Schizophrenia Bulletin

Background and HypothesisVoice atypicalities are potential markers of clinical features of schizophrenia (e.g., negative symptoms). A recent meta-analysis identified an acoustic profile associated with schizophrenia (reduced pitch variability and increased pauses), but also highlighted shortcomings in the field: small sample sizes, little attention to the heterogeneity of the disorder, and to generalizing findings to diverse samples and languages. Study DesignWe provide a critical cumulative approach to vocal atypicalities in schizophrenia, where we conceptually and statistically build on previous studies. We aim at identifying a cross-linguistically reliable acoustic profile of schizophrenia and assessing sources of heterogeneity (symptomatology, pharmacotherapy, clinical and social characteristics). We relied on previous meta-analysis to build and analyze a large cross-linguistic dataset of audio recordings of 231 patients with schizophrenia and 238 matched controls (>4.000 recordings in Danish, German, Mandarin and Japanese). We used multilevel Bayesian modeling, contrasting meta-analytically informed and skeptical inferences. Study ResultsWe found only a minimal generalizable acoustic profile of schizophrenia (reduced pitch variability), while duration atypicalities replicated only in some languages. We identified reliable associations between acoustic profile and individual differences in clinical ratings of negative symptoms, medication, age and gender. However, these associations vary across languages. ConclusionsThe findings indicate that a strong cross-linguistically reliable acoustic profile of schizophrenia is unlikely. Rather, if we are to devise effective clinical applications able to target different ranges of patients, we need first to establish larger and more diverse cross-linguistic datasets, focus on individual differences, and build self-critical cumulative approaches.

Patients with schizophrenia have substantial comorbidity contributing to reduced life expectancy of 10-20 years. Identifying which comorbidities might be modifiable could improve rates of premature mortality in this population. We hypothesize that conditions that frequently co-occur but lack shared genetic risk with schizophrenia are more likely to be products of treatment, behavior, or environmental factors and therefore potentially modifiable. To test this hypothesis, we calculated phenome-wide comorbidity from electronic health records (EHR) in 250,000 patients in each of two independent health care institutions (Vanderbilt University Medical Center and Mass General Brigham) and association with schizophrenia polygenic risk scores (PRS) across the same phenotypes (phecodes) in linked biobanks. Comorbidity with schizophrenia was significantly correlated across institutions (r = 0.85) and consistent with prior literature. After multiple test correction, there were 77 significant phecodes comorbid with schizophrenia. Overall, comorbidity and PRS association were highly correlated (r = 0.55, p = 1.29x10-118), however, 36 of the EHR identified comorbidities had significantly equivalent schizophrenia PRS distributions between cases and controls. Fifteen of these lacked any PRS association and were enriched for phenotypes known to be side effects of antipsychotic medications (e.g., "movement disorders", "convulsions", "tachycardia") or other schizophrenia related factors such as from smoking ("bronchitis") or reduced hygiene (e.g., "diseases of the nail") highlighting the validity of this approach. Other phenotypes implicated by this approach where the contribution from shared common genetic risk with schizophrenia was minimal included tobacco use disorder, diabetes, and dementia. This work demonstrates the consistency and robustness of EHR-based schizophrenia comorbidities across independent institutions and with the existing literature. It identifies comorbidities with an absence of shared genetic risk indicating other causes that might be more modifiable and where further study of causal pathways could improve outcomes for patients.

