Schizophrenia Bulletin

Background. Clinical meaningfulness in schizophrenia trials requires evidence that symptom change is recognizable in clinical practice. Established anchors include a 1 point minimal improvement on the CGI-S and a 7 to 10-point minimal improvement on the Personal and Social Performance scale (PSP), alongside a minimal improvement of 20% on the PANSS negative symptom factor score (NSFS) conventionally applied in negative-symptom trials. Methods. These clinically meaningful anchors were applied as benchmarks to pooled 12 week completer data from two randomized, placebo-controlled trials of roluperidone in adults with schizophrenia and moderate to severe negative symptoms. Results. Roluperidone was associated with higher responder rates than placebo on all three endpoints: at least 20% NSFS improvement (66/176 [38%] vs 39/182 [21%]; RR=1.75, 95% CI: 1.25 to 2.45; p=0.0008), at least 1 point CGI-S improvement (68/167 [41%] vs 48/177 [27%]; RR=1.50, 95% CI: 1.11 to 2.03; p=0.0077), and at least 10 point PSP improvement (63/176 [36%] vs 44/181 [24%]; RR=1.47, 95% CI: 1.06 to 2.04; p=0.018). Effects were consistent across symptoms, global-severity, and functional outcomes, with no evidence of heterogeneity between studies. Conclusions. Across all three anchor criteria, roughly 1.5 to 1.75 times as many patients treated with roluperidone as with placebo reached thresholds of improvement that clinicians and patients would recognize as clinically meaningful, supporting its relevance for persistent negative symptoms in schizophrenia.

Recent computational approaches to speech in psychosis generate large multidimensional feature spaces capturing semantic and acoustic aspects of language production. However, the clinical relevance of individual measures often becomes difficult to interpret due to redundancy, interaction effects, and high intercorrelation among features. Building upon previous work constructing a single composite index derived from semantic features based on language model embeddings, we here build a second acoustic index derived from speech acoustic features. Our aim was to evaluate the differential performance of both indices in conjunction in PANSS symptom prediction in psychosis in a cross-linguistic setting, including positive symptoms (P1, P2, P3), negative symptoms (N1, N4, N6), and general measures (G5, G9). The dataset comprised five languages and 221 patients with schizophrenia spectrum disorder (SSD). Both indices showed predictive power for individual PANSS scores, while also demonstrating clinically important complementarity: semantic indices were more strongly associated with positive symptom dimensions (P2, P3, Total Positive), whereas acoustic indices showed stronger relationships with negative and general symptoms (N1, N4, G5, G9). Both domains shared predictive overlap for global measures, such as PANSS Total scores. These findings suggest that both indices capture complementary and partially overlapping dimensions of psychopathology. The proposed composite index framework contributes to the advancement of low-dimensional speech-derived markers of symptom severity variation, potentially informing vulnerability to relapse and remission in psychosis.

Background and Hypothesis Identifying which patients with schizophrenia will remain stable and which will relapse is relevant to both trial design and clinical practice. We hypothesized that higher negative and lower positive symptoms at baseline would predict longer time to relapse. Study Design This analysis pooled individual participant data on 557 patients with schizophrenia randomized to placebo from five placebo-controlled antipsychotic withdrawal relapse-prevention studies, each with a double-blind phase of at least 6 months. Relapse was defined as a [≥]12-point increase in Positive and Negative Syndrome Scale (PANSS) total score from double-blind baseline at any time during the double-blind phase. Cox regression analyses modeled time to relapse with the three PANSS subscales, age and sex. Study Results PANSS Negative and Positive symptom subscales predicted relapse in opposite directions and at almost equal magnitude in the pooled model. Higher negative symptoms predicted lower relapse risk (hazard ratio (HR)=0.94 per point, 95% confidence interval (CI)=0.90-0.99) and higher positive symptoms predicted higher relapse risk (HR=1.06, 95% CI=1.00-1.13). PANSS General Psychopathology did not predict relapse (HR=0.98, 95% CI=0.95-1.02). Based on the pooled estimates, a 4-point higher baseline PANSS Positive score corresponded to a 27% higher relapse risk, while a 4-point higher baseline PANSS Negative score corresponded to a 21% lower risk. Conclusions These results may help predict relapse risk in clinical care and in clinical trial samples but require prospective validation.

