ZO-1 Serum Levels as a Potential Biomarker for Psychotic Disorder.

OBJECTIVE: There are limited studies in the literature on the relationship between intestinal and blood-brain barrier permeability and the etiology of schizophrenia. We hypothesized that the difference in serum ZO-1 levels in patients with schizophrenia may affect the severity of the disease. The aim of this study was to investigate the role of changes in serum ZO-1 concentrations in the etiopathogenesis of patients with schizophrenia. METHODS: A total of 46 patients, 34 with schizophrenia, 12 with a first psychotic attack, and 37 healthy controls, were included in the study. Symptom severity was determined by applying the Positive and Negative Syndrome Scale and the Clinical Global Impression-Severity Scale. Serum ZO-1 levels were measured from venous blood samples. RESULTS: Serum ZO-1 levels were higher in patients with psychotic disorder compared to healthy controls. There was no statistically significant difference between the groups in the first psychotic attack group and the schizophrenia patients. There was a statistically significant positive correlation between serum ZO-1 levels and Positive and Negative Syndrome Scale positive symptom score. CONCLUSIONS: These findings regarding ZO-1 levels suggest that dysregulation of the blood-brain barrier in psychotic disorder may play a role in the etiology of the disorder.

Downregulation of miR-29a as a possible diagnostic biomarker for schizophrenia.

BACKGROUND: MicroRNAs (miRNAs) are epigenetic factors regulating many genes involved in brain development. Dysregulation of miRNA could result in dysregulation of genes which may contribute to diseases affecting the brain and behavior (e.g., schizophrenia). miR-29 family is a miRNA family contributing to brain maturation. miR-29 knockout in animal studies is reported to correlate with psychiatric disorders very similar to those seen in schizophrenia. In this study, we aimed to evaluate the miR-29a level in patients with schizophrenia and its potential value in the diagnosis of schizophrenia. MATERIALS AND METHODS: The serum sample of 42 patients with schizophrenia and 40 healthy subjects were obtained from the Azeri Recent onset/Acute phase psychosis Survey (ARAS) Cohort study. After preparations, the expression level of miR-29a was investigated by real-time PCR. The SPSS and GraphPad prism software were used to analyze the relation between miR-29a level and clinical parameters and its potential as a biomarker for the diagnosis of schizophrenia. RESULTS: Our study showed a significantly lower miR-29a level in patients compared to healthy controls (p = 0.0012). Furthermore, miR-29a level was significantly lower in some types of schizophrenia (p = 0.024). miR-29a level was not related to sex, age, or heredity (p > 0.05). miR-29a also showed 80% specificity and 71.43% sensitivity in the diagnosis of schizophrenia. CONCLUSION: Downregulation of miR-29a in schizophrenia is significantly related to the development of this illness. It might have the potential as a biomarker for schizophrenia.

[Clinical and psychometric characteristics of cognitive and negative disorders in schizophrenia]. / Klinicheskie i psikhometricheskie osobennosti kognitivnykh i negativnykh rasstroistv pri shizofrenii.

OBJECTIVE: To establish the characteristics of clinical manifestations and cognitive tests in patients with schizophrenia, with a predominance of cognitive and negative disorders. MATERIAL AND METHODS: We examined 76 patients, 66 in the main group, 10 in the comparison group, who were treated in Psychiatric Hospital No. 1 and Psychiatric Hospital No. 4 (Moscow). Clinical-psychopathological, psychometric and statistical methods were used. Features of cognitive functioning were studied using the Frontal Assessment Battery (FAB) and the Edinburgh Cognitive and Behavioural Amyotrophic Lateral Sclerosis (ALS) Screen (ECAS). Emotional intelligence scores were assessed using the Ekman Face Emotion Recognition (EFER) test. RESULTS: Patients with schizophrenia showed dominance of one of 3 types of deficit symptoms: cognitive, emotional, and volitional. Cognitive functions were significantly reduced in patients with schizophrenia when compared with the comparison group (mean FAB score (M±SD) 13.44±2.97 in patients with schizophrenia vs. 16.10±1.70 in the comparison group; t=4.10; p<0.001). Cognitive functions were particularly reduced in patients with volitional deficit (mean EFER total score 42.40±9.0 in patients with volitional deficit vs. 47.21±633 in patients with cognitive deficit; t=2.12; p=0.039; mean FAB score 12.83±3.29 in patients with volitional deficit vs. 16.10±1.70 in the comparison group; t=4.24; p<0.001; mean ECAS score specific to ALS 78.80±9.07 in patients with volitional deficit vs. 84.50±6.71 in the comparison group; t=2.18; p=0.034). CONCLUSION: The greatest contribution to the development of cognitive disorders in schizophrenia is made by dysfunction of frontal (especially) and temporal cortex. Executive functions, speech skills and verbal fluency are most severely damaged.

