Schizophrenia

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.

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 [&ge;]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.

**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.

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.

The potential role of the cerebellum in the pathophysiology of schizophrenia (SCZ) has received relatively insufficient attention. Prior studies characterized cerebellar abnormalities mainly at the group level, without fully considering the heterogeneity of the disease. Here, we applied a machine learning approach (Subtype and Stage Inference, SuStaIn) to cross-sectional 3D volumetric MRIs of the cerebellum, including 1,588 individuals with SCZ (638 females; mean age: 31.9 +/- 12.0 years) and 2,341 healthy controls (HC) (1,041 females; mean age: 33.3 +/- 13.6 years), from 17 sites worldwide. SuStaIn identified three distinct spatiotemporal trajectories in cerebellar gray matter volume (GMV) reduction, respectively originating in lobule X (subtype 1), lobule III (subtype 2) and lobule VIIb (subtype 3), with subtypes 1 and 3 corresponding to the posterior lobe and subtype 2 to the anterior lobe. These cross-sectionally inferred trajectories were replicated in two independent samples of 1,334 and 530 patients, respectively. Multimodal analyses using neuroimaging, transcriptomic and behavioral data revealed subtype-specific biological characteristics in brain morphological patterns, cerebellar-cortical connectivity, gene expression and clinical symptoms. Specifically, subtype-related genes were enriched in metabolism-related processes and immunity-related processes, respectively, in subtype 1 and subtype 2; subtype 3 showed more severe brain abnormalities and worse cognitive symptoms. Treatment data from 381 patients, with up to 12 months of follow-up, revealed poorer response to antipsychotic medications (APM) in subtype 2 and subtype 3, but better response to transcranial magnetic stimulation (TMS) in subtype 1, which also had worse emotion-related symptoms. Together, our findings offer a comprehensive characterization of the heterogeneity of cerebellar pathophysiological progresses in SCZ, which may help in developing future clinical stratification and targeted intervention strategies.

Objective. NMDA-receptor hypofunction has been implicated as a core pathophysiological mechanism in schizophrenia, but conflicting evidence exists regarding the locus of the hypofunction--i.e. predominantly affecting receptors on pyramidal neurons or interneurons. To investigate the locus of NMDA-receptor hypofunction we integrated imaging data from humans and mice with biophysical model simulations. Methods. Translational imaging measures--intrinsic neural timescales (INT) and spontaneous co-activation pattern similarity--were investigated in three schizophrenia-relevant mouse models (chronic ketamine administration, n=6; Df(16)A+/-, n=7; and SETD1A+/-, n=9) and matched control mice (n=5; n=7; and n=8, respectively) using two-photon calcium imaging, and individuals with schizophrenia (n=127) and matched non-clinical controls (n=152) using functional MRI. The stabilized supralinear network biophysical model was used to simulate NMDA-receptor hypofunction and its impact on the translational imaging measures. Results. We identified convergent neural phenotypes of shorter intrinsic neural timescales (INT) and reduced spontaneous co-activation pattern similarity in both the schizophrenia-relevant mouse models and the individuals with schizophrenia compared to matched controls. In silico simulations using the stabilized supralinear network biophysical model recapitulated the convergent neural phenotypes through reduced excitatory-to-excitatory connection strength (i.e. NMDA-receptor hypofunction on pyramidal neurons) and predicted that INT and pattern similarity are correlated; this prediction was confirmed in both the mouse and human data. Conclusions. These results identify NMDA-receptor hypofunction on pyramidal neurons as a general and translationally valid mechanism for NMDA-receptor hypofunction in schizophrenia. These convergent translational findings suggest prioritizing the development of treatments for schizophrenia that increase recurrent excitation.

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.

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.

Background: Schizophrenia Spectrum Disorders (SSD) is associated with spatially heterogeneous structural abnormalities across distributed brain systems, but the longitudinal evolution and neurochemical context of subcortical subregional changes remain poorly understood. We investigated longitudinal volume changes across thalamic, hippocampal, amygdalar, and brainstem subregions and their relationships with clinical characteristics and normative neurotransmitter distribution. Methods: A total of 357 SSD individuals with first-episode and 196 healthy controls were included. Clinical assessments were performed at baseline and at 1, 3, 5, 10, 15, and 20 years of follow-up. Using T1-weighted structural MRI, subregional volumes were quantified across the thalamus, hippocampus, amygdala, and brainstem. We examined baseline group differences, longitudinal volume changes, and associations with clinical variables, including symptom severity. Spatial patterns of regional associations were compared with normative maps of neurotransmitter receptor and transporter distributions. Results: Patients showed heterogeneous structural alterations across the examined subcortical subregions. The hippocampus showed prominent early volume deficits in specific subfields and demonstrated a strong association with antipsychotic medication doses. Amygdalar volumetric reductions were more widespread and were strongly associated with positive symptom severity. Localised structural abnormalities were also observed in the thalamus and brainstem, although these showed weak associations with clinical symptomatology. The spatial co-location of normative neurotransmitter maps with the structural and clinical associations revealed distinct overlapping with dopaminergic, GABAergic, glutamatergic, serotonergic and cholinergic systems. Conclusions: Longitudinal structural alterations in SSD showed marked anatomical heterogeneity across thalamic, limbic, and brainstem systems. Their spatial correspondence with normative neurochemical architecture suggests that regional and subregional structural vulnerability and clinical heterogeneity may be organized along distinct, partially overlapping neurotransmitter profiles.

