Schizophrenia

Backgroundspeech 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. MethodsWe 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. OutcomesBest-performing models predicted symptoms with prediction errors of 1{middle dot}5 PANSS points or lower: P1 1{middle dot}494/1{middle dot}527, P2 1{middle dot}318/1{middle dot}107, P3 1{middle dot}407/1{middle dot}542, N1 1{middle dot}029/1{middle dot}030, N4 1{middle dot}452/1{middle dot}430, N6 0{middle dot}860/0{middle dot}855, G5 0{middle dot}850/0{middle dot}882, G9 1{middle dot}213/1{middle dot}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. InterpretationSpeech 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. FundingEU Horizon research and innovation programme. Research in contextO_ST_ABSEvidence before this studyC_ST_ABSAutomatic assessment of disease severity is a key issue in schizophrenia research, for which spontaneous speech offers a cost-effective, automatable solution. To evaluate existing evidence for speech-based symptom assessment, two reviewers (RHe, MK) searched PubMed, IEEE Xplore, arXiv, bioRxiv, and medRxiv for publications from inception to Aug 25, 2025, using the terms: ("symptom" OR "PANSS" OR "Positive and Negative Syndrome Scale") AND ("psychosis" OR "schizophrenia") AND ("language" OR "speech" OR "spontaneous speech") AND ("prediction" OR "machine learning" OR "deep learning" OR "algorithm" OR "neural network" OR "AI" OR "artificial intelligence"). Fourteen studies on symptom-level modelling were identified. Ten studies dichotomized clinical scores (e.g., PANSS) into low vs high for classification: five used conventional ML (e.g., random forests) and five used neural networks, with F1 scores ranging from 0{middle dot}60-0{middle dot}85. The remaining four studies, and two of the ten studies as mentioned above, modelled raw scores directly as regression tasks. Two relied solely on conventional regressors and the rest used neural networks, with errors from 0{middle dot}487 for single items (scale 1-7) to 8{middle dot}04 for summed scores (scale 18-126). All studies used free speech for elicitation, except one study, which used a reading task. Three studies incorporated additional tasks, such as picture description and immediate recall. None were multilingual: nine were in English, three in Chinese, one in Swiss German, and one in Brazilian Portuguese. Features spanned a wide range, including acoustic-prosodic profiles, morpho-syntactic structure, semantic organization, pragmatics (including sentiments), and even visual features capturing movement during talking. Representations from pretrained language models were also widely employed. Sample sizes (counting patients with schizophrenia) were generally small: eleven studies enrolled <50 patients, one had 65, and only two exceeded 100 patients. Some increased their effective sample size via multiple recordings per patient or by adding healthy controls and/or patients with other psychiatric disorders (e.g., depression). Added value of this studyTo our knowledge, this is the first multilingual, speech-based study modelling schizophrenia symptom severity with machine learning approach, and it includes the largest cohort of patients with schizophrenia to date. We further increased effective sample size by using diverse elicitation tasks and segmenting recordings into clips. This multilingual corpus empowers the usage of complex models and supports transfer learning from high-resource languages (e.g., English) to low-resource ones (e.g., Czech). For each of eight selected relapse-related PANSS items, the best audio-only models achieved RMSE < 1{middle dot}5, underscoring clinical relevance. We assessed potential biases: no effects were found for age, sex, or education (except poorer N4 performance in males), though performance declined at higher symptom severity. Trained models are released for use. Implications of all the available evidenceWe show that speech is a powerful signal for automatic assessment of schizophrenia symptom severity and holds promise for relapse prediction, even without transcripts. The approach readily extends to incorporate textual features (from manual or automatic transcripts) and more advanced models. Prospective studies with repeated recordings across relapse episodes are needed to validate the utility of our models on relapse prediction, for the sake of supporting precision psychiatry while reducing clinician burden.