Theta-burst rTMS in schizophrenia to ameliorate negative and cognitive symptoms: study protocol for a double-blind, sham-controlled, randomized clinical trial.

BACKGROUND: Treatment effects of conventional approaches with antipsychotics or psychosocial interventions are limited when it comes to reducing negative and cognitive symptoms in schizophrenia. While there is emerging clinical evidence that new, augmented protocols based on theta-burst stimulation can increase rTMS efficacy dramatically in depression, data on similar augmented therapies are limited in schizophrenia. The different patterns of network impairments in subjects may underlie that some but not all patients responded to given stimulation locations. METHODS: Therefore, we propose an augmented theta-burst stimulation protocol in schizophrenia by stimulating both locations connected to negative symptoms: (1) the left dorsolateral prefrontal cortex (DLPFC), and (2) the vermis of the cerebellum. Ninety subjects with schizophrenia presenting negative symptoms and aging between 18 and 55 years will be randomized to active and sham stimulation in a 1:1 ratio. The TBS parameters we adopted follow the standard TBS protocols, with 3-pulse 50-Hz bursts given every 200 ms (at 5 Hz) and an intensity of 100% active motor threshold. We plan to deliver 1800 stimuli to the left DLPFC and 1800 stimuli to the vermis daily in two 9.5-min blocks for 4 weeks. The primary endpoint is the change in negative symptom severity measured by the Positive and Negative Syndrome Scale (PANSS). Secondary efficacy endpoints are changes in cognitive flexibility, executive functioning, short-term memory, social cognition, and facial emotion recognition. The difference between study groups will be analyzed by a linear mixed model analysis with the difference relative to baseline in efficacy variables as the dependent variable and treatment group, visit, and treatment-by-visit interaction as independent variables. The safety outcome is the number of serious adverse events. DISCUSSION: This is a double-blind, sham-controlled, randomized medical device study to assess the efficacy and safety of an augmented theta-burst rTMS treatment in schizophrenia. We hypothesize that social cognition and negative symptoms of patients on active therapy will improve significantly compared to patients on sham treatment. TRIAL REGISTRATION: The study protocol is registered at "ClinicalTrials.gov" with the following ID: NCT05100888. All items from the World Health Organization Trial Registration Data Set are registered. Initial release: 10/19/2021.

Multiple serum anti-glutamate receptor antibody levels in clozapine-treated/naïve patients with treatment-resistant schizophrenia.

BACKGROUND: Glutamatergic function abnormalities have been implicated in the etiology of treatment-resistant schizophrenia (TRS), and the efficacy of clozapine may be attributed to its impact on the glutamate system. Recently, evidence has emerged suggesting the involvement of immune processes and increased prevalence of antineuronal antibodies in TRS. This current study aimed to investigate the levels of multiple anti-glutamate receptor antibodies in TRS and explore the effects of clozapine on these antibody levels. METHODS: Enzyme linked immunosorbent assay (ELISA) was used to measure and compare the levels of anti-glutamate receptor antibodies (NMDAR, AMPAR, mGlur3, mGluR5) in clozapine-treated TRS patients (TRS-C, n = 37), clozapine-naïve TRS patients (TRS-NC, n = 39), and non-TRS patients (nTRS, n = 35). Clinical symptom severity was assessed using the Positive and Negative Symptom Scale (PANSS), while cognitive function was evaluated using the MATRICS Consensus Cognitive Battery (MCCB). RESULT: The levels of all four glutamate receptor antibodies in TRS-NC were significantly higher than those in nTRS (p < 0.001) and in TRS-C (p < 0.001), and the antibody levels in TRS-C were comparable to those in nTRS. However, no significant associations were observed between antibody levels and symptom severity or cognitive function across all three groups after FDR correction. CONCLUSION: Our findings suggest that TRS may related to increased anti-glutamate receptor antibody levels and provide further evidence that glutamatergic dysfunction and immune processes may contribute to the pathogenesis of TRS. The impact of clozapine on anti-glutamate receptor antibody levels may be a pharmacological mechanism underlying its therapeutic effects.

