In medical data, measures of central tendency (e.g., mean) and temporal variability (e.g., standard deviation, SD) are widely used as biomarkers to quantify physiological states and disease dynamics. However, both metrics are sensitive to skewed distributions, which can obscure their true predictive value and thereby reduce interpretability. This study investigates whether temporal variability in actigraphy data carries outcome-relevant information about clinical states in bipolar disorder (BD), or whether its apparent predictive capacity is confounded by mean activity levels. Actigraphy recordings from 326 individuals with BD were analyzed, and a subset of 34 participants who experienced both manic and remission periods was used for statistical modeling. Daily features were power-transformed (Box-Cox, Yeo-Johnson) and aggregated weekly by mean (μ7) and SD (σ7). Mixed-effects logistic regression models were fitted to distinguish manic weeks from remission. Power transformations reduced μ7-σ7 correlations, and variability remained a retained outcome-relevant predictive information for most features; however, for some features (27%), the effects were partially explained by mean-variance coupling.

Abstract Symptom severity in schizophrenia has been repeatedly associated with thinner cortical gray matter. While global and regional white matter microstructure alterations in schizophrenia are well-documented, their association with clinical symptom severity remains unclear. As this is likely due to methodological heterogeneity across studies, we tested whether symptom severity in schizophrenia was associated with regional and global white matter alterations using standardized methods. We hypothesized that positive symptom severity would be associated with temporal white matter changes and that negative symptom severity would be associated with alterations in frontal white matter. Using a standardized fractional anisotropy (FA) analysis pipeline developed by the ENIGMA consortium, we conducted a meta-analysis of the association between white matter microstructure and symptom severity in n = 1025 (ages 16–68 years; 369 women/656 men) across 19 ENIGMA sites. Where significant heterogeneity was detected across sites, we examined whether variation in association strength between white matter microstructure and symptom severity was explained by duration of illness and/or current antipsychotic use. Positive symptom severity was significantly associated with white matter microstructure as measured using temporal lobe FA and global FA. Negative symptom severity showed no significant association with white matter microstructure as measured using frontal lobe FA or global FA. Significant heterogeneity across sites was observed for the negative symptom analysis, explained partly by duration of illness. Post-hoc exploratory analyses identified one site as disproportionately contributing to this heterogeneity, and when removed, negative symptom severity was significantly associated with both global and frontal FA. These findings support the view of schizophrenia as a disorder of brain connectivity, in a manner relevant to understanding variation in clinical symptom severity.

Schizophrenia is often conceptualized as a brain network disorder, yet the organizational principles and heterogeneity underlying widespread cortical abnormalities remain poorly understood. Leveraging multisite MRI data from 3,958 individuals diagnosed with schizophrenia and 5,489 neurotypical individuals, we studied the cortical organization and its subtyping by analyzing individualized cortical network similarity. We used eigenvector decompositions to study spatial patterning of the gradients and graph theory to study small-world topology. Individuals with schizophrenia showed widespread alterations of gradient loadings, which followed inferior-superior and frontal-temporal axes. Alterations in small-world topology were localized in key network hubs, including the insula and anterior cingulate cortex. Brain-symptom association analyses identified a latent dimension linking disorganization symptoms to topological alterations. Finally, clustering cortical alterations identified two robust subtypes, characterized by divergent anterior cingulate (S1) versus temporoparietal (S2) thickness differences aligned with the intrinsic gradient-topology patterns. Both subtypes were present early in the illness and stable across disease stages and age groups. These findings reveal systematic disruptions of cortical organization in schizophrenia, providing a network-level framework for macroscale brain organization and inter-individual heterogeneity.

