[Application of the Trail Making Test in the schizophrenia research]. / A Trail Making Teszt alkalmazása a szkizofrénia-kutatásban.

Even the Kraepelinian concept of dementia praecox suggests a link between schizophrenia and various cognitive deficits. Although cognitive impairment is not a fundamental symptom of schizophrenia, it is considered to be one of the basic features of the disease. The deficit can affect a number of cognitive domains and is most often specific. One of the most pronounced cognitive symptoms of schizophrenia is impairment in attentional and executive functions. The Trail Making Test (TMT) is a screening test commonly used in the clinic that is very sensitive to impairments in attention and executive functions. The aim of the present study is to summarise the research conducted in the last five years in which the Trail Making Test has been used to screen schizophrenics. A search was conducted in the PubMed database using the keywords "schizophrenia" and "Trail Making Test". A total of 43 relevant studies have been published on this topic since 2018. A review of the research on this topic shows that the TMT can be used to identify cognitive deficits in schizophrenics, affecting executive functions and attention. It also shows that schizophrenic patients performed significantly worse on the test than healthy individuals.

Revisiting Rorschach test and Minnesota Multiphasic Personality Inventory-II patterns in Kraepelinian vs. DSM-wise Schizophrenia: How They Differ and What It Means for Diagnosis.

OBJECTIVE: This retrospective chart review study aimed to investigate the differences in the Rorschach test and Minnesota Multiphasic Personality Inventory (MMPI)-II profiles among patients with Kraepelinian schizophrenia, those with DSM-wise schizophrenia, and controls. Kraepelinian schizophrenia is characterised by a chronic, deteriorative disease course and a predominance of negative symptoms. METHODS: Patients with Kraepelinian schizophrenia were selected based on medical record reviews. We then compared their Rorschach test and MMPI-II results with those of the DSM-wise schizophrenia group and the control group. RESULTS: The Rorschach test revealed a significant increase in DV2 score and a decrease in D score in patients with Kraepelinian schizophrenia compared to those with DSM-wise schizophrenia. In the MMPI-II profiles, patients with Kraepelinian schizophrenia exhibited an elevated L relative to those with DSM-wise schizophrenia. CONCLUSION: Our results suggested the value of revisiting psychological tests in clinically delineated subgroups, such as Kraepelinian schizophrenia. Although patients fall under the same diagnostic category of schizophrenia, considering different phenotypes is important when interpreting psychological test outcomes. Additionally, our study indicated that both schizophrenia groups did not show as many abnormalities as expected compared to controls. This highlights the potential value of revisiting established profiles of certain psychological tests and calls for further research on other psychological tests.

Media Use and Its Associations With Paranoia in Schizophrenia and Bipolar Disorder: Ecological Momentary Assessment.

Background: Paranoia is a spectrum of fear-related experiences that spans diagnostic categories and is influenced by social and cognitive factors. The extent to which social media and other types of media use are associated with paranoia remains unclear. Objective: We aimed to examine associations between media use and paranoia at the within- and between-person levels. Methods: Participants were 409 individuals diagnosed with schizophrenia spectrum or bipolar disorder. Measures included sociodemographic and clinical characteristics at baseline, followed by ecological momentary assessments (EMAs) collected 3 times daily over 30 days. EMA evaluated paranoia and 5 types of media use: social media, television, music, reading or writing, and other internet or computer use. Generalized linear mixed models were used to examine paranoia as a function of each type of media use and vice versa at the within- and between-person levels. Results: Of the 409 participants, the following subgroups reported at least 1 instance of media use: 261 (63.8%) for using social media, 385 (94.1%) for watching TV, 292 (71.4%) for listening to music, 191 (46.7%) for reading or writing, and 280 (68.5%) for other internet or computer use. Gender, ethnoracial groups, educational attainment, and diagnosis of schizophrenia versus bipolar disorder were differentially associated with the likelihood of media use. There was a within-person association between social media use and paranoia: using social media was associated with a subsequent decrease of 5.5% (fold-change 0.945, 95% CI 0.904-0.987) in paranoia. The reverse association, from paranoia to subsequent changes in social media use, was not statistically significant. Other types of media use were not significantly associated with paranoia. Conclusions: This study shows that social media use was associated with a modest decrease in paranoia, perhaps reflecting the clinical benefits of social connection. However, structural disadvantage and individual factors may hamper the accessibility of media activities, and the mental health correlates of media use may further vary as a function of contents and contexts of use.

