Schizophrenia Bulletin

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.

Background: The highly influential predictive processing theory of psychosis posits that symptoms arise from imbalances in the weighting of predictions (priors) and sensory evidence. Despite this theory's increasing prominence, studies often present conflicting results. This is particularly problematic as findings from single tasks with modest sample sizes are frequently used to advance a theory for a generalised altered reliance on priors in psychosis. Methods: This study presents a random-effects, multi-level meta-analysis (PROSPERO CRD42024574379) evaluating evidence for aberrant reliance on priors in psychosis across perceptual tasks. The search identified articles in Embase, MEDLINE, APA PsycINFO, and APA PsycArticles published between 1st January 2005 and 31st October 2024, with risk of bias assessed using the Newcastle-Ottawa Scale. Included articles (34 results from 27 studies) compared adults with schizophrenia-spectrum psychosis (SZ; n = 904) to healthy controls (n = 1,039) on behavioural measures representing reliance on priors. Results: Results provided no evidence for atypical reliance on priors in psychosis (g = .03, 95% CI [-0.27, 0.34]; p = .818) or associations with delusions (6 results; SZ = 183; r = -.16, 95% CI [-0.51, 0.19]; p = .293) or hallucinations (10 results; SZ = 370; r = .04, 95% CI [-0.28, 0.36]; p = .780). In contrast with the theory that psychosis may differentially affect priors at different levels of the cognitive hierarchy, a sub-group analysis indicated that a two-level hierarchical model of priors did not account for conflicting results (F(1,32) = 0.1, p = .758). Conclusion: These findings do not suggest that psychosis is associated with a generalised predictive processing deficit spanning multiple aspects of perception. Key words: psychosis, schizophrenia, predictive processing, prior expectations, perception

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.

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 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.

Schizophrenia frequently follows a chronic relapsing-remitting course, comprising alternating episodes with and without psychotic symptoms (hereafter: psychosis and psychotic remission). One potential neurobiological correlate of this course is aberrant dopamine synthesis and storage (DSS) in the striatum, which can be estimated by 18F-DOPA positron emission tomography (PET). We hypothesised that striatal DSS in patients with schizophrenia decreases from psychosis to psychotic remission, with lower striatal DSS in patients during psychotic remission compared to healthy subjects. Additionally, we explored whether striatal DSS is associated with psychotic relapse after remission. 18F-DOPA PET scans and clinical assessments were conducted in 28 patients with schizophrenia at two timepoints, first during psychosis and second during early psychotic remission 6 weeks to 12 months after the first timepoint, as well as in 21 healthy controls, assessed twice in a comparable time interval. The averaged influx constant kicer as proxy for DSS was calculated for striatal subregions (i.e., nucleus accumbens, caudate, and putamen) using voxel-wise Patlak modelling with a cerebellar reference region. Mixed-effects models and post hoc analyses were used to test for longitudinal changes in kicer and cross-sectional group differences. An exploratory clinical follow-up 12 months after the second scan was conducted to assess psychotic relapse, and post hoc ANCOVAs were used to test for differences in kicer at each session between relapsing and non-relapsing patients. Kicer in both caudate and nucleus accumbens significantly changed from psychosis to psychotic remission compared to healthy controls, with a significant longitudinal decrease of caudate kicer in patients. Furthermore, kicer in both caudate and accumbens was significantly lower in patients during early psychotic remission compared to controls. At the exploratory clinical follow-up, 32% of patients had experienced a psychotic relapse; they showed higher caudate kicer compared to non-relapsing patients during psychosis, with no difference during psychotic remission. These findings provide evidence for the link between striatal, particularly caudate, DSS and the relapsing-remitting course of psychotic symptoms in schizophrenia, with lower caudate DSS during early psychotic remission. Data suggest altered striatal dopamine synthesis together with impaired DSS dynamics along the course of psychotic symptoms in schizophrenia.