Schizophrenia may involve a fundamental disruption in the ability to construct a shared understanding of social reality, yet conventional neuroimaging studies have largely examined social cognition using simplified and highly structured paradigms that may not capture how individuals interpret complex and ambiguous interpersonal events in everyday life. Using an ecologically valid naturalistic fMRI paradigm, we examined shared and paranoia-related neural representations during ambiguous social narrative processing in 38 patients with schizophrenia and 46 healthy controls. Intersubject correlation was used to quantify neural convergence across individuals, whereas intersubject representational similarity analysis was used to examine how individual differences in paranoid ideation were reflected in neural representations. Compared with healthy controls, patients with schizophrenia showed reduced neural convergence within distributed systems supporting social inference, contextual integration, and salience attribution, including temporoparietal, temporal, insular, and inferior frontal regions. Furthermore, paranoid ideation was associated with altered neural representational structure. In healthy controls, higher paranoia was linked to reduced neural similarity across social-cognitive and affective regions, suggesting reduced convergence toward shared interpretations of ambiguous social information. In schizophrenia, paranoia-related representations showed sparse within-group associations but elevated patient-control differences across a distributed social-affective circuit, indicating a reorganization rather than a simple amplification of paranoia-related processing. Together, these findings demonstrate that schizophrenia involves both impaired alignment of neural responses during shared social meaning construction and organization of paranoia-related representations, providing an ecologically grounded framework for understanding abnormal social inference in psychosis.

Introduction: Disturbed interpersonal attunement is a core but poorly operationalised feature of the psychopathology of schizophrenia. Language Style Matching (LSM), the largely unconscious alignment of two speakers' function words during ordinary conversation, offers an observable, dialogue-derived index of this dyadic attunement. An open question is where altered alignment mark who a patient (a stable trait of inner experience) is or how they are (a fluctuating state that shifts with symptom severity)? Objective: To characterise LSM over 12 months in early psychosis relative to controls, and to test, at both between- and within-person levels, whether alignment covaries with core psychopathological dimensions across self-referential (autobiographical) and externally directed discourse. Methods: First-episode and recent-onset patients (n = 109) and controls (n = 60) completed semi-structured interviews at baseline and 12 months. LSM was computed per context and modelled with linear mixed-effects; a Mundlak decomposition partitioned the LSM-symptom association into between- and within-person components. Results: LSM is not a fixed trait: groups were indistinguishable at baseline but diverged by 12 months (Group x Timepoint {beta}=-0.018, p = .029), and the deficit was specific to autobiographical speech. Within individuals, autobiographical alignment tightened as formal thought disorder rose above a patient's own average and as negative symptoms worsened, independent of antipsychotic dose. Conclusion: Patients aligned less than controls when speaking about themselves, yet aligned more as symptoms deteriorated, a shift from self-generated toward partner-scaffolded speech when self-organisation fails. LSM indexes disordered self-anchoring and interpersonal attunement in the negative-disorganized dimension of psychosis.