Identifying robust neuroimaging markers associated with schizophrenia is essential for advancing research and informing clinical understanding. However, a major obstacle to clinical translation is the limited ability of neuroimaging-based classification models to generalise across scanning sites. In this study, we first establish best performing within-site models, and then systematically investigate cross-site generalisation in first-episode schizophrenia (FES) classification and evaluate strategies for mitigating site-related distribution shifts. Using data from two acquisition sites (n=389 in total), we perform train-on-site/test-on-site experiments to analyze performance degradation under domain shift and examine the effectiveness of ComBat, optimal transport, and adversarial adaptation strategies. Across functional, structural, and diffusion-based features, both traditional machine learning and neural network models achieve comparable performance in within-site classification, with resting state fMRI functional connectivity providing the most robust unimodal features. When models are transferred across sites, performance degrades substantially across all approaches, highlighting the impact of site-related variability. Distribution-alignment methods partially mitigate this degradation, with ComBat and optimal transport yielding more consistent cross-site improvements than adversarial adaptation. Increasing model complexity alone does not result in systematic performance gains, and simple models combined with effective alignment strategies often perform comparably to more complex neural architectures, while multimodal feature fusion does not consistently outperform functional connectivity alone. Overall, our findings indicate that controlling for site effects is more critical than model complexity for achieving generalisable classification in FES, underscoring the importance of rigorous evaluation designs and explicit distribution-alignment strategies for neuroimaging-based predictive models with potential clinical utility. ### Competing Interest Statement The authors have declared no competing interest. Support Program for Promising Human Resources Czech Academy of Sciences, AV21-VP34/2025, Czech Health Research Council, NU21-08-00432 European Regional Development Fund, CZ.02.01.01/00/22_008/0004643, CZ.02.01.01/00/23_020/0008560

Introduction The course of psychotic disorders typically involves relapses. Early warning signs vary between individuals and are difficult to detect in clinical practice, especially in outpatient settings. Speech provides a quantitative clinical marker for detecting such early warning signs. The EU Horizon project TRUSTING (A TRUSTworthy speech-based AI monitoring system for the prediction of relapse in individuals with schizophrenia) aims to develop and evaluate a speech-based monitoring system for predicting imminent psychotic relapses. The study will examine the potential for prospective relapse prediction, and feasibility and usability of the monitoring system.Methods and analysis In this multicentre observational study, n=240 remitted and at-risk-of-relapse adults with psychotic disorders and a comparison group with n=120 healthy participants (matched by age and sex) will be examined at six sites and in six different languages (German, French, Dutch, English, Czech and Turkish). The follow-up period is 6 months. The TRUSTING smartphone app will be used to collect weekly voice recordings through speech tasks; information on medication adherence, substance use, mood, anxiety and sleep quality; and motor data from a tapping task. Primary endpoints encompass model performance for relapse prediction, user adherence, transcription quality, usability of recordings and overall system usability. The primary analysis of user adherence, transcription quality, usability of recordings and overall system usability will be an unadjusted description of the respective proportions using 95% Wilson confidence intervals. Regarding relapse prediction, the predictive value of the risk estimates for relapse occurrence will be assessed using the area under the receiver operating characteristic curve. Exploratory analysis will be performed on potential speech-based markers associated with relapse risk.Ethics and dissemination This study has been approved by swissethics (Business Administration System for Ethics Committees number: 2025–01177). Findings from this project will be disseminated through peer-reviewed journal publications and presentations at relevant scientific conferences, as well as at public events related to mental health.Trial Registration ClinicalTrials.gov ID: NCT07397975.

BACKGROUND AND HYPOTHESIS:Cognitive impairment is a key contributor to disability and poor outcomes in schizophrenia, yet it is not adequately addressed by currently available treatments. Thus, it is important to search for preventable or treatable risk factors for cognitive impairment. Here, we hypothesized that obesity-related neurostructural alterations will be associated with worse cognitive outcomes in people with first episode of psychosis (FEP). STUDY DESIGN:This observational study presents cross-sectional data from the Early-Stage Schizophrenia Outcome project. We acquired T1-weighted 3D MRI scans in 440 participants with FEP at the time of the first hospitalization and in 257 controls. Metabolic assessments included body mass index (BMI), waist-to-hip ratio (WHR), serum concentrations of triglycerides, cholesterol, glucose, insulin, and hs-CRP. We chose machine learning-derived brain age gap estimate (BrainAGE) as our measure of neurostructural changes and assessed attention, working memory and verbal learning using Digit Span and the Auditory Verbal Learning Test. STUDY RESULTS:Among obesity/metabolic markers, only WHR significantly predicted both, higher BrainAGE (t(281)=2.53, p=0.012) and worse verbal learning (t(290) = -2.51, P = .026). The association between FEP and verbal learning was partially mediated by BrainAGE (average causal mediated effects, ACME = -0.04 [-0.10, -0.01], P = .022) and the higher BrainAGE in FEP was partially mediated by higher WHR (ACME = 0.08 [0.02, 0.15], P = .006). CONCLUSIONS:Central obesity-related brain alterations were linked with worse cognitive performance already early in the course of psychosis. These structure-function links suggest that preventing or treating central obesity could target brain and cognitive impairments in FEP.

