or sham stimulation followed by the same cognitive training. Assessments will be conducted at three time points: prior to the intervention (V1), immediately after the intervention (V2), and during the follow-up visit 8 weeks after the intervention (V3). The primary outcome is change in cognitive performance measured with the CANTAB battery. Secondary outcomes include symptom severity assessed with the PANSS, global clinical status (CGI-S), and neurophysiological changes measured by EEG. Written informed consent will be obtained from all participants, and the study has received ethics committee approval. Discussion This trial will evaluate whether tDCS administered prior to cognitive training enhances cognitive improvement compared with cognitive training alone. The findings may inform the development of more effective interventions targeting cognitive deficits in schizophrenia. Trial registration ClinicalTrials.gov Identifier: NCT07273175. Registered on 25 November 2025.

Importance: Suicide is a leading cause of death in the United States with risk strongly influenced by Interpersonal trauma, contributing to treatment resistance and clinical complexity. Objective: To assess clinical and genetic factors in individuals who died from suicide, with and without interpersonal trauma exposure. Design: Individuals who died from suicide with and without trauma were compared in a retrospective case-case design. Prevalence of 19 broad clinical categories was assessed between groups. Results directed selection of 42 clinical subcategories, and 40 polygenic scores (PGS) for further assessment. Multivariable logistic regression models, adjusted for critical covariates and multiple tests, were formulated. Models were also stratified by age group (<26yo and >=26yo), sex, and age/sex. Setting: A population-based evaluation of comorbidity and polygenic scoring in two suicide death subgroups. Participants: A total of 8 738 Utah Suicide Mortality Research Study individuals (23.9% female, average age = 42.6 yo) who died from suicide were evaluated, divided into trauma (N = 1 091) and non-trauma exposed (N = 7 647) individuals. A subset of unrelated European genotyped individuals was also assessed in PGS analyses (Trauma N = 491; Non-trauma N = 3 233). Exposures: Trauma is here defined as interpersonal trauma exposure, including abuse, assault, and neglect from International Classification of Disease coding. Main Outcomes and Measures: Prevalence of comorbid clinical sub/categories and PGS enrichment in trauma versus non-trauma exposed suicide deaths. Results: Overall, trauma-exposed individuals died from suicide earlier (mean age of 38.1 yo versus 43.3 yo; P <0.0001) and were disproportionately female (38% versus 21%, OR = 3.3, CI = 2.9-3.8). Prevalence of asphyxiation and overdose methods, prior suicidality, psychiatric diagnoses, and substance use (OR range = 1.3-3.7) were elevated in trauma exposed individuals who died from suicide. Genetic PGS were also elevated in trauma-exposed individuals who died from suicide for depression, bipolar disorder, cannabis use, PTSD, insomnia, and schizophrenia (OR range = 1.1-1.4) with ADHD and opioid use showing uniquely elevated PGS in trauma exposed males (OR range = 1.2-1.4). Conclusions and Relevance: Results demonstrated multiple convergent lines of age- and sex-specific evidence differentiating trauma-exposed from non-trauma exposed suicide death. Such findings suggest unique biological backgrounds and may refine identification and treatment of this high-risk group.

Schizophrenia spectrum disorder (SSD) is a clinically and genetically heterogeneous condition, yet few studies have integrated real-world clinical data with both common and rare genetic variation to explore this complexity. In this study, we analyzed real-world data from 22,092 individuals in the Danish iPSYCH cohort (11,046 SSD cases and 11,046 matched population controls) leveraging nationwide registry data on diagnoses, hospitalizations, and parental history. Using a variational autoencoder (VAE), we compressed these features into a latent space and identified ten clinically distinct SSD subgroups that varied in comorbidity, parental diagnoses, hospital burden, and early-life adversity. Polygenic scores (PGSs) for five psychiatric disorders showed subgroup-specific enrichment, highlighting potential links between complex clinical profiles and common variant liability. In a subset with exome data (N=5,969), we assessed rare deleterious variant burden across SCZ-informed gene sets and Protein-Protein Interaction (PPI) networks, observing suggestive network-specific trends. This framework for integrating real world-based stratification with genetic evidence is scalable and transferable across cohorts, offering a path toward biologically informed patient classification.