BackgroundPeople with schizophrenia exhibit notable difficulties in the use of everyday language. This directly impacts ones ability to complete education and secure employment. An impairment in the ability to understand and generate the correct grammatical structures (syntax) has been suggested as a key contributor; but studies have been underpowered, often with conflicting findings. It is also unclear if syntactic deficits are restricted to a subgroup of patients, or generalized across the broad spectrum of patients irrespective of symptom profiles, age, sex, and illness severity. MethodsWe conducted a systematic review and meta-analysis, registered on OSF, adhering to PRISMA guidelines, searching multiple databases up to May 1, 2024. We extracted effect sizes (Cohens d) and variance differences (log coefficient of variation ratio) across 6 domains: 2 in comprehension (understanding complex syntax, detection of syntactic errors) and 4 in production (global complexity, phrasal/clausal complexity, utterance length, and integrity) in patient-control comparisons. Study quality/bias was assessed using a modified Newcastle-Ottawa Scale. Bayesian meta-analysis was used to estimate domain-specific effects and variance differences. We tested for potential moderators with sufficient data (age, sex, study quality, language spoken) using conventional meta-regression to estimate the sources of heterogeneity between studies. FindingsOverall, 45 studies (n=2960 unique participants, 64{middle dot}4% English, 79 case-control contrasts, weighted mean age(sd)=32{middle dot}3(5{middle dot}6)) were included. Of the patient samples, only 29{middle dot}2% were women. Bayesian meta-analysis revealed extreme evidence for all syntactic domains to be affected in schizophrenia with a large-sized effect (model-averaged d=0{middle dot}65 to 1{middle dot}01, with overall random effects d=0{middle dot}86, 95% CrI [0{middle dot}67-1{middle dot}03]). Syntactic comprehension was the most affected domain. There was notable heterogeneity between studies in global complexity (moderated by the age), production integrity (moderated by study quality), and production length. Robust BMA revealed weak evidence for publication bias. Patients had a small-to-medium-sized excess of inter-individual variability than healthy controls in understanding complex syntax, and in producing long utterances and complex phrases (overall random effects lnCVR=0{middle dot}21, 95% CrI [0{middle dot}07-0{middle dot}36]), hinting at the possible presence of subgroups with diverging syntactic performance. InterpretationThere is robust evidence for the presence of grammatical impairment in comprehension and production in schizophrenia. This knowledge will improve the measurement of communication disturbances in schizophrenia and aid in developing distinct interventions focussed on syntax - a rule-based feature that is potentially amenable to cognitive, educational, and linguistic interventions. Research in ContextO_ST_ABSEvidence before this studyC_ST_ABSPrior studies have documented significant language deficits among individuals with psychosis across multiple levels. However, syntactic divergence--those affecting sentence structure and grammar--have not been consistently quantified or systematically reviewed. An initial review of the literature indicated that the specific nature and severity of syntactic divergence, as well as their impact on narrative speech production, symptom burden, and daily functioning, remain poorly defined. We conducted a comprehensive search of the literature up to May 1, 2024, using databases such as PubMed, PsycINFO, Scopus, Google Scholar, and Web of Science. Our search terms combined psychosis, schizophrenia, language production, comprehension, syntax, and grammar, and we identified a scarcity of meta-analytic studies focusing specifically on syntactic comprehension and production divergence in psychosis. Added value of this studyThis systematic review and meta-analysis is the first to quantitatively assess syntactic comprehension and production divergence in individuals with psychosis. This study provides estimated effect sizes associated with syntactic impairments as well as a quantification of the variance within patient groups for each domain of impairment. Besides a detailed examination of this under-researched domain, we also identify critical research gaps that need to be addressed to derive benefits for patients from knowledge generated in this domain. Implications of all the available evidenceThis study provides robust evidence of grammatical impairments in individuals with schizophrenia, particularly in syntactic comprehension and production. These findings can enhance early detection approaches via speech/text readouts and lead to the development of targeted cognitive, educational, and linguistic interventions. By highlighting the variability in linguistic deficits, the study offers valuable insights for future therapeutic trials. It also supports the creation of personalized formats of information and educational plans aimed at improving the effectiveness of any therapeutic intervention offered to patients with schizophrenia via verbal medium.