Wavelet Transform, Reconstructed Phase Space, and Deep Learning Neural Networks for EEG-Based Schizophrenia Detection.

This study proposes an innovative expert system that uses exclusively EEG signals to diagnose schizophrenia in its early stages. For diagnosing psychiatric/neurological disorders, electroencephalogram (EEG) testing is considered a financially viable, safe, and reliable alternative. Using the reconstructed phase space (RPS) and the continuous wavelet transform, the researchers maximized the differences between the EEG nonstationary signals of normal and schizophrenia individuals, which cannot be observed in the time, frequency, or time-frequency domains. This reveals significant information, highlighting more distinguishable features. Then, a deep learning network was trained to enhance the accuracy of the resulting image classification. The algorithm's efficacy was confirmed through three distinct methods: employing 70% of the dataset for training, 15% for validation, and the remaining 15% for testing. This was followed by a 5-fold cross-validation technique and a leave-one-out classification approach. Each method was iterated 100 times to ascertain the algorithm's robustness. The performance metrics derived from these tests - accuracy, precision, sensitivity, F1 score, Matthews correlation coefficient, and Kappa - indicated remarkable outcomes. The algorithm demonstrated steady performance across all evaluation strategies, underscoring its relevance and reliability. The outcomes validate the system's accuracy, precision, sensitivity, and robustness by showcasing its capability to autonomously differentiate individuals diagnosed with schizophrenia from those in a state of normal health.

Importance of Early Diagnosis and Treatment of Perioperative Catatonia: A Case Report.

Symptoms of catatonia include silence, motionlessness, and postural retention. Although it is important to detect and treat catatonia early, before it becomes severe, postoperative cases have inherent risks that hinder diagnosis and treatment. A 60-year-old man with schizophrenia underwent endoscopic/thoracoscopic esophagectomy and was extubated in the operating room. In the intensive care unit (ICU), he had stiffness in the neck, ankles, and knees, catalepsy during passive knee flexion, mild disturbance of consciousness, mild creatine kinase elevation, and respiratory depression. Intravenous diazepam was administered for diagnosis, and the patient's rapid improvement indicated catatonia. He was intubated and started on lorazepam; tapering produced no recurrence of symptoms. The patient was extubated and transferred to the general ward on postoperative Day 2. Because this patient was extubated in the operating room and was managed postoperatively in the ICU with a full-time doctor, his symptoms were easily recognized and early diagnosis was possible. Thus, we were able to administer drug therapy quickly and adequately and perform forward management that accounted for postoperative risks, thereby achieving a favorable outcome.

Psychometric evaluation of the patient-reported experience of cognitive impairment in schizophrenia (PRECIS) scale.