Integrating Clinical Neuropsychology and Psychotic Spectrum Disorders: A Systematic Analysis of Cognitive Dynamics, Interventions, and Underlying Mechanisms.

Background and Objectives: The study aims to provide a comprehensive neuropsychological analysis of psychotic spectrum disorders, including schizophrenia, bipolar disorder, and depression. It focuses on the critical aspects of cognitive impairments, diagnostic tools, intervention efficacy, and the roles of genetic and environmental factors in these disorders. The paper emphasizes the diagnostic significance of neuropsychological tests in identifying cognitive deficiencies and their predictive value in the early management of psychosis. Materials and Methods: The study involved a systematic literature review following the PRISMA guidelines. The search was conducted in significant databases like Scopus, PsycINFO, PubMed, and Web of Science using keywords relevant to clinical neuropsychology and psychotic spectrum disorders. The inclusion criteria required articles to be in English, published between 2018 and 2023, and pertinent to clinical neuropsychology's application in these disorders. A total of 153 articles were identified, with 44 ultimately included for detailed analysis based on relevance and publication status after screening. Results: The review highlights several key findings, including the diagnostic and prognostic significance of mismatch negativity, neuroprogressive trajectories, cortical thinning in familial high-risk individuals, and distinct illness trajectories within psychosis subgroups. The studies evaluated underline the role of neuropsychological tests in diagnosing psychiatric disorders and emphasize early detection and the effectiveness of intervention strategies based on cognitive and neurobiological markers. Conclusions: The systematic review underscores the importance of investigating the neuropsychological components of psychotic spectrum disorders. It identifies significant cognitive impairments in attention, memory, and executive function, correlating with structural and functional brain abnormalities. The paper stresses the need for precise diagnoses and personalized treatment modalities, highlighting the complex interplay between genetic, environmental, and psychosocial factors. It calls for a deeper understanding of these neuropsychological processes to enhance diagnostic accuracy and therapeutic outcomes.

A preliminary metabolomics study of the database for biological samples of schizophrenia among Chinese ethnic minorities.

BACKGROUND: Schizophrenia (SCZ) is a profound mental disorder with a multifactorial etiology, including genetics, environmental factors, and demographic influences such as ethnicity and geography. Among these, the studies of SCZ also shows racial and regional differences. METHODS: We first established a database of biological samples for SCZ in China's ethnic minorities, followed by a serum metabolomic analysis of SCZ patients from various ethnic groups within the same region using the LC-HRMS platform. RESULTS: Analysis identified 47 metabolites associated with SCZ, with 46 showing significant differences between Miao and Han SCZ patients. These metabolites, primarily fatty acids, amino acids, benzene, and derivatives, are involved in fatty acid metabolism pathways. Notably, L-Carnitine, L-Cystine, Aspartylphenylalanine, and Methionine sulfoxide demonstrated greater diagnostic efficacy in Miao SCZ patients compared to Han SCZ patients. CONCLUSION: Preliminary findings suggest that there are differences in metabolic levels among SCZ patients of different ethnicities in the same region, offering insights for developing objective diagnostic or therapeutic monitoring strategies that incorporate ethnic considerations of SCZ.

Serum HMGB1 and Beclin 1 Levels in Patients with a Diagnosis of Schizophrenia. / Sizofreni Tanili Hastalarda Serum HMGB1 ve Beklin 1 Düzeyleri.

OBJECTIVE: It is known that inflammation plays a role in the etiopathogenesis of schizophrenia. In this study, we examined high mobility group box 1 protein (HMGB1) and Beclin 1 levels and their relationship with clinical variables in patients with schizophrenia. METHOD: Forty-three patients with schizophrenia and 43 healthy controls were included in this study. The patients were administered sociodemographic data form, the Positive Negative Symptoms Assessment Scale (PANSS) and the Clinical Global Impressions (CGI) scale. After the scales were filled, venous blood samples were taken from both the patient and control groups to measure serum HMGB1 and Beclin 1 levels. Serum samples obtained at the end of centrifugation were measured by Enzyme-Linked ImmunoSorbent Assay (ELISA) method. RESULTS: The mean serum HMGB1 levels were significantly increased and the mean serum Beclin 1 levels were significantly decreased in the schizophrenia group compared to the control group. In addition, a negative correlation was found between HMGB1 and Beclin 1 levels. CONCLUSION: In conclusion, current research shows that HMGB1 is increased and Beclin 1 is decreased in patients with schizophrenia, and these findings may contribute to the role of autophagy in the pathogenesis of schizophrenia.

