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: Predicting long-term outcomes in first-episode schizophrenia (FES) remains difficult, despite being especially important early in the illness, when timely intervention is most critical. Many potential predictors have been studied, but few are reliable enough to guide early treatment decisions. It also remains unclear how much data from the initial phase of illness is required to improve prognostic accuracy. METHODS: We analysed 68 patients with first-episode schizophrenia (FES) assessed at baseline (V1; mean 0.5 years post-onset, YPO), one-year follow-up (V2; mean 1.2 YPO), and outcome (V3; mean 4.9 YPO). We trained elastic-net models to predict three V3 outcomes-negative symptoms (PANSS Negative factor; Wallwork/Fortgang), global functioning (GAF), and quality of life (WHOQOL-BREF psychological domain)-using either 23 V1 predictors alone or V1 predictors plus V2 data (43 predictors). Performance was evaluated with nested cross-validation on held-out data. RESULTS: Using predictors from the first year (V1+V2), we achieved statistically significant out-of-sample prediction for all three V3 outcomes: PANSS Negative factor (Wallwork/Fortgang) R2=0.22 driven mainly by log(DUP), PANSS Negative at V1/V2, and PANSS Disorganized at V2; WHOQOL-BREF Psychological Health R2=0.22 driven mainly by WHOQOL Psychological Health at V2 and GAF at V2; and GAF R2=0.14 driven mainly by GAF at V2, PANSS Positive at V2, WHOQOL Psychological Health at V2, and hospitalization burden (before V1 and between V1-V2). With baseline-only predictors (V1), only PANSS Negative showed meaningful predictive power (R2=0.15); GAF and WHOQOL-BREF did not outperform the intercept-only baseline. CONCLUSION: In FES, long-term functioning (GAF) and quality of life (WHOQOL-BREF) can not be predicted well from first-episode (V1) measures; at least an additional 1 year of follow-up is needed, implying these outcomes are driven by changes after onset that V1 misses. Negative symptoms differ: they are comparatively stable after initial antipsychotic treatment, and duration of untreated psychosis is their strongest predictor beyond baseline severity-consistent with early biology and treatment timing shaping their level and persistence. These contrasting patterns indicate different outcome phenotypes.

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.

BackgroundPrediction of response to antipsychotic medications remains elusive, and a biomarker assisting in treatment selection would drastically improve prognosis. The 40 Hz auditory steady state response (ASSR) is an EEG biomarker, mirroring the GABA-glutamate signaling and the excitation/inhibition balance, consistently been reported to be impaired in schizophrenia, on, with inconsistent evidence of an association with specific symptoms. MethodsN=69 schizophrenia inpatients with an acute psychotic episode underwent an EEG recording to assess event related spectral perturbation (ERSP), intertrial phase coherence (ITC) and phase amplitude coupling (PAC) during the ASSR task, aimed to assess their relationship with response to antipsychotics and with positive, negative, disorganized, excited and depressive symptoms. Moreover, patients were compared with controls (N=36), to delineate schizophrenia acute phase ASSR dynamics. ResultsResponders to treatment showed a decreased 40 Hz ERSP in both the early (0-0.2s post-stimulus; P=0.0013; d=-0.936) and late (0-2-1.2s post-stimulus; P=0.0022; d=-0.932) time windows compared to non-responders. Using logistic regression and bootstrap optimism correction, ERSP classified the two groups with 70% accuracy. Responders but not non-responders showed a reduced ERSP compared to controls (P=0.0211; d=-0.558). Patients had reduced early ITPC (P=0.0001; d=-1.015) vs controls. responders compared to non-responders had increased PAC in the early (P=0.0215; d00.65) and in patients vs controls, in both the early (P=0.0002; d=0.57) and the late (P=0.0006; d=0.74) windows. No association emerged between ASSR metrics and symptoms severity. ConclusionsASSR is a candidate biomarker for antipsychotic treatment personalization. Only responders to treatment presented a significant gamma-band impairment, in line with previous literature on stabilized outpatients, but not non-responders, indicating that a distinct neurobiology could exist.