ObjectiveCognitive deficits are a leading cause of disability in schizophrenia and are linked to poor functional outcomes. There are no first line treatments for these deficits, and their neural basis is poorly understood. While schizophrenia is associated with widespread cognitive deficits, information processing speed is most profoundly impaired. Processing speed deficits have been associated with hyperconnectivity in the Default Mode Network (DMN). We therefore tested if modulating DMN connectivity with single or multiple sessions of transcranial magnetic stimulation (TMS) applied to an individualized DMN target would affect processing speed. MethodsIn the first study, 10 individuals with schizophrenia received single TMS sessions and underwent resting-state neuroimaging and processing speed assessment (Brief Assessment of Cognition in Schizophrenia digit symbol coding) acutely before and after each session. These sessions included excitatory (intermittent theta burst stimulation, iTBS); inhibitory (continuous theta burst stimulation, cTBS); and sham stimulation sessions. In the second study, 29 individuals (17 schizophrenia, 12 non-psychosis controls) received 5 accelerated sessions of cTBS with resting-state neuroimaging and processing speed assessment before and after the course of TMS sessions. ResultsIn the accelerated, multi-session DMN-targeted TMS trial, cTBS improved processing speed in the schizophrenia group (p=0.0124). In individuals with schizophrenia, reduction in DMN connectivity was linked to improvement in processing speed (p=0.021). These changes were dependent on age, where younger participants experienced greater processing speed improvements than older participants (p=0.006). ConclusionsIn sum, personalized network targeted TMS is a novel method for reducing cognitive impairment associated with schizophrenia.

Schizophrenia is a severe neuropsychiatric disorder characterized by positive and negative symptoms and cognitive impairments. The present study aimed to investigate the potential interference of ambient noise on the performance of executive function (EF) tasks in individuals with schizophrenia. The sample consisted of 40 participants, divided equally into two groups: a group of individuals with schizophrenia (SchG) and a healthy control group without neuropsychiatric disorders (HC). All participants did three EF assessment instruments: Trail Making Test, Corsi Block Test, and Maze Test. The experimental design included a test-retest procedure with order counterbalancing: half of the sample began the assessment in the noise condition and the other half in the no-noise condition, to control for order and learning effects. The results indicate that ambient noise has a negative impact on the cognitive performance of individuals with schizophrenia. Specifically, the SchG group performed significantly worse on the Maze Test in the noise condition compared to the no-noise condition. These findings contribute to the understanding of the interactions between sensory and cognitive processes underlying the symptoms of schizophrenia. In addition to their theoretical potential, the results have practical implications, as they support the development of intervention strategies and ambiental adaptations that can improve the functionality and quality of life of people with the disorder.

Background and HypothesisClozapine is the only antipsychotic with protective effects against suicide in schizophrenia (SCZ). Newer third-generation antipsychotics (TGA) have better tolerability and modulate serotonin, dopamine, and N-methyl-d-aspartate neurotransmission pathways implicated in suicide. We aimed to investigate the effects of TGAs on suicide in SCZ. MethodsWe searched seven databases up to December 2023 for SCZ studies that reported suicide data. The primary outcome was suicide deaths and attempts; suicidal ideation was added as a secondary outcome. Random effects meta-analyses quantified suicide risk in randomized controlled trials (RCT) while single proportion meta-analyses assessed longitudinal suicide risk in open label extension trials (OLE). For RCTs, sensitivity analyses were conducted and subgroup analyses explored the impact of dose, drug type, and comparator arm. Study ResultsTwenty articles were included; thirteen excluded higher suicide risk participants. Compared to placebo control, TGAs did not significantly change the risk of primary [RR = 0.65, p = 0.38] or secondary [RR = 0.63, p = 0.15] suicide outcomes. Subgroup and sensitivity analyses were not statistically significant. For OLEs, there was a significant increase in the incidence of primary [Ip = 0.004, p = 0.048] and secondary [Ip = 0.024, p = 0.0013] suicide outcomes, but there was marked study heterogeneity. ConclusionThere is no current trial evidence to show that TGAs significantly impact suicide outcomes in SCZ. The signal from OLEs should be interpreted cautiously due to heterogeneity and requires replication. An effective clozapine alternative is needed for suicide prevention in SCZ.

