Psychiatry and Clinical Neurosciences

Purpose: Involuntary psychiatric hospitalization under court orders requires careful balancing of legal obligations and clinical needs. Identifying factors that influence the length of these hospital stays helps clarify the relationship between legal frameworks and psychiatric treatment. This study aims to describe the socio-demographic, clinical, and legal profiles of individuals hospitalized under court warrants and to identify factors independently associated with the duration of forensic hospitalization. Methods: A retrospective study was conducted on 119 patients discharged between 2018 and 2023. Data were collected from medical and legal records, including socio-demographic details, psychiatric diagnoses, offense types, hospital stay lengths, and legal proceedings. Results: Most patients were men (91.6%) diagnosed with schizophrenia or schizoaffective disorder (97.5%), with high rates of comorbid substance use disorder (79.0%) and unemployment (85.7%). The median hospital stay was 19.0 months, representing 40% of the maximum statutory sentence. Patients with low-severity offenses served a larger share of their maximum sentence (47%) than those with high-severity offenses (24%). Time to first discretionary leave was the strongest predictor of total stay duration in univariable analysis. Conclusion: The finding that patients with minor offenses have longer hospital stays than those with serious offenses confirms that clinical factors, rather than offense severity, primarily influence discharge decisions. These findings support moving toward personalized, clinically focused, and family-inclusive forensic discharge planning while maintaining public safety.

Accurate and scalable assessment of quantitative neuroimaging biomarkers, such as white matter hyperintensities (WMH) and hippocampal (HIP) volumes, is essential for understanding and monitoring brain health, preventing neurological diseases and improving healthspan. However, population-level evaluation of these neuroimaging biomarkers relies on inaccessible, costly and time-consuming magnetic resonance imaging (MRI). Here we propose RetiBrain, a cross-modal deep learning framework that predicts these neuroimaging biomarkers from retinal color fundus photography (CFP) images. By distilling latent structural representations from MRI-based models into a CFP-based model, RetiBrain establishes biologically grounded eye-to-brain mapping. In a CFP-MRI paired cohort, RetiBrain accurately estimates six WMH- and HIP-related biomarkers and outperforms the state-of-the-art retinal foundation model RETFound, improving the mean Pearson correlation coefficient by 0.309 (from 0.240 to 0.549) and achieving a coefficient of 0.640 for periventricular WMH prediction. By integrating structural, topological and geometric feature analyses from CFP images, RetiBrain identifies interpretable retinal representations associated with neurodegeneration and cerebrovascular injury, hallmarks of major neurological diseases such as dementia and stroke. In a longitudinal cohort comprising 2,082 participants (4,164 CFP images with up to 15 years of follow-up), RetiBrain-predicted neuroimaging biomarkers robustly estimated neurological disease risk, as illustrated by dementia prediction (AUROC of 0.824, hazard ratio 2.500 per standard deviation increase, 95% CI: 2.201-2.840). RetiBrain provides a robust, scalable, cost-effective and convenient approach for the assessment of neuroimaging biomarkers, and has potential for long-term brain health monitoring in large-scale general population settings.