BackgroundLipidomic alterations have been reported across schizophrenia (SCZ) and bipolar disorder (BD), but findings are heterogeneous and often overlap across diagnoses, limiting diagnostic specificity. Associations between lipid profiles and illness severity have also been inconsistent when assessed using single symptom scales, raising the possibility that unidimensional measures fail to capture biologically relevant variation. Whether plasma lipidomic alterations relate to multidimensional psychosis severity, and how they relate to polygenic liability, remains unclear. MethodsWe examined associations among psychiatric and cognitive polygenic risk scores (PRS), plasma lipidomics (361 species across 16 classes), and a machine-learning-derived severe psychosis probability score in a transdiagnostic cohort of individuals with SCZ or BD (PRS n=1,320; lipid subset n=428). Regression and lipid class enrichment analyses tested severity associations. Mediation and canonical correlation analyses assessed integrated genetic-lipid-severity relationships. ResultsSCZ-PRS (positive), BD-PRS (negative), and educational attainment PRS (negative) showed modest associations ({beta} = |0.02|) with severe psychosis probability. Lipid class enrichment analysis identified nine classes associated with severity, including increased sphingolipids (dSM, dCer), phosphatidylcholines (PC), triacylglycerides (TAG), and phosphatidylethanolamine plasmalogens (PE-P), alongside decreased phosphatidylcholine plasmalogens (PC-P). Most lipid class associations were robust to adjustment for diagnosis and medication. No significant mediation or shared multivariate genetic-lipid structure was observed. ConclusionsPlasma lipidomic variation tracks multidimensional psychosis severity across diagnostic boundaries. These findings suggest that lipidomic alterations may reflect transdiagnostic biological processes linked to illness burden that are not fully captured by categorical diagnoses, single symptom scales, or common-variant polygenic risk.

Cognitive heterogeneity is a core feature of schizophrenia (SCZ). Conventional approaches examine this heterogeneity using domain-specific scores, which may not fully reflect the underlying cognitive structure. In this study, a norm-anchored cognitive structural deviation (NCSD) framework was developed to examine such heterogeneity from a structure-informed perspective. The HC-derived latent cognitive structure (N-LCS) captured performance across the assessed tasks and remained stable under external validation in an independent cohort. Patients with SCZ showed greater global deviation from the N-LCS, along with altered loading directions of Wisconsin Card Sorting Test (WCST)-derived executive indicators which were consistent across robustness analyses, and altered correlation patterns among cognitive measures relative to HC. These features were quantified using three NCSD-derived indices: the cognitive normative deviation index (CNDI), loading pattern divergence (LPD), and correlation structure discrepancy (CSD). CNDI discriminated SCZ from HC with stable performance under cross-validation. LPD and CSD were associated with anxiety ratings, with LPD also showing a trend-level association with positive symptoms. Exploratory clustering identified a three-cluster solution with clear separation and moderate stability. Together, these findings show that cognitive heterogeneity in SCZ involves both global deviation from the N-LCS and structural alteration. NCSD provides a refined framework to characterize such heterogeneity and may inform precision psychiatry and functional recovery.