Importance: Conventional repetitive transcranial magnetic stimulation (rTMS) can be ineffective in individuals who have previously failed brain stimulation, ketamine and/or multiple lines of therapies. Modern accelerated rTMS protocols using image-guided targets have not been systematically investigated in these individuals. The goal of this study was to assess the feasibility and efficacy of personalized, connectivity-guided, accelerated intermittent theta-burst stimulation (iTBS) in patients with treatment-resistant depression (TRD) of varying refractoriness. Objective: To assess whether connectivity-guided, accelerated iTBS produces significant reductions in depression severity, and to what extent this benefit extends to ultra treatment-resistant depression (UTRD). Design: This was an open-label feasibility trial of connectivity-guided, accelerated iTBS in patients with TRD. Two distinct groups of participants were recruited from a neurosurgical-psychiatry clinic with UTRD and an interventional psychiatry clinic with TRD. Patients were stratified into a priori treatment-resistance subgroups. Patients received five days of open-label treatment. Outcome measures were collected immediately prior to and after treatment, as well as at 4- and 12-weeks post-treatment. Setting: This trial (NCT05813093) was conducted between November 2023 and July 2025 at Sunnybrook Health Sciences Centre in Toronto, Ontario, Canada. Participants: Patients with major depressive disorder. A total of 96 participants were screened, with 73 meeting eligibility criteria (UTRD=30, TRD=43). One withdrew due to inability to tolerate the baseline MRI, and the other withdrew voluntarily prior to treatment. Intervention: Participants underwent a neuronavigated accelerated iTBS (600 pulses) protocol using personalized left dorsolateral prefrontal cortex (dlPFC) targets derived from functional magnetic resonance imaging (fMRI), comprising eight daily treatments, repeated over five days. Main Outcomes: Primary outcomes were i) change in Hamilton Depression Rating Scale (HAM-D17) from baseline to the end of the fifth day of treatment, and ii) the difference in change in HAM-D17 between UTRD and TRD subgroups. Results: Connectivity-guided fMRI targeting yielded personalized targets clustered around the anterolateral dlPFC. Accelerated iTBS elicited rapid antidepressant effects ({Delta}HAM-D17 -9.01 [SD 6.06], t = -12.45, p < 0.001) regardless of treatment-resistance group ({Delta}HAM-D17 -9.64 [SD 5.94] vs -8.10 [SD 6.12], t = -1.05, p = 0.299), which were sustained up to 12 weeks after treatment. Overall response and remission rates at the end of treatment were 40.8% and 16.9%. Self-report scales revealed broad symptomatic relief outside of core depressive symptoms. Conclusions & Relevance: This study demonstrated that fMRI connectivity-guided, accelerated iTBS induces sustained antidepressant effects and broader psychiatric benefits in patients across the spectrum of TRD. In a cohort unlikely to respond to most antidepressant therapies, connectivity-guided, accelerated iTBS offers a safe, well-tolerated option that can achieve benefit, or when ineffective, allow patients to expeditiously proceed with subsequent therapies than conventional rTMS. Trial Registration: This clinical trial was registered at clinicaltrials.gov with NCT05813093.

Schizophrenia is often conceptualized as a brain network disorder, yet the organizational principles and heterogeneity underlying widespread cortical abnormalities remain poorly understood. Leveraging multisite MRI data from 3,958 individuals diagnosed with schizophrenia and 5,489 neurotypical individuals, we studied the cortical organization and its subtyping by analyzing individualized cortical network similarity. We used eigenvector decompositions to study spatial patterning of the gradients and graph theory to study small-world topology. Individuals with schizophrenia showed widespread alterations of gradient loadings, which followed inferior-superior and frontal-temporal axes. Alterations in small-world topology were localized in key network hubs, including the insula and anterior cingulate cortex. Brain-symptom association analyses identified a latent dimension linking disorganization symptoms to topological alterations. Finally, clustering cortical alterations identified two robust subtypes, characterized by divergent anterior cingulate (S1) versus temporoparietal (S2) thickness differences aligned with the intrinsic gradient-topology patterns. Both subtypes were present early in the illness and stable across disease stages and age groups. These findings reveal systematic disruptions of cortical organization in schizophrenia, providing a network-level framework for macroscale brain organization and inter-individual heterogeneity.