**Importance** Average gray matter volume (GMV) loss in early psychosis may conceal clinically relevant differences in brain change. **Objective** To identify longitudinal GMV-change patterns, their cross-site correspondence, and clinical associations. **Design, Setting, and Participants** Prospective cohort with T1-weighted MRI acquired during 2011-2026 at 2 Czech sites; median follow-up, approximately 13 months. Participants were 222 patients with first-episode schizophrenia-spectrum psychosis and 178 healthy controls frequency-matched by site and baseline age. **Exposure** First-episode psychosis vs healthy control status. **Main Outcomes and Measures** Diagnosis-blind clustering by site of image-quality-corrected, subsequently annualized GMV change across 333 cortical parcels; longitudinal voxel-based morphometry (VBM); Global Assessment of Functioning and consensus 5-factor Positive and Negative Syndrome Scale negative-symptom scores. **Results** Among 400 participants (mean [SD] age, 28.2 [7.5] years), 208 (52.0%) were male. Each site independently yielded 3 clusters: C1 (near-stable GMV), C2 (pronounced decrease), and C3 (increase). Cross-site centroid correlations across 333 parcels were strong for C2 and C3 (Spearman rho = 0.865 and 0.861); C1 correlation was weak (rho = 0.138), as expected for near-zero profiles with little regional variation. C2/C3 correspondence supported pooling matched site-specific clusters for post hoc analyses. Pooled clusters contained 77 patients and 125 controls in C1, 66 patients and 16 controls in C2, and 79 patients and 37 controls in C3. Suffixes -P and -HC identify patient and control subgroups. To situate patient changes relative to controls, post hoc VBM revealed an unanticipated gradient of increasingly pronounced GMV reduction across C1-HC to C1-P to C2-P, prominently involving association cortex, and bilateral ventral temporal GMV increase in C3-P (familywise error-corrected P < .05). The opposite contrast along this ordering showed periventricular changes consistent with progressively greater ventricular enlargement. C2-P had the lowest mean follow-up functioning and highest mean negative-symptom severity (adjusted omnibus P = .005 and .02); clinical changes from baseline did not differ significantly among patient groups. **Conclusions and Relevance** Early GMV change in psychosis was not uniform but comprised two contrasting directions: a clinically relevant, control-anchored pattern of increasingly pronounced GMV loss involving default mode regions and a distinct, directionally opposing ventral temporal increase.

BackgroundSchizophrenia liability reflects both the aggregate effects of many common genetic variants and rare structural variation at recurrent neurodevelopmental copy-number variant (ND-CNV) loci. Although both components of genetic liability have been associated with schizophrenia and related phenotypes, it remains unclear whether they exhibit similar neuropsychiatric association profiles or show evidence of interaction when examined jointly. MethodsWe analyzed 71,992 participants from the All of Us Research Program Controlled Tier version 8 dataset, including 2,115 participants carrying structural variants overlapping curated recurrent ND-CNV loci. We integrated binary ND-CNV carrier status, an ancestry-adjusted and standardized reduced schizophrenia polygenic risk score (PRS), and longitudinal electronic health record-derived psychiatric and neurodevelopmental phenotypes. Logistic regression models evaluated pooled, deletion-overlapping, and duplication-overlapping carrier associations, schizophrenia PRS effects, and PRS x ND-CNV interactions. Dimensional analyses, confirmatory factor analysis (CFA), and structural equation modeling (SEM) provided complementary summaries of phenotype covariance and assessed the robustness of primary associations across alternative representations of the phenotype space. ResultsSchizophrenia PRS and ND-CNV carrier status showed distributed association profiles extending beyond schizophrenia diagnosis across psychosis-spectrum, neurodevelopmental, cognitive, and internalizing phenotypes. ND-CNV carriers had higher estimated odds of schizophrenia diagnosis than non-carriers (OR = 1.49, 95% CI 1.09-2.03), while each 1-SD increase in schizophrenia PRS was associated with higher odds of a broad psychosis-spectrum phenotype (OR per SD = 1.07, 95% CI 1.03-1.10). Deletion-overlapping carriers exhibited generally larger and more consistently positive association estimates than duplication-overlapping carriers. Psychosis-spectrum prevalence increased from 5.4% in the lowest PRS decile to 6.7% in the highest, while ND-CNV carriers maintained consistently higher prevalence than non-carriers across the PRS distribution. PRS x ND-CNV interaction estimates remained near the null, providing little evidence of widespread multiplicative interaction. Among tested covariance structures, bifactor models showed the lowest residual misfit and were therefore carried forward for SEM analyses. ConclusionsSchizophrenia-associated polygenic liability and structural variation overlapping recurrent ND-CNV loci exhibit distributed neuropsychiatric association profiles with limited evidence of widespread multiplicative interaction. Their joint evaluation in the large, ancestrally diverse All of Us cohort demonstrates that distinct forms of genomic liability can be examined within a shared, real-world phenotype space extending beyond schizophrenia diagnosis alone.

