Schizophrenia Research

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.

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.

BackgroundWe investigated treatment effects of Acceptance and Commitment Therapy in Daily Life (ACT-DL) on psychological flexibility (PF) and the moderating role of the therapeutic working alliance on these effects in patients with early psychosis. MethodsACT-DL is an ecological momentary intervention (EMI) combining face-to-face ACT with a smartphone app. In the multi-center INTERACT randomized controlled trial, n=148 early psychosis individuals were randomized to either treatment as usual (TAU as the control condition, n=77) or to ACT-DL in addition to TAU (ACT-DL + TAU as the experimental condition, n=71). We assessed global PF and the therapeutic alliance with self-report questionnaires. In addition, we used the experience sampling methodology (ESM) to assess PF with a momentary (in-the-moment and since-the-previous-beep openness) and an evening (daily PF) questionnaire. Assessments took place at baseline, post-intervention (POST), six (FU6), and twelve months (FU12) follow-up. ResultsGlobal (B=19.49 to 33.14; all P-values<.001) and daily PF (B=0.68; P-value<.001) improved equally in both conditions at each time point. Individuals in the ACT-DL condition improved more than those in TAU on momentary openness (in-the-moment openness at POST (B=0.32; P-value=0.007) and since-the-previous-beep openness at POST (B=0.33; P<.001) and FU6 (B=0.23; P-value=0.025). Client-perceived working alliance moderated in-the-moment openness such that larger improvements in openness at POST (B=0.05; P-value<.001) were found in ACT-DL in individuals with higher working alliance scores. ConclusionOur results provide partial support for the capability of ACT-DL to improve daily life measures of openness, and emphasize the importance of the therapeutic relationship in supporting processes of change.

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.

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.

Background and HypothesesWe sought to evaluate the ability of automated speech and language features to track fluctuations in the major psychosis symptoms domains: Thought Disorder, Negative Symptoms, and Positive Symptoms. Study DesignSixty-six participants with psychotic disorders were longitudinally assessed soon after inpatient admission, at discharge, and at 3- and 6-months. Psychosis symptoms were measured with semi-structured interviews and standardized scales. Recordings were collected from paragraph reading, fluency, picture description, and open-ended tasks. Longitudinal relationships between psychosis symptoms and 357 automated speech and language features were analyzed using a single component score and as individual features, using linear mixed models. Study ResultsAll three psychosis symptom domains demonstrated significant longitudinal relationships with the single component score. Thought Disorder was particularly related to features describing more subordinated constructions, less efficient identification of picture elements, and decreased semantic distance between sentences. Negative Symptoms was related to features describing decreased speech complexity. Positive Symptoms appeared heterogeneous, with Suspiciousness relating to greater use of nouns, and Hallucinations related to decreased semantic distances. These relationships were largely robust to interactions with gender and race. However, interactions with timepoint revealed variable relationships during different phases of illness (acute vs. stable). ConclusionsAutomated speech and language features show promise as scalable, objective markers of psychosis severity. The three symptom domains appear to be distinguishable with different features. Detailed attention to clinical setting and patient population is needed to optimize clinical translation; there are substantial implications for facilitating differential diagnosis, improving psychosis outcomes and enhancing therapeutic discovery.

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".

Background and HypothesisIdentifying schizophrenia spectrum disorders (SSD) from spontaneous speech features is a key focus in computational psychiatry today. Study DesignWe present a task-voting procedure using different speech-elicitation tasks to predict SSD in Spanish, followed by ablation studies highlighting the roles of specific tasks and feature domains. Speech from five tasks was recorded from 92 subjects (49 with SSD and 41 controls). A total of 319 features were automatically extracted, from which 24 were pre-selected based on between-feature correlations and ANOVA F-values, covering acoustic-prosody, morphosyntax, and semantic similarity metrics. Study ResultsExtraTrees-based classification using these features yielded an accuracy of 0.840 on hold-out data. Ablating picture descriptions impaired performance most, followed by story reading, retelling, and free speech. Removing morphosyntactic measures impaired performance most, followed by acoustic and semantic measures. Mixed-effect models suggested significant group differences on all 24 features. In SSD, speech patterns were slower and more variable temporally, while variations in pitch, amplitude, and sound intensity decreased. Semantic similarity between speech and prompts decreased, while minimal distances from embedding centroids to each word increased, and word-to-word similarity arrays became more predictable, all replicating patterns documented in other languages. Morphosyntactically, SSD patients used more first-person pronouns together with less third-person pronouns, and more punctuations and negations. Semantic metrics correlated with a range of positive symptoms, and multiple acoustic-prosodic features with negative symptoms. ConclusionsThis study highlights the importance of combining different speech tasks and features for SSD detection, and validates previously found patterns in psychosis for Spanish.