Abstract Resting-state functional MRI (rs-fMRI) studies in schizophrenia commonly rely on normalization to volumetric templates and fixed atlas parcellations derived from neurotypical populations. While these approaches enable group-level comparisons, they may obscure individual variation in cortical organization and intrinsic brain dynamics. In this study, we compared four preprocessing and parcellation strategies across two independent schizophrenia cohorts (MRI site 1, n=159; MRI site 2, n=255) to evaluate how analytic choices affect static functional connectivity and dynamic quasi-periodic pattern (QPP) measures, including default mode-dorsal attention network opposition, QPP component rank, explained variance, event rate, and associations with PANSS symptom severity. Across datasets, individualized surface-based parcellation (IndiPar) consistently detected more pronounced QPP dynamics, stronger default mode / dorsal attention network opposition, and greater explained variance of QPPs relative to atlas-based pipelines. IndiPar also produced larger and more reproducible patient-control differences in functional connectivity and QPP event-rate measures, suggesting improved sensitivity through preservation of subject-specific organization. IndiPar additionally detected a significantly increased QPP event rate and more symptom associations in patients in the larger dataset. However, associations between fMRI measures and symptom severity showed limited stability across cohorts. These findings extend previous reports of altered resting-state activity in schizophrenia, and demonstrate that preprocessing and parcellation choices substantially influence both static and dynamic rs-fMRI results. Individualized surface-based parcellation appears to better preserve subject-specific variability and improves detection of intrinsic brain dynamics. At the same time, the limited cross-dataset replication of symptom associations highlights the challenges of deriving stable brain-symptom relationships from heterogeneous psychiatric cohorts.

Introduction:Neuroinflammation is increasingly recognized as a core pathophysiological mechanism in schizophrenia and can be indirectly assessed through peripheral inflammatory markers. Therefore, this pilot study investigated the impact of antipsychotic treatment on inflammation in patients with first-episode psychosis (FEP) who were antipsychotic-naive at study entry. Methods:Thirty-three drug-naïve FEP patients provided blood samples upon admission (V0) and follow-up (V1), from which peripheral inflammatory markers-i.e., neutrophil/lymphocyte ratio (NLR), monocyte/lymphocyte ratio (MLR), platelet/lymphocyte ratio (PLR), and systemic immune-inflammation index (SII)-were calculated. Antipsychotic doses during continuous hospitalization between V0 and V1 (34 days, IQR: 21-49 days) were converted into cumulative chlorpromazine equivalents (cCPZ). Results:In multiple regression models adjusting for sex, age, BMI, DUP, and clozapine use, cumulative antipsychotic exposure significantly predicted reductions in ΔNLR (p = 0.048), ΔMLR (p = 0.041), ΔPLR (p = 0.028), and ΔSII (p = 0.028). All associations remained significant following false discovery rate adjustment (pFDR = 0.048 for all outcomes). Conclusion:These findings suggest a consistent dose-dependent anti-inflammatory effect during early antipsychotic treatment in FEP. Given the exploratory nature of this study, larger studies are needed to confirm these findings.

BACKGROUND:Predicting long-term outcomes in first-episode schizophrenia (FES) remains difficult, despite being especially important early in the illness, when timely intervention is most critical. It also remains unclear how much data from the initial phase of illness is required to improve prognostic accuracy. METHODS:We analysed 68 FES patients assessed at baseline (V1; mean 0.5 years post-onset), one-year follow-up (V2; mean 1.2 years), and outcome (V3; mean 4.9 years). Elastic-net models were trained to predict three V3 outcomes - negative symptoms (PANSS Negative factor; Wallwork/Fortgang), global functioning (GAF), and quality of life (WHOQOL-BREF psychological domain) - using either V1 predictors alone (23 variables) or V1 + V2 combined (43 variables). Performance was evaluated using nested cross-validation on held-out data. RESULTS:With V1 + V2 predictors, all three outcomes were predicted at statistically significant levels: PANSS Negative R2 = 0.22 (driven by log(DUP), PANSS Negative at V1/V2, and PANSS Disorganised at V2); WHOQOL-BREF Psychological Health R2 = 0.22 (driven by WHOQOL Psychological Health and GAF at V2); and GAF R2 = 0.14 (driven by GAF, PANSS Positive, WHOQOL Psychological Health at V2, and hospitalisation burden). With V1 predictors alone, only PANSS Negative showed meaningful predictive power (R2 = 0.15); GAF and WHOQOL-BREF did not outperform the intercept-only baseline. CONCLUSION:Long-term functioning and quality of life in FES cannot be predicted from first-episode data alone; at least one year of follow-up is required, suggesting post-onset changes shape these outcomes. Negative symptoms are an exception: comparatively stable after initial treatment and predictable from baseline, with past symptomatology along with DUP selected as predictors - indicating stronger persistency and predictability than in the other two investigated outcomes.

