Schizophrenia

Cognitive deficits in schizophrenia significantly hinder functional outcomes and often remain unresponsive to conventional treatments. While initial evidence suggested potential pro-cognitive effects of electrical brain stimulation in schizophrenia, recent meta-analyses have not supported these findings, warranting further investigation on intervention optimization. This sham-controlled crossover study explored cognitive and emotional effects of bilateral dorsolateral prefrontal cortex (DLPFC) anodal transcranial direct current stimulation (tDCS) and high-frequency transcranial random noise stimulation (tRNS) in schizophrenia. Thirty-six male patients with schizophrenia participated in a crossover trial, receiving three sessions (tDCS, tRNS, sham) in counterbalanced order with one-week intervals. tDCS and tRNS sessions involved 20-minute 2 mA anodal stimulation (tDCS) and 2 mA 100-640 Hz random noise stimulation targeting the left and right DLPFCs (F3-F4) with two extracephalic return electrodes. Executive functions (working memory, planning) were assessed during stimulation, and emotional changes were measured with the Positive and Negative Affect Schedule (PANAS) pre- and post-stimulation. Side effects and blinding efficacy were evaluated. Both bilateral tDCS and tRNS significantly improved executive functions (i.e., problem solving) compared to sham, with tRNS additionally enhancing working memory accuracy and strategy score. Both interventions increased positive affect and reduced negative affect after the intervention, with tRNS showing greater enhancement of positive emotions. Reduced negative affect correlated with better executive functions during tRNS. Side effects were minimal, and blinding was effective for the sham condition. Bilateral DLPFC anodal tDCS and high-frequency tRNS show promise as adjunctive treatments for schizophrenia, especially for cognitive deficits, with broader cognitive and emotional benefits observed with tRNS.

BackgroundCurrently, there is no accepted system for the classification of psychotherapies for application within systematic reviews is timely. ObjectiveTo devise a system for classification of psychotherapy interventions - for use, initially, in systematic reviews. MethodsCochrane Schizophrenias Register used as the source of RCTs. After being piloted and refined at least twice, finally we applied it to all relevant trials within the register. Basic statistical data already held within the register were extracted and used to calculate the distribution of schizophrenia research by form of psychotherapy. FindingsThe final classification system consisted of six definable broad boughs two of which were further subdivided into branches. The taxonomy accommodated all psychotherapy interventions described in the Register. Of the initial 1645 intervention categories within the Register, after careful recoding, 539 (33%) were psychotherapies (234 coded as Thought/Action (cognitive & behavioural) - 1495 studies; 135 Cognitive Functioning - 652 studies; 113 Social - 684 studies; 55 Humanistic - 272 studies; 23 Psychoanalytic/dynamic - 40 studies; and 63 Other - 387 studies). For people with schizophrenia, across categories, the average size of psychotherapy trial is small (107) but there are notable and important exceptions. ConclusionWe reported a practical method for categorising psychotherapy interventions in evaluative studies with applications beyond schizophrenia. A move towards consensus on the classification and reporting of psychotherapies is needed. Clinical ImplicationsThis classification can help the clinicians, clinical practice guideline developers, and evidence synthesis experts to recognise and compare the interventions from same or different classes. Summary BoxO_ST_ABSWhat is already known about this subject?C_ST_ABSO_LIEffective classification of medical interventions is a perquisite for their effective identification, detection, and grouping. This in turn is essential for comprehensive identification of randomised control trials (RCTs) for inclusion in systematic reviews. C_LIO_LIA vast range of psychological therapies for schizophrenia exist, however there is a great degree of heterogeneity in their methods, and little consistency in their nomenclature. C_LIO_LIClassification of interventions for schizophrenia exists for pharmacological therapies. However only limited attempts have been made to develop such a classification for psychotherapies, and no literature-based classifications have been attempted for use in research. C_LI What are the new findings?O_LIThe vast majority of psychotherapy interventions for schizophrenia can be consistently and systematically assigned to five broad categories: Thought/Action, Cognitive Functioning, Social, Humanistic, and Psychoanalytic/Psychodynamic. A small minority of emerging or unique psychotherapy interventions do not fit into any of these five categories. C_LIO_LIUsing the same classification system these categories can in turn be subdivided into branches, allowing similar forms of psychotherapy to identified with greater detail, and allowing systematic reviews of greater specificity to be conducted. C_LIO_LIThis classification was applied to Cochrane Schizophrenias comprehensive register of schizophrenia RCTs. It was demonstrated to be an effective method for identifying and grouping different schizophrenia psychotherapy RCTs for the purposes of conducting systematic reviews. C_LIO_LIThe mean size of schizophrenia psychotherapy RCTs is approximately one hundred participants, consistent across different categories of psychotherapies. Thought/Action interventions - such as cognitive behavioural therapies - account for the largest proportion of schizophrenia psychotherapy RCTs. Only a small minority of schizophrenia psychotherapy RCTs investigate humanistic and psychoanalytic/psychodynamic therapies. C_LI How might it impact on clinical practice in the foreseeable future?O_LIThe classification system we have developed can be used for the accurate identification and grouping of different types of psychotherapies. This will allow more comprehensive, accurate, and specific systematic reviews to be conducted - in turn producing better quality evidence on the effectiveness of different forms of psychotherapy for schizophrenia. C_LIO_LIThe classification system also has applications beyond research - and likely beyond schizophrenia - including providing a framework for laypersons and clinicians to better understand and recognise different forms of psychotherapy. It also provides a contribution, and an impetus, towards improving consensus around common language and classification of psychotherapies. C_LIO_LIThe data on study size and distribution by category of psychotherapy - which we have produced by applying our classification system to Cochrane Schizophrenias comprehensive register of schizophrenia RCTs - may illuminate avenues for future research into schizophrenia psychotherapy, and identify areas in which RCTs in this area can be improved. C_LI