Abstract Background Despite a rising global psychiatric burden, a treatment gap persists where the majority of symptomatic individuals remain unmedicated. Traditional epidemiological analyses treat this untreated population as a single, uniform block, obscuring specific barriers to care. This study uses an unsupervised machine learning pipeline to identify distinct socio-behavioural and biological sub-populations within the untreated cohort to guide targeted public health interventions. Methods Data were pooled from the 2015-2018 National Health and Nutrition Examination Survey (NHANES) cycles (N=11,848 total adult respondents). A symptomatic cohort of 3,075 individuals experiencing daily or weekly anxiety or depression symptoms was isolated, excluding severe liver pathology outliers ("GGT"[≥]80" U/L" ). A 22-feature matrix combining continuous clinical biomarkers (systolic blood pressure, waist circumference, HbA1c) and categorical social variables was projected using Factor Analysis of Mixed Data (FAMD). Latent sub-populations were identified via Gaussian Mixture Modelling (GMM), optimized by the Bayesian Information Criterion (BIC). Results The broad baseline population revealed a substantial mental health burden, with 30.4% reporting active psychiatric symptoms, of whom 71.6% were entirely unmedicated. The GMM pipeline successfully isolated three distinct sub-populations (k=3) separated by age, clinical strain, and treatment rates: Cluster 0 (Mature Adults, mean age 55.03): high psychiatric severity (34.1% severe untreated), central obesity, and hypertensive strain (135.82 mmHg), with 64.1% untreated despite frequent primary care contact; Cluster 1 (Working Professionals, mean age 38.38): highly educated, female-dominated (70.5%), with 77.7% untreated driven by moderate distress; Cluster 2 (Emerging Youth, mean age 18.49): a highly vulnerable late-adolescent group with a staggering 90.2% untreated rate. Conclusion The unmedicated symptomatic population is highly diverse and segmented by life stage. These profiles show that the treatment gap is driven by age-specific barriers, specifically workforce-age symptom masking and late-adolescent developmental transitions. Closing this deficit requires shifting from uniform public health approaches toward targeted interventions, such as digital peer support networks for youth and integrated primary care screenings for older adults.

Transdiagnostic psychiatric research on reward-guided learning has largely focused on simple associative processes, leaving it unclear whether or how higher-level processes are disrupted. Here, we studied how abstraction, the ability to extract relevant features from complex information, and metacognition, the ability to monitor and evaluate one's own mental processes, map onto specific transdiagnostic dimensions. Using an online sample (N = 249), we examined associations between these processes and three cross-culturally robust transdiagnostic dimensions derived from a large existing dataset (N = 19,505): Compulsive hypersensitivity, Social withdrawal, and Addictive behaviours. Computational modelling of an abstract representation learning task with confidence judgments revealed that Compulsive hypersensitivity was negatively associated with both abstraction ability (pboot = 0.003) and metacognitive sensitivity (pboot = 0.005), while Social withdrawal was positively associated with metacognitive sensitivity alone (pboot = 0.002). Moreover, transdiagnostic dimensions revealed more coherent associations with higher-order cognition than symptom-level analyses, highlighting the added value of examining psychopathology at the factor rather than the symptom level. These findings portray a hierarchical view of cognitive dysfunctions in psychopathology and point to representational and metacognitive processes as potential targets for transdiagnostic intervention.

Objective: Attention-deficit/hyperactivity disorder (ADHD) is clinically and etiologically heterogeneous, and diagnostic decisions may benefit from integrating multiple sources of information. We developed an explainable deep learning approach to test whether genetic, environmental, cognitive, demographic, and temperament data could classify ADHD diagnosis and identify features contributing to model decisions. Method: We analyzed participants from the Oregon ADHD-1000 cohort split into training, validation, and test subsets. We trained modular neural network models classifying ADHD case-control status using SNP-level genotype data with biological annotations, polygenic scores, demographics, parenting and family conflict, stress and trauma, geocoded measures, cognitive task measures, temperament factor scores, and missingness indicators. Hyperparameter optimization selected model architecture and feature block inclusion. We evaluated model performance using AUC, precision-recall curves, calibration analyses, prediction certainty analyses, and decision curve analysis. We used integrated gradients to quantify block-level, feature-level, and individualized feature importance. Results: The best model using temperament features had an AUC of 0.97 in the held-out test subset, with high accuracy, sensitivity, and specificity and a Brier score of 0.06. The best model excluding temperament had an AUC of 0.75. Feature importance analyses highlighted temperament, demographic, and cognitive domains in the temperament-inclusive model. Individualized explanations showed that prediction drivers varied across participants and could help reveal conflicting or supporting evidence across domains. Conclusion: Explainable, multi-modal classification models can integrate heterogeneous ADHD-relevant information and identify features that contribute to individual predictions. These types of models may advance ADHD risk modeling research and clinician-led decision support, especially in complex or diagnostically uncertain cases.