Background and HypothesisClozapine is the only medication with proven efficacy for treatment-resistant schizophrenia, yet many patients experience delays of several years before initiation. Our aim was to develop and validate a dynamic prediction model for clozapine initiation among patients with schizophrenia trained solely on electronic health record (EHR) data from routine clinical practice. Study DesignEHR data from all adults ([&ge;] 18 years) with a schizophrenia (ICD10: F20) or schizoaffective disorder (ICD10: F25) diagnosis who had been in contact with the Psychiatric Services of the Central Denmark Region between 1 January 2013 and 1 June 2024 were retrieved. 179 structured predictors were engineered (covering, e.g.,diagnoses, medications, coercive measures) and 750 predictors derived from clinical notes. At every psychiatric hospital visit, we predicted if an incident clozapine prescription occured within the next 365 days. XGBoost and logistic regression models were trained on 85% of the data with 5-fold stratified cross-validation. Performance was evaluated on the remaining 15% of the data (held out) using the area under the receiver operating characteristic curve (AUROC). Study ResultsThe training/test set comprised of 194,234/35,527 hospital visits, distributed on 4928/878 unique patients. In the test set, the best XGBoost model achieved an AUROC of 0.81, sensitivity of 32%, positive predictive value of 23% at a 7.5% predicted positive rate. ConclusionsA dynamic prediction model based solely on EHR data predicts clozapine initiation with high discrimination. If implemented as a clinical decision support tool, this model may guide clinicians towards more timely initiation of clozapine treatment.

Background Hospital admissions for mental health (MH) and stress related presentations (SRP; symptoms without a clear medical cause which may be psychosomatic in nature) among children and young people (CYP) have risen over time. Rehospitalisation contributes to service costs, may indicate gaps in community based care, and can also disrupt education and social development. Methods This retrospective cohort study used NHS Hospital Episode Statistics to identify all CYP aged 10 to 25 with >1 MH/SRP related hospital admissions in England between 1 April 2014 and 31 March 2018, with follow up until 31 March 2019. Admissions were classified from ICD10 codes into internalising, externalising, personality, and eating disorders, psychosis, self-harm, substance use, postpartum, or potentially psychosomatic diagnostic groups. Outcomes included 30 day all cause readmission, 1 year all cause readmission, and 1 year MH/SRP-specific rehospitalisation. Time to rehospitalisation, and number of MH/SRP readmissions were also evaluated. Clinical and sociodemographic characteristics associated with rehospitalisation were assessed using regression models, time to rehospitalisation using Kaplan Meier analyses, and diagnostic transitions were visualised using Sankey diagrams. Results Of 492,061 CYP with hospital admission for MH/SRP, approximately one third were rehospitalised within one year. Females, older CYP and those from more deprived areas had higher odds of all cause readmission. The odds of MH/SRP rehospitalisation were highest among those aged 14 to 15 years. Co occurring chronic physical health conditions, personality and eating disorders were associated with higher odds, and shorter time, to readmission. Conclusions Rehospitalisation following MH/SRP admissions is common and socioeconomically patterned among CYP. Targeted discharge planning and continuity of care interventions are needed, particularly for high risk CYP admitted with eating and personality disorders.