Backgroundspeech carries cues to variation in mental state in schizophrenia spectrum disorders/psychotic disorders, typically indexed with clinician-rated scales such as the PANSS. Progress in the automation of speech-based symptom modelling has been constrained by data scale and the underrepresentation of low-resource languages. In this study, we aggregate multi-center recordings to assemble a large corpus and assess symptom-prediction models at scale, to enable more objective and efficient assessments and the early detection of relapse-related signals from speech. MethodsWe compiled data from 453 patients with schizophrenia spectrum disorders, recruited from ten global sites, and clipped their speech recordings into 6,664 segments. Across three feature sets, acoustic-prosodic profile, pretrained multilingual embeddings, and their concatenation, we compared 16 algorithms to predict eight relapse-related PANSS items, including three positive (P1, P2, P3), three negative (N1, N4, N6), and two general (G5, G9) items, on speaker-disjoint splits (80% train, 10% test, and 10% validation). Performance was assessed by root-mean-squared-error (RMSE) at both segment and participant (median aggregation) levels. Best model per item underwent bias checks for age, sex, education, and symptom severity. OutcomesBest-performing models predicted symptoms with prediction errors of 1{middle dot}5 PANSS points or lower: P1 1{middle dot}494/1{middle dot}527, P2 1{middle dot}318/1{middle dot}107, P3 1{middle dot}407/1{middle dot}542, N1 1{middle dot}029/1{middle dot}030, N4 1{middle dot}452/1{middle dot}430, N6 0{middle dot}860/0{middle dot}855, G5 0{middle dot}850/0{middle dot}882, G9 1{middle dot}213/1{middle dot}282 (segment/participant). Performance of the pretrained multilingual embeddings surpassed acoustic-prosodic features and their concatenation. Results were comparable in low-resource languages (e.g., Czech). We found no bias by age, sex, or education, aside from reduced N4 accuracy in males; but performance degraded with higher symptom severity. InterpretationSpeech can support automatic assessment of schizophrenia symptoms using pretrained multilingual embeddings, even without the use of transcripts. Such models show promise as clinically meaningful, efficient, and low-burden tools for real-time monitoring of symptom trajectories. FundingEU Horizon research and innovation programme. Research in contextO_ST_ABSEvidence before this studyC_ST_ABSAutomatic assessment of disease severity is a key issue in schizophrenia research, for which spontaneous speech offers a cost-effective, automatable solution. To evaluate existing evidence for speech-based symptom assessment, two reviewers (RHe, MK) searched PubMed, IEEE Xplore, arXiv, bioRxiv, and medRxiv for publications from inception to Aug 25, 2025, using the terms: ("symptom" OR "PANSS" OR "Positive and Negative Syndrome Scale") AND ("psychosis" OR "schizophrenia") AND ("language" OR "speech" OR "spontaneous speech") AND ("prediction" OR "machine learning" OR "deep learning" OR "algorithm" OR "neural network" OR "AI" OR "artificial intelligence"). Fourteen studies on symptom-level modelling were identified. Ten studies dichotomized clinical scores (e.g., PANSS) into low vs high for classification: five used conventional ML (e.g., random forests) and five used neural networks, with F1 scores ranging from 0{middle dot}60-0{middle dot}85. The remaining four studies, and two of the ten studies as mentioned above, modelled raw scores directly as regression tasks. Two relied solely on conventional regressors and the rest used neural networks, with errors from 0{middle dot}487 for single items (scale 1-7) to 8{middle dot}04 for summed scores (scale 18-126). All studies used free speech for elicitation, except one study, which used a reading task. Three studies incorporated additional tasks, such as picture description and immediate recall. None were multilingual: nine were in English, three in Chinese, one in Swiss German, and one in Brazilian Portuguese. Features spanned a wide range, including acoustic-prosodic profiles, morpho-syntactic structure, semantic organization, pragmatics (including sentiments), and even visual features capturing movement during talking. Representations from pretrained language models were also widely employed. Sample sizes (counting patients with schizophrenia) were generally small: eleven studies enrolled <50 patients, one had 65, and only two exceeded 100 patients. Some increased their effective sample size via multiple recordings per patient or by adding healthy controls and/or patients with other psychiatric disorders (e.g., depression). Added value of this studyTo our knowledge, this is the first multilingual, speech-based study modelling schizophrenia symptom severity with machine learning approach, and it includes the largest cohort of patients with schizophrenia to date. We further increased effective sample size by using diverse elicitation tasks and segmenting recordings into clips. This multilingual corpus empowers the usage of complex models and supports transfer learning from high-resource languages (e.g., English) to low-resource ones (e.g., Czech). For each of eight selected relapse-related PANSS items, the best audio-only models achieved RMSE < 1{middle dot}5, underscoring clinical relevance. We assessed potential biases: no effects were found for age, sex, or education (except poorer N4 performance in males), though performance declined at higher symptom severity. Trained models are released for use. Implications of all the available evidenceWe show that speech is a powerful signal for automatic assessment of schizophrenia symptom severity and holds promise for relapse prediction, even without transcripts. The approach readily extends to incorporate textual features (from manual or automatic transcripts) and more advanced models. Prospective studies with repeated recordings across relapse episodes are needed to validate the utility of our models on relapse prediction, for the sake of supporting precision psychiatry while reducing clinician burden.

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.

BackgroundEEG microstates provide a window into rapid, large-scale brain network dynamics. Despite showing alterations in schizophrenia, evidence in first-episode schizophrenia spectrum psychosis (FESSP) is limited. We assessed whether microstate temporal and transition features could identify a multivariate signature of FESSP, and whether these dynamics can track symptom severity. MethodsResting-state EEG was analysed in 69 participants (FESSP n=41, mean age: 22.49 years; healthy controls n=28, mean age: 21.33 years). Twenty-eight microstate temporal and transition features were extracted across microstate classes (A-D). Group classification accuracy was assessed using a linear support vector machine with stratified cross-validation and permutation testing. Within the FESSP group, we further assessed associations between microstate features and clinical scores using the Brief Psychiatric Rating Scale (BPRS), Scale for the Assessment of Positive Symptoms (SAPS), and Scale for the Assessment of Negative Symptoms (SANS). ResultsMultivariate microstate features provided above-chance discrimination of FESSP from controls (balanced accuracy=0.644; AUC=0.688; p=0.030). However, when comparing individual features between groups, no feature survived multiple-comparison correction consistent with characterisation of FESSP via a distributed multivariate pattern across correlated features. Within the FESSP group, microstate dynamics were most strongly linked to negative symptoms, with higher SANS scores associated with shorter microstate D durations ({rho}=-0.507, pFDR=0.020) and higher occurrence of microstates A and B ({rho}=0.434-0.443, pFDR=0.042). BPRS-18 and SAPS showed no associations with any features. ConclusionsUsing EEG microstate temporal and transition features with multivariate classification, we identified a pattern that differentiated FESSP from controls and showed selective associations with negative symptom severity.