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.

BackgroundAtypical antipsychotics (AAPs) are increasingly prescribed for paediatric patients with autism spectrum disorder (ASD). However, pharmacovigilance data from Saudi Arabia remain scarce. The aim of this study is to detect and characterise signals associated with AAPs and assess gender- and age-related ADR reporting patterns. MethodsA retrospective cross-sectional study with disproportionality analysis was conducted on paediatric patients (0-19 years) using the Saudi Food and Drug Authority (SFDA) database (2021-2024). Disproportionality analyses were performed using the reporting odds ratio (ROR), proportional reporting ratio (PRR), and Bayesian confidence propagation neural network (BCPNN). Age and gender were assessed using odds ratios (ORs), with Firth penalized logistic regression to confirm associations. ResultsOverall, 701 ADR reports were analysed. Most involved males (59.91%) and children aged 0-9 years (82.88%). Olanzapine had the highest proportion of serious reports (44.44%). At the system organ class (SOC) level, signals included nervous system disorders with risperidone (ROR 32.61 [16.62-64.00]; PRR 3.49 [2.22-5.47]; IC025 0.08); cardiac and general disorders with quetiapine; haematological, metabolic, and psychiatric disorders with olanzapine; and gastrointestinal disorders with aripiprazole. Gender-adjusted analyses indicated significant variation in selected ADR signals across age groups, while no significant differences were observed by gender. ConclusionsThis study highlights disproportionality signals associated with AAP use in paediatric patients. Age-related ADR variation supports consideration of age-specific ADR monitoring within current paediatric recommendations. While these findings do not establish causality, they generate hypotheses for further evaluation. Regional pharmacovigilance may complement international paediatric safety monitoring frameworks.

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: Auditory verbal hallucinations (AVH) are among the most disabling symptoms of schizophrenia, yet the network-level mechanisms linking auditory dysfunction to hallucination severity remain poorly characterized, limiting targeted therapeutic development. Methods: Using resting-state fMRI and high-resolution surface-based parcellation (HCP-MMP), we examined auditory-language functional connectivity and mesoscale network organization in 56 patients with schizophrenia and 22 healthy controls (primary dataset, N=78). Pairwise auditory-language connectivity associations with hallucination severity (PANSS P3) were examined alongside auditory network community structure, assortativity, hub organization, and routing efficiency. To further characterize these parcel-level findings at the vertex level, we applied a region-growing approach to identify auditory-frontal connectivity patterns maximally associated with severity. In silico perturbation analyses tested the causal role of belt cortex connectivity. Key findings were independently replicated in 93 patients from the Human Connectome Project for Early Psychosis (HCP-EP), for a combined total of 171 participants. Results: Auditory-language connectivity disruptions were concentrated in belt cortex and specifically implicated ventral what-stream connections, with a vertex-wise analysis converging on the same anatomy. Auditory network segregation scaled with hallucination severity, with belt cortex emerging as the primary integrative hub governing auditory-to-language routing efficiency. Perturbation analyses showed that strengthening earlier hierarchy auditory regions reduced network segregation more than strengthening later-hierarchy regions. Reduced belt cortex coupling and elevated network segregation replicated independently in the HCP-EP sample. Conclusions: Auditory network disorganization scales with hallucination severity and replicates across independent samples, with belt cortex as the critical locus of connectivity disruption and network integration failure, identifying it as a candidate neuromodulatory target.

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.

Altered white matter microstructure, commonly indexed by fractional anisotropy (FA), has been reported in individuals at clinical high-risk of psychosis (CHR-P) across multiple white matter tracts. However, the relationship between these alterations and later transition to psychosis remains unclear. To address this gap, we investigated whether baseline and longitudinally updated FA measurements were associated with risk of transition to psychosis in a large cohort of individuals at CHR-P. We analysed diffusion tensor imaging data from N = 205 participants at CHR-P, 23 of whom developed psychosis within 3 years of follow-up. Data were collected at baseline, 6 months, and 12 months. Associations between global and tract-specific FA and transition risk were examined using Cox proportional hazards models of baseline data and longitudinally updated data. Point estimates were directionally consistent, with hazard ratios <1 for FA across all white matter tracts in both baseline and longitudinal models. Associations reached nominal significance in the hippocampal cingulum, anterior thalamic radiation, and forceps minor, but none of these remained significant after correcting for multiple comparisons (pcorr > .05). The directionally consistent estimates are compatible with small, distributed effects across multiple white matter pathways being associated with transition to psychosis, although this requires replication. Longitudinally updated FA measurements did not yield stronger associations than were available from baseline imaging data alone. Studies at an even larger scale are needed to establish whether these associations are robust and clinically meaningful.