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

While hallucinations are often considered to be the sine qua non of serious mental illness, they are commonly experienced by those without a need for care. Such non-clinical, hallucinating individuals offer a window into the etiology of hallucinations. Garrison et al. (2015) recently assessed structural variability in paracingulate sulcus (PCS) of medial prefrontal cortex. They found a reduction of 10mm in left hemisphere PCS length increased the likelihood of hallucinations by 19.9%. However, these findings were in clinical hallucinators, and reduced leftward asymmetry in PCS has been repeatedly demonstrated in schizophrenia and associated with reduced reality monitoring. Similar analyses have been conducted in non-clinical voice hearers and in those particpants, no reduction in PCS length was reported. However, there is considerable variability in the voice experiences of non-clinical voice hearers. Put simply, some may be more clinical than others. We recently identified a relatively homogenous group of non-clinical voice hearers who identify as clairaudient psychics. Their voice experiences are extremely redolent of those reported by a matched patient voice hearing group and their brain and behavioral responses to a perceptual inference task were, at least at the level of prior beliefs, similarly perturbed to patients who hear voices. These observations are consistent with a continuum model of voice hearing, along which clairaudient voice hearers are nearer to patients with schizophrenia in some respects. The PCS data reported by Garrison et al would militate against such a continuum. Before rejecting the continuum model, we sought to determine if the differences in PCS length also applied to the clairaudient non-clinical voice hearers. If PCS length is to be developed as a biomarker for clinical hallucination risk, then it is important to demonstrate that it is not similarly reduced in non-clinical voice hearers. We find that it is reduced, both in clinical and non-clinical voice-hearers.

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.

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.

Background and HypothesisSchizophrenia (SZ) is considered a "thought disorder". Therefore, language assessment is crucial in diagnosing SZ. Linguistic analysis and emerging computational language models provide objective biomarkers for diagnosis. Against this background, the main hypothesis is that the language patterns of SZ patients are significantly different from those of healthy controls (HCs) in Turkish, as has previously been shown in other languages. MethodsSpeech characteristics of 50 native Turkish-speaking SZ patients were compared with 50 HCs matched for age, sex, length of education, and right/left-handedness. Speech data were collected in 15-minute interviews. The interview recordings were transcribed and analysed for various lexical, syntactic and phonological measures in CLAN and compared for discourse measures using fastText word embedding models. ResultsThe number of words produced per minute, the number of different words, mean length of utterance, average word frequency, the number of filled pauses, discourse coherence and question-response similarity were lower in the patient group than in the control group. On the other hand, content words/function words ratio, sentence prediction loss, different words/total words ratio, the number of silent pauses, and silent pauses/total speech ratio were higher in the patient group than in the control group. ConclusionThe hypothesis is confirmed. The results from Turkish-speaking SZ patients show similarities with results from other languages from other language families. The findings are important as Turkish is a low-resource and relatively under-researched language in the literature. The manuscript is under peer review. Please do not cite this preprint.

