Psychiatry and Clinical Neurosciences

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.

Purpose Delirium affects around 40% of intensive care unit (ICU) patients and adversely affects survival, length of stay, and cognitive recovery. Despite this, implementation of systematic clinical delirium assessments is variable. We sought to validate a point-of-care electroencephalography (EEG)-based system (Ceribell, Inc.) for the detection of delirium in the ICU. Methods In this multicenter, prospective cohort study, we monitored adult ICU patients with the Ceribell EEG system for 6-8 hours for a minimum of three days. Up to three times per day, expert clinicians assessed participants for delirium and research coordinators completed Confusion Assessment Method for the ICU (CAM-ICU) assessments, which we used to calculate CAM-ICU-7 severity scores. We compared expert clinician diagnoses with Ceribell EEG algorithm output using data from 30-minute periods surrounding each clinician assessment. Results Among 225 participants analyzed (median age: 64 years, 60% male, 21% mechanically ventilated), expert clinicians performed 857 assessments and found 24% of participants were delirious during at least one assessment. The Ceribell EEG system detected delirium with a sensitivity of 81% (95% CI: 76% to 87%) and specificity of 81% (95% CI: 78% to 84%). The continuous Ceribell EEG system score correlated with CAM-ICU-7 delirium severity scores (bootstrapped median rho 0.58, IQR: 0.54-0.62, p <0.001). Conclusion In a mixed adult ICU population, the Ceribell EEG system identified delirium with good sensitivity and specificity. Its continuous output was associated with delirium severity, highlighting its potential ability to characterize underlying physiological patterns across the clinical delirium spectrum.

Background: Alcohol use disorder (AUD) is associated with increased dementia risk, but its long-term relationship with dementia, variation by age and subtype, and how this risk compares with that associated with alcohol consumption remain poorly characterised. We investigated these questions in a large prospective cohort of UK women. Methods: We analysed Million Women Study participants recruited aged 50-64 years and followed for more than 20 years through linked health records. Women with hospital-recorded AUD were matched to up to five controls. Cox models estimated associations between time-varying AUD and incident dementia overall, by subtype and attained age, with exposure lags of up to 15 years and competing-risk analyses. Self-reported alcohol consumption was analysed separately. Findings: Among 1,288,359 women, 20,070 with AUD were matched to 100,339 controls. Dementia occurred in 3,774 women with AUD and 8,239 matched controls. AUD was associated with all-cause dementia (HR=3.73 [95% CI 3.52-3.95]), with strongest associations for mixed (5.03 [3.88-6.51]), unspecified (4.29 [3.92-4.70]), and vascular dementia (3.96 [3.44-4.56]). Associations were substantially stronger before age 70 years (10.68 [9.02-12.63]) than at ages 70-79 years (3.75 [3.47-4.04]) or 80 years or older (2.34 [2.11-2.60]). Risk remained elevated with a 15-year lag (2.16 [1.81-2.58]) and after accounting for competing mortality. Associations with self-reported alcohol consumption were substantially weaker. Interpretation: Clinically recognised AUD identifies women at markedly increased and prolonged dementia risk, particularly at younger ages and for vascular and mixed dementia, not captured by drinking quantity alone. People with AUD should be considered a priority population for earlier dementia prevention and risk reduction. Funding: Wellcome Trust Career Development Award to AT (306069/Z/23/Z); NIH grants P50AA012870 and R01DA054869 (JG). The Million Women Study is supported by the UK Medical Research Council (grant number UKRI1464).

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.