Objective: To quantify the burden, structure, and downstream analytic consequences of "Unable to Assess" (UTA) delirium documentation in the intensive care unit (ICU). Design: Retrospective cross-sectional and repeated-measures study. Setting: A single US academic medical center (Medical Information Mart for Intensive Care IV [MIMIC-IV], 2008-2019). Patients: 72,944 adult ICU stays with at least 1 delirium screen. Interventions: None. Measurements and Main Results: Among 610,632 screens, 130,455 (21.4%; 95% CI, 21.0%-21.8%) were recorded as UTA, exceeding the 119,052 (19.5%) scored positive. The UTA fraction rose from 2.0% at a Richmond Agitation-Sedation Scale (RASS) score of 0 to 97.8% at RASS -4; 22.0% of UTA screens occurred in arousable patients, where UTA was associated with mechanical ventilation (odds ratio [OR], 3.43; 95% CI, 3.17-3.71) and non-English primary language (OR, 3.74; 95% CI, 3.43-4.08). Building the delirium label three ways from the same patients shifted prevalence modestly (32.1% to 30.8%) and prediction (area under the curve, 0.737 to 0.719) but most affected the delirium-mortality association: in a baseline-adjusted model the OR was 4.12 (95% CI, 3.88-4.36) under complete-case handling and fell to 2.16 (95% CI, 2.06-2.27) when UTA was recoded as negative. UTA was recoverable from the observed clinical state (area under the curve, 0.95). Conclusions: In this ICU cohort, Unable to Assess was the most common recorded delirium result other than Negative, exceeding positive screens; recoding it as negative roughly halved the apparent delirium-mortality association by relabeling deeply sedated, high-mortality patients. Delirium datasets should preserve and report UTA, whose concentration among arousable non-English-speaking patients is a measurable equity target.

Background and Hypothesis: Schizophrenia (SZ) and bipolar disorder (BD) share overlapping yet distinct clinical profiles and system-wide brain alterations. Macroscale functional connectivity gradients capture principal axes of cortical organization, including the separation of unimodal and transmodal systems, offering a low-dimensional lens on individual differences in brain architecture. Whether these axes reflect shared or diagnosis-specific variation across the SZ-BD spectrum is unknown. Study Design: Using resting-state fMRI from 187 adults (110 HC, 37 SZ, 40 BD) from the UCLA Consortium for Neuropsychiatric Phenomics, we derived individual low-dimensional gradients and applied three analyses: case-control comparisons at both the cortical network and subcortical region-of-interest level, Partial Least Squares (PLS) regression linking gradients to clinical phenotypes, and individual-level similarity indices (SI-PLS) positioning participants within a gradient-behaviour space. Study Results: While the gradient structure (G1: visual-somatomotor and G2: unimodal-transmodal) was preserved across groups, patient groups showed greater deviations along both axes. Network analyses revealed transdiagnostic frontoparietal compression in G2, alongside disorder-specific effects: visual pole contraction and subcortical amygdala displacement in SZ, and somatomotor displacement in BD. PLS identified a BD-associated profile of preserved gradient architecture and lower symptom burden, contrasting with an SZ-associated profile of greater cognitive impairment and symptom severity. SI-PLS scores placed SZ and BD in distinct regions of a shared two-dimensional neural space, with HC between them. Conclusions: Differences across the SZ-BD spectrum organize along two principal axes, revealing transdiagnostic alterations in higher-order association systems alongside disorder-specific sensory signatures. These findings support a multi-axis dimensional framework for understanding clinical heterogeneity in psychosis.