BackgroundEEG microstates provide a window into rapid, large-scale brain network dynamics. Despite showing alterations in schizophrenia, evidence in first-episode schizophrenia spectrum psychosis (FESSP) is limited. We assessed whether microstate temporal and transition features could identify a multivariate signature of FESSP, and whether these dynamics can track symptom severity. MethodsResting-state EEG was analysed in 69 participants (FESSP n=41, mean age: 22.49 years; healthy controls n=28, mean age: 21.33 years). Twenty-eight microstate temporal and transition features were extracted across microstate classes (A-D). Group classification accuracy was assessed using a linear support vector machine with stratified cross-validation and permutation testing. Within the FESSP group, we further assessed associations between microstate features and clinical scores using the Brief Psychiatric Rating Scale (BPRS), Scale for the Assessment of Positive Symptoms (SAPS), and Scale for the Assessment of Negative Symptoms (SANS). ResultsMultivariate microstate features provided above-chance discrimination of FESSP from controls (balanced accuracy=0.644; AUC=0.688; p=0.030). However, when comparing individual features between groups, no feature survived multiple-comparison correction consistent with characterisation of FESSP via a distributed multivariate pattern across correlated features. Within the FESSP group, microstate dynamics were most strongly linked to negative symptoms, with higher SANS scores associated with shorter microstate D durations ({rho}=-0.507, pFDR=0.020) and higher occurrence of microstates A and B ({rho}=0.434-0.443, pFDR=0.042). BPRS-18 and SAPS showed no associations with any features. ConclusionsUsing EEG microstate temporal and transition features with multivariate classification, we identified a pattern that differentiated FESSP from controls and showed selective associations with negative symptom severity.

Background and HypothesisSchizophrenia is a disease characterized by various symptoms and has severe lifelong impacts on patients and their families. Despite various hypotheses and associated studies, the key mechanism in schizophrenia is not fully elucidated. In the present study, we focused on adropin, a peptide regulating energy metabolism, antioxidation, and neuroprotection. Study DesignIn both the group of healthy volunteers (HV) and the group of patients with some schizophrenia spectrum and other psychotic disorders (SZ), we evaluated adropin along with other variables such as anthropological factors, psychological well-being indicators, and laboratory test results. Study ResultsThe adropin levels in SZ were not significantly different from those in HV. Correlation analysis indicated five significant correlations beyond various natural correlations arising from fundamental proportional relationships and multifaceted psychological well-being indicators: (1) adropin versus right handgrip strength in the SZ group ({tau} = -0.82, P = 0.066); (2) adropin versus selenium in the total group ({tau} = 0.44, P = 0.053); (3) ferritin versus perceived stress in the total group ({tau} = -0.44, P = 0.053); (4) right versus left handgrip strength in the total group ({tau} = 0.70, P = 0.001) and in the SZ group ({tau} = 0.82, P = 0.075); and (5) selenium versus state anxiety in the total group ({tau} = 0.44, P = 0.053) and the SZ group ({tau} = 0.84, P = 0.066). ConclusionsThe present study provides a foundation for future studies and sheds light on the role of adropin in schizophrenia.

Cognitive heterogeneity is a core feature of schizophrenia (SCZ). Conventional approaches examine this heterogeneity using domain-specific scores, which may not fully reflect the underlying cognitive structure. In this study, a norm-anchored cognitive structural deviation (NCSD) framework was developed to examine such heterogeneity from a structure-informed perspective. The HC-derived latent cognitive structure (N-LCS) captured performance across the assessed tasks and remained stable under external validation in an independent cohort. Patients with SCZ showed greater global deviation from the N-LCS, along with altered loading directions of Wisconsin Card Sorting Test (WCST)-derived executive indicators which were consistent across robustness analyses, and altered correlation patterns among cognitive measures relative to HC. These features were quantified using three NCSD-derived indices: the cognitive normative deviation index (CNDI), loading pattern divergence (LPD), and correlation structure discrepancy (CSD). CNDI discriminated SCZ from HC with stable performance under cross-validation. LPD and CSD were associated with anxiety ratings, with LPD also showing a trend-level association with positive symptoms. Exploratory clustering identified a three-cluster solution with clear separation and moderate stability. Together, these findings show that cognitive heterogeneity in SCZ involves both global deviation from the N-LCS and structural alteration. NCSD provides a refined framework to characterize such heterogeneity and may inform precision psychiatry and functional recovery.