The present study aims to explain how to use the precision nomothetic approach to analyze the interconnections between the negative symptoms, cognitive dysfunctions and biomarkers of schizophrenia. We review our data obtained in different study groups of patients with (deficit) schizophrenia and show, using examples extracted from these studies, how Partial Least Squares (PLS) path analysis should be used to examine these complex associations. PLS path analysis combines factor and multiple regression analysis in mediated models. We show that a single latent trait can be extracted from negative symptom domains and psychosis, hostility, excitation, mannerism, formal thought disorders and psychomotor retardation (PHEMFP). Both the negative and PHEMFP concepts miss discriminant validity whilst a common latent construct may be extracted from the 6 negative and 6 PHEMFP subdomains, dubbed overall severity of schizophrenia (OSOS). A common latent factor may be extracted from neurocognitive test scores including executive functions, and semantic and episodic memory dubbed the general cognitive decline (G-CoDe) index. PLS analysis shows that the effects of neuroimmunotoxic pathways on OSOS are partly mediated by the G-CoDe and indicate that those pathways have also direct effects on OSOS. We explain that the intercorrelations between those features should be assessed in an unrestricted study group combining patients and controls. Moreover, further bifactorial factor analysis with the restricted schizophrenia group may disclose illness-specific covariations among the features. Machine learning discovered a new schizophrenia phenotype characterized by increased severity of AOPs, G-CoDe, and OSOS, dubbed "major neurocognitive psychosis".

Cognitive impairment is a central characteristic of schizophrenia. Executive functioning (EF) impairments are often seen in mental disorders, particularly schizophrenia, where they relate to adverse outcomes. As a heterogeneous construct, how specifically each dimension of EF to characterize the diagnostic and prognostic aspects of schizophrenia remains opaque. We used classification models with a stacking approach on systematically measured EFs to discriminate 195 patients with schizophrenia from healthy individuals. Baseline EF measurements were moreover employed to predict symptomatically remitted or non-remitted prognostic subgroups. EF feature importance was determined at the group-level and the ensuing individual importance scores were associated with four symptom dimensions. EF assessments of inhibitory control (interference and response inhibitions), followed by working memory, evidently predicted schizophrenia diagnosis (area under the curve [AUC]=0.87) and remission status (AUC=0.81). The models highlighted the importance of interference inhibition or working memory updating in accurately identifying individuals with schizophrenia or those in remission. These identified patients had high-level negative symptoms at baseline and those who remitted showed milder cognitive symptoms at follow-up, without differences in baseline EF or symptom severity compared to non-remitted patients. Our work indicates that impairments in specific EF dimensions in schizophrenia are differentially linked to individual symptom-load and prognostic outcomes. Thus, assessments and models based on EF may be a promising tool that can aid in the clinical evaluation of this disorder.

BackgroundSchizophrenia is a severe neuropsychiatric disorder accompanied by debilitating cognitive and psychosocial impairments over the course of the disease. As disease trajectories exhibit considerable inter-individual heterogeneity, early clinical and neurobiological predictors of long-term outcome are desirable for personalized treatment and care strategies. MethodsIn a naturalistic longitudinal approach, 381 schizophrenia patients from the Iowa Lon-gitudinal Study (ILS) cohort underwent an extensive characterization, including repeated magnetic resonance imaging (MRI) scans, over a mean surveillance period of 11.07 years. We explored whether pre-diagnostic markers, clinical markers at the first psychotic episode, or magnetic resonance imaging (MRI) measures at the onset of the disease were predictive of relapse or remission of specific symptom patterns later in life. ResultsWe identified a set of clinical parameters - namely premorbid adjustment during adolescence, symptom patterns, and neuropsychological profiles at disease onset - that were highly correlated with future disease trajectories. In general, brain measures at baseline did not correlate with outcome. Progressive regional brain volume losses over the observation period, however, were highly correlated with relapse patterns and symptom severity. ConclusionsOur findings provide clinicians with a set of highly robust, easily acquirable, and cost-effective predictors for long-term outcome in schizophrenia. These results can be directly translated to a clinical setting to improve prospective care and treatment planning for schizophrenia patients. (Funding sources: NIH MH68380, MH31593, MH40856, and MH43271).

