Schizophrenia

Schizophrenia is associated with cognitive impairments, including deficits in implicit learning. Probabilistic serial reaction time tasks (SRTT) offer an objective approach to characterizing these deficits through both motor execution (ME) and motor imagery (MI), the mental simulation of movement without physical action. Whether implicit probabilistic sequence learning is impaired across both modalities in schizophrenia remains poorly understood. Thirty individuals with schizophrenia (ME: n=12; MI: n=13) and 40 healthy controls (ME: n=20; MI: n=20) completed an auditory probabilistic SRTT. Symptom severity was assessed with the PANSS and cognitive functioning with the MCCB. Healthy controls demonstrated a robust signature of implicit probabilistic sequence learning, whereas participants with schizophrenia exhibited weaker and less consistent learning signatures, particularly during motor imagery. Sensitivity to probabilistic structure differed significantly between groups during motor execution but not motor imagery. Participants with schizophrenia also showed significantly longer reaction times than controls across both modalities, consistent with generalized psychomotor slowing. Greater PANSS-General severity was associated with greater deviation from the probabilistic learning patterns observed in healthy controls during ME, whereas higher MCCB verbal learning scores were associated with greater similarity to these learning patterns during MI. These findings indicate that implicit probabilistic sequence learning is impaired in schizophrenia across both motor execution and motor imagery, and that these deficits are meaningfully associated with clinical symptom severity and cognitive functioning.

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

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

Resting-state electroencephalography (EEG) studies consistently report increased slow-wave activity and reduced alpha power in schizophrenia. However, the regional distribution of EEG relative power (RP) across finer frequency bands, particularly in adolescent schizophrenia, remains poorly characterized. We analyzed a publicly available resting-state EEG dataset comprising 45 male adolescents with schizophrenia (SCZ; mean age: 12.3 {+/-} 1.2 years) and 39 age- and sex-matched healthy controls (CON; mean age: 12.3 {+/-} 1.3years), recorded using 16 scalp electrodes (10-20 system). Following band-pass filtering, artefact subspace reconstruction, spline interpolation, average re-referencing and independent component analysis, RP was computed for nine frequency bands across five cortical regions (frontal, central, parietal, temporal, and occipital). Group differences were assessed using three-way repeated-measures analysis of variance (ANOVA), followed by Bonferroni false discovery rate (FDR)-corrected post-hoc pairwise comparisons using estimated marginal means (emmeans). Compared to controls, the SCZ group showed significantly higher delta RP in the frontal, parietal, and occipital regions and significantly lower alpha2 RP, dissociable from alpha1, in the central, parietal, temporal, and occipital regions (all FDR < 0.001). The occipital cortex showed the greatest increase in delta RP (|Cohens d|=0.82) and the greatest reduction in alpha2 RP (|d|=0.73), with moderate-to-large effect sizes. Topographic maps demonstrated widespread delta enhancement and attenuated occipito-parietal alpha2 activity in the SCZ group. These findings demonstrate region-specific alterations in resting-state EEG relative power in adolescent schizophrenia, particularly within the occipital cortex, and suggest that regional RP may serve as a promising neurophysiological feature for early-onset schizophrenia. HighlightsO_LIResting-state EEG relative power was analyzed in adolescent schizophrenia. C_LIO_LIDelta relative power increased (occipital > parietal > frontal). C_LIO_LIAlpha2, but not alpha1, relative power decreased (occipital > parietal > temporal > central). C_LIO_LIOccipital cortex showed the largest EEG spectral abnormalities. C_LIO_LINine-band analysis improved regional spectral characterization. C_LI