BACKGROUND: Cognitive impairment associated with schizophrenia (CIAS) represents a distinct, persistent, and core group of schizophrenia symptoms. Cognitive symptoms have been shown to have an impact on quality of life. There are several published CIAS measures, but none based on direct patient self-report. It is important to capture the patient's perspective to supplement performancebased outcome measures of cognition to provide a complete picture of the patient's experience. This paper describes additional validation work on the Patient-Reported Experience of Cognitive Impairment in Schizophrenia (PRECIS) instrument. METHODS: Data from two large, international, pharmaceutical clinical trials in medically and psychiatrically stable English-speaking patients with schizophrenia and 88 healthy controls were analyzed. An exploratory factor analysis (EFA) was conducted in one trial (n = 215), using the original 35-item PRECIS. The factor structure suggested by EFA was further evaluated using item response theory (IRT; Samejima's graded response model), and tested using confirmatory factor analysis (CFA). Both EFA and CFA results were tested in a second trial with similar inclusion/exclusion characteristics (n = 410). Additional statistical properties were evaluated in healthy controls. RESULTS: EFA suggested that the best solution after item reduction suggested a factor structure of 6 factors based on 26 items (memory, communication, self-control, executive function, attention, sharpness of thought), supporting a total score, with an additional 2-item bother score (28 items in all). IRT analysis indicated the items were well-ordered within each domain. The CFA demonstrated excellent model fit, accounting for 69% of the variance. The statistical properties of the 28-item version of the PRECIS were confirmed in the second trial. Evidence for internal consistency and test-retest reliability was robust. Known-groups validity was supported by comparison of healthy controls with patients with schizophrenia. Correlations indicated moderate associations between PRECIS and functioning instruments like the Schizophrenia Cognition Rating Scale (SCoRS), but weak correlations with performance-based outcomes like MATRICS Consensus Cognitive Battery (MCCB). DISCUSSION: Using two clinical trial samples, we identified a robust factor structure for the PRECIS and were able to replicate it in the second sample. Evaluation of the meaningful score difference (MSD) should be repeated in future studies, as these samples did not show enough change for it to be evaluated. CONCLUSIONS: This analysis provides strong evidence for the reliability and validity of the PRECIS, a 28-item, patient-reported instrument to assess cognitive impairment associated with schizophrenia. The correlation with functioning and the weak correlation with performance on cognitive tasks suggests that patient reports of cognitive impairment measure a unique aspect of patient experience.

Severe psychiatric disorders are associated with increased risk of dementia.

BACKGROUND: Individuals with psychiatric disorders have an increased risk of developing dementia. Most cross-sectional studies suffer from selection bias, underdiagnosis and poor population representation, while there is only limited evidence from longitudinal studies on the role of anxiety, bipolar and psychotic disorders. Electronic health records (EHRs) permit large cohorts to be followed across the lifespan and include a wide range of diagnostic information. OBJECTIVE: To assess the association between four groups of psychiatric disorders (schizophrenia, bipolar disorder/mania, depression and anxiety) with dementia in two large population-based samples with EHR. METHODS: Using EHR on nearly 1 million adult individuals in Wales, and from 228 937 UK Biobank participants, we studied the relationships between schizophrenia, mania/bipolar disorder, depression, anxiety and subsequent risk of dementia. FINDINGS: In Secure Anonymised Information Linkage, there was a steep increase in the incidence of a first diagnosis of psychiatric disorder in the years prior to the diagnosis of dementia, reaching a peak in the year prior to dementia diagnosis for all psychiatric diagnoses. Psychiatric disorders, except anxiety, were highly significantly associated with a subsequent diagnosis of dementia: HRs=2.87, 2.80, 1.63 for schizophrenia, mania/bipolar disorder and depression, respectively. A similar pattern was found in the UK Biobank (HRs=4.46, 3.65, 2.39, respectively) and anxiety was also associated with dementia (HR=1.34). Increased risk of dementia was observed for all ages at onset of psychiatric diagnoses when these were divided into 10-year bins. CONCLUSIONS: Psychiatric disorders are associated with an increased risk of subsequent dementia, with a greater risk of more severe disorders. CLINICAL IMPLICATIONS: A late onset of psychiatric disorders should alert clinicians of possible incipient dementia.

A novel blood-based epigenetic biosignature in first-episode schizophrenia patients through automated machine learning.