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.

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 a major determinant of functional outcome in psychotic disorders, yet the contribution of psychotropic medication is incompletely understood. This study aimed to determine whether personalised receptor-engagement profiles derived from prescribed psychotropic medications are associated with cognitive performance in individuals with psychotic disorders. Methods: We performed a cross-sectional study using clinical, cognitive, and medication data from the Bipolar-Schizophrenia Network on Intermediate Phenotypes studies. In 1,067 (51.5% female) individuals with psychotic disorders, we estimated personalised receptor-engagement profiles based on the prescribed dose and known pharmacological properties of 49 psychotropic medications across 32 drug targets. Correlated receptor-target modulation profiles were summarized into seven pharmacological clusters. Extreme gradient boosting models were trained using repeated nested cross-validation, with the contribution of pharmacological profiles to predicted cognition quantified using out-of-sample SHapley Additive exPlanations (SHAP) values. Overall pharmacological contribution was tested using within-diagnosis permutation testing. Results: Estimated receptor-engagement profiles were significantly associated with cognitive performance independent of diagnosis. A receptor profile involving alpha2-adrenergic, dopaminergic (DRD4, DRD5) and several serotonergic receptors was associated with poorer cognitive performance, whereas dopamine transporter (SLC6A3) engagement was associated with better cognitive performance. Muscarinic receptor modulation was additionally associated with poorer verbal memory and motor speed, while the DRD2-containing receptor profile was not associated with cognitive performance after correction for multiple testing. Conclusions: Modelling psychotropic medications according to receptor pharmacology identified receptor-engagement profiles associated with cognitive performance in psychotic disorders. This framework may help identify receptor systems for future investigation as potential targets for cognition-enhancing therapies and support the development of personalised prescribing strategies.

Early detection of individuals at clinical high risk for psychosis (CHR-P) improves access to preventive care. We developed and internally validated the E-DetectioN Tool for Emerging mental disoRders (ENTER), a multimodal digital screener designed to improve community identification of CHR-P compared with an established approach (PQ-16 cut-off). Participants aged 12-35 years from the UK and Italy completed the online ENTER screener, assessing attenuated psychotic symptoms, processing speed and environmental risk factors. Participants above the PQ-16 cut-off and a random subset below were assessed with the Comprehensive Assessment of At Risk Mental States (CAARMS). PQ-16 cut-off and ENTER models were developed using (penalised) logistic regression, random forest and extreme gradient boosting (XGBoost) within a repeated nested cross-validation framework with semi-supervised learning incorporating unlabelled data. Models were evaluated by discrimination, calibration and clinical utility. 2,522 participants completed the ENTER screener. 495 underwent CAARMS assessments. 124 (25.1%) met CHR-P criteria; 2,027 participants were not assessed with the CAARMS (unlabelled). ENTER showed improved discrimination (C=0.744, 95%CI: 0.697-0.790) compared to the PQ-16 cut-off (C=0.625, 95%CI: 0.570-0.682, p=0.003) and similar calibration (ENTER: slope=1.02, 95%CI: 0.78-1.32; PQ-16 cut-off: slope=0.39, 95%CI: 0.19-0.52, p=0.074). At a clinically relevant threshold, ENTER identified approximately 2 additional CHR-P individuals per 100 assessments compared with the PQ-16 cut-off. Compared with established PQ-16 cut-off pre-screening, ENTER showed higher performance for community identification of CHR-P individuals. Multimodal digital screening might support more informative community triage before specialist assessment. External validation and implementation studies are required to establish real-world clinical utility.

Brain-imaging models predicting psychiatric outcomes are rarely tested where they would be used. Clinical high risk for psychosis is a demanding test case because services differ in whom they recruit and how they record transition. We re-estimated a published structural magnetic resonance imaging (MRI) risk model and applied it unchanged to four independent cohorts (685 participants, 106 transitions). Discrimination was weak (area under the curve, 0.56) and near chance in the two cohorts not previously used to evaluate the model (0.53 and 0.56). Predicted risks did not transfer (calibration slope, 0.17). Exploratory performance landscapes, which map local discrimination across participant characteristics, showed that discrimination varied with negative-symptom severity, also in external cohorts, whereas apparent age- and brain-age-related variation depended on single cohorts; no global test survived multiple-testing correction. Reporting where a model works, not only its average accuracy, can reveal when one diagnostic label hides several prediction problems.