Data-driven phenotypes have the potential to accelerate biomedical research but must be vetted thoroughly for robustness, interpretability, and generalizability. This study sought to create and evaluate the predictive validity of empirical phenotypes derived from assessments of signs and symptoms of psychopathology collected in a large cohort of patients using a validated semi-structured clinical interview based on the OPCRIT system. Data was available for N = 17,719 individuals diagnosed with schizophrenia (n = 10,429; 30% female), bipolar I disorder (n = 4,520; 53% female), or schizoaffective disorder (n = 2,770; 44% female) using DSM-IV-TR criteria. The best-fitting latent class analysis (LCA) model was a 6-class solution. Three of the six class profiles replicated results from previous studies. Stratifying by sex did not alter class profiles. Latent classes were significantly associated with established DSM diagnoses for primary and sex-stratified results but not for the LCA solution based primarily on symptoms and lacking indicators related to illness course and relative prevalence of psychotic versus affective symptoms in the overall clinical presentation. All LCAs (i.e., primary, sex-stratified, and symptom-only analyses) produced latent classes that showed statistically significant associations with demographic, etiological, and clinical correlates, but the effect sizes were modest for most associations and comparable in magnitude to those observed for established DSM diagnosis. These results collectively suggest that symptom-based empirical phenotypes derived from LCA may not outperform DSM diagnoses and reaffirm the long-observed importance of illness course for differentiating schizophrenia and bipolar I disorder.

Schizophrenia is a highly heritable brain disorder with a typical symptom onset in early adulthood. The two-hit hypothesis posits that schizophrenia results from deviant early neurodevelopment, predisposing an individual, followed by a disruption of later brain maturational processes that trigger the onset of symptoms. Here, we investigate how the timing of expression of 345 putative schizophrenia risk genes may aid in understanding the interplay of neurobiological processes in the pathophysiology of schizophrenia. Clustering of brain transcriptomic data across the lifespan revealed a set of 183 genes that was significantly upregulated prenatally and downregulated postnatally and 162 genes that showed the opposite pattern. The prenatally upregulated set of genes was functionally annotated to fundamental cell cycle processes, while the postnatally upregulated set was associated with the immune system and neuronal communication. We subsequently calculated two set-specific polygenic risk scores for 743 individuals with schizophrenia and 743 sex- and age-matched healthy controls. We found an interaction between the two scores; higher prenatal polygenic risk was only significantly associated with schizophrenia diagnosis and severity, at higher levels of postnatal polygenic risk. We therefore provide genetics-based evidence in favor of the two-hit hypothesis, supporting that schizophrenia may be shaped by disruptions of separable biological processes acting at distinct phases of neurodevelopment.

IntroductionTo tackle the phenotypic heterogeneity of schizophrenia, data-driven methods are often applied to identify subtypes of its (sub)clinical symptoms though there is no systematic review. AimsTo summarize the evidence from cluster- and trajectory-based studies of positive, negative and cognitive symptoms in patients with schizophrenia spectrum disorders, their siblings and healthy people. Additionally, we aimed to highlight knowledge gaps and point out future directions to optimize the translatability of cluster- and trajectory-based studies. MethodsA systematic review was performed through searching PsycINFO, PubMed, PsycTESTS, PsycARTICLES, SCOPUS, EMBASE, and Web of Science electronic databases. Both cross-sectional and longitudinal studies published from 2008 to 2019, which reported at least two statistically derived clusters or trajectories were included. Two reviewers independently screened and extracted the data. ResultsOf 2,285 studies retrieved, 50 studies (17 longitudinal and 33 cross-sectional) conducted in 30 countries were selected for review. Longitudinal studies discovered two to five trajectories of positive and negative symptoms in patient, and four to five trajectories of cognitive deficits in patient and sibling. In cross-sectional studies, three clusters of positive and negative symptoms in patient, four clusters of positive and negative schizotypy in sibling, and three to five clusters of cognitive deficits in patient and sibling were identified. These studies also reported multidimensional predictors of clusters and trajectories. ConclusionsOur findings indicate that (sub)clinical symptoms of schizophrenia are more heterogeneous than currently recognized. Identified clusters and trajectories can be used as a basis for personalized psychiatry.