Schizophrenia (SCZ) is a chronic, severe, and complex psychiatric disorder that affects all aspects of personal functioning. While SCZ has a very strong biological component, there are still no objective diagnostic tests. Lately, special attention has been given to epigenetic biomarkers in SCZ. In this study, we introduce a three-step, automated machine learning (AutoML)-based, data-driven, biomarker discovery pipeline approach, using genome-wide DNA methylation datasets and laboratory validation, to deliver a highly performing, blood-based epigenetic biosignature of diagnostic clinical value in SCZ. Publicly available blood methylomes from SCZ patients and healthy individuals were analyzed via AutoML, to identify SCZ-specific biomarkers. The methylation of the identified genes was then analyzed by targeted qMSP assays in blood gDNA of 30 first-episode drug-naïve SCZ patients and 30 healthy controls (CTRL). Finally, AutoML was used to produce an optimized disease-specific biosignature based on patient methylation data combined with demographics. AutoML identified a SCZ-specific set of novel gene methylation biomarkers including IGF2BP1, CENPI, and PSME4. Functional analysis investigated correlations with SCZ pathology. Methylation levels of IGF2BP1 and PSME4, but not CENPI were found to differ, IGF2BP1 being higher and PSME4 lower in the SCZ group as compared to the CTRL group. Additional AutoML classification analysis of our experimental patient data led to a five-feature biosignature including all three genes, as well as age and sex, that discriminated SCZ patients from healthy individuals [AUC 0.755 (0.636, 0.862) and average precision 0.758 (0.690, 0.825)]. In conclusion, this three-step pipeline enabled the discovery of three novel genes and an epigenetic biosignature bearing potential value as promising SCZ blood-based diagnostics.

Automated diagnosis of schizophrenia based on spatial-temporal residual graph convolutional network.

BACKGROUND: Schizophrenia (SZ), a psychiatric disorder for which there is no precise diagnosis, has had a serious impact on the quality of human life and social activities for many years. Therefore, an advanced approach for accurate treatment is required. NEW METHOD: In this study, we provide a classification approach for SZ patients based on a spatial-temporal residual graph convolutional neural network (STRGCN). The model primarily collects spatial frequency features and temporal frequency features by spatial graph convolution and single-channel temporal convolution, respectively, and blends them both for the classification learning, in contrast to traditional approaches that only evaluate temporal frequency information in EEG and disregard spatial frequency features across brain regions. RESULTS: We conducted extensive experiments on the publicly available dataset Zenodo and our own collected dataset. The classification accuracy of the two datasets on our proposed method reached 96.32% and 85.44%, respectively. In the experiment, the dataset using delta has the best classification performance in the sub-bands. COMPARISON WITH EXISTING METHODS: Other methods mainly rely on deep learning models dominated by convolutional neural networks and long and short time memory networks, lacking exploration of the functional connections between channels. In contrast, the present method can treat the EEG signal as a graph and integrate and analyze the temporal frequency and spatial frequency features in the EEG signal. CONCLUSION: We provide an approach to not only performs better than other classic machine learning and deep learning algorithms on the dataset we used in diagnosing schizophrenia, but also understand the effects of schizophrenia on brain network features.

Is There a Relationship between Psychotic Disorders and the Radicalization Process? A Systematic Review.

Background and Objectives: Radicalization, a complex and multifaceted phenomenon, has been a subject of increasing concern in recent years, particularly due to its potential connection to acts of mass violence and terrorism. This systematic review examines the intricate link between radicalization and psychotic disorders, utilizing various sources such as observational studies, case reports, and series. It aims to highlight the prevalence of schizophrenia spectrum and other psychotic disorders among radicalized individuals and to define the role of mental health professionals in dealing with this issue, contributing to the development of prevention and treatment strategies. Materials and Methods: The methodology involved an extensive literature search across PubMed, Scopus, and APA PsycINFO up to 1 February 2024, adhering to PRISMA guidelines. The study focused on radicalization and psychotic disorders as defined by DSM-5 criteria, excluding other mental disorders. A population sample of 41 radicalized individuals diagnosed with psychotic disorders was selected, among which schizophrenia was identified as the predominant condition. Results: It was observed that 24% of these individuals passed away soon after committing their crimes, leading the researchers to rely on retrospective data for their diagnoses. The use of diverse assessment tools for psychiatric diagnosis and the lack of a standardized method for diagnosing or assessing involvement in the radicalization process were also noted. Despite limitations like reliance on observational studies and case reports, which result in low evidence quality and varied methodologies, our work provides a valuable contribution to clarifying the relationship between radicalization and psychotic disorders. However, further clinical studies are needed to delve deeper into these aspects. Conclusions: In conclusion, our review points out that individuals with psychotic disorders do not have a higher crime rate than the general population and warns against associating crimes with mental illness due to the stigma it creates. The lack of uniform psychiatric diagnostic tools and radicalization assessment highlights the need for more standardized risk assessment tools and validated scales in psychiatric diagnosis to better understand the relationship between radicalization and psychotic disorders and to develop integrated protocols.