Cognitive deficits are established as a fundamental feature of schizophrenia; however, their pattern and how they are affected by chronicity are still unclear. Although a generalized stable impairment affecting multiple cognitive domains is commonly seen from the onset, some longitudinal studies have shown evidence of neuroprogression, and selective deterioration in certain cognitive domains. We assessed cognitive performance in patients with recent-onset (n = 17, duration of illness [&le;] 2 years) and chronic schizophrenia (n = 14, duration [&ge;] 15 years), and healthy adults (n = 16) using the Global Neuropsychological Assessment and examined correlations between cognitive scores and gray matter volumes computed from T1-weighted MRI images. We also measured and analyzed differences between patient groups for negative and positive symptoms, psychotic exacerbations, and medication exposure, and studied their correlations with cognitive performances. We observed cognitive deficits affecting multiple domains in both recent-onset and chronic schizophrenia samples. Selectively greater impairment of perceptual comparison/processing speed was found in adults with chronic schizophrenia (p = 0.009, {eta}2partial = 0.25). In the full sample (n = 47), perceptual comparison speed correlated significantly with gray matter volumes in the anterior and medial temporal lobes, predominantly on the left side (TFCE, FWE p < 0.01). These results indicate that along with generalized deficit across multiple cognitive domains, selectively greater impairment of perceptual comparison/processing speed appears to characterize chronic schizophrenia. This pattern might indicate an accelerated or premature cognitive aging. Gray matter volumetric deficits in the anterior-medial temporal lobes especially of left side might underlie the impaired perceptual comparison/processing speed seen in 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.

Although the Positive and Negative Syndrome Scale (PANSS) is widely utilized in schizophrenia research, variability in specific item loading exist, hindering reproducibility and generalizability of findings across schizophrenia samples. We aim to establish a common metric PANSS factor structure from a large multi-ethnic sample and validate it against a meta-analysis of existing PANSS models. Schizophrenia participants (N = 3511) included in the current study were part of the Singapore Translational and Clinical Research Program (STCRP) and the Clinical Antipsychotic Trials for Intervention Effectiveness (CATIE). Exploratory Factor Analysis (EFA) was conducted to identify the factor structure of PANSS and validated with a meta-analysis (N = 16,171) of existing PANSS models. Temporal stability of the PANSS model and generalizability to individuals at ultra-high risk (UHR) of psychosis were evaluated. A five-factor solution best fit the PANSS data. These were the i) Positive, ii) Negative, iii) Cognitive/disorganization, iv) Depression/anxiety and v) Hostility factors. Convergence of PANSS symptom architecture between EFA model and meta-analysis was observed. Modest longitudinal reliability was observed. The schizophrenia derived PANSS factor model fit the UHR population, but not vice versa. We found that two other domains, Social Amotivation (SA) and Diminished Expression (DE), were nested within the negative symptoms factor. Here, we report one of the largest transethnic factorial structures of PANSS symptom domains (N = 19,682). Evidence reported here serves as crucial consolidation of a common metric PANSS that could aid in furthering our understanding of schizophrenia.