PsiOvi Staging Model for Schizophrenia (PsiOvi SMS): A New Internet Tool for Staging Patients with Schizophrenia.

BACKGROUND: One of the challenges of psychiatry is the staging of patients, especially those with severe mental disorders. Therefore, we aim to develop an empirical staging model for schizophrenia. METHODS: Data were obtained from 212 stable outpatients with schizophrenia: demographic, clinical, psychometric (PANSS, CAINS, CDSS, OSQ, CGI-S, PSP, MATRICS), inflammatory peripheral blood markers (C-reactive protein, interleukins-1RA and 6, and platelet/lymphocyte [PLR], neutrophil/lymphocyte [NLR], and monocyte/lymphocyte [MLR] ratios). We used machine learning techniques to develop the model (genetic algorithms, support vector machines) and applied a fitness function to measure the model's accuracy (% agreement between patient classification of our model and the CGI-S). RESULTS: Our model includes 12 variables from 5 dimensions: 1) psychopathology: positive, negative, depressive, general psychopathology symptoms; 2) clinical features: number of hospitalizations; 3) cognition: processing speed, visual learning, social cognition; 4) biomarkers: PLR, NLR, MLR; and 5) functioning: PSP total score. Accuracy was 62% (SD = 5.3), and sensitivity values were appropriate for mild, moderate, and marked severity (from 0.62106 to 0.6728). DISCUSSION: We present a multidimensional, accessible, and easy-to-apply model that goes beyond simply categorizing patients according to CGI-S score. It provides clinicians with a multifaceted patient profile that facilitates the design of personalized intervention plans.

The evaluation study for social cognition measures in Japan: Psychometric properties, relationships with social function, and recommendations.

AIM: Patients with schizophrenia can have significant subjective difficulties in social cognition, but few undergo testing or treatment for social cognition. The Social Cognition Psychometric Evaluation (SCOPE) study recommends six social cognitive measures; however, the reliability and validity of these measures in different cultural and linguistic areas has not been adequately examined. We examined the psychometric properties of nine social cognitive measures and the relationship to social function, with the aim of determining the level of recommendation for social cognitive measures in clinical practice in Japan. METHODS: For our test battery, an expert panel previously selected nine measures: the Bell Lysaker Emotion Recognition Task (BLERT); Facial Emotion Selection Test (FEST); Hinting Task (Hinting); Metaphor and Sarcasm Scenario Test (MSST); Intentionality Bias Task (IBT); Ambiguous Intentions and Hostility Questionnaire (AIHQ); Social Attribution Task-Multiple Choice (SAT-MC); SAT-MCII; and Biological Motion (BM) task. In total, 121 outpatients with schizophrenia and 70 healthy controls were included in the analysis, and the results were provided to an expert panel to determine the recommendations for each measure. The quantitative psychological indices of each measure were evaluated for practicality, tolerability, test-retest reliability, correlation with social function, and the incremental validity of social function. RESULTS: Hinting and FEST received the highest recommendations for use in screening, severity assessment, and longitudinal assessment, followed by BLERT, MSST AIHQ, SAT-MC, and SAT-MCII, with IBT and BM receiving the lowest recommendations. CONCLUSION: This study provides a uniform assessment tool that can be used in future international clinical trials for social cognitive impairment.

Identification and treatment of individuals with childhood-onset and early-onset schizophrenia.