Protein Profiles and Novel Molecular Biomarkers of Schizophrenia Based on 4D-DIA Proteomics.

Schizophrenia is a severe psychological disorder. The current diagnosis mainly relies on clinical symptoms and lacks laboratory evidence, which makes it very difficult to make an accurate diagnosis especially at an early stage. Plasma protein profiles of schizophrenia patients were obtained and compared with healthy controls using 4D-DIA proteomics technology. Furthermore, 79 DEPs were identified between schizophrenia and healthy controls. GO functional analysis indicated that DEPs were predominantly associated with responses to toxic substances and platelet aggregation, suggesting the presence of metabolic and immune dysregulation in patients with schizophrenia. KEGG pathway enrichment analysis revealed that DEPs were primarily enriched in the chemokine signaling pathway and cytokine receptor interactions. A diagnostic model was ultimately established, comprising three proteins, namely, PFN1, GAPDH and ACTBL2. This model demonstrated an AUC value of 0.972, indicating its effectiveness in accurately identifying schizophrenia. PFN1, GAPDH and ACTBL2 exhibit potential as biomarkers for the early detection of schizophrenia. The findings of our studies provide novel insights into the laboratory-based diagnosis of schizophrenia.

Discriminative analysis of schizophrenia and major depressive disorder using fNIRS.

BACKGROUND: Research into the shared and distinct brain dysfunctions in patients with schizophrenia (SCZ) and major depressive disorder (MDD) has been increasing. However, few studies have explored the application of functional near-infrared spectroscopy (fNIRS) in investigating brain dysfunction and enhancing diagnostic methodologies in these two conditions. METHODS: A general linear model was used for analysis of brain activation following task-state fNIRS from 131 patients with SCZ, 132 patients with MDD and 130 healthy controls (HCs). Subsequently, seventy-seven time-frequency analysis methods were used to construct new features of fNIRS, followed by the implementation of five machine learning algorithms to develop a differential diagnosis model for the three groups. This model was evaluated by comparing it to both a diagnostic model relying on traditional fNIRS features and assessments made by two psychiatrists. RESULTS: Brain activation analysis revealed significantly lower activation in Broca's area, the dorsolateral prefrontal cortex, and the middle temporal gyrus for both the SCZ and MDD groups compared to HCs. Additionally, the SCZ group exhibited notably lower activation in the superior temporal gyrus and the subcentral gyrus compared to the MDD group. When distinguishing among the three groups using independent validation datasets, the models utilizing new fNIRS features achieved an accuracy of 85.90 % (AUC = 0.95). In contrast, models based on traditional fNIRS features reached an accuracy of 52.56 % (AUC = 0.66). The accuracies of the two psychiatrists were 42.00 % (AUC = 0.60) and 38.00 % (AUC = 0.50), respectively. CONCLUSION: This investigation brings to light the shared and distinct neurobiological abnormalities present in SCZ and MDD, offering potential enhancements for extant diagnostic systems.

Study Protocol: Global Research Initiative on the Neurophysiology of Schizophrenia (GRINS) project.