Background and aims Over the past decade, there has been increased lifetime use of illicit and medical cannabis, underscoring the need to examine public health impacts. This included reports of increased cannabis-related psychosis. In this context, this study aimed to determine all cannabis-related hospital admissions over a ten-year period to characterise poisonings and mental health harms. Design This is a retrospective observational study of the Victorian Admitted Episodes Dataset (VAED) records, which include all hospital admissions in the state of Victoria and are supplied by the Victorian Department of Health. Setting Victoria, Australia. Cases Hospital records for poisoning (T40.7) and mental health and behavioural disorders (F12.0-F12.9) related to use of cannabis for the period July 2013 to June 2023 were selected for this study. Measurements Crude and age-standardised rates per 100,000 population are reported. Net percentage change over the ten-year study window was estimated using generalised linear models with a log-population offset, contrasting the final year (2022/23) against the baseline (2013/14). Findings There were 39,565 cannabis-related admissions in the ten-year period, including 7263 admissions where cannabis-related harm was the principal diagnosis. The age-standardised rate per 100,000 population was 63.6 for all cannabis-related harm admissions and 11.7 for principal diagnosis cases. The peak annual rate for all cannabis-related harm was observed in 2020/2021 (77.3 per 100,000). There was a 49.0% (p<0.0001) increase in the rate of hospital admissions with a cannabis-related principal diagnosis, when comparing 2022/23 with baseline (2013/14). Overall, harmful use (F12.1) was the most common cannabis-related mental and behavioural disorder. Psychotic disorder (F12.5) was the most common diagnosis (45.3%; n=2,909/6,421). When considering principal diagnosis only, the rates were highest in males aged 15-24 years (36.9/100,000), followed by females in the same age group (20.5/100,000). While most principal diagnosis cases involved males, a greater percentage change between the first and last years of the study period was observed for females (+75.1%, p<0.0001) compared with males (+34.6%, p=0.003). Conclusions A 49% increase in the rate of cannabis-related hospital admissions, particularly mental and behavioural disorders due to use of cannabinoids, has occurred over the past decade. These trends are non-linear, rates in the last two years were lower than the peak in 2020/2021.

Smooth pursuit eye movement abnormalities are well established in psychosis, but the specific components of pursuit performance that vary across clinical states and symptom profiles remain insufficiently characterized. Here, we used a rapid smooth pursuit paradigm combining standard linear tracking (repeated short trials moving in different directions) with structured target occlusion to examine oculomotor performance in individuals with acute psychosis, chronic psychosis, and healthy controls. The occlusion condition allowed assessment of tracking when the target was temporarily hidden and gaze had to be maintained along the expected trajectory. Basic oculomotor measures, including full pursuit gain and initial catch-up saccade properties, were largely preserved in patients. In contrast, more specific trajectory-based measures revealed distinct abnormalities. Saccade-free smooth tracking gain was selectively reduced in acute psychosis, whereas tracking deviation during non-occluded pursuit was altered in both patient groups, reflecting reduced forward tracking relative to controls. During structured occlusion, patients showed reduced forward gaze progression along the expected trajectory, with a graded pattern across groups: controls showed the strongest predictive lead, chronic patients an intermediate response, and acute patients the weakest predictive lead. Tracking deviation and occlusion-related deviation were both associated with positive symptom severity, whereas smooth tracking gain was not. These findings suggest that smooth pursuit abnormalities in psychosis are measure-specific rather than uniform, involving both broader psychosis-related alterations in gaze-target alignment and state-sensitive disruptions in occlusion-related tracking. Structured occlusion may therefore offer a useful extension of conventional smooth pursuit paradigms for probing prediction-related sensorimotor control in psychosis and distinguishing acute from chronic clinical states.

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

Apathy is a major driver of long-term disability in schizophrenia, yet existing accounts focus on how patients evaluate the cost of action while neglecting their capacity to generate options for action in the first place. Using a brief, culture-fair option generation task (OGT) within the international CHANSS study, we examined this process in 150 patients with different stages of schizophrenia and 100 healthy controls across the UK, Spain, and China. The OGT requires drawing as many different paths as possible between two points on a touchscreen, yielding two measures: fluency (the number of paths that they generate) and uniqueness (the distinctiveness of those paths). Patients drew as many paths as controls but produced options roughly half as unique. Across patients, uniqueness was positively associated with verbal fluency and depressive symptom severity, but negatively associated with self-reported behavioural apathy. This suggests that increased apathy is associated with reduced ability to generate unique options (paths on the task). To examine the cognitive mechanisms further, we decomposed uniqueness into four exploratory measures: roaming entropy (spatial breadth of exploration), option elaboration (within-path development), perseveration (local repetition), and novelty maintenance (sustaining diversity over time). Together these explained 69% of the variance in motor-residualised uniqueness and attenuated the group difference by 80%, with roaming entropy as the dominant contributor. K-means clustering revealed three distinct patient profiles, each with a different clinical signature. A small subgroup generated options much like the control group. A larger subgroup retained spatial exploration but developed each option less and reported more depressive symptoms. A third subgroup showed broadly impaired exploration and the lowest verbal fluency. These findings establish option generation as a measurable behavioural phenotype in schizophrenia that relates to apathy and can be decomposed into specific cognitive mechanisms. They suggest that an impoverished option space, marked by reduced generation of unique possibilities for action, may be a remediable component of apathy.