Understanding factors that predict the course of schizophrenia remains essential for improving long-term clinical management. Rate and severity of symptom exacerbations vary widely across individuals, and although prior studies have examined potential predictors, findings have been inconsistent and often limited by small samples, infrequent assessments, and non-standardized measures. Using data from phase 1 of the Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE), which includes a large cohort with monthly standardized evaluations, this study investigated whether baseline negative symptom severity predicts risk of symptom exacerbation over time. Participants were 1139 adults aged 18-65 years meeting DSM-IV criteria for schizophrenia. Symptoms worsening or exacerbation was defined as a [&ge;]12-point increase from baseline on the PANSS total score. Cox regression survival models examined the association between baseline PANSS negative symptom tertiles and time to exacerbation, adjusting for age, sex, PANSS positive and general psychopathology subscales, and CGI-Severity. Overall, 25.5% of participants experienced exacerbation over a 18-month period of follow-up. Survival curves demonstrated significant separation across negative symptom tertiles (p=0.047), with higher baseline negative symptoms associated with longer time to exacerbation. Compared with the lowest tertile, medium and high negative symptom groups showed reduced exacerbation risk (HR=0.73 and HR=0.69, respectively; both p=0.03). Findings indicate that greater baseline negative symptom severity is associated with a lower likelihood of short-term symptom worsening, suggesting a relatively stable illness course among individuals with more severe negative symptoms. These results have implications for prognosis and treatment planning, while underscoring the persistent functional burden imposed by negative symptoms despite lower exacerbation risk.

Background and HypothesesSchizotypy is a complex model containing a broad spectrum of personality traits that can be observed in the general population as well as in psychiatric patients. There is compelling evidence that Adverse Childhood Experiences (ACEs) are correlated with schizotypal traits in healthy individuals. We hypothesize that associations between specific forms of abuse and distinct schizotypal traits will demonstrate gender-specific differences. Study DesignThe present study relies on a dataset designed and collected for the VELAS-study (VELAS: Ventral language stream in schizophrenia with regard to semantic and visuo-spatial processing anomalies) in Zurich, Switzerland. Young adults completed an online questionnaire which included the Childhood Trauma Questionnaire (CTQ) and the Oxford-Liverpool Inventory of Feelings and Experiences (O-LIFE). To test individual associations, gender-specific linear regression models were calculated. Study ResultsA total of 597 healthy young adults completed the online questionnaire. Gender-specific linear regression models revealed strong associations of emotional abuse with all schizotypal traits in both sexes with stronger effect sizes for male subjects. In men, sexual abuse was associated with Unusual Experiences, while in women it was associated with Disorganisation. Emotional neglect showed an association with Introvertive Anhedonia in both genders, with stronger effect sizes for male than female participants. Physical neglect exhibited an association with Introvertive Anhedonia solely in male subjects. ConclusionsOur gender-specific results give a deeper insight into associations of ACEs with schizotypal traits and serve as a puzzle piece in understanding risk constellations in the development of schizophrenia spectrum disorders.

Negative symptoms in schizophrenia, particularly motivational deficits, pose significant challenges to treatment and recovery. Despite their profound impact on functional outcomes, these symptoms remain poorly understood and inadequately addressed by current interventions. The CHANSS (Characterising Negative Symptoms in Schizophrenia) study aims to dissect the cognitive mechanisms underlying motivational impairments by focusing on three interconnected domains: executive cognition, motivational cognition, and meta-cognition. This large, international, cross-sectional study recruits a heterogeneous sample of patients across illness stages (from first-episode psychosis to treatment-resistant schizophrenia) and uses a comprehensive cognitive battery, clinical scales, self-report measures, and computerised cognitive tasks. Four novel tasks assess key processes in motivated behaviour: option generation, reward-based decision-making, risk sensitivity, and performance self-evaluation. By incorporating control for secondary influences like depression, psychosis, sedation, and illness chronicity, the study seeks to identify distinct cognitive and behavioural subtypes within motivational dysfunction. CHANSS tests the hypothesis that specific patient profiles exhibit predominant impairments in one or more cognitive domains, which may differentially affect goal-directed behaviour. The study design allows exploration of hierarchical relationships between cognitive processes, such as how executive deficits may cascade to impair motivation and self-evaluation. Ultimately, CHANSS aims to advance mechanistic understanding of motivational deficits in schizophrenia and pave the way for personalised, targeted interventions. Its findings may inform future clinical trials and contribute to a shift away from one-size-fits-all approaches toward more effective, stratified treatment strategies in schizophrenia.