Psychiatric disorders are fundamentally challenged by symptom heterogeneity, high comorbidity, and the absence of objective biomarkers, which together result in substantial variability in clinical assessment and treatment selection. Patient-generated language captures rich information about subjective experience and symptom severity, which can be systematically encoded and analyzed using computational models, making it a scalable signal for psychiatric assessment. We compare two approaches: (i) a domain-specialized transformer fine-tuned on clinical language, based on the Bio-ClinicalBERT encoder architecture, and (ii) a large-scale instruction-tuned generalist encoder (Instructor-XL) used as a frozen feature extractor with a shallow classification head. A corpus of N = 151,228 de-identified texts was compiled from five public sources, covering four psychiatric phenotypes: anxiety, depression, schizophrenia, and suicidal intention. Models were evaluated using stratified 10-fold cross-validation with cost-sensitive training, prioritizing imbalance-aware metrics, including Macro-F1 and Matthews Correlation Coefficient (MCC), over accuracy. Bio-ClinicalBERT achieved superior overall performance (Macro-F1 = 0.78, MCC = 0.6752), indicating more reliable separation of diagnostically overlapping affective categories. In contrast, Instructor-XL achieved its highest class-specific performance for schizophrenia (F1 = 0.798). Explainability analyses suggest that the domain-specialized model places greater weight on clinically relevant terms, whereas the generalist model relies on a broader set of lexical features.

ObjectiveWe examined the clinical utility of resting state electroencephalography (rsEEG) by evaluating its temporal stability, discriminant validity for B-SNIP psychosis Biotypes, and suitability as a treatment target for brain stimulation. MethodsWe collected 5 minutes of eyes-open rsEEG from 1401 participants with psychosis and 750 healthy persons. A subset of participants was re-tested after 6 months and 12 months (N=109). In a pilot target engagement study (n=5) we collected rsEEG before and after 2 high-definition transcranial direct current stimulation (HD-tDCS) interventions targeting the left dorsolateral prefrontal cortex (dlPFC) and temporoparietal junction (TPJ). Data were reduced with principal component analyses to delta/theta, alpha, beta, and gamma frequency bands, and compared between groups and timepoints. ResultsrsEEG frequency bands displayed good-to-excellent stability and significantly distinguished psychosis Biotypes with large effect sizes. Compared to healthy, Biotype-1 had low activity (average ES=-.58), Biotype-2 had high activity (ES=1.07), and Biotype-3 had slightly elevated activity (ES=.33). There were no rsEEG differences between DSM psychosis groups. After anodal dlPFC stimulation, alpha and gamma power slightly increased while positive symptoms and verbal fluency improved. After cathodal TPJ stimulation, delta/theta power slightly increased while psychoticism and digit sequencing improved. ConclusionsResting state brain activity is a trait-like marker that differentiates B-SNIP psychosis Biotypes, suggesting differing underlying neurophysiology. The pilot intervention supports the feasibility of targeting this underlying neurophysiology with HD-tDCS. Integrating rsEEG in diagnostic procedures and stratified intervention selection may be beneficial for psychosis patients.

In-scanner head motion is a recognized source of bias in structural magnetic resonance imaging (sMRI), yet it remains under-addressed in psychiatric neuroimaging where structural difference in patient populations are considered foundational. We examined motion-related bias in grey matter volume estimates across eight independent cohorts comprising 9,664 individuals, including 8,979 neurotypical controls (NC), 497 patients with schizophrenia (SCZ), and 188 patients with bipolar disorder (BD). Motion estimates were derived from multiple fMRI scans acquired within the same scanning session and summarized using principal component analysis. In NC, motion accounted for 1-6% of regional grey matter variance, a magnitude comparable to reported psychiatric case-control effect sizes. Adjusting for motion attenuated SCZ-NC group differences, reducing effect sizes in 85% of brain regions and yielding 5% fewer significant ROIs (pFDR<0.05). In BD, motion correction reduced effect sizes in 97% of regions, with a 24% reduction in significant ROIs. Cross-diagnostic spatial patterns were significantly correlated (r=0.63, p=3x10-{superscript 1}3), explaining a sizable portion of SCZ-BD commonalities. Critically, a falsification analysis in UK Biobank (N=5,123) showed that stratifying NC by motion alone produced grey matter differences accounting for 45-62% of SCZ case-control effect magnitude, underscoring how difficult it is to interpret SCZ-like morphometric differences as tissue properties rather than as motion-driven patterns. These findings urge caution in interpretations of sMRIdifferences in patient-control comparisons and use of systematic fMRI based motion control as standard practice in sMRI analyses.