Psychosis has been linked to changes in diffusion-derived fractional anisotropy (FA) across multiple white matter tracts, yet most evidence is cross-sectional. Longitudinal findings are inconsistent, and the clinical relevance of white matter changes over time remains unclear. To address this gap, we investigated longitudinal white matter changes and their clinical correlates in early psychosis in a large, multi-centre diffusion-tensor imaging study. Across 18 sites, 407 participants (healthy controls: n = 97, 59.8% men, mean age 23.9{+/-}4.6 years; clinical high risk of psychosis: n = 158, 53.5% men, mean age 23.0{+/-}4.9 years; first-episode psychosis: n = 152, 71.1% men, mean age 25.1{+/-}5.4 years) were scanned at up to three time points over 12 months. FA was assessed in the cingulum bundle, the superior longitudinal fasciculus, the inferior fronto-occipital fasciculus, and at the whole brain level. FA trajectories were analysed using linear mixed-effects models to test effects of group, social and occupational functioning, and (attenuated) psychotic symptoms, while controlling for demographic and socioeconomic covariates. No significant group differences in FA were observed, either globally or within tracts (p > .05). Longitudinal FA trajectories were not associated with changes in (attenuated) psychotic symptoms or functioning (p > .05). In contrast, higher baseline antipsychotic medication dose was significantly associated with lower FA (pcorr < .05). Overall, these findings indicate that white matter microstructure is relatively stable during the clinical-high-risk and first-episode phases of psychosis, and that it is not clearly associated with variation in symptom severity or functional outcomes over time.

Individuals with schizophrenia show well-documented impairment in metaphor comprehension, often exhibiting a bias toward concrete, literal interpretations. While this tendency has traditionally been linked to psychopathological and cognitive factors, the contribution of perceptual processes remains underexplored. Here, we tested the hypothesis that figurative language impairment reflects altered perceptual processing, whereby the visual representations evoked by metaphors remain abnormally active and hinder abstraction. A sample of 143 individuals with schizophrenia and healthy controls was administered a novel paradigm where metaphors (e.g., Wisdom is a flashlight) served as primes for target words related to the metaphor vehicle based on visual (e.g., microphone), action (e.g., remote), or semantic features (e.g., lamp). While in healthy participants metaphors activated semantically associated words, individuals with schizophrenia showed sustained visual priming, emerging 1000 ms after metaphor presentation and persisting up to 1400 ms, with both groups showing reverse priming for action targets. Critically, greater visual priming predicted lower metaphor comprehension in patients, whereas greater semantic priming was correlated with better metaphor skills in controls. These results suggest that visual-perceptual representations are not only overactivated in patients compared to controls during metaphor processing but may also interfere with figurative comprehension. We argue that concretism arises from an imbalance between bottom-up sensory signals and top-down contextual priors, leading to the persistence of the visual representations and impaired abstraction. More broadly, these results support multimodal and predictive accounts of metaphor processing and point to altered perceptual dynamics as a previously unappreciated mechanism contributing to pragmatic impairment in schizophrenia.

Background & Hypothesis: Dysfunctional predictive processing (PP), specifically the aberrant weighting of priors, is a frequently-proposed mechanism for psychosis and psychosis-like phenomena (schizotypy). Evidence for this theory mostly originates from single-task studies, which assume that all tasks load onto a single latent construct of PP performance, but the underlying factor structure of PP tasks is unknown. PP deficits in psychosis may be better described by a two-factor, hierarchical model: weakened lower-level (perceptual) priors compensated by higher-level (cognitive) priors. Study Design: This study implements a multi-paradigm approach in healthy participants to investigate latent constructs underlying PP and their relationship to schizotypy. Participants (N = 73) completed 6 tasks measuring reliance on priors across language, memory, visual, and auditory domains. A factor analysis investigated whether performance across tasks is captured by a single or two-factor model. Study Results: Although a two-factor model best described performance, factors reflected within-task correlations rather than a PP hierarchy. Cross-task PP measures were poorly correlated, suggesting that individuals' weighting of priors was task-specific. A full model including all task outcomes (not factors) significantly predicted the severity of schizotypal aberrant beliefs but no other schizotypal measures. Conclusions: These results do not evidence a single factor underpinning PP performance. It is therefore inappropriate to use results from single tasks to propose a generalised PP deficit in psychosis. Variation was also not captured by a two-factor hierarchical model of priors. Further multi-paradigm research is required to evaluate alternative models or additional variables that describe aberrant PP in psychosis.