Approximately 8 % of patients with schizophrenia are diagnosed before age 18, and 18 % experience their first symptoms before age 18. This narrative review explores the management of patients with early-onset schizophrenia (EOS) and childhood-onset schizophrenia (COS) from diagnosis to their transition to adult care settings. Early diagnosis of schizophrenia in children and adolescents is essential for improving outcomes, but delays are common due to overlapping of symptoms with developmental phenomena and other psychiatric conditions, including substance use, and lack of clinicians' awareness. Once diagnosed, antipsychotic treatment is key, with specific second-generation agents generally being preferred due to better tolerability and their broader efficacy evidence-base in youth. Dosing should be carefully individualized, considering age-related differences in drug metabolism and side effect liability. Clinicians must be vigilant in detecting early non-response and consider switching or dose escalation when appropriate. Since early age of illness onset is a consistent risk factor for treatment-resistant schizophrenia (TRS), clinicians need to be competent in diagnosing TRS and using clozapine. Since COS and EOS are associated with cognitive deficits and impaired functioning, psychosocial interventions should be considered to improve overall functioning and quality of life. Good long-term outcomes depend on continuous treatment engagement, and successful transitioning from pediatric to adult care requires careful planning, early preparation, and collaboration between pediatric and adult clinicians. Targeting functional outcomes and quality of life in addition to symptom remission can improve overall patient well-being. Comprehensive evaluations, age-specific assessments, and targeted interventions are needed to address the unique challenges of EOS and COS.

Diagnosing schizophrenia using deep learning: Novel interpretation approaches and multi-site validation.

Schizophrenia is a profound and enduring mental disorder that imposes significant negative impacts on individuals, their families, and society at large. The development of more accurate and objective diagnostic tools for schizophrenia can be expedited through the employment of deep learning (DL), that excels at deciphering complex hierarchical non-linear patterns. However, the limited interpretability of deep learning has eroded confidence in the model and restricted its clinical utility. At the same time, if the data source is only derived from a single center, the model's generalizability is difficult to test. To enhance the model's reliability and applicability, leave-one-center-out validation with a large and diverse sample from multiple centers is crucial. In this study, we utilized Nine different global centers to train and test the 3D Resnet model's generalizability, resulting in an 82% classification performance (area under the curve) on all datasets sourced from different countries, employing a leave-one-center-out-validation approach. Per our approximation of the feature significance of each region on the atlas, we identified marked differences in the thalamus, pallidum, and inferior frontal gyrus between individuals with schizophrenia and healthy controls, lending credence to prior research findings. At the same time, in order to translate the model's output into clinically applicable insights, the SHapley Additive exPlanations (SHAP) permutation explainer method with an anatomical atlas have been refined, thereby offering precise neuroanatomical and functional interpretations of different brain regions.

Taste dysfunction as a predictor of depression in schizophrenia: A systematic review and meta-analysis.

OBJECTIVE: This study aims to investigate the relationship between taste dysfunction and depression among patients with schizophrenia, to achieve early detection of depression in clinical practice. METHODS: Following PRISMA guidance, a comprehensive literature search was conducted globally, covering papers published from 1961 to June 2023. A total of 17 manuscripts were selected through meta-analysis and sensitivity analysis after examining available materials from seven databases to determine the correlation between depression and taste dysfunction. RESULTS: The comparison of the 17 selected manuscripts revealed that individuals with gustatory dysfunction may be more likely to experience depressive symptoms (SMD, 0.51, 95% CI, 0.08 to 0.93, p = 0.02). Depression is associated with taste dysfunction in certain aspects, as indicated by the pleasantness ratings of sucrose solutions (SMD, -0.53, 95% confidence interval [CI] -1.11 to 0.05, p = 0.08), gustatory identification ability (SMD, 0.96, 95% CI, 0.03 to 1.89, p = 0.04), and the perception threshold of sweet taste (MD, 0.80, 95% CI, 0.79 to 0.81, p < 0.00001). CONCLUSIONS: Due to variations in the methods, designs, and selection criteria employed in the included studies, it is necessary to establish a feasible framework. Future research using detailed and targeted approaches can provide clearer and more unified conclusions on the relationship between taste dysfunction and depression. Moreover, further high-quality research is needed to obtain clearer conclusions and explore the potential of taste dysfunction as an effective tool for early screening of depression. TRIAL REGISTRATION: This review has been registered in the PROSPERO on April 2022 with the identifier CRD42023400172.

Psychometric evaluation of the Arabic language version of the Birchwood Insight Scale in patients with schizophrenia.