Schizophrenia spectrum disorders (SSD) are characterized by altered brain structure, reflecting widespread dysconnectivity across brain-specific networks. However, the role of hierarchical organization on cortical morphometric networks in shaping clinical outcomes over the course of the disease remains unclear. Connectome-derived gradients have increasingly been used to investigate spatial transitions in brain organization. Here, we computed cortical and subcortical Morphometric INverse Divergence (MIND) similarity networks from 1293 structural MRI data of 193 healthy controls (HC) and 350 individuals with SSD followed for up to 20 years. MIND features were calculated for each subject-specific network by computing regional averages and performing gradient decomposition. We found that MIND in SSD was longitudinally associated with treatment duration and medication. These associations were co-localized with hierarchical axes of cortical organization and schizophrenia epicenters. Moreover, psychiatric symptoms were associated with these alterations in structural similarity, which were also related to treatment duration. Collectively, these findings advance our understanding of how brain organization, treatment duration, and medication shape clinical symptoms throughout the course of SSD.

Evidence-based psychotherapies are first-line treatments for psychiatric disorders, yet response rates remain suboptimal. Noninvasive brain stimulation (NIBS) may augment psychotherapy by modulating treatment-engaged circuits. We conducted a systematic review and meta-analysis of randomized controlled trials comparing active NIBS plus evidence-based psychotherapy versus sham NIBS plus psychotherapy. Following Cochrane methods, we searched six databases through February 2025, screening 1,017 records. Twenty-eight trials (31 treatment arms; 1,506 participants) met inclusion criteria. Active NIBS combined with psychotherapy produced significantly greater symptom improvement than sham NIBS with psychotherapy (standardized mean difference = -0.38, 95% confidence interval [-0.68, -0.08]), with substantial heterogeneity. Moderator analyses revealed critical implementation parameters: repetitive transcranial magnetic stimulation (rTMS) showed significant benefit while transcranial direct current stimulation did not. Non-concurrent delivery--stimulation before or after psychotherapy sessions--was significantly effective, whereas concurrent administration was not. Among psychotherapy modalities, cognitive behavioral therapy combined with NIBS produced significant benefit. Human-delivered psychotherapy, but not computerized formats, significantly enhanced outcomes. By diagnosis, significant effects were observed only for anxiety disorders. Secondary analyses revealed significant anxiety symptom reduction specific to rTMS. Treatment integrity was under-reported: only 39.3% of studies used fully manualized protocols and 10.7% documented therapist adherence. Non-concurrent rTMS paired with human-delivered, manualized cognitive behavioral therapy emerges as the most effective strategy, particularly for anxiety disorders. These findings provide an evidence-based framework for optimizing combined treatment protocols and highlight the need for standardized psychotherapy fidelity monitoring in future trials.

Psychotic disorders are increasingly recognized as the extreme end of a progressive psychopathology continuum, with less advanced stages including the asymptomatic familial high-risk state (FHR), the help-seeking clinical high-risk state (CHR), and first episode psychosis (FEP). However, we lack a comprehensive study of clinical, cognitive, functional, and neuroanatomical markers across all three early stages of psychosis, limiting our understanding of how the multimodal phenotypes which define psychotic disorders emerge in the broader course of psychopathology. We leveraged a sample of 70 FEP, 40 CHR, 43 FHR, and 41 healthy participants recruited from the same clinical and sociodemographic setting - the first such dataset to be described in the literature. Several markers were elevated in CHR but did not worsen in FEP, including depression/anxiety and difficulties functioning, while FEP was uniquely defined by cognitive impairments and cortical thickness reductions characteristic of those seen in schizophrenia. Across the sample, the dominant axis of joint brain-behaviour variability captured a relationship between reduced cortical thickness and lower cognitive performance, a pattern which was equally established in both CHR and FEP. Initial longitudinal data revealed that depressive and negative symptoms best predicted lower functioning at 6-month follow-up, regardless of group status. Together, our analysis suggests that affective and functional disturbances emerge in earlier stages of psychosis, while cognitive and anatomical abnormalities characterize more advanced ones - though the overlap we observed across groups demonstrates that clinically relevant phenotypes can cut across group boundaries, requiring personalized care to manage.