Schizophrenia often features low-grade neuroinflammation. Because latent toxoplasmosis (LT) is more prevalent among individuals with schizophrenia, we tested whether LT yields a biomarker pattern resembling that reported in schizophrenia. We quantified 15 cytokines and 15 blood markers of brain injury in 65 LT-positive individuals and 103 matched LT-negative controls using multiplex immunoassays. Multivariate effects of infection, age, sex, and their interaction were assessed by MANCOVA and PERMANOVA. Effects on individual biomarkers were tested by partial Kendall correlation (controlling for age and sex). Differences in the internal correlation structure were evaluated with Mantel tests on dissimilarity matrices derived from partial correlations. LT was associated with higher KLK6, S100B, and TDP-43, and lower MIF; several other markers showed nonsignificant but sizable trends. Cytokines showed reduced IFN-γ, IL-1β, and MCP-1, and elevated IL-13 and IL-17 in the infected group. Sex-stratified analyses suggested stronger effects on brain-injury markers in women and on cytokines in men. Correlation structure also diverged: infected individuals exhibited more negative links between brain-injury markers and cytokines, whereas controls showed predominantly positive associations (Mantel r = 0.461, p = 0.043). The LT profile overlapped with schizophrenia in elevated KLK6 and S100B and, in men, reduced GDNF, but contrasted for MIF and for the overall cytokine pattern (no consistent IL-6/TNF-α elevation). LT entails neuroinflammatory and neuroimmune alterations that only partly recapitulate schizophrenia; the biomarker pattern and interrelationships differ, arguing against LT as the main driver of schizophrenia-related neuroinflammation.

Background: Schizophrenia often features low-grade neuroinflammation. Because latent toxoplasmosis (LT) is common in this population, we tested whether LT yields a biomarker pattern resembling that reported in schizophrenia. Methods: We quantified 15 cytokines and 15 blood markers of brain injury in 65 LT-positive individuals and 103 matched LT-negative controls using multiplex immunoassays. Multivariate effects of infection, age, sex, and their interaction were assessed by MANCOVA and PERMANOVA. Effects on individual biomarkers were tested by partial Kendall correlation (controlling for age and sex). Differences in the internal correlation structure were evaluated with Mantel tests on dissimilarity matrices derived from partial correlations. Results: LT was associated with higher KLK6, S100B, and TDP-43 and lower MIF; several other markers showed nonsignificant but sizable trends. Cytokines showed reduced IFN-γ, IL-1β, and MCP-1 and elevated IL-13 and IL-17 in the infected group. Sex-stratified analyses suggested stronger effects on brain-injury markers in women and on cytokines in men. Correlation structure also diverged: infected individuals exhibited more negative links between brain-injury markers and cytokines, whereas controls showed predominantly positive associations (Mantel r = 0.461, p = 0.043). The LT profile overlapped with schizophrenia in elevated KLK6 and S100B and, in men, reduced GDNF, but contrasted for MIF and for the overall cytokine pattern (no consistent IL-6/TNF-α elevation). Conclusions: LT entails neuroinflammatory and neuroimmune alterations that only partly recapitulate schizophrenia; the biomarker pattern and interrelationships differ, arguing against LT as the main driver of schizophrenia-related neuroinflammation. Keywords: Chronic toxoplasmosis; Neuroimmune communication; HPA axis dysregulation; Inflammatory signaling; Glial activation markers; Brain injury biomarkers; Sex-specific immune modulation; aging. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement Czech Science Foundation supported this work (grant no. 22-20785S) ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: The study was approved by the Institutional Review Board of the Faculty of Science, Charles University (approval number 2021/4), and conducted in accordance with applicable regulations and ethical guidelines. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes All data produced are available online at figshare 10.6084/m9.figshare.30408286