BACKGROUND: Objective and quantifiable markers are crucial for developing novel therapeutics for mental disorders by 1) stratifying clinically similar patients with different underlying neurobiological deficits and 2) objectively tracking disease trajectory and treatment response. Schizophrenia is often confounded with other psychiatric disorders, especially bipolar disorder, if based on cross-sectional symptoms. Awake and sleep EEG have shown promise in identifying neurophysiological differences as biomarkers for schizophrenia. However, most previous studies, while useful, were conducted in European and American populations, had small sample sizes, and utilized varying analytic methods, limiting comprehensive analyses or generalizability to diverse human populations. Furthermore, the extent to which wake and sleep neurophysiology metrics correlate with each other and with symptom severity or cognitive impairment remains unresolved. Moreover, how these neurophysiological markers compare across psychiatric conditions is not well characterized. The utility of biomarkers in clinical trials and practice would be significantly advanced by well-powered transdiagnostic studies. The Global Research Initiative on the Neurophysiology of Schizophrenia (GRINS) project aims to address these questions through a large, multi-center cohort study involving East Asian populations. To promote transparency and reproducibility, we describe the protocol for the GRINS project. METHODS: The research procedure consists of an initial screening interview followed by three subsequent sessions: an introductory interview, an evaluation visit, and an overnight neurophysiological recording session. Data from multiple domains, including demographic and clinical characteristics, behavioral performance (cognitive tasks, motor sequence tasks), and neurophysiological metrics (both awake and sleep electroencephalography), are collected by research groups specialized in each domain. CONCLUSION: Pilot results from the GRINS project demonstrate the feasibility of this study protocol and highlight the importance of such research, as well as its potential to study a broader range of patients with psychiatric conditions. Through GRINS, we are generating a valuable dataset across multiple domains to identify neurophysiological markers of schizophrenia individually and in combination. By applying this protocol to related mental disorders often confounded with each other, we can gather information that offers insight into the neurophysiological characteristics and underlying mechanisms of these severe conditions, informing objective diagnosis, stratification for clinical research, and ultimately, the development of better-targeted treatment matching in the clinic.

Multi-scale entropy assessment of magnetoencephalography signals in schizophrenia.

Schizophrenia is a severe disruption in cognition and emotion, affecting fundamental human functions. In this study, we applied Multi-Scale Entropy analysis to resting-state Magnetoencephalography data from 54 schizophrenia patients and 98 healthy controls. This method quantifies the temporal complexity of the signal across different time scales using the concept of sample entropy. Results show significantly higher sample entropy in schizophrenia patients, primarily in central, parietal, and occipital lobes, peaking at time scales equivalent to frequencies between 15 and 24 Hz. To disentangle the contributions of the amplitude and phase components, we applied the same analysis to a phase-shuffled surrogate signal. The analysis revealed that most differences originate from the amplitude component in the δ, α, and ß power bands. While the phase component had a smaller magnitude, closer examination reveals clear spatial patterns and significant differences across specific brain regions. We assessed the potential of multi-scale entropy as a schizophrenia biomarker by comparing its classification performance to conventional spectral analysis and a cognitive task (the n-back paradigm). The discriminative power of multi-scale entropy and spectral features was similar, with a slight advantage for multi-scale entropy features. The results of the n-back test were slightly below those obtained from multi-scale entropy and spectral features.

Schizophrenia, the very idea: On self-disorder, hyperreflexivity, and the diagnostic concept.

The purpose of the present article is to consider schizophrenia-the very idea-from the perspective of phenomenological psychopathology, with special attention to the problematic nature of the diagnostic concept as well as to the prospect and challenges inherent in focusing on subjective experience. First, we address historical and philosophical topics relevant to the legitimacy of diagnostic categorization-in general and regarding "schizophrenia" in particular. William James's pragmatist approach to categorization is discussed. Then we offer a version of the well-known basic-self or ipseity-disturbance model (IDM) of schizophrenia, but in a significantly revised form (IDMrevised). The revised model better acknowledges the diverse and even seemingly contradictory nature of schizophrenic symptoms while, at the same time, interpreting these in a more unitary fashion via the key concept of hyperreflexivity-a form of exaggerated self-awareness that tends to undermine normal world-directedness and the stability of self-experience. Particular attention is paid to forms of exaggerated "self-presence" that are sometimes neglected yet imbue classically schizophrenic experiences involving subjectivism or quasi-solipsism and/or all-inclusive or ontological forms of paranoia. We focus on the distinctively paradoxical nature of schizophrenic symptomatology. In concluding we consider precursors in the work of Klaus Conrad, Kimura Bin and Henri Grivois. Finally we defend the concept of schizophrenia by considering its distinctive way of altering certain core aspects of the human condition itself.