BackgroundLipidomic alterations have been reported across schizophrenia (SCZ) and bipolar disorder (BD), but findings are heterogeneous and often overlap across diagnoses, limiting diagnostic specificity. Associations between lipid profiles and illness severity have also been inconsistent when assessed using single symptom scales, raising the possibility that unidimensional measures fail to capture biologically relevant variation. Whether plasma lipidomic alterations relate to multidimensional psychosis severity, and how they relate to polygenic liability, remains unclear. MethodsWe examined associations among psychiatric and cognitive polygenic risk scores (PRS), plasma lipidomics (361 species across 16 classes), and a machine-learning-derived severe psychosis probability score in a transdiagnostic cohort of individuals with SCZ or BD (PRS n=1,320; lipid subset n=428). Regression and lipid class enrichment analyses tested severity associations. Mediation and canonical correlation analyses assessed integrated genetic-lipid-severity relationships. ResultsSCZ-PRS (positive), BD-PRS (negative), and educational attainment PRS (negative) showed modest associations ({beta} = |0.02|) with severe psychosis probability. Lipid class enrichment analysis identified nine classes associated with severity, including increased sphingolipids (dSM, dCer), phosphatidylcholines (PC), triacylglycerides (TAG), and phosphatidylethanolamine plasmalogens (PE-P), alongside decreased phosphatidylcholine plasmalogens (PC-P). Most lipid class associations were robust to adjustment for diagnosis and medication. No significant mediation or shared multivariate genetic-lipid structure was observed. ConclusionsPlasma lipidomic variation tracks multidimensional psychosis severity across diagnostic boundaries. These findings suggest that lipidomic alterations may reflect transdiagnostic biological processes linked to illness burden that are not fully captured by categorical diagnoses, single symptom scales, or common-variant polygenic risk.

BackgroundEarly detection of individuals at risk for clinical deterioration in first-episode psychosis (FEP) remains a vital challenge in psychiatric care. Emerging evidence indicates that immune dysregulation might play a crucial role in the pathophysiology and progression of psychotic disorders. AimsThis study examined the predictive potential of a plasma cytokine and chemokine panel in anticipating clinical stage transition of FEP patients. MethodUsing multiplex immunoassays, plasma samples from a cohort of 35 FEP patients were screened for the quantification of 21 analytes. Participants were clinically assessed at baseline and follow-up and classified according to a validated staging model. Data was used to predict clinical stability over a 12-month follow-up period. ResultsIL-17A was found to be significantly increased in transitioning patients (p = 0.045), with a medium standardized effect size and wide confidence interval (Hedges g = - 0.687, 95% CI [-1.379, 0.004]). A logistic regression model was determined, which revealed that higher baseline levels of IL-17A were significantly linked to progression to a more advanced clinical stage, while higher baseline levels of MIP-3 and IFN-{gamma} were associated with clinical stability. This combined cytokine model exhibited strong predictive capacity (AUC = 0.853), indicating its potential as a biomarker panel for early risk assessment. ConclusionsThese findings highlight the importance of neuroimmune mechanisms in the development of psychotic disorders and advocate for the inclusion of immunological markers within staging-based models of care. Incorporating cytokine profiling into clinical practice could improve personalised treatment strategies and lead to better long-term outcomes for individuals with FEP.

