First-episode Schizophrenia Research Articles

Brain Age Gap as a Predictor of Early Treatment Response and Functional Outcomes in First-Episode Schizophrenia: A Longitudinal Study: L'écart d'âge cérébral comme prédicteur de la réponse en début de traitement et des résultats fonctionnels dans un premier épisode de schizophrénie : une étude longitudinale.

Accelerated brain aging, i.e., the age-related structural changes in the brain appearing earlier than expected from one's chronological age, is a feature that is now well established in schizophrenia. Often interpreted as a feature of a progressive pathophysiological process that typifies schizophrenia, its prognostic relevance is still unclear. We investigate its role in response to antipsychotic treatment in first-episode schizophrenia. We recruited 49 drug-naive patients with schizophrenia who were then treated with risperidone at a standard dose range of 2-6 mg/day. We followed them up for 3 months to categorize their response status. We acquired T1-weighted anatomical images and used the XGboost method to evaluate individual brain age. The brain age gap (BAG) is the difference between the predicted brain age and chronological age. Patients with FES had more pronounced BAG compared to healthy subjects, and this difference was primarily driven by those who did not respond adequately after 12 weeks of treatment. BAG did not worsen significantly over the 12-week period, indicating a lack of prominent brain-ageing effect induced by the early antipsychotic exposure per se. However, highly symptomatic patients had a more prominent increase in BAG, while patients with higher BAG when initiating treatment later showed lower gains in global functioning upon treatment, highlighting the prognostic value of BAG measures in FES. Accelerated brain aging is a feature of first-episode schizophrenia that is more likely to be seen among those who will not respond adequately to first-line antipsychotic use. Given that early poor response indicates later treatment resistance, measuring BAG using structural MRI in the first 12 weeks of treatment initiation may provide useful prognostic information when considering second-line treatments in schizophrenia.

Improved patient identification by incorporating symptom severity in deep learning using neuroanatomic images in first episode schizophrenia.

Brain alterations associated with illness severity in schizophrenia remain poorly understood. Establishing linkages between imaging biomarkers and symptom expression may enhance mechanistic understanding of acute psychotic illness. Constructing models using MRI and clinical features together to maximize model validity may be particularly useful for these purposes. A multi-task deep learning model for standard case/control recognition incorporated with psychosis symptom severity regression was constructed with anatomic MRI collected from 286 patients with drug-naïve first-episode schizophrenia and 330 healthy controls from two datasets, and validated with an independent dataset including 40 first-episode schizophrenia. To evaluate the contribution of regression to the case/control recognition, a single-task classification model was constructed. Performance of unprocessed anatomical images and of predefined imaging features obtained using voxel-based morphometry (VBM) and surface-based morphometry (SBM), were examined and compared. Brain regions contributing to the symptom severity regression and illness identification were identified. Models developed with unprocessed images achieved greater group separation than either VBM or SBM measurements, differentiating schizophrenia patients from healthy controls with a balanced accuracy of 83.0% with sensitivity = 76.1% and specificity = 89.0%. The multi-task model also showed superior performance to single-task classification model without considering clinical symptoms. These findings showed high replication in the site-split validation and external validation analyses. Measurements in parietal, occipital and medial frontal cortex and bilateral cerebellum had the greatest contribution to the multi-task model. Incorporating illness severity regression in pattern recognition algorithms, our study developed an MRI-based model that was of high diagnostic value in acutely ill schizophrenia patients, highlighting clinical relevance of the model.

Glucose Metabolism and Sex Hormones in Male Patients with Medication-naïve First-episode Schizophrenia: A Large-scale Cross-sectional Study.