Aberrant salience mediates the interplay between emotional abuse and positive symptoms in schizophrenia.

INTRODUCTION: Childhood trauma and adversities (CTA) and aberrant salience (AS) have a pivotal role in schizophrenia development, but their interplay with psychotic symptoms remains vague. We explored the mediation performed by AS between CTA and psychotic symptomatology in schizophrenia. METHODS: We approached 241 adults suffering from schizophrenia spectrum disorders (SSDs), who have been in the unit for at least 12 consecutive months, excluding the diagnosis of dementia, and recent substance abuse disorder, and cross-sectional evaluated through the Aberrant Salience Inventory (ASI), Childhood Trauma Questionnaire Short-Form (CTQ-SF), and Positive and Negative Symptom Scale (PANSS). We tested a path-diagram where AS mediated the relationship between CTA and psychosis, after verifying each measure one-dimensionality through confirmatory factor analysis. RESULTS: The final sample comprised 222 patients (36.9% female), with a mean age of 42.4 (± 13.3) years and an average antipsychotic dose of 453.6 (± 184.2) mg/day (chlorpromazine equivalents). The mean duration of untreated psychosis was 1.8 (± 2.0) years while the mean onset age was 23.9 (± 8.2) years. Significant paths were found from emotional abuse to ASI total score (ß = 0.39; p < .001) and from ASI total score to PANSS positive (ß = 0.17; p = .019). Finally, a statistically significant indirect association was found from emotional abuse to PANSS positive mediated by ASI total score (ß = 0.06; p = .041; CI 95% [0.01, 0.13]). CONCLUSION: Emotional abuse has an AS-mediated effect on positive psychotic symptomatology. AS evaluation could allow a better characterization of psychosis as well as explain the presence of positive symptoms in adults with SSDs who experienced CTA.

The Future-oriented Repetitive Thought (FoRT) scale: Validation in Chinese samples and application in the schizophrenia spectrum.

BACKGROUND: Repetitive thoughts are usually associated with psychopathology. The Future-oriented Repetitive Thought (FoRT) Scale is a measure designed to capture frequency of repetitive thought about positive and negative future events. However, the validity of the scale in Chinese population and its application in the schizophrenia spectrum have not been examined. METHODS: The current study aimed to examine the psychometric properties of the Chinese version of the FoRT scale and to apply it to the schizophrenia spectrum. In Study 1, three samples (total N = 1875) of university students were recruited for exploratory factor analysis, confirmatory factor analysis, and validity test, respectively. In Study 2, we identified subsamples with high schizotypal traits (N = 89) and low schizotypal traits (N = 89), and recruited 36 inpatients with schizophrenia and 41 matched healthy controls. RESULTS: The three-factor (pessimistic repetitive future thinking, repetitive thinking about future goals, and positive indulging about the future) structure of the FoRT scale with one item deleted, fitted the Chinese samples. And the scale could distinguish patients with schizophrenia and individuals with high schizotypal traits from controls. CONCLUSION: These findings support that the Chinese version of the FoRT scale is a valid tool and provide evidence for the potential applications in the schizophrenia spectrum.

Tablet-Based Cognitive and Eye Movement Measures as Accessible Tools for Schizophrenia Assessment: Multisite Usability Study.