OBJECTIVE: Sepsis-associated encephalopathy (SAE) and sepsis-associated delirium (SAD) lack confirmatory laboratory tests or biomarkers. We evaluated whether the Th17/interleukin (IL)-17 axis or neuroglial-injury biomarkers reflect acute brain dysfunction beyond systemic inflammation and whether biomarker profiles differ between these phenotypes. DATA SOURCES: EMBASE, MEDLINE/PubMed, and the Cochrane Library from inception to August 1, 2026, supplemented by backward and forward citation searching, following PRISMA 2020. STUDY SELECTION: Observational studies of adults with sepsis classified by SAE or SAD status and reporting circulating biomarker concentrations by neurological group or biomarker-outcome associations. Eighteen studies (2,388 participants; 1,863 with sepsis) were included. DATA EXTRACTION: Two reviewers independently extracted study, phenotype, assay, and outcome data. Risk of bias was assessed with the Newcastle-Ottawa Scale and certainty with GRADE. DATA SYNTHESIS: Random-effects models pooled standardized mean differences (SMDs) separately against non-septic and septic controls and odds ratios (ORs) for biomarker-outcome associations. IL-6 was the only mediator elevated in both comparative frameworks and associated with outcomes (non-septic controls: SMD, 0.54 [0.25-0.82]; septic controls: SMD, 1.05 [0.51-1.59]; OR, 1.85 [1.50-2.29]). S100B, neuron-specific enolase, IL-10, tumor necrosis factor; and interferon-gamma; were elevated versus septic controls, whereas IL-17 was modestly elevated only versus non-septic controls (SMD, 0.37 [0.01-0.73]). The IL-6 septic-control effect was concentrated in SAE (p<0.0001) and remained robust in leave-one-out and risk-of-bias sensitivity analyses. Certainty was low to very low, and all 14 computable prediction intervals included the null. CONCLUSIONS: IL-6 emerges as the leading circulating candidate biomarker of sepsis-associated brain dysfunction, while S100B and NSE capture neuroglial injury beyond sepsis. Current evidence does not establish a discriminating role for the Th17 axis. Future studies require standardized neurological phenotyping and appropriately matched septic controls to establish diagnostic value.

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.

**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.

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.

Genetic studies of serious mental illness (SMI, including schizophrenia and bipolar disorder) have implicated two classes of risk variant: common variants1-3, each of small effect but collectively accounting for most population-level risk, and ultra-rare coding variants4,5 which confer large risk in a small number of individuals. Most such studies cannot directly assess the impact of specific variants on SMI, transdiagnostically, as the phenotypic information that they collect is focused on a single diagnosis. Here we report results of association analysis of predicted deleterious variants from exome sequencing of 21,958 cases (SMI and severe major depressive disorder) and 19,826 controls from the Mision Origen biobank in the Paisa genetic isolate of Colombia. We identified transdiagnostic associations to seven genes, three at an exome-wide and four at a false discovery rate significance threshold. Five of these genes have not previously been implicated in SMI (ADAP1, WIPI1, NFE2L1, FBXO10, and HLA-DPB1 ); for the first four of these the association is driven by variants enriched in the Paisa due to a founder effect, filling the gap between ultra-rare and common variants. The strongest association that we observe is to a frameshift variant (7:904169:T:TC) in ADAP1 that is genome-wide significant for SMI, schizophrenia, and bipolar disorder and increases risk for both diagnoses by more than threefold; it truncates ADAP1, a brain-enriched Arf6 GTPase-activating/scaffolding protein that has been mechanistically linked to post-mitotic neuronal morphogenesis and regulation of dendritic differentiation6. Variant carriers differ significantly from other SMI cases in Mision Origen for psychotic, manic, and cognitive item-level phenotypes assessed through analyses of electronic health records7-9 and displayed impaired executive function on neurocognitive testing. Variant carriers differ significantly from other SMI cases in Mision Origen for multiple item-level phenotypes (psychotic, manic, and cognitive) assigned through natural language processing of longitudinal electronic health records. Although the 7:904169:T:TC variant is not significantly associated with major depressive disorder, carriers compared to others with this diagnosis display a significant increase in symptoms typical of bipolar disorder. The variant's high allele frequency in Mision Origen (~0.4%) will enable population-level studies to stratify SMI based on causation and identify additional factors influencing risk and resilience.