BackgroundNegative symptoms are core features of schizophrenia that relate strongly to functional impairment, yet interventions targeting these symptoms remain largely ineffective. Emerging theoretical work highlights how environmental factors may shape and maintain negative symptoms. Although racial disparities in schizophrenia diagnosis among Black Americans are well documented and linked to racial stress and psychosis, the impact of racial stress on negative symptoms has not been examined. This study provides an initial test of a novel theory proposing that racial stress - here measured by racial discrimination - influences negative symptom severity through exacerbation of negative cognitions about the self, particularly defeatist performance beliefs (DPB). Study DesignParticipants diagnosed with schizophrenia-spectrum disorder (SSD) (N = 208; 80 Black, 128 White) completed the Positive and Negative Syndrome Scale (PANSS), the Defeatist Beliefs Scale, and self-report measures of subjective racial and ethnic discrimination (Racial and Ethnic Minority Scale and General Ethnic Discrimination Scale). Relationships among variables were tested using linear regression and mediation analysis. Study ResultsBlack participants exhibited significantly greater total and experiential negative symptoms than White participants with no group difference in DPB. Racial discrimination explained 46% of the relationship between race and negative symptoms. Among Black participants, higher DPB were associated with greater negative symptom severity. Discrimination was positively related to both DPB and negative symptoms. DPB partially mediated the relationship between discrimination and negative symptoms. ConclusionsFindings suggest that racial stress contributes to negative symptom severity via defeatist beliefs among Black individuals, highlighting potential targets for culturally informed interventions.

BackgroundPsychosis has been conceptualised as a continuum extending from healthy individuals with psychotic-like experiences to clinical populations with schizophrenia. It is unclear which biological mechanisms found in chronic schizophrenia extend across the psychosis continuum to healthy individuals with high positive schizotypy (HS). In this study, we used computational modeling to test whether changes in effective connectivity and excitation/inhibition (E/I) balance reported in schizophrenia are also found in HS. MethodsA total of 2425 individuals from the general population were screened for HS. A subset (N=141) was invited for in-depth phenotyping. Resting-state functional magnetic resonance imaging (rsfMRI) and proton magnetic resonance spectroscopy (1H-MRS) were recorded in n=69 HS individuals and n=72 group-matched controls with low schizotypy (LS). We used dynamic causal modeling to estimate effective connectivity between bilateral primary auditory cortex (A1), superior temporal gyrus (STG), and inferior frontal gyrus (IFG). ResultsBilateral backward connectivity from IFG to STG was significantly reduced in HS compared to LS. Widespread cortical disinhibition in the auditory cortex-IFG network correlated with more severe positive schizotypy scores and impulsive nonconformity. Reduced excitability in the same network was correlated with stronger cognitive disorganisation. ConclusionsOur results favour a psychosis-continuum hypothesis, suggesting that reduced top-down drive from frontal cortex and compensatory allostatic upregulation of cortical excitability, as observed in chronic schizophrenia, also extend to groups with sub-clinical psychotic symptoms. Frontal cortex dysfunction may serve as a biologically interpretable biomarker of psychosis risk and a target for preventative interventions.

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.

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.

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.

Catatonia is a severe psychomotor syndrome that occurs across psychiatric diagnoses and is increasingly conceptualized as reflecting neurodevelopmental vulnerability. The anterior cingulate cortex (ACC) plays a central role in motor initiation and cognitive-affective integration and displays substantial interindividual variability in its sulcal morphology, which is established prenatally and remains stable across life. In this MRI study, we examined whether ACC sulcal patterns represent a structural trait marker of catatonia. We analyzed high-resolution T1-weighted images from a hospital-based cohort comprising patients with catatonia (N = 109), psychiatric patients without catatonia (N = 323), and healthy controls (N = 91). The presence of the paracingulate sulcus (PCS) in each hemisphere was determined through blinded visual inspection, and regression analyses tested associations with diagnostic group, adjusting for age, sex, scanner type, intracranial volume, and benzodiazepine and antipsychotic exposure. Patients with catatonia exhibited a significantly reduced prevalence of the left PCS and diminished hemispheric asymmetry compared with both non-catatonic patients and healthy controls. These effects were independent of whether catatonia occurred within psychotic or mood disorders. PCS size did not differ across groups, and sulcal pattern did not correlate with catatonia severity among affected individuals. The findings demonstrate that ACC sulcal deviations are specifically associated with catatonia across diagnostic categories, supporting a neurodevelopmental etiology and reinforcing ACC involvement in its pathophysiology. Early-determined sulcal morphology may represent a trait-level marker contributing to vulnerability for catatonia, with implications for early identification, risk stratification, and targeted intervention strategies.

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.