Background and HypothesisAuditory-verbal hallucinations (AVH), hearing voices without external speakers, are a core symptom of schizophrenia. A prominent account proposes that AVH reflect failures to recognize self-generated speech. Similar effects in hallucination-prone individuals suggest that these mechanisms span a continuum from subclinical to clinical manifestations. We used a self-other voice discrimination (SOVD) task, previously identified as a potential biomarker of self-disturbance, to assess self-recognition deficits in patients with (AVH+) and without (AVH-) a history of AVH, as well as in healthy individuals. Study Design41 schizophrenia patients (23 AVH+, 18 AVH-) and 40 healthy controls completed the SOVD task. In a follow-up experiment, 26 additional healthy participants performed a familiar-other voice discrimination task identical to SOVD, but without the self-voice. In patients, performance was tested in relation to symptom severity (PANSS), and in controls to hallucination proneness scores. Study ResultsSOVD performance was selectively impaired in AVH+ patients, while AVH- patients did not substantially differ from controls. Across groups, higher PANSS and hallucination proneness scores were specifically related to reduced self-voice recognition, with no impact on other-voice recognition. This effect did not extend to familiar-other voice discrimination. ConclusionsImpaired self-voice recognition is a promising selective marker of AVH occurrence in the acute phase of schizophrenia and extends to hallucination proneness in the general population. These findings support a dimensional view of hallucinations and point to deficits specific to self-voice processing rather than general voice perception. They also highlight SOVD as a promising cognitive biomarker of AVH, with direct implications for early identification and targeted interventions.

BackgroundThe insula is a heterogeneous cortical region with three cytoarchitectural subregions-- agranular, dysgranular, and granular--that have distinct functional roles. Previous cross- sectional studies have shown smaller volume of all insula subregions in individuals with psychotic disorders. However, longitudinal trajectories of insula subregions in early psychosis, and the relationship between subregional volumes and relevant clinical phenomena, such as perceptual aberrations, have not been previously examined. Methods66 early psychosis (EP) and 65 healthy comparison (HC) participants completed 2-4 study visits over 2 years. T1-weighted structural brain images were processed using longitudinal voxel- based morphometry in CAT12 and segmented into anatomic subregions. At baseline, participants completed the Perceptual Aberrations Scale (PAS) to capture bodily distortions. The EP group was further examined based on diagnostic trajectory over two years (stable schizophrenia, stable schizophreniform, and conversion from schizophreniform to schizophrenia). ResultsEP participants had smaller insula volumes in all subregions compared to HC participants, and these volumes were stable over two years. Compared to HC, insula volumes were significantly smaller in EP participants with a stable diagnosis of schizophrenia, but other diagnostic trajectory groups did not significantly differ from HC or the stable schizophrenia group. While perceptual aberrations were significantly elevated in EP participants, PAS scores were not significantly related to insula volume. ConclusionsWe find that all insula subregions are smaller in early psychosis and do not significantly decline over two years. These data suggest that all insula subregions are structurally impacted in schizophrenia-spectrum disorders and may be the result of abnormal neurodevelopment.

Aberrant microstate features of resting-state electroencephalography (rsEEG) have been proposed as potential endophenotypic markers for schizophrenia. However, longitudinal investigations across the psychosis continuum remain limited, particularly regarding treatment effects and illness-related alterations over time. We examined 47 antipsychotic-naive, first-episode patients with psychosis (FEP), 32 individuals at ultra-high risk for psychosis (UHR), and 94 matched healthy controls (HC). All participants under-went rsEEG at baseline, FEP patients and HC were reassessed after six weeks and two years, during which the FEP-patients received antipsychotic treatment. We analyzed microstate features: Duration, Occurrence, Coverage, and Sample Entropy, and investigated group differences at baseline, as well as within group and between group effects over time. Additionally, we explored the effect of medication, and associations with symptom level using a linear model. Post hoc correlation analyses were performed to investigate the stability of microstate features at an individual level over time. At baseline, no differences were observed among HC, UHR, and FEP groups. The main linear mixed models including HC and FEP, as well as all microstates A, B, C, and D at the 3 timepoints indicated an overall effect of time between baseline and two years for Occurrence (p=0.044), and Coverage (p=0.036), but not for Duration (p=0.986). Post-hoc tests for each microstate showed a significant effect of time within FEP between baseline and two years (Occurrence p=0.047, Duration C p=0.011, and Coverage C p=0.007). Furthermore, a group effect emerged between HC and FEP at two years for Coverage C (p=0.039), p-values uncorrected. No other effects of time or group were observed. Occurrence of Microstate D was negatively correlated with general symptom level at baseline (p=0.008, corrected), but not at any follow-up. No associations were found between microstates and antipsychotic medication at six weeks. These findings indicate that Microstate C increases in antipsychotic naive patients the first two years after first episode psychosis, contrasted with the temporal stability in controls. This highlights the need for further research disentangling longitudinal effects of pharmacological and pathophysiological modulation of EEG microstates in large samples.