Genetic studies of post-traumatic stress disorder (PTSD) implicate immune regulation, yet linking peripheral immune-related proteins to brain molecular regulation and experimentally tractable targets remains challenging. Cellular and spatial genetic analyses were used to characterize the cellular and spatial genetic context of these traits. We then conducted bidirectional Mendelian randomization (MR) across 30 predefined Ig-like plasma proteins and 16 behavioral and neuropsychiatric traits. The forward MR screen identified four associations meeting the prespecified screening criteria, all of which underwent the same post-screening robustness assessment. CD33-PTSD showed the most consistent support across instrument definitions and applicable robust estimators and was therefore advanced as the prioritized candidate to regional genetic, brain molecular QTL, and functional analyses. Standard CD33 pQTL-PTSD colocalization did not support a simple shared single-variant model, while multi-signal analyses remained scientifically unresolved under the available LD reference. Meanwhile, plasma CD33 pQTL and cortical CD33 eQTL showed strong regional sharing (PP4=0.96468). Regional localization, cross-analysis candidate selection, and functional annotation further suggested a complex regulatory context across the CD33-SIGLEC region. Among 284 compounds with prior CD33 binding evidence that entered structure-assisted screening, 11 were retained as candidates for experimental follow-up. Overall, CD33-PTSD was the prioritized association identified by our screening; subsequent regional genetic, brain molecular QTL, and functional annotation analyses further delineated the complex regulatory context of the CD33- SIGLEC locus, while the CD33-centered structure-assisted exploration yielded 11 compound candidates for subsequent experimental validation.

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.

Background: Psychiatric disorders pose a significant public health burden, and their complex genetic architecture underlies multiple biological pathways, contributing to a lack of diagnostic biomarkers. Laboratory-Wide Association Studies (LabWAS) leverage electronic health record laboratory data as intermediary phenotypes to link genetic risk to physiology. Methods: Polygenic Scores (PGS) for 25 health-related, psychiatric, and substance use phenotypes were generated for individuals of African (AFR; N=10,473) and European (EUR, N=35,131) ancestry from the Penn Medicine Biobank. Laboratory data was prepared using the LabWAS pipeline, encompassing 78 laboratory tests and 6 vital signs. Linear regression models tested the association of each lab value with PGS adjusting for sex, age, and ten within-ancestry genetic principal components. Results: Four PGS were significantly associated with at least one laboratory value in AFR individuals and 22 PGS in EUR individuals. In EUR individuals, psychiatric PGS partitioned into three physiological profiles: anxiety, attention-deficit/hyperactivity disorder, major depressive disorder, and post-traumatic stress disorder shared a metabolic/inflammatory signature (higher BMI, glucose, and monocytes; lower high density lipoprotein cholesterol and oxygen saturation); anorexia nervosa, obsessive compulsive disorder, and schizophrenia were characterized by lower BMI; and bipolar disorder demonstrated a more circumscribed profile with lower electrolytes and renal makers. Substance use disorder PGS similarly clustered: tobacco use disorder and externalizing showed metabolic/inflammatory profiles; alcohol-related phenotypes showed lower red blood cell indices; and cannabis use disorder had a more circumscribed profile. Conclusions: LabWAS reveals physiologic signatures associated with genetic risk for psychiatric disorders, offering new biological insights into their pathology.