BACKGROUND: Clinical insight (i.e., impaired insight into illness) is increasingly recognized by the scientific community as a significant contributor to an array of psychological and clinical outcomes in schizophrenia. Therefore, its assessment using a reliable, rapid, easy and economic tool is important for clinical practice. This study proposes to investigate the psychometric properties of an Arabic translation of the Birchwood Insight Scale (BIS) in Arabic-speaking chronic patients with schizophrenia. Our objectives were to identify the most adequate factor structure of the BIS among the several measurement models previously proposed in the literature, verify the reliability and measurement invariance of the BIS across sex groups, and explore the concurrent validity of the BIS through examining its patterns of correlations with psychotic symptoms. METHOD: One hundred seventeen Arabic-speaking chronic, remitted patients with schizophrenia took part in this study. An Arabic translated version of the BIS and the Positive and Negative Syndrome Scale (PANSS) were administered to participants. RESULTS: Confirmatory factor analyses (CFA) showed that, after omitting two items with low loadings (items 1 and 2), the unidimensional factor model of the BIS showed good fit indices and a reliability of α = 0.68 and ω = 0.68. However, analyses failed to show good fit for the full-length one-, two-, and three-factor models of the BIS in its Arabic version. Measurement invariance of the Arabic 6-item one-factor BIS was established between males and females at the configural, metric and scalar levels; no statistically significant difference between males and females was found in terms of BIS scores. Finally, BIS scores correlated significantly with the PANSS scores in our sample, thus demonstrating adequate concurrent validity. CONCLUSION: This study offers valuable additional psychometric information about the BIS based on results of CFA and other analyses in schizophrenia from a non-Western cultural environment. We believe that making the BIS available in Arabic might benefit clinicians working with Arabic-speaking patients with schizophrenia, open new avenues of research and gain a better knowledge into the nature of clinical insight and its relevance to psychopathology.

Speech markers to predict and prevent recurrent episodes of psychosis: A narrative overview and emerging opportunities.

Preventing relapse in schizophrenia improves long-term health outcomes. Repeated episodes of psychotic symptoms shape the trajectory of this illness and can be a detriment to functional recovery. Despite early intervention programs, high relapse rates persist, calling for alternative approaches in relapse prevention. Predicting imminent relapse at an individual level is critical for effective intervention. While clinical profiles are often used to foresee relapse, they lack the specificity and sensitivity needed for timely prediction. Here, we review the use of speech through Natural Language Processing (NLP) to predict a recurrent psychotic episode. Recent advancements in NLP of speech have shown the ability to detect linguistic markers related to thought disorder and other language disruptions within 2-4 weeks preceding a relapse. This approach has shown to be able to capture individual speech patterns, showing promise in its use as a prediction tool. We outline current developments in remote monitoring for psychotic relapses, discuss the challenges and limitations and present the speech-NLP based approach as an alternative to detect relapses with sufficient accuracy, construct validity and lead time to generate clinical actions towards prevention.

A naturalistic cohort study of first-episode schizophrenia spectrum disorder: A description of the early phase of illness in the PSYSCAN cohort.

BACKGROUND: We examined the course of illness over a 12-month period in a large, international multi-center cohort of people with a first-episode schizophrenia spectrum disorder (FES) in a naturalistic, prospective study (PSYSCAN). METHOD: Patients with a first episode of schizophrenia, schizoaffective disorder (depressive type) or schizophreniform disorder were recruited at 16 institutions in Europe, Israel and Australia. Participants (N = 304) received clinical treatment as usual throughout the study. RESULTS: The mean age of the cohort was 24.3 years (SD = 5.6), and 67 % were male. At baseline, participants presented with a range of intensities of psychotic symptoms, 80 % were taking antipsychotic medication, 68 % were receiving psychological treatment, with 46.5 % in symptomatic remission. The mean duration of untreated psychosis was 6.2 months (SD = 17.0). After one year, 67 % were in symptomatic remission and 61 % were in functional remission, but 31 % had been readmitted to hospital at some time after baseline. In the cohort as a whole, depressive symptoms remained stable over the follow-up period. In patients with a current depressive episode at baseline, depressive symptoms slightly improved. Alcohol, tobacco and cannabis were the most commonly used substances, with daily users of cannabis ranging between 9 and 11 % throughout the follow-up period. CONCLUSIONS: This study provides valuable insight into the early course of a broad range of clinical and functional aspects of illness in FES patients in routine clinical practice.