Identifying generalizable brain-based biotypes across independent cohorts is critical for parsing heterogeneity in Major Depressive Disorder (MDD), yet robust subtypes spanning micro- and macroscales remain poorly defined. We applied stability-based clustering to cortical thickness data from 1,531 MDD individuals in UK Biobank (UKB), with external validation in 144 inpatients from IRCCS Ospedale San Raffaele (HSR). Two distinguishable clusters emerged (accuracy=87.5%), with one showing widespread cortical thinning, anergy-related symptoms, childhood trauma, and diabetes comorbidity. This profile generalized with 96.5% accuracy in a hold-out UKB sample and 80.6% in HSR. Mapping clusters cortical profiles onto Neurosynth meta-analytic activation patterns revealed a ventral-dorsal gradient linked with emotion regulation, interoceptive, and motivational processes. Spatial correlations with 19 neurotransmitter receptors and transporters obtained from positron emission tomography identified dopamine transporter as the dominant contributor in UKB, and histamine receptor H3 in HSR. These findings provide a reproducible framework linking MDD subtypes to multiscale biological complexity.

Background: Predicting 28-day mortality in ICU patients with alcoholic cirrhosis is challenging because clinical deterioration is dynamic and heterogeneous. Methods: Using MIMIC-IV (v3.1), this study included 1,907 patients (training n = 1,334; validation n = 573), engineering 208 temporal and static predictors from 64 base variables and reducing them to 40 through multi-stage selection. Seven classifiers and a weighted gradient-boosting ensemble (XGBoost, CatBoost, LightGBM) were compared with Optuna tuning. Results: The ensemble achieved the highest internal validation AUC (0.9276; 95% CI: 0.9011-0.9507) and lowest Brier score (0.0870), with strong discrimination on eICU-CRD (AUC 0.9347) and related MIMIC-III (AUC 0.9071). Ablation indicated that temporal features, especially deltas, were major contributors ({triangleup}AUC {approx} 0.17 when removed). SHAP highlighted APS III score, anion gap, oxygen saturation (delta), lactate, and INR as leading predictors. Conclusions: The framework supports interpretable, trajectory-informed risk stratification in critically ill cirrhotic patients; prospective validation is needed before clinical use.

Importance: The impact of COVID-19 containment policies (e.g., physical distancing, school closures) on population anxiety has been debated and difficult to resolve. Objective: To estimate the joint effects of state-level COVID-19 containment policies on anxiety symptoms during the early pandemic. Design: Retrospective analysis of a prospective cohort with cross-sectional outcome assessment. Setting: All of Us Research Program, a U.S. national research cohort. Participants: 40,610 adult participants who completed the All of Us COPE survey in July 2020. Exposures: Seven state-level COVID-19 containment policies (school closures, workplace closures, cancellation of public events, restrictions on gatherings, public transport closures, stay-at-home requirements, and restrictions on internal movement) measured from March 22 to May 23, 2020, via the Oxford COVID-19 Government Response Tracker (OxCGRT). Main outcomes and measures: The primary outcome was anxiety symptoms (GAD-7) in July 2020. Using quantile g-computation, we classified policies as anxiety-increasing or anxiety-decreasing by the sign of their training-set contributions, then re-estimated joint effects in a holdout testing set. Results: Among participants (64% female; mean age: 57.8 years), 13.3% (n=5398) reported moderate-to-severe anxiety (GAD-7 score 10-21) in July 2020. The joint effect of all seven containment policies was not significant ({beta} = 1.88, 95% CI: -0.51 to 4.28, p = 0.12). An anxiety-increasing joint effect from 4 policies (school, workplace, public events, internal movement; {beta} = 2.98, 95% CI: 0.30 to 5.66, p = 0.03) and an anxiety-decreasing joint effect from 3 policies (gatherings, public transport, stay-at-home; {beta} = -1.10, 95% CI: -1.75 to -0.44, p = 0.002) reached significance. Effects were largest in adults 18-44 (anxiety-increasing {beta} = 8.93, 95% CI: 1.50 to 16.37, p = 0.02; anxiety-decreasing {beta} = -2.81, 95% CI: -4.98 to -0.64, p = 0.01), with no significant effects in adults 45 and older. Conclusions and Relevance: Modeling seven containment policies jointly showed no net anxiety effect, a result that masked opposing-direction effects. Partitioning by effect direction revealed significant joint effects exceeding single-policy estimates, with young-adult point estimates above the 4-point GAD-7 minimal clinically important difference (MCID) though lower CI bounds fell below it. These findings may inform the use of containment policies in future pandemics, given their differing association with population anxiety