Objective: To determine whether plasma neurofilament light chain (NfL) and glial fibrillary acidic protein (GFAP) are elevated in hospitalised patients with delirium relative to patients with dementia and primary psychiatric disorders (PPD). Methods and analysis: This was a cross-sectional observational study conducted at the Royal Melbourne Hospital, Melbourne, Australia, between April and October 2024, with comparator samples from a research biobank. In Phase 1 (Delirium Discovery Cohort), we measured plasma NfL and GFAP using the Single molecule array (Simoa) platform in hospitalised patients with delirium and compared them with dementia and PPD groups. Biomarker concentrations were the primary outcome and were compared using general linear models adjusted for age, sex, and estimated glomerular filtration rate, with bootstrapped confidence intervals. Discriminative performance was assessed as a secondary outcome using receiver operating characteristic (ROC) curve analysis. In Phase 2, we applied this threshold to independent dementia and PPD groups. Results: In Phase 1 (n =176), inpatients with delirium showed higher concentrations of both NfL and GFAP than patients with dementia ({beta} = 0.78; 95% CI [0.37, 1.20] and {beta} = 0.39 [0.07, 0.73] respectively) or PPD groups ({beta} = 1.17; [0.69, 1.66] and {beta} = 0.83 [0.51, 1.24] respectively). ROC curve analysis for differentiating delirium from dementia and PPD groups yielded an optimal NfL threshold of 24.1 pg/mL (AUC = 0.89, 96% sensitivity, 70% specificity). In comparison, GFAP showed lower accuracy (AUC = 0.85), and combining NfL with GFAP (AUC = 0.90) did not significantly improve performance. In Phase 2 (n = 154), the NfL threshold of > 24 pg/ml maintained strong performance (AUC = 0.95, 96% sensitivity, and 77% specificity). Conclusions: Plasma NfL shows promise as a biomarker for delirium in hospitalised patients, distinguishing it with high sensitivity from patients with dementia and PPD. Larger multicentre studies are needed to refine cut-offs, evaluate temporal dynamics, and determine integration into acute hospital delirium pathways.

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.

Circadian disruption is a transdiagnostic feature of psychiatric illness, particularly in bipolar disorder (BD), where evening chronotype is linked to greater symptom severity. The suprachiasmatic nucleus (SCN) times pineal gland (PG) melatonin synthesis through a well-described feedforward pathway. Moreover, melatonin can also phase-shift the SCN-pacemaker, consistent with a functional feedback limb. Directed effective connectivity between the SCN-containing hypothalamus and the PG has not been estimated in human neuroimaging, and it is unknown whether that directed coupling varies by psychiatric diagnosis or chronotype. We applied cross-spectral dynamic causal modeling (DCM) to resting-state fMRI data in 431 UK Biobank (healthy controls [HC, N=136], BD [N=78], major depressive disorder [MDD, N=154] and psychotic disorders [PY, N=63]). Random-effects Bayesian model selection compared a fully connected model with reduced archiature and, separately, feedforward-only (SCN[-&gt;]PG) vs feedback-only (PG[-&gt;]SCN) models. A per-subject log Bayes factor difference (dF) indexed directionality preference and was tested against diagnosis, chronotype and their interaction using ANCOVA. The fully connected model was strongly preferred (posterior probability=97.8%, protected exceedance probability [PXP]=1.00). Feedforward architecture dominated overall (70% vs 30%, PXP=1.00). Diagnosis did not predict dF (p=.403), but chronotype did (p=.036), and there was a significant diagnosis-by-chronotype interaction (p<.001). Feedforward dominance was strongest in evening chronotypes (p<.001). Model evidence favored a fully connected architecture with overall feedforward dominance. That preference varied by chronotype and, qualified by the interaction, was strongest in BD evening chronotypes. The pattern supports BD-evening chronotype as a high-risk subgroup and argues for routine chronotype assessment in BD care.