Machine learning approaches may support individual-level classification in psychiatry, but many EEG-based schizophrenia studies have relied on small samples or conventional summary features. We evaluated whether trial-level auditory oddball event-related potential (ERP) waveforms could support schizophrenia versus healthy-control classification using deep learning. The study included 258 patients with schizophrenia and 142 healthy controls. EEG recordings from an auditory duration oddball paradigm were segmented into -100 to 500 ms epochs, and trial-level mismatch waveforms were generated by subtracting each participant's mean standard response from accepted deviant trials. Models were trained using a fixed participant-level training, validation, and test split, with demographic residualization fit only in the training set. Five deep learning architectures were trained on full residualized ERP waveforms and compared with classical machine learning models trained on 18 conventional ERP summary features. Deep learning models achieved higher test set discrimination than classical feature-based models, with AUROC values ranging from 0.797 to 0.857 versus 0.705 to 0.720. Benchmark analyses suggested that performance depended on the combination of waveform-level input and deep learning architecture. These findings support trial-level auditory oddball ERP waveforms as promising classification inputs and candidate electrophysiological biomarkers of schizophrenia-related neural information processing.

In about a quarter of people with schizophrenia-spectrum disorder (SSD), the illness is unresponsive to standard antipsychotic treatment, yet the biological mechanisms underlying this remain poorly understood. Although such treatment-resistant schizophrenia (TRS) shares a substantial genetic liability with treatment-responsive schizophrenia, the limited efficacy of dopamine antagonists in TRS indicates that mechanisms beyond dopamine signalling likely contribute to treatment-resistance, requiring the identification of alternative biological pathways. This is the first cross-ancestry genetic study to investigate the genetic architecture of TRS, by directly comparing patients with treatment-resistant and treatment-responsive schizophrenia in two independent Hong Kong (N=798) and STRATA-G consortium (N=1243) cohorts. Using an integrated multi-level analytic framework, we conducted a genome-wide association study (GWAS) with gene-based and gene set-based analyses, pathway polygenic-risk-scores, and transcriptome-wide association study (TWAS). We further conducted gene-set enrichment analysis focusing on expert-curated synaptic pathways and brain tissues. Genetic signals at the gene, pathway, and genetically predicted expression levels were identified within each ancestry. Whereas limited power constrained individual loci discovery and cross-ancestry concordance, enrichment analyses indicated heterogeneous signals across cohorts, including differences in effect direction, but highlighted cohort-specific, synapse-related biology, particularly pathways involved in presynaptic vesicle dynamics, neurotransmission, and synaptic organization. Collectively, these findings highlight synaptic biology as one potential pathway-level signal from common-variant genetic effects associated with treatment resistance in SSD, despite minimal SNP-level discovery. Our work suggests there is promise in pathway-level and multi-omics approaches to elucidate biologically meaningful heterogeneity within SSD and provides support for synaptic mechanisms as potential targets for understanding and stratifying treatment-resistance.

Background: Predictive-processing accounts propose altered prediction-error signaling in psychosis. However, event-related potential (ERP) findings in first-episode psychosis (FEP) are inconsistent and focused predominantly on mismatch negativity (MMN). Examining anticipatory and later post-stimulus ERP responses alongside MMN within a single passive paradigm may broaden the assessment of predictive processing in FEP. Methods: 34 participants with non-affective FEP and 34 healthy controls (HC) underwent EEG during a passive auditory pattern-based paradigm. Mean amplitudes of contingent negative variation (CNV), MMN, and post-imperative negative variation (PINV) were quantified at five frontocentral electrodes. Exploratory analyses examined N1 and P2 mean amplitudes. Mixed models tested group, electrode, and group-by- electrode effects adjusting for age, sex and years of education. Regressions examined ERP associations with psychotic-like experiences, neurocognition, and social cognition across groups, and with symptoms, functioning, and antipsychotic dose within FEP. Results: FEP participants reported more psychotic-like experiences and showed poorer global and social cognition than HC (all p-FDR [&le;] .042). Standardized adjusted FEP-HC differences at FCz were nonsignificant for CNV ({beta} = -0.46), MMN ({beta} = -0.30), and PINV ({beta} = -0.10; all p-FDR [&ge;] .265). Exploratory analyses identified lower P2 amplitude in FEP at FCz ({beta} = -0.73; p-FDR = .019) and in the averaged frontocentral ROI ({beta} = -0.64; p-FDR = .027). No ERP-phenotype associations survived FDR correction. Conclusions: Despite phenotypic differences between FEP and HC, no significant differences emerged in the primary ERP indices of predictive processing examined. The exploratory P2 finding and its possible relationship to predictive processing and frontal dysfunction in FEP warrant further investigation.