IntroductionSchizophrenia is a severe psychiatric condition associated with cognitive impairment and premature dementia. Furthermore, metabolic syndrome (MetS)--combined central obesity, diabetes, dyslipidemia and hypertension--is highly prevalent in patients with schizophrenia and is believed to contribute to cognitive impairment and premature dementia in patients with schizophrenia. However, the mechanisms by which MetS contributes to cognitive impairment in patients with schizophrenia is unclear. Based on the association of MetS with platelet activation and the ability of activated platelets to impact blood-brain-barrier function, we tested the hypothesis that platelet activation is associated with both MetS and cognitive impairment in two independent pilot samples of patients with schizophrenia. MethodsIn the first pilot sample (sample A) we recruited 13 veterans with either schizophrenia or schizoaffective disorder with MetS (MetS+, n=6), and without MetS (MetS-, n=7). We administered the Measurement and Treatment Research to Improve Cognition in Schizophrenia (MATRICS) Consensus Cognitive Battery (MCCB) on all 13 veterans and assessed platelet activation using flow cytometry. In the second pilot sample (sample B), we identified 10 non-veteran MetS+ patients with schizophrenia and 10 age-, and sex-matched MetS-patients with schizophrenia from previously collected data on 106 patients enrolled in a non-MetS study. Participants in sample B had data on the NIH Toolbox cognitive battery (NIH Toolbox) and plasma soluble P-selectin (sP-selectin), a marker of platelet activation. We compared flow cytometry platelet activation in MetS+ and MetS- using the Mann Whitney test and the median test to compare sP-selectin and cognitive measures. We also measured the correlation between platelet activation and cognition using Spearmans rho correlation. ResultsPlatelet activation was significantly higher in MetS+ than MetS- (mean rank 8.60 vs. 3.83, p=0.017). Median score for the picture vocabulary test (language ability) was significantly lower in MetS+ relative to MetS- (82.35 vs. 104, p=0.015). In addition, platelet activation correlated negatively (rho = -0.74, p= 0.009) with the Wechsler Memory Scale: Spatial Span (nonverbal working memory) and plasma sP-selectin correlated negatively (rho = -0.55, p= 0.029) with the List Sorting Working Memory Test. ConclusionOur preliminary findings suggest that platelet activation is involved in the association of MetS with cognitive impairment in patients with schizophrenia. Future studies are needed to elucidate the role of platelets in MetS-related cognitive impairment in patients with schizophrenia.

VII.ObjectivesThinning of retinal layers has been documented in patients with chronic schizophrenia using standard metrics of optical coherence tomography (OCT) devices. We demonstrate the effectiveness of machine learning (ML) techniques to differentiate between schizophrenia patients and healthy controls using OCT images. MethodsFeatures extracted from a convolutional neural network (CNN) designed to segment retinal layers from OCT images represented abstracted data from the OCT images of 14 first episode (FEP) and 18 chronic schizophrenia patients, and their respective 20 and 18 age-matched controls. The abstracted data and OCT machine metrics were used separately to train support vector classification (SVC) models to differentiate between control and schizophrenia samples and test them. ResultsSVCs operating on OCT machine metrics did not classify unseen samples of FEP schizophrenia patients and controls with performance better than chance, while those looking at chronic schizophrenia did, paralleling results obtained using parametric statistics. In contrast, SVCs operating on OCT image data extracted from the CNN classified unseen samples from both populations with performance greater than chance. ConclusionThese results suggest that ML techniques can detect patterns in patients with FEP schizophrenia with greater performance using features extracted from OCT images than metrics provided by OCT machines.