Patterns of brain circuit dysfunction underlying depression and anxiety have been increasingly characterized, including dimensional and subtype variation. A key challenge is determining how such patterns generalize across populations and measurement frameworks. Here, we introduce HARMONY, a harmonized multimodal neuroimaging dataset supporting large-scale investigation of brain behavior associations across symptom-defined dimensions. HARMONY integrates four Human Connectome Project style Connectomes Related to Human Disease cohorts spanning adolescence to later adulthood and capturing anxious misery symptoms. The resource combines standardized HCP style preprocessing, quality control, imaging-derived phenotypes, and harmonized symptom measures into a clinically enriched public dataset. Proof of concept analyses using HARMONY showed that pooling heterogeneous cohorts increased statistical power for detecting associations between imaging-derived phenotypes and anhedonia and depression severity. Effect sizes remained modest, consistent with symptom-based measures across heterogeneous samples. Functional imaging derived phenotypes showed the strongest multivariate predictive performance. In summary, HARMONY provides a large multi cohort resource for reproducible mental health neuroimaging research.

Social anxiety disorder (SAD) is among the most prevalent anxiety disorders, and it has been associated with signs of advanced biological ageing. Despite this, brain age research on anxiety disorders remains limited. This mega-analysis investigated brain ageing in adults with SAD within the ENIGMA-Anxiety Working Group. Structural MRI scans from 576 participants with SAD and 1 355 non-affected healthy controls (HCs) across 26 international samples were included. Brain age was estimated from 77 cortical and subcortical regions using a publicly available ENIGMA brain age model. The brain-predicted age difference (brain-PAD) was calculated as the difference between brain age and chronological age. Group and subgroup differences (comorbidity, medication) were assessed using linear mixed-effect models. In the full sample, there was no group difference in brain-PAD ({beta}diagnosis (SE)=0.70 (0.37) years, p=0.061). In a subgroup of participants with SAD with comorbid anxiety disorders (n=184 SAD, n=1 355 HCs), a brain-PAD of +2.39 (0.93) years (Cohen's d=0.23, pFDR=0.003) was observed. This brain-PAD became smaller after exclusion of participants with comorbid agoraphobia and specific phobia, suggesting that these disorders may partly drive the advanced brain-PAD. In conclusion, this ENIGMA-Anxiety mega-analysis did not find evidence of advanced brain ageing in the full sample of adult participants with SAD relative to HCs. However, a sub-analysis suggested that SAD with co-occurring phobic disorders, or the phobic disorders themselves, are associated with neurostructural patterns typical of older brains. Future research could utilise transdiagnostic samples with information on age of onset and disorder duration to further clarify this relation.

Psilocybin shows therapeutic promise for several psychiatric disorders, but the acute perceptual and cognitive alterations produced by conventional doses (10-25 mg) require in-clinic supervision, which limits scalability. Whether the therapeutically relevant pharmacology of psilocybin can be separated from its hallucinogenic activity remains unresolved. To address this gap, we conducted a Phase 1, randomized, double-blind, placebo-controlled, single ascending dose study to characterize the safety, pharmacokinetics and pharmacodynamics of low doses of psilocybin. Fifty-six healthy adults received a single oral dose of psilocybin (0.5, 1.0, 1.5, 2.5, 3.5 or 4.0 mg) or matching placebo across seven sequential cohorts, with each dose escalation reviewed by a Drug Safety Review Committee. All participants completed the study with no serious adverse events or discontinuations. Treatment-emergent adverse events were comparable to placebo and most prominently arose as somnolence. Plasma psilocin appeared rapidly with a median time to maximum concentration < 1 h with dose-proportional exposure and a short terminal half-life. Subjective drug effects were dose-related and became distinguishable from placebo at doses at or below 2.5 mg. Peak subjective ratings increased with dose, while any signs of hallucinations or altered-states scores remained low and not different than placebo. Psychophysiological engagement was confirmed by a clear dose-dependent pupillary dilation while cognitive performance (attention, vigilance, working memory, impulse control) showed no dose-dependent decrement and state anxiety did not increase at any dose. These findings indicate that the perceptible pharmacology of psilocybin can be dissociated from significant perceptual alterations and cognitive impairment at low doses. They further support controlled investigations in outpatient Phase 2 studies evaluating the safety and feasibility of repeated, self-administered low-dose psilocybin. ClinicalTrials.gov #NCT07710027