Background: Interoception refers to the processing, integration, interpretation, and regulation of bodily signals by the brain. It is crucial to motivational and affective functioning such as subjective experience of arousal (i.e., intensity of emotion). Previous studies found altered self- report of arousal in people with schizophrenia, but the underlying biological mechanism is unclear. This study aimed to investigate interoceptive processing and emotional experience in schizophrenia by triangulating self-report, cardiac dynamics, and heartbeat-evoked potential (HEP; an event-related potential that reflects cortical processing of heartbeats). Methods: A total of 39 participants with schizophrenia spectrum disorders (SZ; 36% female) and 31 healthy controls (HC; 35% female) reported subjective arousal level when viewing images of varying emotional intensity while electroencephalogram and electrocardiogram were being recorded. Results: We found that intense images led to larger heartrate decelerations and higher subjective arousal ratings in HC, but did not modulate heartrate changes in SZ and were less predictive of their subjective ratings. Subjective ratings were predictive of cardiac dynamics in HC, but not in SZ. SZ showed a less negative HEP amplitude than HC when viewing less intense images. Lastly, SZ with less severe positive and negative symptoms exhibited cardiac dynamics more similar to those of HC. Conclusions: These findings suggest both reduced cardiac reactivity to emotional stimuli and a disconnect between interoceptive afferent signals and cortical processing, leading to different subjective emotional experience in people with schizophrenia. Studying interoception using a multimodal approach may reveal important insights into the underlying mechanisms of altered emotional experience in schizophrenia.

Large Language Models (LLMs) are proposed as tools for high-throughput, deep phenotyping of psychiatric disorders. Applied to electronic health records, LLMs could in principle extract patient symptoms, outcome trajectories, risk factors, and treatment history at scale and these, when combined with increasingly available biological data, such as genomics or neuroimaging, could provide powerful resources for health research. Although proof-of-principle LLM-based symptom extractions have been carried out for some medical conditions, the heterogeneous nature of psychiatric disorders, including schizophrenia, requires extensive domain-specific evaluations. Here, we evaluate 14 general-purpose LLMs for extracting eight symptoms of schizophrenia from clinical summaries in a severe mental illness cohort (N = 704). Performance across symptoms was poor to moderate (macro F1 = 0.500 - 0.647), with positive symptoms more accurately extracted than negative symptoms. Few-shot prompting, a common strategy for improving LLM task performance, did not significantly improve these results. Nevertheless, LLM-predicted and gold-standard positive symptoms demonstrated comparable associations with clinical variables in regression analyses. Individual-level extraction errors attenuated group-level associations but did so unevenly across symptom domains. This indicates that general-purpose LLMs may not be able to extract psychosis-related phenotypes from clinical summaries with the quality required for clinical use but might nevertheless be useful for exploratory research on large cohorts. However, closing the performance gap between positive and negative symptom extractions seems essential groundwork to prevent a systematic bias in any LLM-derived characterisations of psychotic symptoms.

Background and Hypothesis: First-line antipsychotics are ineffective in ~40% of people with first episode psychosis (FEP). Electroencephalogram (EEG)-based predictive biomarkers could be a scalable solution to reduce trial-and-error. Study Design: We acquired EEG in 56 minimally treated FEP individuals before treatment initiation, and 34 matched healthy controls (HC). Treatment response was defined as >50% improvement in BPRS scores by 12 weeks of treatment. Modified k-means clustering of resting-state EEG yielded six microstates including canonical topographies (A-D). The first principal component (PC1) of microstate properties derived from HCs served as a normative reference onto which FEP patients were projected. Baseline PC1 was used to predict treatment response in adjusted regression analyses. To test stability of results, an independent dataset of HCs (n=92) was fitted using the same microstate solution and compared to our native HC dataset. PC1 scores derived from this independent normative dataset were also used for predicting response in FEP. Study Results: Individual microstates replicated established patient-control differences (e.g., higher presence of microstate C compared to D, p<0.001). PC1 explained 34.8% of variance. Baseline PC1 predicted subsequent treatment response (adjusted OR=0.63, p<0.001), yielding AUC=72.3% in leave-one-out cross-validation (pperm<0.001) when combined with clinical information. PC1 from our HC transported well to the second healthy control EEG dataset (n=92) (r=0.9,p<0.001; r=0.89,p<0.001 ). PC1 in FEP derived using PC1 weights from a second normative dataset also predicted response in FEP (OR=0.70, p=0.03). Conclusions: EEG microstate architecture differing from the expected norm is associated with lower odds of treatment response in FEP.