Alternative-splicing events (ASE) increase transcriptomic variability and play key roles in biological functions. The contribution of ASE to bipolar disorder (BD) remains largely unexplored. We performed a Transcriptome-Wide Alternative-Splicing Analysis (TWASA) to identify ASEs and genes potentially involved in BD. The study comprised 635 individuals: a discovery sample (DS) of 31 individuals from eight multiplex BD families (16 BD cases; 15 unaffected relatives), and a replication sample (RS) of 604 subjects (372 BD cases; 232 controls). Sequencing was conducted on RNA from lymphoblastoid cell lines (DS) and whole blood (RS). TWASA was performed using VAST-TOOLS (VT), rMATS (RM), and MAJIQ/MOCCASIN (MCC). Gene-set association analyses of genes containing ASEs were performed across six psychiatric disorders. Novel ASE (nASE) were investigated in the DS using FRASER. Limited gene overlap was observed across TWASA tools. MCC identified 2,031 complex ASEs involving 1,508 genes, showing the strongest genetic association with BD across psychiatric phenotypes. Prioritization of MCC-identified ASE genes yielded 441 candidates, including DOCK2 as top candidate from the DS. Replication was obtained for 98 genes, five with an identical ASE, and four (RBM26, QKI, ANKRD36, and TATDN2) showing a concordant percentage-spliced-in direction with the DS. Finally, 578 nASE were identified in the DS, with no evidence of familial segregation or differences in ASE types. This first TWASA in BD reveals tool-specific variability, complex ASE for genes specifically associated with BD, and novel candidate genes for BD. Alternative transcript isoform abundance may represent a mechanism contributing to BD pathophysiology.

Schizophrenia (SCZ) is increasingly linked to neuroimmune dysregulation and impaired synaptic plasticity, yet the cellular mechanisms connecting inflammatory signaling to neural dysfunction remain poorly understood. Using human induced pluripotent stem cell (iPSC)-derived cortical spheroids (hCS) and astrocytes from patients with SCZ and matched controls, we investigated the effects of GABAA receptor modulation on immune signaling and neuroplasticity. Inflammatory stimulation induced robust interferon-responsive transcriptional programs, prominently involving the antiviral effector MX1 and related interferon-stimulated genes. Computational deconvolution and cell type-specific analyses identified astrocytes as key mediators of these responses. Muscimol, a non-classic psychedelic and GABAA receptor agonist, suppressed inflammatory gene expression, reduced secretion of proinflammatory cytokines, and attenuated interferon-associated signaling. In addition, muscimol induced neuroplasticity-associated transcriptional programs, including upregulation of NTRK2 and ELK1 in hCSs, and restored impaired glutamate uptake in iPSC-derived SCZ astrocytes. These effects were blocked by GABAA receptor inhibition, confirming receptor-dependent mechanisms. Proteomic analyses of hCS cultures, and independent human dorsolateral prefrontal cortex datasets revealed baseline dysregulation of GABAergic and neurotrophin signaling in SCZ, supporting translational relevance. Together, these findings demonstrate that GABAA receptor activation by muscimol suppresses inflammatory signaling while promoting neuroplasticity in hCSs, and identify astrocytes as central regulators of interferon-dependent neuroimmune dysfunction in SCZ. These results establish non-classic psychedelic compounds as potential modulators of neuroimmune-plasticity coupling and suggest that targeting astrocyte GABAergic signaling may represent a therapeutic strategy for restoring neural homeostasis in SCZ.