Background and HypothesisPrevious studies have suggested that choroid plexus (ChP) enlargement occurs in individuals with schizophrenia-spectrum disorders (SSD) and is associated with peripheral inflammation. However, it is unclear whether such an enlargement delineates a biologically defined subgroup of SSD. Moreover, it remains elusive how ChP is linked to brain regions, associated with peripheral inflammation in SSD. Study DesignA cross-sectional cohort of 132 individuals with SSD and 107 age-matched healthy controls (HC) underwent magnetic resonance imaging (MRI) of the brain and clinical phenotyping to investigate the ChP and associated regions. Case-control comparison of ChP volumes was conducted and structural variance was analysed by employing the variability ratio (VR). K-means clustering analysis was used to identify subgroups with distinct patterns of the ventricular system and the clusters were compared in terms of demographic, clinical and immunological measures. The relationship between ChP volumes and brain regions, previously associated with peripheral inflammation, was investigated. Study ResultsWe could not find a significant enlargement of the ChP in SSD compared to HC but detected an increased VR of ChP and lateral ventricle volumes. Based on these regions we identified 3 clusters with differences in age, symbol coding test scores and possibly inflammatory markers. Larger ChP volume was associated with higher volumes of hippocampus, putamen, and thalamus in SSD, but not in HC. ConclusionsThis study suggests that ChP variability, but not mean volume, is increased in individuals with SSD, compared to HC. Larger ChP volumes in SSD were associated with higher volumes of regions, previously associated with peripheral inflammation.

Background and HypothesisAntipsychotic medications are the first-line treatment for schizophrenia. However, around 40% of people with schizophrenia who are treated with antipsychotics could develop extrapyramidal side-effects (EPSE) including: 1) Dyskinesias, 2) Parkinsonism, 3) Akathisia, and 4) Dystonia. Study DesignWe conducted Genome-wide association (GWAS) and Epigenome-wide association (EWAS) meta-analysis of EPSE utilising data from previous schizophrenia case control studies. We integrated significant EWAS findings to an EPSE GWAS meta-analysis to enhance our understanding of the functional impact of common variants on EPSE. We also investigated whether polygenic risk scores (PRS) for schizophrenia, Parkinsons disease, and Lewy-body dementia could be predictive of EPSE development. Study ResultsThe top index SNP rs2709733 (A/G) from EPSE GWAS (p=2.214x10-7) mapped to a long intergenic non-protein coding RNA, LINC01162 with consistent effects across all cohorts. We identified 9 differentially methylated positions (DMPs) associated with EPSE when controlling for methylation age, sex, derived estimates of cell composition, smoking score, and schizophrenia PRS. Four of the DMPs cg14531564, cg20647656, cg12004641, cg22845912, and their affiliated genes (SDF4, ANKMY1, TNS1, SLA) were associated with the risk of developing EPSE and not with schizophrenia risk. Another DMP (cg12044923) which mapped to the STK32B gene, showed significant enrichment for association with risk of EPSE. ConclusionsOur study sheds new light on the potential biological mechanisms underlying EPSE development in schizophrenia, highlighting the importance of exploring both methylation shifts and common SNP associations. Further research with larger samples sizes and a focus on the role of STK32B are encouraged.