Schizophrenia is associated with distributed genetic, molecular, and functional brain network abnormalities, where sex differences have been well documented. However, whether sex difference exists in genetically regulated molecular variation, and how sex impacts the relationship between regulatory liability and large-scale functional dysconnectivity in schizophrenia, remain largely unclear. Here, we integrated brain eQTL-predicted gene-expression features with resting-state functional network connectivity (FNC) using an independent multimodal decomposition framework. In 367 participants from FBIRN and COBRE, gene-expression and FNC features were independently decomposed into latent components and screened for sex differences and schizophrenia associations. We identified a schizophrenia-associated gene-expression component that also showed a significant sex difference and was enriched in cerebellar tissues and biological processes related to vesicle and membrane organization and intracellular trafficking. This gene-expression component was significantly associated with a schizophrenia-associated FNC component involving cortical, subcortical, and cerebellar systems. Particularly, the gene-expression and FNC components also presented a significant sex-by-gene interaction characterized by a stronger negative association in males and a weaker positive association in females. In UK Biobank, the schizophrenia-associated gene-expression effect replicated in 2,273 individuals with schizophrenia and matched controls, while the multimodal association and its sex-dependent interaction also replicated in 4,206 participants with matched genetic and imaging data. These findings suggest that schizophrenia-related regulatory and functional-network abnormalities may converge across biological scales and that their coupling may be moderated by sex.

We conducted an open-label pilot study of bilateral nucleus accumbens-anterior limb of the internal capsule (NAc-ALIC) deep brain stimulation (DBS) in three patients with treatment-resistant schizophrenia (TRS). Clinical symptoms, global functioning, cognition, stimulation parameters and intracranial local field potential (LFP) were assessed over 24 weeks. Two patients met the prespecified clinical response criterion, defined as a [&ge;]40% reduction in Positive and Negative Syndrome Scale (PANSS) total score, by week 8 and showed PANSS reductions of 69.0% and 69.4%, respectively, at week 24. The third patient showed a 18.7% reduction at week 24. Responders showed improvements in global functioning and selected cognitive domains. Longitudinal LFP recordings revealed increased gamma-band relative power and reduced theta-band activity in responders, whereas these changes were less consistent in the non-responder. DBS was generally well tolerated, with transient stimulation-related adverse effects resolving after reprogramming. These findings provide preliminary clinical evidence supporting further investigation of NAc-ALIC DBS in TRS and suggest that individualized programming and intracranial physiological measures may contribute to the identification of treatment-response signatures.

Background: Schizophrenia is characterized by widespread structural and functional brain abnormalities, yet it remains unclear whether these alterations converge across imaging modalities and whether such convergence is associated with underlying cellular distribution and evolution of the human cortex. Methods: We performed coordinate-based meta-analyses of seven whole-brain neuroimaging modalities, including cortical thickness, gyrification, voxel-based morphometry, amplitude of low-frequency fluctuations, regional homogeneity, regional cerebral blood flow (all based on magnetic resonance imaging), and glucose metabolism based on [18F]-Fluorodeoxyglucose positron emission tomography. The analyses included 200 studies (209 datasets) comprising 11,794 patients with schizophrenia and 10,342 healthy controls. After single-modality meta-analyses using seed-based d-mapping, the meta-analytic result maps were integrated using orthonormal projective non-negative matrix factorization. Finally, the resulting component maps were related to maps of cortical cell type-specific gene expression and human-specific cortical evolution. Results: Multimodal integration identified a robust convergence pattern centered in bilateral insular, mediofrontal, and lateral sensorimotor cortices, with the strongest contributions from regional homogeneity, cerebral blood flow, and amplitude of low-frequency fluctuations. The convergence pattern showed significant spatial correspondence with cortical gene expression profiles of astrocytes, microglia, and oligodendrocyte progenitor cells. In contrast, we found no evidence for a preferential localization to regions of human-specific cortical expansion or connectivity. Conclusions: Structural and functional brain abnormalities in schizophrenia converge on an insular-mediofrontal-sensorimotor pattern that aligns with glial gene expression profiles. These results support glia-related regional vulnerability as an organizing principle of multimodal brain abnormalities in schizophrenia.