IntroEvidence from neuroimaging has implicated abnormal cerebral cortical patterns in schizophrenia. Application of machine learning techniques is required for identifying structural signature reflecting neurobiological substrates of schizophrenia at the individual level. We aimed to detect and develop a method for potential marker to identify schizophrenia via the features of cerebral cortex using high-resolution magnetic resonance imaging (MRI). MethodIn this study, cortical features were measured, including volumetric (cortical thickness, surface area, and gray matter volume) and geometric (mean curvature, metric distortion, and sulcal depth) features. Patients with first-episode schizophrenia (n = 52) and healthy controls (n = 66) were included from the Department of Psychiatry at Xijing Hospital. Multivariate computation was used to examine the abnormalities of cortical features in schizophrenia. Features were selected by least absolute shrinkage and selection operator (LASSO) method. The diagnostic capacity of multi-dimensional neuroanatomical patterns-based classification was evaluated based on diagnostic tests. ResultsMean curvature (left insula and left inferior frontal gyrus), cortical thickness (left fusiform gyrus), and metric distortion (left cuneus and right superior temporal gyrus) revealed both group differences and diagnostic capacity. Area under receiver operating characteristic curve was 0.88, and the sensitivity, specificity, and accuracy of were 94%, 82%, and 88%, respectively. Confirming these findings, similar results were observed in the independent validation. There was a positive association between index score derived from the multi-dimensional patterns and the severity of symptoms (r = 0.40, P < .01) for patients. DiscussionOur findings demonstrate a view of cortical differences with capacity to discriminate between patients with schizophrenia and healthy population. Structural neuroimaging-based measurements hold great promise of paving the road for their clinical utility in schizophrenia.

ObjectiveCharacterization of psychosis typically relies on cognitive and behavioral assessments. This study suggests the use of feature-specific sensory experiments to detect subtle perceptual alterations in early psychosis. MethodsPatients (N=120) diagnosed with schizophrenia (SCHZ, N=45), bipolar disorder (BIP, N=36), or first-episode psychosis (FEP, N=39), recruited from public mental health facilities in Brazil, were compared with age-matched healthy controls (HCSCHZ, HCBIP, and HCFEP; pooled from N=94). Independent psychophysical measurements were obtained within each group. The Pictorial-Size-Test (PST) assessed pictorial size perception. Sound-Appreciation-Test (SAT) assessed auditory discomfort. ResultsSCHZ circled larger perceived sizes than HCSCHZ (power=95%, d=0.63, p<0.0001), FEP circled larger perceived sizes than HCFEP (power=99%, d=2.86, p<0.0001), but BIP did not perceive larger sizes than HCBIP in PST. SCHZ reported higher levels of discomfort than HCSCHZ (power=99%, d=1.29, p<0.0005), BIP reported higher levels of discomfort than HCBIP (power=99%, d=2.73, p<0.0001) and FEP reported higher levels of discomfort than HCFEP (power=99%, d=1.46, p<0.0003) on SAT. ConclusionsThe findings suggest that low-cost psychophysical measurements can provide information about sensory alterations in early psychosis revealing dissimilar patterns between schizophrenia and bipolar disorder. Such patterns are not readily perceived by physician-patient interaction but may add to overall clinical judgement.

We used a 10-question self-report questionnaire, Mini Voice Questionnaire (MVQ), for mapping the phenomenology of auditory verbal hallucinations (AVH). The MVQ contains questions related to daily AVH frequency and duration, the events preceding AVH episode onset and offset, the very first AVH episode, emotional content, coping strategies, if the voice comes from the inside or outside of head, if it is ones own voice heard, and whether the voice is present when filling out the questionnaire. Forty-one patients with a diagnosis of schizophrenia spectrum disorder participated in the study. The construction of the MVQ was originally driven by an interest in whether AVH-episode onsets and offsets, that is, the coming and going of the voice, are initiated by specific environmental events or mental states, or whether they occur spontaneously, which could have both theoretical and clinical implications. MVQ scores were correlated with PANSS and BAVQ questionnaire scores. The results showed that specific events do not precede onset or offset of AVH episodes except for the very first episode which was often associated with trauma or other negative events. This finding could have implications for neurobiological models of AVH, showing that AVH episodes are spontaneously initiated, pointing to a neuronal origin of AVH episode onsets and offsets. The P3 (hallucinatory behavior) item of the PANSS questionnaire correlated significantly with frequency and duration of AVH episodes: More frequent and longer AVH episodes were associated with higher P3 scores, implying more severe symptoms. The results are discussed in terms of recent AVH models.