BACKGROUND: Schizophrenia is a complex mental disorder characterized by significant cognitive and neurobiological alterations. Impairments in cognitive function and eye movement have been known to be promising biomarkers for schizophrenia. However, cognitive assessment methods require specialized expertise. To date, data on simplified measurement tools for assessing both cognitive function and eye movement in patients with schizophrenia are lacking. OBJECTIVE: This study aims to assess the efficacy of a novel tablet-based platform combining cognitive and eye movement measures for classifying schizophrenia. METHODS: Forty-four patients with schizophrenia, 67 healthy controls, and 41 patients with other psychiatric diagnoses participated in this study from 10 sites across Japan. A free-viewing eye movement task and 2 cognitive assessment tools (Codebreaker task from the THINC-integrated tool and the CognitiveFunctionTest app) were used for conducting assessments in a 12.9-inch iPad Pro. We performed comparative group and logistic regression analyses for evaluating the diagnostic efficacy of the 3 measures of interest. RESULTS: Cognitive and eye movement measures differed significantly between patients with schizophrenia and healthy controls (all 3 measures; P<.001). The Codebreaker task showed the highest classification effectiveness in distinguishing schizophrenia with an area under the receiver operating characteristic curve of 0.90. Combining cognitive and eye movement measures further improved accuracy with a maximum area under the receiver operating characteristic curve of 0.94. Cognitive measures were more effective in differentiating patients with schizophrenia from healthy controls, whereas eye movement measures better differentiated schizophrenia from other psychiatric conditions. CONCLUSIONS: This multisite study demonstrates the feasibility and effectiveness of a tablet-based app for assessing cognitive functioning and eye movements in patients with schizophrenia. Our results suggest the potential of tablet-based assessments of cognitive function and eye movement as simple and accessible evaluation tools, which may be useful for future clinical implementation.

Dysregulation of Plasma Growth Factors and Chemokines in Cocaine Use Disorder: Implications for Dual Diagnosis with Schizophrenia and Antisocial Personality Disorder in an Exploratory Study.

INTRODUCTION: Dual diagnosis in individuals with cocaine use disorders (CUDs) presents a mental health challenge marked by an increased susceptibility to disabling morbidities and premature mortality. Despite extensive research on depression and anxiety, other prevalent comorbidities, such as psychotic and personality disorders, have received less attention. This study explores inflammation-related mediators as potential biomarkers for CUD and dual diagnosis with schizophrenia (SCZ) or antisocial personality disorder (APD). METHODS: This exploratory study included 95 participants, comprising 40 healthy subjects and 55 abstinent patients with CUD. Lifetime CUD was diagnosed either as single diagnosis (CUD group, N = 25) or as a dual diagnosis (DD group. N = 30) with SCZ (CUD+SCZ subgroup) or APD (CUD+APD subgroup). Participants were clinically assessed, and the plasma concentrations of growth factors (i.e., G-CSF, BDNF, and VEGF-A) and chemokines (i.e., CCL11/eotaxin-1, CCL2/MCP-1, and CXCL12/SDF-1) were determined and log(10)-transformed for analysis. RESULTS: Growth factors and chemokines were dysregulated by CUD and psychiatric diagnoses. Specifically, patients in the CUD group exhibited significantly lower concentrations of G-CSF and CCL11/eotaxin-1 than the control group. In contrast, the DD group showed significantly higher concentrations of all analytes than both the CUD and control groups. Additionally, no differences in these analytes were observed between the CUD+SCZ and CUD+APD subgroups within the DD group. Regarding cocaine-related variables, significant associations were identified in the CUD group: an inverse correlation between the age at first cocaine use and the concentrations of BDNF and CCL2/MCP-1; and a positive correlation between the duration of the cocaine abstinence and the concentrations of BDNF and CCL11/eotaxin-1. Lastly, a logistic regression model incorporating all these analytes demonstrated high discriminatory power in distinguishing patients with CUD alone from those with dual diagnosis. CONCLUSIONS: Individuals with dual diagnosis of CUD exhibit elevated concentrations of growth factors and chemokines, distinguishing them from those with CUD alone. It is unclear whether the differences in these inflammatory mediators are specific to the presence of SCZ and APD. The study highlights potential biomarkers and associations, providing valuable insights into the intricate interplay of CUD and psychiatric disorders to enhance clinical diagnosis and therapeutics.