IntroductionSepsis is a life-threatening ailment caused by an exaggerated immune response to infection that poses a major health problem, with increasing prevalence, high costs, and poor outcomes. Improved outcomes are seen in patients when providers follow the Surviving Sepsis Campaign recommended clinical practice guidelines for identifying and treating sepsis using a 3-hour and 6-hour bundle after sepsis is suspected. Previous research has shown patients with mental health issues receive worse quality of diabetes and cardiac care and have poorer outcomes compared with those without mental health issues. Similarly, patients with mental health issues may receive worse sepsis care due to inability to explain symptoms, agitation, etc. This study explores sepsis quality of care among patients with vs. without an acute mental health crisis, and whether patients with certain mental health issues were more likely to receive sepsis bundle care than others. MethodsUsing data extracted from 2018-2019 at the Long Island Jewish Medical Center Emergency Department (ED), patients who met sepsis inclusion criteria were grouped into either having, or not having, a severe mental illness crisis on the basis of whether physical or chemical restraints were used in the ED. Patients with a history of a severe mental illness, but who were not in a severe mental health crisis, were grouped with the patients without mental health illness, as, in the absence of an acute psychiatric problem, their mental health issue unlikely affected sepsis care. We describe demographic characteristics of both groups and performed a univariate analysis using Students T-test to compare the percent of those with vs. without acute mental health crisis who received full 3- and 6-hour sepsis bundle care. Patients with an acute mental health crisis were grouped according to "cognitive" (eg, dementia) vs. "non-cognitive" (eg, schizophrenia) disorders. ResultsComparing those with vs. without acute mental health crisis, there was no difference in the percent of patients who received 3-hour sepsis bundle care (80.7% vs 74.9%, p = 0.1456). However, among patients who received the 3-hour bundle, a significantly-greater percent of those with an acute mental health crisis received the 6-hour sepsis bundle (51.0% vs. 30.7%, p <0.0001). There was no difference between different groups of patients with mental health issues (eg, "cognitive" vs. "non-cognitive") with respect to receiving 3- or 6-hour sepsis bundle care. DiscussionSurprisingly, although there was no significant difference in likelihood to receive a 3-hour sepsis bundle among patients with vs. without an acute mental health crisis, those with an acute mental health crisis were more-likely to receive 6-hour care. We suspect this difference might be due to increased attention paid to patients with an acute mental health crisis, including more-frequent room visits by hospital staff or more concerns among family members. No particular set of mental health conditions was associated with receiving or not receiving appropriate care. Future research could address possible confounding factors, go into more detail about the specific component of the sepsis protocol that patients failed to receive, and specify what aspects of a mental health crisis affected treatment plans. Future studies are needed to assess possible associations between severe mental illness crisis, bundle care, and mortality in relation to ED, Intensive Care Unit (ICU), or hospital length-of-stay (LOS).

Background At present, there are no approved pharmacological treatments for people at clinical high risk for psychosis (CHR-P). We sought to assess the acceptability of cannabidiol (CBD): a promising candidate treatment for this population. Methods CHR-P individuals completed a survey which assessed their views on the acceptability of CBD, its expected effectiveness and side effects, and on formulation preferences. Results The sample comprised 55 CHR-P individuals (24.3 years and 69% female). Most (91%) were familiar with CBD, and had previously used cannabis (64%), and around half (42%) had tried over-the-counter CBD. 75% were willing to take CBD as an intervention for mental health problems. Most participants anticipated fewer side effects with CBD than with existing medications, and preferred tablet or capsule formulations over liquids. Discussion CBD is perceived as a highly acceptable treatment among CHR-P individuals.

Deficits in inhibitory control are common across a wide range of psychiatric disorders and are closely linked to symptom severity, including emotional dysregulation, anxiety, substance misuse, and self-harm, making them an appealing target for intervention. Cognitive training offers a low-cost, scalable, and non-invasive strategy to strengthen inhibitory control; however, most existing paradigms target only a single facet of inhibition and rarely account for environmental influences, such as affective context. To address these gaps, we developed a computerized inhibitory control training paradigm to simultaneously engage three components of inhibition: preemptive, proactive, and reactive, while embedding trials within positive and negative affective contexts to assess the impact of emotional stimuli. Across two online experiments, participants completed the GAMBIT task in one session (Experiment 1, N = 300) or repeated over three sessions (Experiment 2, N = 65). The task included No-Go trials to train preemptive inhibition, stop-signal trials for reactive inhibition, and stop-signal anticipation trials to train proactive inhibition. Affective images of differing valence were presented as background stimuli to evaluate their impact on inhibitory performance. In Experiment 1, participants showed higher accuracy on No-Go versus reference Go trials ({beta}=1.45, SE=0.09, p<.001), confirming successful manipulation of preemptive inhibition. Reaction times were slower during anticipation trials across two different conditions ({beta}=0.16, SE=0.04, p<.001; {beta} = 0.07, SE = 0.04, p = 0.047), consistent with proactive slowing when anticipating a potential stop signal. Additionally, positive affective images ({beta} = 0.10, SE= 0.009, p < 0.001) further slowed RTs, indicating emotional interference with proactive control. In Experiment 2, the pattern of higher No-Go accuracy was replicated ({beta} = 0.91, SE = 0.11, p < .001) and accuracy generally improved over sessions ({beta} = 0.38, SE = 0.06, p < .001). In anticipation trials, RTs become shorter across sessions (session 2: {beta} = -0.25, SE = 0.06, p < .001; session 3: {beta} = -0.45, SE = 0.06, p < .001), reflecting practice-related gains, and SSRTs decreased over time (F(2,56) = 6.26, p = .004), consistent with enhanced reactive inhibition. Proactive inhibition was modulated by affective images, with both negative ({beta} = 0.04, SE = 0.02, p = .039) and positive ({beta} = 0.16, SE = 0.02, p < .001) affective images associated with slower RTs. Participants also reported reductions in self-assessed temper control by the last session (W = 25.5, p = .007, q = .037, d = -0.51) and usability ratings were high (all means [&ge;] 3.87/5). Together, these findings show that this paradigm recruits multiple forms of inhibitory control and yields training-related improvements in both performance and affective outcomes. This provides preliminary validation of a scalable, fully online inhibitory control training tool targeting multiple dissociable inhibitory processes within affective contexts. The approach holds promise as an accessible transdiagnostic intervention to support symptom improvement across psychiatric disorders, with future work needed to evaluate clinical efficacy in patient populations.