Multimodal neuroimaging features might enable accurate classification and provide personalized treatment options in psychiatric domain. We conducted a retrospective study to investigate whether structural and functional features for predicting response to overall treatment of schizophrenia at the end of the first or a single hospitalization and in addition cross validate the results. This structural and functional magnetic resonance imaging (MRI) study included 85 and 63 patients with schizophrenia at baseline in dataset 1 and 2, respectively. After treatment, patients were classified as responders and non-responders. Features of gray matter and functional connectivity were extracted. Radiomics analysis was used to explore the predictive performance. Prediction models were based on structural features, functional features, and combined features. We found that the prediction accuracy was 80.38% (sensitivity: 87.28%; specificity 82.47%) for the model using functional features, and 69.68% (sensitivity: 83.96%; specificity: 72.41%) for the model using structural features. Our model combined both structural and functional features accurately predicted 92.04% responder and 80.23% non-responders to overall treatment, with an accuracy of 85.03%. These results highlight the power of structural and functional MRI-derived radiomics features to predict early response to treatment in schizophrenia. Prediction models of the very early treatment response in schizophrenia could augment effective therapeutic strategies.

An effective way to quantify metacognitive abilities is to ask participants to estimate their confidence in the accuracy of their response during a cognitive task. A recent meta-analysis1 raised the issue that most assessments of metacognitive abilities in schizophrenia spectrum disorders may be confounded with cognitive deficits, which are known to be present in this population. Therefore, it remains unclear whether the reported metacognitive deficits are metacognitive in nature, or rather inherited from cognitive deficits. Arbitrating between these two possibilities requires equating task performance between experimental groups. Here, we aimed to characterize metacognitive performance among individuals with schizophrenia across three tasks (visual detection, familiarity, recollection) using a within-subject design, while controlling experimentally for intra-individual task performance and statistically for between-subject task performance. In line with our hypotheses, we found no metacognitive deficit for visual detection and familiarity judgements. While we expected metacognition for recollection to be specifically impaired among individuals with schizophrenia, we found evidence in favor of an absence of a deficit in that domain also. The clinical relevance of our findings is discussed in light of a hierarchical framework of metacognition.

IntroductionMotor abnormalities have been shown to be a distinct component of schizophrenia symptomatology. However, objective and scalable methods for assessment of motor functioning in schizophrenia are lacking. Advancements in machine learning-based digital tools have allowed for automated and remote digital phenotyping of disease symptomatology. Here, we assess the performance of a computer vision-based assessment of motor functioning as a characteristic of schizophrenia using video data collected remotely through smartphones. Methods18 patients with schizophrenia and 9 healthy controls were asked to remotely participate in smartphone-based assessments daily for 14 days. Video recorded from the smartphone front-facing camera during these assessments was used to quantify head movement through a pre-trained computer vision model. The ability of head movement measurements to distinguish between patients and healthy controls as well as their relationship to schizophrenia symptom severity as measured through traditional clinical scores was assessed. ResultsA logistic regression demonstrated that head movement was a significant predictor of schizophrenia diagnosis (p < 0.05). Linear regression between head movement and clinical scores of schizophrenia symptom severity showed that head movement has a negative relationship with schizophrenia symptom severity (p < 0.05), primarily with negative symptoms of schizophrenia. ConclusionsRemote, smartphone-based assessments were able to capture meaningful visual behavior for computer vision-based objective measurement of head movement. The measurements of head movement acquired were able to accurately classify schizophrenia diagnosis and quantify symptom severity in patients with schizophrenia.