Background speech carries cues to variation in mental state in schizophrenia spectrum disorders/psychotic disorders, typically indexed with clinician-rated scales such as the PANSS. Progress in the automation of speech- based symptom modelling has been constrained by data scale and the underrepresentation of low-resource languages. In this study, we aggregate multi-center recordings to assemble a large corpus and assess symptom-prediction models at scale, to enable more objective and efficient assessments and the early detection of relapse-related signals from speech. Methods We compiled data from 453 patients with schizophrenia spectrum disorders, recruited from ten global sites, and clipped their speech recordings into 6,664 segments. Across three feature sets, acoustic-prosodic profile, pretrained multilingual embeddings, and their concatenation, we compared 16 algorithms to predict eight relapse-related PANSS items, including three positive (P1, P2, P3), three negative (N1, N4, N6), and two general (G5, G9) items, on speaker-disjoint splits (80% train, 10% test, and 10% validation). Performance was assessed by root-mean-squared-error (RMSE) at both segment and participant (median aggregation) levels. Best model per item underwent bias checks for age, sex, education, and symptom severity. Outcomes Best-performing models predicted symptoms with prediction errors of 1.5 PANSS points or lower: P1 1.494/1.527, P2 1.318/1.107, P3 1.407/1.542, N1 1.029/1.030, N4 1.452/1.430, N6 0.860/0.855, G5 0.850/0.882, G9 1.213/1.282 (segment/participant). Performance of the pretrained multilingual embeddings surpassed acoustic-prosodic features and their concatenation. Results were comparable in low-resource languages (e.g., Czech). We found no bias by age, sex, or education, aside from reduced N4 accuracy in males; but performance degraded with higher symptom severity. Interpretation Speech can support automatic assessment of schizophrenia symptoms using pretrained multilingual embeddings, even without the use of transcripts. Such models show promise as clinically meaningful, efficient, and low-burden tools for real-time monitoring of symptom trajectories. Funding EU Horizon research and innovation programme. ### Competing Interest Statement LP reports personal fees for serving as chief editor from the Canadian Medical Association Journals, speaker/consultant fee from Janssen Canada and Otsuka Canada, SPMM Course Limited, UK, Canadian Psychiatric Association; book royalties from Oxford University Press; investigator-initiated educational grants from Janssen Canada, Sunovion and Otsuka Canada outside the submitted work. IS reports charity grant fro Janssen, speaker fee from Otzuka and Ludbeck. All other authors report no relevant conflicts. SXT owns equity and serves on the board and as a consultant for North Shore Therapeutics, received research funding and serves as a consultant for Winterlight Labs, is on the advisory board and owns equity for Psyrin, and serves as a consultant for Catholic Charities Neighborhood Services and LB Pharmaceuticals. PH has received grants and honoraria from Novartis, Lundbeck, Takeda, Mepha, Janssen, Boehringer Ingelheim, Neurolite and OM Pharma outside of this work. All other authors reported no conflict of interests. ### Funding Statement This work is part of the project "TRUSTworthy speech-based AI monitorING system for the prediction of relapse in individuals with schizophrenia (TRUSTING)", funded by the European Union Horizon Europe research and innovation programme under grant agreement No. 101080251. The views and opinions expressed are those of the author(s) only and do not necessarily reflect those of the European Union or the European Health and Digital Executive Agency (HaDEA). Neither the European Union nor the granting authority can be held responsible for them. Authors are listed in alphabetical order, except for local members of the leading research group and the TRUSTING Pis. Additional funders for data collection are as follows. English data: Brain and Behavior Research Foundation Young Investigator Grant (K23 MH130750, to SXT). Spanish data: Carlos III Health Institute (PI14/00639, PI14/00918, PI17/00221, PI20/00066, and PI23/00076, to RAA). Chilean Spanish data: National Agency for Research and Development (ANID), Chile (Fondecyt Regular Grant No. 1241618, to AFB). Swiss German data: Swiss National Science Foundation (Grant No. 191938, to PH), Brain and Behavior Research Foundation (Grant No. 28997, to PH), and OPO Foundation (Grant No. 2020-0075, to PH). Dutch data: RAPSODI study funded by ZonMW, Netherlands (Grant No. 80-83600-98-40120), as part of the research program Rational Pharmacotherapy (Goed Gebruik Geneesmiddelen) (Grant No. 836041008, to IS), and the HAMLETT study funded by ZonMW, Netherlands (Grant No. 80-84800-98-41015, to IS). Turkish data: Scientific and Technological Research Council of Turkey (TUBITAK 2247, Project No. 120C141). In addition, RAA was funded by a Miguel Servet contract from the Carlos III Health Institute (Grant No. CP18/00003) and a Consolidator Grant from the Ministerio de Ciencia e Innovacion (Grant No. CNS2022-136110). A.P. was supported by a Marie Sklodowska-Curie Actions H2020 MSCA IF 2018 grant (ID: 832518, Project: MOVES). A.S. was supported by the Carlsberg Foundation. KK was supported by the Japan Society for the Promotion of Science (JSPS). RHe was funded by the China Scholarship Council (Grant No. 202108390062) during part of this work and is currently funded by the DELTA-Lang project (Synergy Grant 2023, Grant No. 101118756). ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: Ethics commission of National Institute of Mental Health (NIMH) of the Czech Republic. IRB at Feinstein Institutes for Medical Research, Northwell Health. Local Institutional Board at Valdecilla Research Institute (IDIVAL) in Santander, Spain. Review board (Ethics Committee for Clinical Research, CEC SSMS of Santiago, Chile). Ethical commission of the Department of Psychiatry in Montperrin Hospital, CH Aix-en-Provence, France. Ethics committee of Kantonale Ethikkommission Zurich. Review boards of the University medical center Utrecht and Groningen. Ethics Committee of Dokuz Eylul University. Ethics Committee of the State Chamber of Physicians Westphalia-Lippe and the University of Muenster. Ethics committee of Renmin Hospital of Wuhan University and the Institutional Review Board of the Institute of Psychology, the Chinese Academy of Sciences. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes The original data cannot be publicly shared due to ethical restrictions. However, all scripts and results will soon be available at https://github.com/RuiHe1999/PANSS_prediction.