Adolescence is a period of rapid neurodevelopment during which psychiatric symptoms may emerge, yet symptom-specific markers show inconsistent associations with cognition and brain structure and can rarely be generalised longitudinally. Using data from the ABCD Study, we derived a transdiagnostic mental-health burden measure that integrates multiple symptom domains and examined its cognitive and structural brain correlates in early adolescence longitudinally. Adolescents with higher burden showed consistently lower performance in vocabulary, memory, and processing-speed, alongside widespread reductions in whole-brain, cortical, and white-matter volumes at baseline and after 2 years. These effects were strongest in a subgroup with persistent high burden and replicated in cross-sectional analyses. After 4 years, mental-health differences remained robust, although brain-behaviour associations weakened, likely reflecting developmental reorganisation and reduced sample size. Our study demonstrates that global mental-health burden provides a scalable, developmentally appropriate marker of early psychiatric vulnerability that overcomes limitations of symptom-specific approaches.

This study examined whether connectivity-guided accelerated intermittent theta-burst stimulation (iTBS) improves depressive symptoms beyond routine multimodal inpatient care in hospitalized patients with treatment-resistant depression (TRD). In this randomized, double-blind, sham-controlled trial, patients with unipolar TRD received active or sham iTBS. Stimulation targeted an individualized left dorsolateral prefrontal cortex site showing most functional anticorrelation with the subgenual anterior cingulate cortex on resting-state functional MRI. Treatment was delivered as 3 daily sessions over 10 weekdays (30 sessions; 54,000 pulses) as an inpatient augmentation strategy. Primary and secondary outcomes were changes in Montgomery-Asberg Depression Rating Scale (MADRS) and Beck Depression Inventory-II (BDI-II) scores during the 2-week stimulation phase. Exploratory endpoints included response and remission rates. Of the 57 randomized patients, 51 completed treatment (active, n=27; sham, n=24). The cohort exhibited moderate-to-severe treatment resistance (mean Maudsley Staging Method score, 10.9) and high psychiatric comorbidity. Active iTBS was associated with significantly steeper MADRS improvement than sham (-3.54 points/week; 95% CI, -5.53 to -1.55; PFDR=.02), corresponding to model-estimated reductions of 12.06 versus 4.98 points with a large effect size (d=-0.89). BDI-II trajectories similarly favored active treatment, though with a smaller effect (group-by-time estimate, -0.23 points/day; 95% CI, -0.41 to -0.05; PFDR=.04; d=-0.22). MADRS response rates were higher with active iTBS (42.3% vs 13.0%), while remission rates were numerically but not significantly higher (26.9% vs 12.5%). No serious adverse events occurred. In conclusion, connectivity-guided iTBS produced significant add-on antidepressant effects during acute inpatient treatment of TRD. Larger multicenter trials are needed to establish durability and optimize implementation.

The increased vulnerability to mental health problems in adolescence is frequently reported but poorly understood, hampered by a rigid diagnostic system which fails to capture intertwining symptoms and only loosely aligns with biological axes of variability. Here, we reconceptualised the mental health symptoms of young adolescents in the ABCD cohort (N=11862) as a latent topology of overlapping symptom dimensions, using an unsupervised machine learning algorithm to establish how transdiagnostic dimensions co-occur and overlap within individuals. Combining this with a novel classification approach, we delineated zones within this landscape, within which specific profiles of symptoms were robustly represented. These data-driven profiles were leveraged to establish associated resting-state functional connectivity and genetic characteristics. In doing so we recaptured the commonly reported p-factor axis as well as further symptom-subtype dimensions. Gene ontology analysis revealed that shared neurobiological and cellular mechanisms embedded in both the genome and transcriptome may confer risk for psychopathology.