Cognitive deficits are a core feature of schizophrenia, yet their neural mechanisms remain poorly understood. Network switching, a measure of how frequently brain networks change their interactions over time, has been linked to cognitive performance in healthy individuals and has been reported to be altered in schizophrenia. Recent evidence further suggests that the relationship between network switching and cognition depends on arousal, which is itself disrupted in schizophrenia. However, whether arousal-related alterations in network switching contribute to cognitive impairment in schizophrenia remains unclear. Here, we used concurrent resting-state functional MRI (fMRI) and pulse oximetry data from 39 healthy controls (HC), 27 psychiatric controls (PC), and 39 individuals with schizophrenia spectrum disorders (SSD) to examine whether network switching relates to indices of autonomic arousal. Additionally, in HC and SSD participants, we tested whether arousal moderated the association between network switching and performance on an attention task. We observed no group differences in autonomic arousal. However, PC exhibited higher dorsal default mode and anterior salience network switching rates compared to SSD participants. Additionally, autonomic arousal significantly moderated the relationship between network switching and cognitive performance in HC, an effect that was absent in SSD. Notably, these findings implicate network switching as a potential neural biomarker that differentiates PC from SSD. They also suggest that disrupted coupling between arousal state and network switching, rather than switching alone, may underlie cognitive dysfunction in SSD.

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.

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.

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.

BackgroundSchizophrenia is marked by impairments in emotional processing and social cognition, yet traditional neuroimaging paradigms often lack the ecological validity to capture these deficits in real-world contexts. MethodsIn this study, we used intersubject correlation (ISC) analysis of functional MRI data to examine shared neural representations of naturalistic visual narratives in individuals with schizophrenia and healthy controls. Participants viewed short films designed to evoke happy, sad, and emotionally neutral responses, allowing us to compare how synchronized brain activity varied with emotional content across and within groups. ResultsHealthy controls showed greater ISC in regions associated with affective salience, emotion recognition, and social understanding, including the amygdala, insula, and temporal cortices. In contrast, participants with schizophrenia displayed higher synchrony in visual, subcortical, and frontal areas, suggesting a reliance on perceptual and executive systems. To isolate the effects of emotion from general visual processing, we compared ISC during emotional clips relative to neutral videos. This revealed significantly reduced synchrony in the bilateral amygdala in patients, highlighting a core dysfunction in affective engagement. Interestingly, neutral stimuli elicited unexpectedly strong synchronization in frontal and limbic regions in the schizophrenia group, possibly reflecting altered salience attribution to ambiguous or emotionally ambiguous content. ConclusionsThese results point to a functional reorganization of affective processing in schizophrenia, where impaired limbic recruitment is accompanied by compensatory engagement of perceptual and cognitive control networks. ISC during naturalistic stimulation emerges as a powerful tool for capturing subtle disruptions in shared emotional experience in psychiatric populations.

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.

BackgroundLoneliness is a psychosocial stressor associated with elevated risk of severe mental illness (SMI), including major depressive disorder (MDD), schizophrenia (SCZ), and bipolar disorder (BD). Loneliness is theorized to become biologically embedded via inflammation-related mechanisms, yet its causal relationship with SMI and the role of inflammatory signaling remain unclear. AimsTo investigate whether loneliness causally influences SMI risk and whether inflammatory cytokines mediate this relationship. MethodWe applied univariable Mendelian randomization (MR) to estimate the causal effect of loneliness on SMI and multivariable MR (MVMR) to assess mediation by inflammatory signaling. We integrated genome-wide association study (GWAS) summary statistics for loneliness and SMI with genetic instruments for inflammatory cytokines. MVMR models estimated the direct effect of loneliness after accounting for inflammatory signaling using eQTL and pQTLs for interleukin-1 receptor antagonist (IL-1RA), interleukin-6 (IL-6), IL-6 receptor (IL-6R), tumor necrosis factor alpha (TNF-), and TNF receptors (TNF-R1/2). Bidirectional MR examined potential reverse causal pathways between inflammation, SMI, and loneliness. ResultsMR provided evidence consistent with a causal effect of loneliness on SCZ and MDD. Results were also consistent with inflammatory cytokine pathways for IL-1RA, IL-6R, and TNF-R1, partially mediating the loneliness-SCZ and loneliness-MDD causal effect. No significant effects were identified for BD in UVMR or MVMR models. Bidirectional MR suggested evidence of reverse causation between SCZ and loneliness. ConclusionsThe findings support a causal risk-increasing effect of loneliness on SCZ and MDD, partially mediated by systemic inflammatory signaling, implicating pathways as a plausible mechanistic link between psychosocial stress and mental illness risk and highlighting potential opportunities for prevention and targeted intervention through inflammation and social pathways.