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.

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.

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.

0.Clozapine is the gold-standard for treatment-resistant schizophrenia despite its severe metabolic complications, including metabolic syndrome (MetS) and type 2 diabetes (T2D) risk. A better understanding of the genetic factors influencing clozapine pharmacokinetics and their relationship to metabolic risk could help inform precision medicine approaches to clozapine prescribing. Using a series of genetic-epidemiological approaches, we aimed to identify candidate biomarkers associated with clozapine-induced metabolic dysfunction. We first used two-sample Mendelian randomization (MR) leveraging genome-wide association summary data to investigate evidence of causal relationships between clozapine metabolism and cardiometabolic traits. These analyses indicated that higher plasma clozapine levels and a higher clozapine-norclozapine ratio were both associated with a higher risk of T2D and higher blood pressure. We then applied a Phenome-scan-colocalization-MR pipeline to identify traits influenced by clozapine-metabolism loci that might serve as biomarkers of cardiometabolic risk. This pipeline identified 28 colocalizing candidate biomarkers associated with clozapine metabolising genetic loci. Subsequent MR highlighted associations for 16 of these 28 biomarker candidates with cardiometabolic outcomes, which included haematological markers and excretory traits (e.g. gamma-glutamyl transferase, red cell distribution width, and urea). These findings may inform the development of biomarker-guided monitoring approaches for risk stratification and early intervention, enabling a shift from reactive monitoring to predictive approaches in managing clozapine-induced metabolic dysfunction with appropriate clinical validation. These findings may also help to mitigate the risk of metabolic dysfunction associated with other antipsychotic medications.

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.

Individuals at clinical high risk for psychosis (CHR) are cognitively and neurobiologically heterogeneous, which encourages the use of a clustering approach to parse this heterogeneity. Multimodal approaches are assumed to be superior to unimodal approaches in identifying subgroups. With the success of the use of cognition and electrophysiological measures collectively in established psychotic disorders, and the lack of such an approach in CHR, we were motivated to address this gap. Using the North American Psychosis-Risk Longitudinal Study (NAPLS) 2 consortia (CHR (N=764)), we applied unsupervised cluster analysis on the combined cognitive and electrophysiology measures to identify CHR subgroups and assess their relationship with clinical and functional outcomes. A two-cluster solution with modest separability was found, which prompted the use of an alternative probabilistic, rather than discrete, clustering approach. Individuals who were more likely to be in Cluster 1 exhibited poorer cognitive performance, larger N100, mismatch negativity, and P300 amplitudes, and worse functioning, as well as a younger age of onset. These findings were largely replicated in NAPLS 3 (CHR (N=628)). Taken together, the results of our previous study of cognition-only clustering and the current study of combining cognition and electrophysiology indicate that multimodal clustering, if not developmentally informed, may obscure meaningful subtyping.

ObjectiveCognitive deficits are a leading cause of disability in schizophrenia and are linked to poor functional outcomes. There are no first line treatments for these deficits, and their neural basis is poorly understood. While schizophrenia is associated with widespread cognitive deficits, information processing speed is most profoundly impaired. Processing speed deficits have been associated with hyperconnectivity in the Default Mode Network (DMN). We therefore tested if modulating DMN connectivity with single or multiple sessions of transcranial magnetic stimulation (TMS) applied to an individualized DMN target would affect processing speed. MethodsIn the first study, 10 individuals with schizophrenia received single TMS sessions and underwent resting-state neuroimaging and processing speed assessment (Brief Assessment of Cognition in Schizophrenia digit symbol coding) acutely before and after each session. These sessions included excitatory (intermittent theta burst stimulation, iTBS); inhibitory (continuous theta burst stimulation, cTBS); and sham stimulation sessions. In the second study, 29 individuals (17 schizophrenia, 12 non-psychosis controls) received 5 accelerated sessions of cTBS with resting-state neuroimaging and processing speed assessment before and after the course of TMS sessions. ResultsIn the accelerated, multi-session DMN-targeted TMS trial, cTBS improved processing speed in the schizophrenia group (p=0.0124). In individuals with schizophrenia, reduction in DMN connectivity was linked to improvement in processing speed (p=0.021). These changes were dependent on age, where younger participants experienced greater processing speed improvements than older participants (p=0.006). ConclusionsIn sum, personalized network targeted TMS is a novel method for reducing cognitive impairment associated with schizophrenia.

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.