ObjectiveDecades of research have identified a strong association between heavy cannabis use and schizophrenia, with evidence of correlated genetic factors. However, many studies on the genetic relationship between cannabis use and psychosis have lacked data on both phenotypes within the same individuals, creating challenges due to unmeasured confounding. We aimed to address this by using multi-modal data from the All of Us Research Program, which contains genetic data as well as information on schizophrenia diagnosis and cannabis use. MethodsWe tested the association between cannabis use disorder (CUD) and schizophrenia polygenic scores (PGS) and schizophrenia and heavy cannabis use. We tested models where both CUD and schizophrenia PGS were included as joint predictors of heavy cannabis use and schizophrenia case status. We defined three sets of cases based on comorbidities: relaxed (assessing for only the primary condition), strict (excluding for both conditions), and dual-comorbidity (including both conditions). ResultsCUD and schizophrenia polygenic liability were independently associated with heavy cannabis use; the schizophrenia PGS effect was very modest. In contrast, both schizophrenia and CUD PGS were independently associated with schizophrenia, with independent significant effects of CUD PGS. Polygenic liability to CUD was associated with schizophrenia in individuals without a documented history of cannabis use, suggesting widespread pleiotropy. ConclusionsThese findings underscore the need for comprehensive models that integrate genetic risk factors for heavy cannabis use to advance our understanding of schizophrenia aetiology.

BackgroundClinical biobanks linking electronic health records (EHRs) with genotype data are expanding, enabling investigation of genomic risk factors for psychiatric disorders. However, few recall-by-genotype (RbG) studies have been published--particularly for psychiatric risk variants in diverse healthcare systems--indicating a need for further research to inform implementation. Some rare copy number variants (CNVs) confer substantially increased risk for neurodevelopmental disorders (NDDs) and cognitive impairment. We recalled NDD CNV carriers from BioMe, a multi-ancestry biobank within the Mount Sinai Health System, for in-depth phenotyping and empirical insights into the implementation of RbG in psychiatry. MethodsFrom BioMe, 892 adults were recontacted: 335 NDD CNV carriers, 217 with schizophrenia, and 340 neurotypical controls. Of these, 18% responded to recontact, 12% were screened for participation, and 10% began the study. Participants completed structured clinical and cognitive assessments. ResultsSeventy-three participants (8% of those recontacted) completed the study: 30 NDD CNV carriers, 20 schizophrenia cases, and 23 controls. The mean age was 48.8 years, 66% were female, and ancestry was 37% African, 34% Hispanic, and 26% European. Seventy percent of NDD CNV carriers had at least one neuropsychiatric or developmental condition, including 40% with mood or anxiety disorders. Among 22 NDD CNV carriers at loci previously examined for cognitive effects, performance was impaired on digit span backward ({beta}= -1.76, FDR = 0.04) and sequencing ({beta}= -2.01, FDR = 0.04) compared with controls but outperformed schizophrenia cases on verbal learning ({beta}= 4.5, FDR = 0.05). ConclusionsThis proof-of-concept RbG study of rare psychiatric risk variants from a multi-ancestry biobank demonstrates both opportunities and challenges for recontact within healthcare systems. Despite modest enrollment, recalling individuals--including those affected by psychiatric illness and cognitive impairment--yielded a genotypically defined cohort and phenotypes not captured in EHRs, underscoring the potential of RbG to advance precision psychiatry.

Brief abstractSchizophrenia is hypothesized to be caused by impaired functional integration in the brain, and this could hypothetically be caused by white matter disruptions, synaptic dysfunction, or both. Neuroimaging studies consistently show reduced fractional anisotropy, a measure of white matter integrity, in patients with schizophrenia. Using Mendelian randomization, we show that these white matter changes are likely to be the consequence of a primary synaptopathy in schizophrenia.

Disruption of conscious access contributes to the advent of psychotic symptoms in schizophrenia but could also explain lack of insight in other psychiatric disorders. In this study, we explored how insight and psychotic symptoms related to disruption of consciousness. We explored consciousness in patients with schizophrenia, patients with obsessive-compulsive disorder (OCD) with good vs. poor insight and matched controls. Participants underwent clinical assessments and performed a visual masking task allowing us to measure individual consciousness threshold. We used a principal component analysis to reduce symptom dimensionality and explored how consciousness measures related to symptomatology. We found that clinical dimensions could be well summarized by a restricted set of principal components which also correlated with the extent of consciousness disruption. More specifically, positive symptoms were associated with impaired conscious access in patients with schizophrenia whereas the level of insight delineated two subtypes of OCD patients, those with poor insight who had consciousness impairments similar to patients with schizophrenia, and those with good insight who resemble healthy controls. Our study provides new insights about consciousness disruption in psychiatric disorders, showing that it relates to positive symptoms in schizophrenia and with insight in OCD. In OCD, it revealed a distinct subgroup sharing neuropathological features with schizophrenia. Our findings refine the mapping between symptoms and cognition, paving the way for a better treatment selection.