BACKGROUND: Clinical high risk (CHR) for psychosis is characterized by substantial heterogeneity in symptoms, functioning, and outcomes. Although distributed resting-state functional connectivity (RSFC) abnormalities have been reported, it remains unclear whether this heterogeneity can be organized into consistent multivariate brain-symptom dimensions and clinically meaningful subgroups. METHODS: We analyzed RSFC and 34 clinical variables from 701 CHR participants in the Accelerating Medicines Partnership Schizophrenia Program (AMP SCZ). Partial least squares analysis identified latent brain-symptom dimensions, which were further examined using transcriptomic annotation, independent psychosocial and cognitive measures, hierarchical clustering, and Month 2 follow-up data. RESULTS: Two significant brain-symptom dimensions emerged. The first reflected general psychopathology and was associated with somatomotor-salience and higher-order connectivity, with enrichment for genes related to neuronal development. The second, capturing symptom differentiation, spanned negative/internalizing features with psychobehavioral overactivation and involved cerebellar-thalamic-somatomotor circuitry, with enrichment for genes related to synaptic signaling. These dimensions showed dissociable associations with broader psychosocial and cognitive measures, and their loading patterns were substantially reproducible at Month 2. Clustering based on these dimensions identified two biotypes with distinct clinical and connectivity profiles that remained broadly similar at Month 2 despite divergent short-term RSFC changes. CONCLUSIONS: These findings support complementary dimensional and categorical representations of CHR heterogeneity that link clinical variation with distributed brain connectivity and molecular signatures, while demonstrating short-term reproducibility of the identified dimensions and divergent RSFC changes across biotypes. This multilevel framework may help characterize biologically meaningful variation in psychosis risk.

Background: Cognitive impairment is the leading cause of disability in schizophrenia with limited treatments. A major barrier to treatment development is the absence of reproducible, mechanistically grounded neural targets. Cross-sectional studies have identified dorsomedial prefrontal cortex (DMPFC)-somatomotor connectivity as a neural marker of cognitive performance on the Auditory Continuous performance task (ACPT), a measure of attention. To test the stability of this marker, we tested the relationship between DMPFC-somatomotor connectivity and ACPT performance in a longitudinal psychosis sample. Methods: Individuals with early psychosis (n=251) and matched controls (n=90) were enrolled and underwent resting-state neuroimaging and neurocognitive assessment. A subset completed longitudinal assessments over 2-4 years. We calculated DMPFC-somatomotor resting-state functional connectivity using a previously identified DMPFC region and a seed in the somatomotor cortex. We performed linear mixed effects models to predict ACPT performance based on connectivity, time, psychosis type, and their interaction. Results: In the psychosis sample, time (p=.0037) and affective psychosis diagnosis (p<.0001) predicted better ACPT performance. In a model predicting ACPT performance, we observed a significant interaction effect of DMPFC-somatomotor connectivity*psychosis subtype (p=.0079) such that DMPFC-somatomotor connectivity predicted ACPT performance only in individuals with non-affective psychosis (p=.0051). We then tested the specificity of this connectivity-cognitive performance relationship. In a model predicting DMPFC-somatomotor connectivity, only ACPT performance (p=.017), but not fluid cognition, was a significant predictor. Conclusions: DMPFC-somatomotor connectivity is longitudinally associated with cognitive performance in early psychosis. This relationship is strongest in nonaffective psychosis, suggesting a novel, reliable target for intervention for cognitive deficits in early psychosis.