Schizophrenia (SCZ) usually begins in early adult life. The underlying molecular mechanisms of SCZ remain unclear. There is evidence for the involvement of abnormalities in metabolic and endocrine systems in SCZ, even in drug-naïve first-episode schizophrenia patients (DNFES). However, the association between impaired regulation of glucose metabolism and sex hormones was not studied in SCZ. This study aimed to evaluate the interrelationship between sex hormones and high fasting glucose levels in male DNFES patients. A total of 99 patients with SCZ were recruited, and fasting glucose, fasting insulin, the insulin resistance index (HOMA-IR), and sex hormones were measured. We found that some male patients with SCZ had abnormal levels in glucose metabolism parameters and gonadal hormones that were not within the normal range. Linear regression analysis adjusted for age, waist circumference, and body mass index showed that testosterone levels were negatively associated with fasting insulin in male patients (β = -0.21, t = -2.2, p = 0.03). Our findings confirm the abnormalities in glucose metabolism parameters and gonadal hormones at the onset of the illness in male DNFES patients with SCZ. In addition, there was an interaction effect between abnormal glucose metabolism and sex hormones in male patients.

Altered intrinsic neural activity and its molecular analyses in first-episode schizophrenia with auditory verbal hallucinations.

Auditory verbal hallucinations (AVHs) are one of the signature positive symptoms of schizophrenia, affecting a substantial portion of patients with schizophrenia. These hallucinations seriously impact the lives of patients, resulting in a substantial social burden. Recent studies have shown a significant correlation between abnormal local brain activity and the neurobiological mechanisms of AVHs. However, it is not fully clear whether altered intrinsic brain activity in schizophrenia patients with AVHs is correlated with specific neurotransmitter systems. We included 50 first-episode, drug-naïve schizophrenia patients with AVHs, 50 patients without AVHs (NAVHs), and 50 age- and sex-matched healthy controls (HCs). The amplitude of low-frequency fluctuation (ALFF) was utilized to explore the altered intrinsic brain activity in the AVH group. Subsequently, we spatially correlated the altered ALFF with neurotransmitter maps using JuSpace. In our study, compared to HCs, the AVH group exhibited significantly reduced ALFF in multiple brain regions, mainly including the left precuneus, bilateral supplementary motor areas, bilateral paracentral lobules, bilateral precentral gyri, and bilateral postcentral gyri. The NAVH group showed significantly reduced ALFF in the left inferior occipital gyrus, left calcarine gyrus, and left lingual gyrus compared to HCs. Furthermore, the AVH group showed higher ALFF in the right inferior frontal gyrus compared to the NAVH group. Additionally, these ALFF alterations in the AVH group were closely related to three neurotransmitters, including dopamine, serotonin and norepinephrine. We link neurotransmitters to abnormal intrinsic brain activity in first-episode, drug-naïve schizophrenia patients with AVHs, contributing to a comprehensive understanding of the pathophysiological processes and treatment pathways underlying AVHs.

High-order brain network feature extraction and classification method of first-episode schizophrenia: an EEG study.

A multimodal persistent topological feature extraction and classification method is proposed to enhance the recognition accuracy of first-episode schizophrenia patients. This approach addresses the limitations of traditional higher-order brain network analyses that rely on single persistent features (e.g., persistent images). The study utilized resting-state EEG data from 198 subjects recruited at Huilongguan Hospital in Beijing, comprising 102 males and 96 females, with a mean age of 30 years and mean education of 14 years. Persistent topological features were extracted using adaptive thresholding during persistent homology (PH) filtrations. The distribution of these features was visualized through heatmaps and persistence entropies, while the generation process was elucidated using Betti curves and persistence landscapes. The classification performance of the multimodal persistent topological features was assessed using various machine learning classifiers. The classifier yielding the highest performance was selected for comparison with traditional brain network features derived from graph theory and single persistent topological features. The results revealed significant topological changes in first-episode schizophrenia patients throughout the persistent homology filtering compared to healthy subjects. The univariate feature selection algorithm achieved a classification accuracy of 94.6% with a combination of attributes meeting the criterion of AC ≥ 0.6. The proposed method demonstrates clinical significance for the early identification and diagnosis of first-episode schizophrenia patients, offering a new research perspective for constructing higher-order functional connectivity networks and extracting topological structure features.