Quantum Machine-Based Decision Support System for the Detection of Schizophrenia from EEG Records.

Schizophrenia is a serious chronic mental disorder that significantly affects daily life. Electroencephalography (EEG), a method used to measure mental activities in the brain, is among the techniques employed in the diagnosis of schizophrenia. The symptoms of the disease typically begin in childhood and become more pronounced as one grows older. However, it can be managed with specific treatments. Computer-aided methods can be used to achieve an early diagnosis of this illness. In this study, various machine learning algorithms and the emerging technology of quantum-based machine learning algorithm were used to detect schizophrenia using EEG signals. The principal component analysis (PCA) method was applied to process the obtained data in quantum systems. The data, which were reduced in dimensionality, were transformed into qubit form using various feature maps and provided as input to the Quantum Support Vector Machine (QSVM) algorithm. Thus, the QSVM algorithm was applied using different qubit numbers and different circuits in addition to classical machine learning algorithms. All analyses were conducted in the simulator environment of the IBM Quantum Platform. In the classification of this EEG dataset, it is evident that the QSVM algorithm demonstrated superior performance with a 100% success rate when using Pauli X and Pauli Z feature maps. This study serves as proof that quantum machine learning algorithms can be effectively utilized in the field of healthcare.

Simultaneous SERS Sensing of Cysteine and Homocysteine in Blood Based on the CBT-Cys Click Reaction: Toward Precisive Diagnosis of Schizophrenia.

At present, there is a lack of sufficiently specific laboratory diagnostic indicators for schizophrenia. Serum homocysteine (Hcy) levels have been found to be related to schizophrenia. Cysteine (Cys) is a demethylation product in the metabolism of Hcy, and they always coexist with highly similar structures in vivo. There are few reports on the use of Cys as a diagnostic biomarker for schizophrenia in collaboration with Hcy, mainly because the rapid, economical, accurate, and high-throughput simultaneous detection of Cys and Hcy in serum is highly challenging. Herein, a click reaction-based surface-enhanced Raman spectroscopy (SERS) sensor was developed for simultaneous and selective detection of Cys and Hcy. Through the efficient and specific CBT-Cys click reaction between the probe containing cyan benzothiazole and Cys/Hcy, the tiny methylene difference between the molecular structures of Cys and Hcy was converted into the difference between the ring skeletons of the corresponding products that could be identified by plasmonic silver nanoparticle enhanced molecular fingerprint spectroscopy to realize discriminative detection. Furthermore, the SERS sensor was successfully applied to the detection in related patient serum samples, and it was found that the combined analysis of Cys and Hcy can improve the diagnostic accuracy of schizophrenia compared to a single indicator.

EEG alpha reactivity on eyes opening discriminates patients with schizophrenia and schizoaffective disorder.

OBJECTIVE: Alpha activity in the electroencephalogram (EEG) is typically dominant during rest with closed eyes but suppressed by visual stimulation. Previous research has shown that alpha-blockade is less pronounced in schizophrenia patients compared to healthy individuals, but no studies have examined it in schizoaffective disorder. METHODS: A resting state EEG was used for the analysis of the alpha-reactivity between the eyes closed and the eyes opened conditions in overall (8 - 13 Hz), low (8 - 10 Hz) and high (10 - 13 Hz) alpha bands in three groups: schizophrenia patients (SC, n = 30), schizoaffective disorder (SA, n = 30), and healthy controls (HC, n = 36). All patients had their first psychotic episode and were receiving antipsychotic therapy. RESULTS: A significant decrease in alpha power was noted across all subjects from the eyes-closed to eyes-open condition, spanning all regions. Alpha reactivity over the posterior regions was lower in SC compared to HC within overall and high alpha. SA showed a trend towards reduced alpha reactivity compared to HC, especially evident over the left posterior region within the overall alpha. Alpha reactivity was more pronounced over the middle and right posterior regions of SA as compared to SC, particularly in the high alpha. Alpha reactivity in SC and SA patients was associated with various negative symptoms. CONCLUSIONS: Our findings imply distinct alterations in arousal mechanisms in SC and SA and their relation to negative symptomatology. Arousal is more preserved in SA. SIGNIFICANCE: This study is the first to compare the EEG features of arousal in SC and SA.