Background and Hypothesis: Although psychotic disorders are characterized by widespread cortical thinning, translating these findings into individualized biomarkers remains challenging. Regional Vulnerability Index (RVI) and Neuroscore are candidate individual-level biomarkers that quantify the similarity between an individual's structural brain pattern and generic psychosis-associated alterations. This study investigated cortical thickness (CT)-based RVI and Neuroscore, along with mean CT deviation from healthy individuals, across the psychosis spectrum. Study Design: Using structural 3T MRI data from 289 participants (clinical high risk for psychosis [CHR], n=39; first episode psychosis [FEP], n=44, chronic psychosis [CP], n=86; healthy controls [HC], n=120), RVI, Neuroscore, and mean CT deviation (z) scores were computed from region-wise CT measures quantified using FreeSurfer v7.2. Group differences and associations with symptoms were evaluated. Study Results: RVI and Neuroscore were significantly higher, and mean CT z-scores lower, in FEP and CP compared with CHR and HC, with moderate-to-large effect sizes (|Cohen's d| = 0.55-1.15). No differences were observed between FEP and CP or CHR and HC. Indices were not associated with psychosis-spectrum symptom severity in the CHR, FEP, or CP groups. RVI (r = -0.561, p < .001) and Neuroscore (r = -0.997, p < .001) were strongly negatively correlated with the mean CT z-score. Conclusions: CT-based RVI and Neuroscore appear to index established disease status but not risk status, while showing substantial overlap with mean CT deviation. These findings highlight the need to further refine these biomarkers through data-driven and multimodal approaches to improve their utility for risk and prognostic prediction.

Abstract Background Negative symptoms are persistent determinants of disability in schizophrenia and often respond incompletely to antipsychotic treatment. Objective To compare the efficacy of pharmacological and non-pharmacological add-on treatments for prominent or persistent negative symptoms using network meta-analysis. Methods This systematic review followed PRISMA 2020 and PRISMA-NMA and was registered in PROSPERO (CRD420261422218). PubMed, Europe PMC, Semantic Scholar and Crossref were searched from inception to 29 July 2026, supplemented by citation chasing. Eligible studies were randomized controlled trials in adults with DSM/ICD schizophrenia or schizoaffective disorder, prominent or persistent negative symptoms, stable antipsychotic treatment and an adjunctive intervention. Outcomes were Positive and Negative Syndrome Scale negative subscale or Scale for the Assessment of Negative Symptoms scores. Random-effects frequentist networks estimated standardized mean differences (SMDs) with 95% confidence intervals (CIs); negative values favoured add-on treatment. Risk of bias was assessed with Cochrane RoB 2. Results Forty-nine unique RCTs met the clinical and design criteria, of which 28 (2,067 randomized; 1,933 analysable participants) contributed to the locked quantitative dataset. The primary connected network included 24 trials, 25 treatments and 31 comparison estimates. Fourteen add-ons had CIs excluding the null versus a broad control node. The highest P-scores were observed for mirtazapine (SMD -2.38, 95% CI -3.55 to -1.21), granisetron (-1.96, -2.74 to -1.17), tropisetron (-1.82, -2.59 to -1.05), minocycline (-1.78, -2.55 to -1.01) and memantine (-1.54, -2.28 to -0.80). Heterogeneity was low ({tau} = 0.119; {tau}2= 0.014), but the predominantly star-shaped network had zero inconsistency degrees of freedom. Overall RoB 2 judgements were low for eight trials, some concerns for 15 and high for five. Conclusions Several pharmacological and non-pharmacological add-ons showed potentially important efficacy signals. Because most nodes were informed by single small trials, direct active comparisons were scarce, inconsistency could not be evaluated and risk-of-bias concerns were common, the treatment hierarchy should be considered hypothesis-generating rather than a basis for firm clinical recommendations. Registration: PROSPERO CRD420261422218 Keywords: schizophrenia; negative symptoms; adjunctive treatment; network meta-analysis; randomized controlled trial; neurostimulation Key Points This review compares pharmacological and non-pharmacological adjuncts in adults selected for prominent or persistent negative symptoms while receiving stable antipsychotic medication. Mirtazapine, granisetron, tropisetron, minocycline and memantine had the highest P-scores, while tDCS, rTMS and body-oriented psychotherapy also showed efficacy signals versus broad control. The evidence network was sparse and star-shaped, most interventions were supported by one small trial, and inconsistency was not estimable; rankings therefore require cautious interpretation.