BackgroundSubtyping schizophrenia can disentangle heterogeneity and help with treatment decision- making. However, current schizophrenia subtypes have not demonstrated adequate clinical utility, limited by sample size, suboptimal clustering methods, and choice of clustering input. Polygenic scores (PGS) reflect the genetic risk of phenotypes including comorbidities and are available before treatment, making them candidate clustering input. MethodsWe derived PGS for schizophrenia, autism spectrum disorder, bipolar disorder type-1, depression, and intelligence in 4,915 schizophrenia cases with register linkage. We randomly divided the sample into discovery and replication partitions and applied a novel clustering workflow on both: preprocessing PGS, feature extraction with uniform manifold approximation and projection (UMAP), and clustering with density-based spatial clustering of applications with noise (DBSCAN). After replication, we re-performed clustering on the entire sample and evaluated treatment-relevant variables of medication and hospitalization (extracted from registers) across clusters. OutcomesWe identified five well-replicated PGS clusters. Cluster 1 (26% of entire sample) with generally lower PGS, had the least use of antipsychotics (including clozapine), and fewer outpatient visits. Cluster 2 (48%) with generally higher PGS, especially schizophrenia PGS, had more prescriptions of antipsychotics including clozapine and longer treatment with clozapine. Each featured by specific PGS, clusters 3 (high IQ-PGS, 11%), 4 (high ASD-PGS, 8%), 5 (high BIP-PGS, 7%) showed sub-threshold level significance in the corresponding phenotypic measures but did not differ significantly in the treatment-relevant variables. Solely categorizing the patients with SCZ-PGS did not generate any significant patterns in the phenotypic and treatment-relevant variables. InterpretationThe results suggest that combinations of PGS of brain disorders and traits can provide clinically relevant clusters, offering a direction for future research on schizophrenia subtyping. Future replications in independent samples are required. The workflow can be generalized to other disorders and with mechanism-informed PGS.

ObjectiveThe pathophysiology of psychosis remains unclear. Preclinical, postmortem, and imaging evidence implicates iron and neuromelanin, but the consistency and magnitude of effects are uncertain. We aimed to characterise brain iron and neuromelanin alterations in psychosis through a systematic review and meta-analysis of iron-sensitive MRI and neuromelanin-sensitive MRI (NM-MRI) studies. MethodsWe searched EMBASE, PubMed, and PsycINFO from inception to October 31, 2025, for case-control studies using iron-sensitive MRI or NM-MRI in patients with psychosis. We used random-effects models to calculate effect sizes (Hedges g) and meta-regressions to examine clinical confounders. The primary outcomes included effect sizes for NM-MRI and iron-sensitive MRI measures--transverse relaxation rate (R2), effective relaxation rate (R2*), and quantitative susceptibility mapping (QSM). ResultsTwenty-seven reports, including 879 individuals with psychosis and 813 controls, were analysed. Meta-analyses were conducted across the caudate nucleus, putamen, globus pallidus, thalamus, and substantia nigra. In psychosis, R2* was significantly lower across all examined regions (g= -0.27 to -0.40), QSM values were lower in the substantia nigra (g= - 0.61; 95% CI, -0.84 to -0.38), and R2 was lower in the caudate nucleus (g= -0.30; 95% CI, - 0.56 to -0.04). NM-MRI values in the substantia nigra were significantly higher (g= 0.39; 95% CI, 0.23 to 0.55), though this effect strongly correlated with chlorpromazine daily equivalent dose ({beta}= 0.001; 95% CI, 0.0003 to 0.0018), suggesting medication-related effects. ConclusionsPsychosis is associated with lower subcortical iron-sensitive MRI values. This was most marked in the substantia nigra, where NM-MRI values--which index neuromelanin-bound iron in dopamine neurones--were significantly higher. This suggests that while subcortical iron is overall lower in psychosis, neuromelanin-bound iron is increased within dopamine neurones. Investigating the mechanisms underlying iron alterations may provide new treatment targets.