BackgroundTo clarify the working mechanisms of psychotherapy for obsessive-compulsive disorder (OCD), we studied the neural effects of two psychotherapies: cognitive behavioral therapy with exposure and response prevention (CBT-ERP) and inference-based cognitive behavioral therapy (I-CBT). MethodsFifty-five individuals with OCD completed an emotional processing task during fMRI before and after 20 weekly psychotherapy sessions, using general fear and OCD-related visual stimuli. Forty-two healthy controls performed the task once. We used Bayesian region-of-interest analyses to assess changes in brain activation in prefrontal, limbic, sensory, subcortical, and visual areas, and their association with symptom improvement. ResultsAfter treatment, the CBT-ERP group (N=28) showed strong credible evidence for decreased activation across all brain regions during fear (but not OCD) versus neutral stimuli, especially in treatment responders. Conversely, the I-CBT group (N=27) showed increased activation during fear versus neutral stimuli in the precentral gyrus and lateral occipital cortex (LOC), which correlated with symptom improvement. A similar but weaker pattern was observed for OCD-related stimuli. Across all ROIs, baseline fear-related activity was associated with symptom improvement in CBT-ERP, while lower baseline activity was associated with improvement in I-CBT in, amongst others, the precentral gyrus and dorsolateral prefrontal cortex. Lower baseline LOC activation during OCD-related stimuli was linked to symptom improvement after both psychotherapies. ConclusionsThe results support CBT-ERPs mechanism of fear reduction and I-CBTs mechanism of sensory engagement. Visual brain activity during emotional processing may predict treatment response across psychotherapies.

There is an unmet need to identify biomarkers of active thyroid eye disease (TED). scRNAseq revealed that orbital fibroblasts from orbital decompressions in people with TED express high levels of thyroid hormone receptors, growth factor receptors, including insulin-like growth factor 1 receptor (IGF1R), and extracellular matrix proteins including SPARC (osteonectin), whereas orbital fat endothelial cells expressed thyroid peroxidase (TPO). SPARC was significantly raised in the serum of people with thyroid disease compared to healthy controls. Furthermore, those with moderate, severe and sight threatening TED had higher SPARC levels than those with thyroid disease but free of TED or mild TED. Free-triiodothyronine (FT3) levels were positively correlated with SPARC in moderate-sight threatening TED. SPARC and IGF1 were positively correlated across people with thyroid disease alone, as well as TED. Thyroid stimulating hormone (TSH) levels were negatively correlated with SPARC in moderate-sight threatening TED. When participants were followed longitudinally, SPARC decreased after the active phase of TED. At the protein level, immunohistochemistry indicated that SPARC was heterogeneously expressed by fibroblasts in both control and TED orbital fat. SPARC is a key mediator of fibrosis and deposition of extracellular matrix and the correlation of SPARC serum levels to TED status and FT3 make it a promising biomarker of active TED.