IntroductionAuditory Verbal Hallucinations (AVH) are among the most frequent and severe symptoms in schizophrenia and related psychotic disorders. Virtual Reality (VR)-assisted interventions have emerged, demonstrating promising potential in reducing AVH severity. This treatment approach may be challenged with regards to feasibility, particularly when therapeutically managing the anxiety-related reactions associated with AVH. This pilot study evaluates the feasibility and acceptability of augmenting VR-assisted therapy with real-time biofeedback to address these challenges. The integration of biofeedback enables continuous adaptation of therapy based on physiological responses while allowing participants to train self-regulation of these parameters. MethodsNeuro-VR is a randomized clinical pilot trial utilizing a mixed-methods design. Thirty participants with schizophrenia spectrum disorders and AVH will be randomized to either eight sessions of VR-assisted therapy or eight sessions of VR-assisted therapy augmented with real-time biofeedback. Assessments will be conducted at baseline and post-treatment. Outcome measures include both clinical metrics, electroencephalogram recordings, and qualitative interviews to evaluate feasibility, acceptability, and potential treatment effects of the combined approach. DiscussionThis study will explore whether integrating biofeedback into VR-assisted therapy enhances personalization, supports emotion regulation, and improves tolerability. The findings will provide preliminary evidence on the utility of physiological markers to guide VR-based interventions for AVH and inform the development of individualized, effective treatments for patients with schizophrenia. Trial registrationClinicalTrials.gov, NCT06628323 (https://clinicaltrials.gov/study/NCT06628323). Registered August 19, 2024.

Social cognition, including theory of mind (ToM), is impaired in people with schizophrenia which can significantly impact daily functioning. Current interventions for social cognitive impairment are often time consuming and have limited ecological validity. Combining emerging technologies such as virtual reality (VR) and transcranial alternating current stimulation (tACS) may help address these limitations. The current study applied theta tACS to the right temporoparietal junction (rTPJ) during VR social cognition training in 15 participants with schizophrenia. Neurophysiological (event-related potentials and spectral power) and behavioural outcome measures (ToM task performance) were assessed. Participants underwent two experimental sessions. One session involved VR with concurrent active theta tACS (5Hz frequency) and the other consisted of VR with concurrent sham theta tACS. Resting state electroencephalography (EEG) and ToM tasks with concurrent EEG were measured pre- and post- VR-tACS. Order of stimulation condition was randomised, and stimulation and assessments were all double-blinded. We found ToM task response time improved after VR, regardless of tACS condition. While only VR and active tACS, but not sham, resulted in a widespread increase in resting state theta power. This is the first study to combine VR and tACS in a psychiatric population to address social cognition and provides initial evidence to support the feasibility and efficacy of a combined VR-tACS protocol in schizophrenia. Implications for future research are discussed.

BackgroundCognitive impairment is highly prevalent in schizophrenia and treatment options are severely limited. Development of effective treatments will rely on successful engagement of biological targets. There is growing evidence that the cognitive impairments in schizophrenia are related to impairments in prefrontal cortical inhibition and dysfunctional cortical oscillations. MethodsIn the current study we sought to investigate whether a short course of transcranial Direct Current Stimulation (tDCS) could modulate these pathophysiological targets. Thirty participants with schizophrenia were recruited and underwent neurobiological assessment (Transcranial Magnetic Stimulation combined with EEG [TMS-EEG] and task-related EEG) and assessment of cognitive functioning (n-back task and the MATRICS Consensus Cognitive Battery). Participants were then randomized to receive 5 sessions of either active or sham anodal tDCS to the left prefrontal cortex. Twenty-four hours after the last tDCS session participants repeated the neurobiological and cognitive assessments. Neurobiological outcome measures were TMS-evoked potentials (TEPs), TMS-related oscillations and oscillatory power during a 2-back task. Cognitive outcome measures were d prime and accurate reaction time on the 2-back and MATRICS scores. ResultsFollowing active tDCS there was a significant reduction in the N40 TEP amplitude in the left parietal occipital region. There were no other significant changes. ConclusionsFuture interrogation of evidence based therapeutic targets in large scale RCTs is required.