Introduction: Auditory verbal hallucinations (AVH) affect 60-80% of people with schizophrenia, yet existing assessment tools inadequately capture their phenomenological complexity. Aim: To determine whether individuals with schizophrenia spectrum disorders could accurately match auditory verbal hallucination loudness to an external audio track, and to explore participant perspectives on this approach. Methods: Eight participants with schizophrenia spectrum disorders and active AVHs rated loudness via a Likert scale and by adjusting a headphone audio track to match their experience. Structured interviews and thematic analysis captured participant viewpoints. Results: No significant correlation was found between audio tool and Likert scale scores. Seven of eight participants reported the audio tool provided greater precision in quantifying AVH loudness and better enabled them to convey their internal experience to others. Discussion: Audio-matching tools may offer meaningful advantages over traditional scales for quantifying AVH loudness, even where statistical convergence with existing measures is absent. Limitations: Small sample size; loudness alone cannot fully capture the qualitative experience of hearing voices. Implications: This tool shows promise for longitudinal tracking of AVH loudness. Recommendations: Further investigation of digital approaches to AVH assessment is warranted.

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

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

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

Background and Hypothesis. Motor alterations represent an important component of psychotic disorders. Chronic cannabis use, a key risk factor for psychosis, is also associated with sensorimotor dysfunctions. Yet, the hypothesis of a common sensorimotor disturbance remains underinvestigated. Study Design. In this study, we examined submovements, elementary units of motor output, to search for common subclinical impairments in these populations. Patients with psychosis (n = 17), heavy cannabis users (n = 21), and healthy controls (n = 17) performed a continuous visuomotor synchronization task, consisting in tracking a dot moving on a screen with a finger. Study Results. Individuals with psychosis and cannabis users exhibited less frequent and more variable submovements compared with healthy controls. Furthermore, when interacting with a pre-recorded human kinematic profile, both groups exhibited attenuated responses to the observed submovements. This alteration was found to be more pronounced in patients with psychosis. Conclusions. These findings suggest that submovement analysis may reveal subtle, shared alterations in sensorimotor integration in psychosis and chronic cannabis use, providing an objective window onto motor dysfunction not readily captured by current clinical tools.

Background: Adverse drug withdrawal events (ADWEs) are a key safety concern during deprescribing but remain poorly explored in pharmacovigilance systems. Objectives: To identify and compare ADWE signals across drug classes, different drugs within drug classes, and across patient characteristics, countries, and over time. Methods: A case/non-case disproportionality analysis was conducted in FDA-FAERS and EMA-EudraVigilance pharmacovigilance databases, with stratification by age (adults: 18-64, older adults: [&ge;]65), sex (male/female), reporting time (2004-2023 in 5-year intervals), and country (for EMA data). Disproportionality analysis (quantitative signal detection) was used to detect signals between ADWEs and drugs using the proportional reporting rate (PRR[&ge;]2), reporting odds ratio (ROR>1), and information component (IC>0) with case count [&ge;]5. Results: Overall, 158,501 reports (FDA-FAERS 145,514; EMA-EudraVigilance 12,987) included drug-event pairs related to ADWEs. In FDA-FAERS, clobetasone (IC=5.58; PRR=79.18; ROR=176.90) showed the strongest ADWE signals, followed by hydromorphone (4.85; 29.94; 37.37), hydrocodone, and paroxetine. In EMA-EudraVigilance, ethyl loflazepate (IC=6.01; PRR=119.80; ROR=197.53), clobetasone (5.39; 102.73; 155.10), veralipride, and levomethadone had the strongest signals. Most drugs maintained positive ADWE signals in analysis stratified into adults and older adults. However, among the top 10 drugs (based on highest IC values), buprenorphine/naloxone, desvenlafaxine, and baclofen in FDA-FAERS (ICs 4.95-6.05) showed stronger signals in older adults. A sex-based difference was observed, with paroxetine, venlafaxine, and buprenorphine/naloxone showing a stronger positive signal in females in both databases, whereas several opioids had stronger signals in males versus females across both databases. Conclusion: This study suggests ADWE signals for some medications differ by age and sex, potentially indicating different risks for withdrawal effects.