Neurodegenerative and psychiatric brain disorders show substantial heterogeneity in clinical and neuropathological manifestations, possibly reflecting both distinct and shared pathogenic mechanisms. Our understanding of brain disorder heterogeneity is limited by the scarcity of deeply phenotyped, multi-modal post-mortem brain bank cohorts. Here, we integrated genotype, clinical and neuropathological data from 2,553 Netherlands Brain Bank donors to investigate how genetic risk contributes to disease manifestations. Disease-specific polygenic risk scores revealed extensive cross-disorder enrichment of genetic risk, suggesting shared pathogenic mechanisms beyond diagnostic boundaries. Donors with frontotemporal lobar degeneration carrying C9orf72 repeat expansions showed elevated polygenic risk for multiple disorders, indicating that genetic variation may modify monogenic disease expression. Neuropsychiatric symptoms were associated with distinct personality-trait polygenic risk profiles across disorders, highlighting diagnosis-dependent genetic contributions to clinical heterogeneity. Together, our findings demonstrate the value of this unique resource and reveal a dynamic interplay of genetic risk across brain disorders.

INTRODUCTION Psychosis affects 40% of individuals with Alzheimer's disease (AD) and is associated with accelerated cognitive decline. Blood-based biomarkers, particularly plasma phosphorylated tau (ptau), have demonstrated utility in predicting cognitive decline in AD, with ptau217 showing superior performance in many studies. However, whether these biomarkers predict differential cognitive trajectories in AD with psychosis (ADP) remains unknown. METHODS Two independent cohorts were analyzed: Alzheimer's Disease Neuroimaging Initiative (ADNI; n=659: 172 cognitively unimpaired [CU], 406 AD, 81 ADP) and Litwin-Zucker Research Center (LZ; n=142: 68 CU, 57 AD, 17 ADP) with 6-year follow-up. Psychosis was defined by non-zero Neuropsychiatric Inventory delusions or hallucinations scores. In ADNI, plasma ptau181, ptau217, ptau231, amyloid-{beta}42/40, GFAP, and NfL were quantified using NULISA. In LZ, ptau181, ptau205, ptau212, ptau217, amyloid-{beta}42/40, GFAP, and NfL were quantified using Simoa. Linear mixed-effects models assessed prediction of cognitive decline across memory, language, visuospatial, and executive function domains. RESULTS In ADNI, baseline ptau181 predicted differential ADP decline in language (p<0.05), visuospatial (p<0.05), and executive function (p<0.05); ptau217 predicted language (p<0.05) and visuospatial (p<0.05) decline; GFAP predicted language (p<0.05) and visuospatial (p<0.05) decline; and NfL visuospatial decline (p=0.01). In LZ, ptau181 predicted decline in memory (p<0.05), language (p<0.0001), visuospatial (p<0.05), and executive function (p<0.05); ptau217 predicted memory (p<0.05) and visuospatial (p<0.05) decline; and GFAP predicted language decline (p<0.05). Johnson-Neyman analyses revealed ADP-AD divergence at low ptau181 thresholds in ADNI, while LZ showed crossover patterns with steeper ADP decline at low biomarker levels that attenuated at high levels where AD decline was steeper. DISCUSSION ADP exhibited accelerated cognitive decline across domains driven by a distinct biomarker landscape compared to non-psychotic AD. Plasma ptau181 demonstrated broader domain-specific associations with decline in ADP than other blood-based biomarkers and associated exclusively with executive function impairment, indicating its unique utility for predicting cognitive trajectories in this pathophysiological subtype.