The present study aims to explain how to use the precision nomothetic approach to analyze the interconnections between the negative symptoms, cognitive dysfunctions and biomarkers of schizophrenia. We review our data obtained in different study groups of patients with (deficit) schizophrenia and show, using examples extracted from these studies, how Partial Least Squares (PLS) path analysis should be used to examine these complex associations. PLS path analysis combines factor and multiple regression analysis in mediated models. We show that a single latent trait can be extracted from negative symptom domains and psychosis, hostility, excitation, mannerism, formal thought disorders and psychomotor retardation (PHEMFP). Both the negative and PHEMFP concepts miss discriminant validity whilst a common latent construct may be extracted from the 6 negative and 6 PHEMFP subdomains, dubbed overall severity of schizophrenia (OSOS). A common latent factor may be extracted from neurocognitive test scores including executive functions, and semantic and episodic memory dubbed the general cognitive decline (G-CoDe) index. PLS analysis shows that the effects of neuroimmunotoxic pathways on OSOS are partly mediated by the G-CoDe and indicate that those pathways have also direct effects on OSOS. We explain that the intercorrelations between those features should be assessed in an unrestricted study group combining patients and controls. Moreover, further bifactorial factor analysis with the restricted schizophrenia group may disclose illness-specific covariations among the features. Machine learning discovered a new schizophrenia phenotype characterized by increased severity of AOPs, G-CoDe, and OSOS, dubbed "major neurocognitive psychosis".

Schizophrenia has been linked to reality monitoring confusions, particularly misattributions of internal productions to external sources. We hypothesized that these misattributions may be related to deficits in processing acoustic cues that distinguish the subjective experience of a sound source inside or outside the head, i.e., sound externalization. This study aimed to investigate sound externalization in patients with schizophrenia, particularly for emotional sounds, as emotion influences auditory perception. In an externalization task, twenty-three patients with schizophrenia and twenty-five healthy controls were exposed to neutral and emotional sounds processed to be perceived as: internalized (diotic) or externalized (filtered with either an anechoic head-related transfer function -HRTF- or a binaural room impulse response -BRIR). Participants had to indicate whether the sound source was perceived inside or outside their head. Exploratory analyses also examined the relationships between externalization, reality monitoring, and symptom severity. Compared to controls, patients with schizophrenia rated the filtered sounds (HRTF, BRIR) as less externalized (pbonf < .001) and the diotic sounds as more externalized (pbonf = 0.004), regardless of emotional content. No significant correlation was found between externalization and reality monitoring. In patients, greater symptom severity was associated with reduced externalization of sounds simulated as originating outside the head. These findings suggest an abnormal perception of sound sources in patients with schizophrenia, who confuse sounds inside and outside the head to a greater extent. Further research is needed to elucidate the relationship between sound externalization and symptoms such as hallucinations.

IntroductionSpeech is a promising marker for schizophrenia-spectrum disorder diagnosis, as it closely reflects symptoms. Previous approaches have made use of different feature domains of speech in classification, including semantic and phonetic features. However, an examination of the relative contribution and accuracy per domain remains an area of active investigation. Here, we examine these domains (i.e. phonetic and semantic) separately and in combination. MethodsUsing a semi-structured interview with neutral topics, speech of 94 schizophrenia-spectrum subjects (SSD) and 73 healthy controls (HC) was recorded. Phonetic features were extracted using a standardized feature set, and transcribed interviews were used to assess word connectedness using a word2vec model. Separate cross-validated random forest classifiers were trained on each feature domain. A third, combinatory classifier was used to combine features from both domains. ResultsThe phonetic domain random forest achieved 81% accuracy in classifying SSD from HC. For the semantic domain, the classifier reached an accuracy of 80% with a sparse set of features with 10-fold cross-validation. Joining features from the domains, the combined classifier reached 85% accuracy, significantly improving on models trained on separate domains. Top features were fragmented speech for phonetic and variance of connectedness for semantic, with both being the top features for the combined classifier. DiscussionBoth semantic and phonetic domains achieved similar results compared with previous research. Combining these features shows the relative value of each domain, as well as the increased classification performance from implementing features from multiple domains. Explainability of models and their feature importance is a requirement for future clinical applications.