Pavlovian bias, a motor bias driven by emotionally-valenced context, has long been considered a proxy for emotion-action processing, which may become aberrant in affective disorders and suicidality. The orthogonal valenced Go/NoGo paradigm was developed to isolate this bias and has been widely adopted in translational psychiatry research under this interpretation. However, several unresolved theoretical inconsistencies highlight the need to dissociate potential confounding factors within this paradigm before assigning mechanistic and clinical interpretations to its findings. Using a novel paradigm that fully orthogonalized valence, action, and aimed transition in a normative sample of 25 individuals, we found that the "active escape bias" previously observed in aversive contexts--and interpreted as reflecting suicidality in the clinical population--was reproduced in appetitive contexts using trials with the same transition structure. Furthermore, computational modeling indicated that the bias patterns isolated by the orthogonal paradigm are primarily explained by the combined effects of aimed transition and cue salience, without requiring emotional-valence modulation. These findings call for a fundamental reconsideration of how orthogonal Go/NoGo paradigms are interpreted in translational psychiatry, particularly how the behavior they isolate is mechanistically linked to affective symptoms.

Psychiatric disorders frequently co-occur with a broad range of non-psychiatric conditions, suggesting shared biological mechanisms that transcend traditional diagnostic boundaries. Although recent studies have demonstrated substantial genetic overlap between psychiatric and other medical disorders, the genomic architecture underlying these relationships and the extent to which shared genetic liability can be leveraged for psychiatric gene discovery remain incompletely understood. Here, we systematically investigated the shared genetic architecture between eleven major psychiatric disorders and thirty-nine non-psychiatric disorders spanning cardiovascular, metabolic, autoimmune, neurological, gastrointestinal, respiratory, and other disease categories. To maximize statistical power, we meta-analyzed genome-wide association study (GWAS) summary statistics from large biobanks and international consortia. Genome-wide analyses identified 298 significant genetic correlations between psychiatric and non-psychiatric disorders, revealing pervasive shared liability across disease domains. Local genetic correlation analyses identified 180 genomic regions contributing to cross-disorder risk, while colocalization analyses revealed 1,470 candidate shared variants. Several highly pleiotropic loci, including regions containing FURIN and TCF4, contributed to risk across multiple psychiatric and other medical conditions. Genomic structural equation modeling identified recurrent dimensions of shared liability, including cardiovascular-kidney-metabolic and neuroimmune-related dimensions, suggesting that psychiatric-medical comorbidity is organized into biologically coherent systems. Leveraging these shared genetic architectures through cross-trait analyses identified 1,569 significant loci, including 382 psychiatric risk loci not reported in the individual psychiatric GWAS used in our analyses. Integrative positional and functional gene-mapping approaches prioritized genes involved in neuronal development, synaptic organization, neurotrophic signaling, and cellular homeostasis, with LRFN5, MED19, PITPNM2, CTNND1, TMEM106B, GLYCTK, and PCLO as the top implicated genes. Pathway and tissue-enrichment analyses further demonstrated convergence on neurodevelopmental and synaptic processes and significant enrichment across multiple brain regions. Our results provide a comprehensive characterization of the genetic architecture underlying comorbidity between psychiatric and other medical disorders and demonstrate that leveraging genetically correlated medical conditions across domains can substantially increase power for psychiatric risk-locus discovery.

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.

Deep brain stimulation (DBS) can be an effective treatment for severe obsessive-compulsive disorder (OCD), but response is variable, and objective, reliable predictors of who will respond positively are absent. In three individuals with OCD, we implanted intracranial electrodes across the cortico-striato-limbic network before DBS. Prefrontal gamma activity, most prominently in the orbitofrontal cortex (OFC), distinguished high- from low-symptom OCD states reproducibly across days. Acute stimulation of the ventral capsule/ventral striatum relieved OCD symptoms and, in one patient, significantly shifted this OFC gamma signal. Effective connectivity from therapeutic stimulation sites to these prefrontal regions defined three patient-specific OCD subnetworks; the baseline strength of the nucleus accumbens/ventral pallidum-OFC subnetwork predicted acute and long-term DBS response. Imaging-based modeling of current propagation in stimulation-activated ventral capsule to the OFC tracked acute response, suggesting that this prediction could be approximated from imaging alone. These findings outline a predictive, subnetwork-level framework for personalized DBS in OCD.