Background. Psychosis spectrum symptoms (PSS) are prevalent in youth and are associated with increased risk for psychotic disorder, suicidality, and functional impairment. Computationally, PSS may stem from altered predictive coding of basic sensory surprises and environmental volatility. Formalized as hierarchical precision-weighted prediction errors (pwPEs), this altered processing is a proposed mechanistic substrate of aberrant perceptual inference across disorders, including psychosis-risk populations. While the auditory mismatch negativity (MMN) provides an electrophysiological index of pwPEs, it remains unknown if distinct hierarchical pwPE components distinguish youth who endorse PSS. Methods. A sample of 131 participants (PSS-=66, PSS+=65; ages 11-24) from the ongoing Toronto Adolescent and Youth (TAY-CAMH) Cohort study were stratified by PSS status using the PRIME Screen-Revised and were assessed for their psychosocial functioning. 64-channel EEG was recorded during an auditory oddball paradigm with stable and volatile phases. A hierarchical Bayesian model applied to the stimulus stream generated trajectories of low-level sensory and high-level volatility-related pwPEs. Alongside standard phase-averaged event-related potentials (ERPs), Bayesian trajectories derived model-based ERPs. Results. Replicating prior findings in non-clinical controls, stable-phase MMN significantly exceeds volatile-phase MMN and lower psychosocial functioning was associated with reduced volatile-phase MMN amplitude. Age significantly modulated oddball MMN and unweighted prediction errors ({delta}1, {delta}2). Group differences between PSS+ and PSS- were statistically significant for volatility-level pwPE ({epsilon}3), peaking at ~180 ms Peri-Stimulus Time (pFWE-peak =.024). Conclusions. Independent of age-related developmental effects, volatility-level pwPE learning ({epsilon}3) constitutes a more sensitive EEG marker associated with PSS status in help-seeking youth than low-level sensory pwPE.

Schizophrenia (SCZ) and bipolar disorder (BIP) share substantial common-variant liability but differ in cognition, medical comorbidity, and treatment response. Here we decomposed this overlap into schizophrenia-predominant, bipolar-predominant, and shared psychosis dimensions to test whether these components show distinct pleiotropic and biological profiles. Using the largest available SCZ and BIP GWAS, we applied bidirectional mtCOJO and Genomic SEM to derive SCZcondBIP, BIPcondSCZ, and PSY-shared and validated them using inter-component genetic correlations, FinnGen psychiatric endpoints, and Genomic SEM latent factors. We then characterized each component across cognitive, cardiometabolic, and immune traits, followed by genomic risk-locus discovery, pathway analysis, developmental expression profiling, and drug-target enrichment. The three components showed marked divergence. SCZcondBIP was negatively genetically correlated with cognition, education, metabolic syndrome, C-reactive protein, and neutrophil percentage, whereas BIPcondSCZ showed the opposite cognitive profile and shifted toward positive cardiometabolic and immune correlations. PSY-shared retained the mixed cognitive pattern seen at the disorder level and intermediate peripheral correlations, indicating that shared psychosis liability masks stronger disorder-specific differences. Between-component contrasts were approximately twice the magnitude of the corresponding SCZ-versus-BIP contrasts. We identified 248 consensus genomic risk loci, including 81 not detected in the input disorder GWAS. Biologically, PSY-shared was enriched for synaptic signalling, ion-channel, and neurodevelopmental pathways; SCZcondBIP primarily implicated synaptic-signalling and cellular-homeostasis pathways; and BIPcondSCZ showed weaker but distinct enrichment for synaptic-vesicular biology. Drug-target enrichment further separated the components, with strong antipsychotic enrichment for PSY-shared and distinct non-antipsychotic signals for the conditional factors. These findings show that SCZ and BIP genetic risk is best understood as biologically distinguishable shared and disorder-predominant dimensions that differentially map onto cognitive, cardiometabolic, immune, and molecular architecture. These findings provide a framework for evaluating whether component-specific polygenic scores improve stratification of cognitive, cardiometabolic, and inflammatory heterogeneity across severe psychiatric illness.