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.

Schizophrenia is increasingly recognized as a disorder with a prominent neuroimmune component. Researchers have observed elevated markers of inflammation (e.g., cytokines, CRP, and NLR) not only during first-episode psychosis but also in chronic stages, suggesting that immune dysregulation may play a key role in the illness's pathophysiology. Yet, current pharmacological treatment mainly targets dopaminergic dysregulation, which is effective in reducing positive symptoms but is ineffective in managing negative symptoms and cognitive decline associated with schizophrenia. Antipsychotics (APs) may exert anti-inflammatory effects, possibly through attenuating glial activation and modulation of the immune pathways, though these effects remain still underexplored. That is why, in this narrative review, we synthesize evidence from in vitro, animal, and human studies to examine whether APs influence inflammatory processes and assess their potential in mitigating the refractory symptoms of schizophrenia through the immune modulation. Despite promising findings, several key uncertainties persist: inflammatory markers exhibit inconsistent patterns across studies, methodological approaches differ considerably, and antipsychotic-induced metabolic alterations further complicate interpretation. To fully understand the anti-inflammatory potential of APs, future research should identify the most effective compounds, determine optimal treatment timing, and rigorously control for confounding factors. Crucially, a paradigm shift is needed: clinical trials must adopt biomarker-guided stratification, and drug development should focus on agents that modulate the innate immunity. These steps are essential for developing more effective treatments for the refractory symptoms of schizophrenia.

Longitudinal neuroimaging studies offer valuable insight into brain development, ageing, and disease progression over time. However, prevailing analytical approaches rooted in our understanding of population variation are primarily tailored for cross-sectional studies. To fully leverage the potential of longitudinal neuroimaging, we need methodologies that account for the complex interplay between population variation and individual dynamics. We extend the normative modelling framework, which evaluates an individual’s position relative to population standards, to assess an individual’s longitudinal change compared to the population’s standard dynamics. Using normative models pre-trained on over 58,000 individuals, we introduce a quantitative metric termed ‘ z-diff ’ score, which quantifies a temporal change in individuals compared to a population standard. This approach offers advantages in flexibility in dataset size and ease of implementation. We applied this framework to a longitudinal dataset of 98 patients with early-stage schizophrenia who underwent MRI examinations shortly after diagnosis and 1 year later. Compared to cross-sectional analyses, showing global thinning of grey matter at the first visit, our method revealed a significant normalisation of grey matter thickness in the frontal lobe over time—an effect undetected by traditional longitudinal methods. Overall, our framework presents a flexible and effective methodology for analysing longitudinal neuroimaging data, providing insights into the progression of a disease that would otherwise be missed when using more traditional approaches.

It is hypothesised that structural brain abnormalities in individuals with schizophrenia are associated with aggressive behaviour, but this has not been tested directly. We pooled magnetic resonance imaging and clinical data from 2095 patients and 2861 healthy control subjects across 20 sites of the ENIGMA-Schizophrenia Working Group. Using normative modelling, we quantified individual-level deviations from controls (z-scores) for global and regional grey matter volume and white matter microstructural integrity. Ordinal regression models were used to estimate the associations between these deviations and concurrent aggression (odds ratios [ORs] with 99