ObjectiveTo describe the clinical outcomes related to the introduction of paliperidone palmitate in a specialty hospital in the Philippines DesignCross-sectional study among patients seen at the psychiatry service of a specialty hospital catering to veterans who were initiated on paliperidone palmitate. We reviewed and abstracted baseline patient data from the medical record of eligible patients. Outcome of treatment was collected through a one-time objective assessment of the patient by a third-party psychiatrist using the Structured Clinical Interview for Symptoms of Remission (SCI-SR) tool. Main ResultA total of 30 patients were recruited for the study from August 2020 and June 2021, the majority of whom were males (80%), residents of the National Capital Region (50%), and single (20%). The median duration from schizophrenia diagnosis to initiation of paliperidone treatment was 19.50 years (IQR: 16.60 - 33.50). In eight patients (22.67%), other antipsychotic drugs were discontinued following initiation of paliperidone treatment; in the remaining 22 participants (73.33%), paliperidone was taken concurrently with other antipsychotic drugs. The median duration from the initiation of paliperidone treatment to follow-up assessment was 27.20 months (IQR: 24.73 - 30.50), with all participants having at least 6 months of treatment. At follow-up assessment, all participants were classified to be in remission. ConclusionIn this study among patients with schizophrenia seen in a specialty hospital in the Philippines, we found evidence that clinical outcomes with paliperidone palmitate were comparable to those given a combination of oral and long-acting antipsychotics.

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.

BackgroundPoor patient retention and family engagement compromise the effectiveness of coordinated specialty care (CSC) for first-episode psychosis (FEP). This mixed methods study aimed to identify program-level characteristics (CSC fidelity and engagement strategies) associated with patient retention and family engagement in Massachusetts CSC programs. MethodsPrimary outcomes were rates of patient retention and family engagement ([&ge;]1 evidence-based family intervention session), based on CSC program census (October 2022 - September 2023). Quantitative analyses explored program characteristics (EPINET Program-Level Core Assessment Battery) and fidelity ratings (Massachusetts Psychosis Fidelity Scale) as predictors using t-tests or univariate linear regressions. Thematic analysis of program interviews compared patient and family engagement strategies employed by high versus low performing programs. ResultsAcross nine programs, mean patient retention was 86% (range: 58-97%) and family engagement was 40% (range: 12-100%). Higher fidelity to evidence-based services (e.g., individual therapy, family intervention, and supported education/employment) was significantly associated with both outcomes (p<.05; R2 range: .51-.72). Mixed-methods analysis showed that high performing programs used case management-related supports to meet service users practical needs. Factors associated with higher patient retention included having comprehensive intake assessments, provider visits during hospitalization, and periodic treatment reviews. Programs that conducted benefits counseling and proactively recommended family services as standard care had higher family engagement. ConclusionsHigher fidelity CSC programs had better patient retention and family engagement. Case management-related supports addressed treatment barriers. Strategies designed to strengthen therapeutic alliance and goal alignment may promote patient engagement, while family engagement may benefit from proactive recommendation of family intervention.

Background and HypothesisMapping a patients speech as a network has proved to be a useful way of understanding formal thought disorder in psychosis. However, to date, graph theory tools have not incorporated the semantic content of speech, which is altered in psychosis. Study DesignWe developed an algorithm, "netts", to map the semantic content of speech as a network, then applied netts to construct semantic speech networks for a general population sample, and a clinical sample comprising patients with first episode psychosis (FEP), people at clinical high risk of psychosis (CHR-P), and healthy controls. Study ResultsSemantic speech networks from the general population were more connected than size-matched randomised networks, with fewer and larger connected components, reflecting the non-random nature of speech. Networks from FEP patients were smaller than from healthy participants, for a picture description task but not a story recall task. For the former task, FEP networks were also more fragmented than those from controls; showing more, smaller connected components. CHR-P networks showed fragmentation values in-between FEP patients and controls. A clustering analysis suggested that semantic speech networks captured novel signal not already described by existing NLP measures. Network features were also related to negative symptom scores and scores on the Thought and Language Index, although these relationships did not survive correcting for multiple comparisons. ConclusionsOverall, these data suggest that semantic networks can enable deeper phenotyping of formal thought disorder in psychosis. We are releasing Netts as an open Python package alongside this manuscript.