BackgroundPeople with schizophrenia exhibit notable difficulties in the use of everyday language. This directly impacts ones ability to complete education and secure employment. An impairment in the ability to understand and generate the correct grammatical structures (syntax) has been suggested as a key contributor; but studies have been underpowered, often with conflicting findings. It is also unclear if syntactic deficits are restricted to a subgroup of patients, or generalized across the broad spectrum of patients irrespective of symptom profiles, age, sex, and illness severity. MethodsWe conducted a systematic review and meta-analysis, registered on OSF, adhering to PRISMA guidelines, searching multiple databases up to May 1, 2024. We extracted effect sizes (Cohens d) and variance differences (log coefficient of variation ratio) across 6 domains: 2 in comprehension (understanding complex syntax, detection of syntactic errors) and 4 in production (global complexity, phrasal/clausal complexity, utterance length, and integrity) in patient-control comparisons. Study quality/bias was assessed using a modified Newcastle-Ottawa Scale. Bayesian meta-analysis was used to estimate domain-specific effects and variance differences. We tested for potential moderators with sufficient data (age, sex, study quality, language spoken) using conventional meta-regression to estimate the sources of heterogeneity between studies. FindingsOverall, 45 studies (n=2960 unique participants, 64{middle dot}4% English, 79 case-control contrasts, weighted mean age(sd)=32{middle dot}3(5{middle dot}6)) were included. Of the patient samples, only 29{middle dot}2% were women. Bayesian meta-analysis revealed extreme evidence for all syntactic domains to be affected in schizophrenia with a large-sized effect (model-averaged d=0{middle dot}65 to 1{middle dot}01, with overall random effects d=0{middle dot}86, 95% CrI [0{middle dot}67-1{middle dot}03]). Syntactic comprehension was the most affected domain. There was notable heterogeneity between studies in global complexity (moderated by the age), production integrity (moderated by study quality), and production length. Robust BMA revealed weak evidence for publication bias. Patients had a small-to-medium-sized excess of inter-individual variability than healthy controls in understanding complex syntax, and in producing long utterances and complex phrases (overall random effects lnCVR=0{middle dot}21, 95% CrI [0{middle dot}07-0{middle dot}36]), hinting at the possible presence of subgroups with diverging syntactic performance. InterpretationThere is robust evidence for the presence of grammatical impairment in comprehension and production in schizophrenia. This knowledge will improve the measurement of communication disturbances in schizophrenia and aid in developing distinct interventions focussed on syntax - a rule-based feature that is potentially amenable to cognitive, educational, and linguistic interventions. Research in ContextO_ST_ABSEvidence before this studyC_ST_ABSPrior studies have documented significant language deficits among individuals with psychosis across multiple levels. However, syntactic divergence--those affecting sentence structure and grammar--have not been consistently quantified or systematically reviewed. An initial review of the literature indicated that the specific nature and severity of syntactic divergence, as well as their impact on narrative speech production, symptom burden, and daily functioning, remain poorly defined. We conducted a comprehensive search of the literature up to May 1, 2024, using databases such as PubMed, PsycINFO, Scopus, Google Scholar, and Web of Science. Our search terms combined psychosis, schizophrenia, language production, comprehension, syntax, and grammar, and we identified a scarcity of meta-analytic studies focusing specifically on syntactic comprehension and production divergence in psychosis. Added value of this studyThis systematic review and meta-analysis is the first to quantitatively assess syntactic comprehension and production divergence in individuals with psychosis. This study provides estimated effect sizes associated with syntactic impairments as well as a quantification of the variance within patient groups for each domain of impairment. Besides a detailed examination of this under-researched domain, we also identify critical research gaps that need to be addressed to derive benefits for patients from knowledge generated in this domain. Implications of all the available evidenceThis study provides robust evidence of grammatical impairments in individuals with schizophrenia, particularly in syntactic comprehension and production. These findings can enhance early detection approaches via speech/text readouts and lead to the development of targeted cognitive, educational, and linguistic interventions. By highlighting the variability in linguistic deficits, the study offers valuable insights for future therapeutic trials. It also supports the creation of personalized formats of information and educational plans aimed at improving the effectiveness of any therapeutic intervention offered to patients with schizophrenia via verbal medium.