Background: Psychotic illnesses are among the most debilitating classes of psychiatric disorders, requiring targeted and effective treatment strategies. Although antipsychotics are the primary pharmacological therapy for psychosis, their full range of effects remain unclear, including effects within the frontal cortex, a brain region linked structurally and functionally to psychotic disorders. Methods: To examine the effects of antipsychotic treatment on the frontal cortex, we conducted a meta-analysis of publicly available rodent (rat, mice) transcriptional profiling datasets (microarray, RNA-Seq). Five datasets (GSE45229, GSE93918, GSE2547, GSE4031.1, GSE66275) were identified within the Gemma database using pre-specified search terms and inclusion/exclusion criteria (date: 7/7/2024), yielding differential expression results for eight drug vs. control comparisons (collective n=68). A random-effects meta-analysis model was fit to the log2 fold changes for each gene, and p-values adjusted for false discovery rate (FDR), with follow-up analyses exploring robustness, heterogeneity, and publication bias. To increase the power and generalizability of our findings, an exploratory meta-analysis was also run incorporating antipsychotic effects from both rodents and nonhuman primates (collective n=101), and compared to findings from individuals with schizophrenia. Results: Our meta-analysis yielded stable estimates for 12,190 genes, identifying 63 genes that were differentially expressed following antipsychotic treatment ("DEGs", FDR<0.05). Differential expression included genes important for serotonergic and cholinergic signalling, and was enriched within pathways linked to oligodendrocyte development and myelination, physiological and cellular stress responses, and cardiovascular function. An exploratory meta-analysis combining rodent and nonhuman primate results confirmed these observations and yielded additional findings (117 DEGs total). Comparisons with human post-mortem findings suggested that some schizophrenia-related gene expression may instead reflect antipsychotic treatment. Conclusion: Further validation is necessary, but our findings suggest that antipsychotics may assist in the regulation of specific structural and functional changes within the frontal cortex linked to psychotic disorders.

Background Large Language Models (LLMs) are increasingly explored for pharmacovigilance tasks, including information extraction, case documentation, and single-case causality assessment. However, their ability to support causality assessment at the case series level -- a complex, time-intensive task requiring clinical reasoning across multiple reports -- remains unexplored. Objective To investigate how a large-scale general-purpose LLM can support pharmacovigilance professionals in assessing causality in a case series, and to explore how prompt design influences the quality of the model's reasoning. Methods GPT-4o was used to assess causality for five drug - adverse event combinations, using an adaptation of the Bradford Hill viewpoints for case series assessment. The combinations represented varying drugs and vaccines, adverse events, and case series sizes (5-402 reports). One combination served as a negative control. Structured prompts were iteratively developed and refined using one combination, then applied to all combinations. LLM-generated assessments for each viewpoint were qualitatively evaluated by human annotators for accuracy (precision), and the LLM's coverage of key aspects from the original signal text was assessed for one combination (recall). Results Across all five combinations, annotators agreed with 79-92% of the LLM's output sentences. Full disagreement was consistently low (3-7%), with errors typically involving misinterpretation of complex report details rather than outright fabrication. Prompt design substantially influenced output quality; providing Bradford Hill viewpoint descriptions, including case series data, and adding explicit anti-hallucination instructions improved specificity and grounding. For the recall assessment, 15 of 23 key segments from the original signal text were reflected in the LLM output. The overall summary assessments demonstrated balanced reasoning, correctly distinguishing between positive safety signals and the negative control, and provided a coherent synthesis suitable as a starting point for human assessors. Conclusions LLMs have the potential to generate contextually nuanced and largely accurate preliminary causality assessments of case series aligned with the Bradford Hill viewpoints, with a low but non-zero hallucination rate. These findings support LLMs as a tool to augment, not replace, expert judgment in signal assessment. Future work should address larger and more diverse signal sets, improved evaluation frameworks for generative output, and the integration of pre-computed summary statistics to reduce errors.

Large genome-wide association studies (GWASs) have generated extensive summary-statistics resources across psychiatric disorders, ancestries, and sex strata. These resources create an opportunity to compare genetic architectures across related datasets, but practical tools for identifying both shared and divergent association signals remain limited. We developed an AI-assisted workflow for local comparative analysis and visualization of multiple psychiatric GWAS summary-statistics datasets. The workflow harmonizes input GWASs, computes pairwise differential association statistics, prioritizes shared loci with concordant evidence across paired datasets, and renders genome-wide and locus-level visualizations with nearby gene context. To improve accessibility and reproducibility, the same analytical workflow can be executed either directly from the command line or through AI-assisted natural-language workflows, while detailed implementation steps remain transparent and locally controlled. We demonstrate the workflow using sex- and ancestry-stratified Psychiatric Genomics Consortium schizophrenia GWAS summary statistics. In the demonstration analysis, the differential workflow highlighted a novel candidate sex-divergent locus at rs185665940 showing protective effect to European females in an intergenic region close to CYP26B1 and EXOC6B, with another independent SNP rs10166057 close to rs185665940 (a risk SNP to schizophrenia and also an brain eQTL of CYP26B1) showing female-specific risk association with schizophrenia in both European and Asian female but not male populations. These results show that the workflow can recover biologically credible shared association signals while also identifying candidate subgroup-differential loci for downstream investigation. The pipeline provides a practical bridge between comparative GWAS analysis, publication-style visualization, and AI-assisted reproducible execution under local user control.