Background: Concomitant gabapentinoid and dihydropyridine calcium channel blocker (DHP-CCB) use amplifies dementia risk, an interaction proposed to involve dual neuronal calcium channel blockade. Whether this risk depends on the sequence of drug initiation - and is therefore preventable by prescribing order - remains unknown. Methods: Using the Rutgers Clinical Research Data Warehouse (2015-2024), we conducted three complementary analyses. The primary analysis (Population 4) compared gabapentin versus pregabalin in 4,451 patients on chronic DHP-CCB therapy who newly initiated a gabapentinoid (55 dementia events; IPTW Cox model). The asymmetry confirmatory analysis (Population 3) compared DHP-CCB versus ACE/ARB initiation in 1,740 patients on chronic gabapentinoid therapy (29 dementia events). A sensitivity analysis replicated prior findings in a broader CCB-first cohort (N=9,383). A dementia acceleration analysis examined outcomes in 273 patients with established dementia initiating gabapentinoid. Results: In Population 4, gabapentinoid initiation on a background of chronic CCB therapy was associated with a 2.23-fold elevated dementia risk compared to pregabalin (IPTW HR 2.23, 95% CI 1.43-3.48, p=0.0004). The Population 3 asymmetry test yielded a null result: adding DHP-CCB to chronic gabapentinoid therapy carried no differential dementia risk versus adding ACE/ARB (IPTW HR 0.995, 95% CI 0.595-1.664, p=0.98). This directional asymmetry - elevated risk only when gabapentinoid is added to pre-existing CCB therapy, not the reverse - is the central finding. Lagged analyses showed HRs increasing monotonically from 2.23 to 2.87 across 0- to 180-day lag windows, reducing concern for protopathic bias. In the dementia acceleration cohort, DHP-CCB use at gabapentinoid initiation was associated with encephalopathy (IPTW HR 2.09, 95% CI 1.19-3.67, p=0.010); zero encephalopathy events occurred among non-DHP CCB users (N=16), consistent with DHP subtype specificity. Conclusions: The gabapentinoid-CCB cognitive interaction is directionally asymmetric: risk concentrates in patients adding gabapentinoid to pre-existing CCB therapy, not the reverse. This pattern is mechanistically consistent with impaired homeostatic synaptic plasticity in neurons compensating for chronic L-type calcium channel blockade. For patients already on CCB therapy requiring neuropathic pain management, pregabalin may be preferable to gabapentin, pending external validation. The asymmetry also implies that initiating a CCB in a patient already on gabapentin may not carry equivalent risk.

Introduction People with bipolar disorder (BD) and concurrent opioid use disorder (OUD) experience more severe clinical outcomes, including higher mortality, treatment complexity, and worse psychiatric symptoms, yet they are underserved due to a lack of tailored clinical guidelines and limited supporting research on competing treatment options. While pharmacological treatments for BD are well-established, their use varies widely across settings, and their effectiveness in individuals with co-occurring OUD is unclear. We propose parallel population-based studies to emulate randomized controlled trials to assess the comparative effectiveness of pharmacological treatment options for BD among people with OUD in British Columbia and Ontario, Canada, 2010-2023. Methods and analysis We propose emulating a series of parallel target trials using linked population-level health administrative data for all individuals aged 18 years or older diagnosed with both BD and OUD and who initiated treatments for BD between 1 January 2010 and 31 December 2023. All analyses will be conducted in parallel in British Columbia and Ontario. We propose a series of four successive target trial emulations, comparing (i) lithium versus non-antipsychotic mood stabilizers such as divalproex, lamotrigine, and valproic acid; (ii) lithium versus 2nd generation antipsychotics with mood stabilizing properties such as risperidone, olanzapine, aripiprazole, and quetiapine; (iii) lithium versus combination treatments such as lithium and divalproex, lithium and olanzapine, lithium and aripiprazole, lithium and quetiapine, divalproex and olanzapine, and olanzapine and quetiapine; (iv) lithium and valproate (LATVAL) versus lithium and olanzapine, lithium and aripiprazole, lithium and quetiapine, divalproex and olanzapine, and olanzapine and quetiapine. Incident user and prevalent new user analyses are planned for proposed target trials (i)-(iv), pending sufficient data. Stratified analyses will be conducted for BD-I, manic and depressive phases of BD illness. We propose an initiator analysis (intention-to-treat, conditional on medication dispensation) to determine the effectiveness of the treatments and per-protocol analyses to determine the efficacy of the treatments after dealing with treatment switching and recommended dose adjustment. The outcomes will include psychiatric acute-care visits (hospitalizations and emergency department visits), BD treatment discontinuation and all-cause mortality. Subgroup and sensitivity analyses, including cohort and study timeline restrictions, eligibility criteria modifications, and outcome reclassifications, are proposed to assess the robustness of our results. Executing analyses in parallel across settings using a co-developed protocol will allow us to evaluate the replicability of findings. Ethics and dissemination The protocol, cohort creation, and analysis plan have been classified and approved as a quality improvement initiative by the Providence Health Care Research Ethics Board and the Simon Fraser University Office of Research Ethics. Results will be disseminated to local advocacy groups, clinical groups and decision-makers, national and international clinical guideline developers, presented at international conferences, and published in peer-reviewed journals.