Effects of 6-week olanzapine treatment on serum IL-2, IL-4, IL-8, IL-10, and TNF-α levels in drug-naive individuals with first-episode schizophrenia

BackgroundSchizophrenia is a complex neuropsychiatric disorder. Growing evidence indicates that the activation of the inflammatory response system with interleukin (IL)-2, IL-4, IL-8, IL-10, and tumor necrosis factor-alpha (TNF-α) plays an important role in the pathogenesis of schizophrenia,. However, clinical data on cytokine levels in patients with schizophrenia treated with antipsychotics are inconsistent or inconclusive. In this study, we have examined inflammatory factors’ alterations and their relationship to changes in clinical symptoms before and after olanzapine treatment of drug-naive patients with first-episode schizophrenia.MethodsWe recruited 142 hospitalized patients with first-episode schizophrenia as a study group; blood samples were collected, and the patients were assessed for clinical symptoms at baseline and after 6 weeks of olanzapine treatment. One hundred individuals with no history of mental illness were also recruited as healthy controls. Blood samples were collected, and the serum levels of IL-2, IL-4, IL-8, IL-10, and TNF-α were determined using an enzyme cycling assay. The severity of clinical symptoms was assessed according to the Positive and Negative Syndrome Scale (PANSS).ResultsIndividuals with schizophrenia had lower IL-8 levels and higher IL-10 levels than healthy controls (P < 0.001). Positive correlations were detected between serum IL-2 and IL-10 concentrations and each subscale of the PANSS (all P < 0.05). Moreover, a negative correlation existed between the serum IL-8 concentration and the PANSS negative score (r = − 0.172, P = 0.040). After 6 weeks of treatment, serum IL-8 levels in the patient group were lower than at baseline (P < 0.001), whereas serum IL-10 and TNF-α levels were higher than at baseline (all P < 0.05). Therefore, serum IL-10 can be determined as an independent risk factor for outcome in patients with first-episode schizophrenia (P = 0.02, OR = 2.327). Furthermore, serum IL-2, IL-10, and TNF-α levels were significantly lower, whereas the serum IL-8 level was significantly higher (P < 0.001) in the healthy control group than in the “response” and “no-response” treatment groups respectively.ConclusionsOur results indicate that serum IL-2, IL-8, IL-10, and TNF-α levels may be involved in the pathophysiological mechanisms of schizophrenia and correlate with the effects of olanzapine.

N-Methyl-d-Aspartate Receptor Antibody and Sensory Gating Deficits in Non-smoking, Minimal Antipsychotic Medication Exposure, and First-Episode Patients With Schizophrenia.

Sensory gating deficit is considered a pathophysiological feature of schizophrenia, which has been linked to N-methyl-d-aspartate receptor (NMDAR) hypofunction as one of the potential underlying mechanisms. Here, we hypothesize that higher levels of NMDAR antibody (Ab) may contribute to the sensory gating deficits in schizophrenia. We enrolled 72 non-smoking inpatients with first-episode schizophrenia (FES), most of them with only a relatively short duration of exposure to antipsychotic medications, and 51 non-smoking healthy controls (HC). Sensory gating was measured by P50 evoked potentials ratio and the difference between the two stimuli in an auditory paired-stimuli paradigm and serum NMDAR Ab levels were quantified by enzyme-linked immunosorbent assay. The FES group showed higher serum NMDAR Ab levels [(9.23 ± 4.15) ng/mL vs. (7.08 ± 2.83) ng/mL; P = .002], higher P50 ratio (P = .002), and less P50 difference (P = .001) than HC. In partial correlation analysis, serum NMDAR Ab levels were positively correlated with the P50 ratio (r = 0.36, P = .003) and negatively with the P50 difference (r = -0.39, P = .001) in the FES group. The NMDAR Ab levels mediated the diagnosis of schizophrenia and P50 sensory gating deficits (P50 ratio and P50 difference). Autoimmunity targeting NMDAR is a crucial intermediate mechanism in impaired sensory gating in patients with schizophrenia. The findings support early intervention targeting NMDAR for patients with schizophrenia.

A Comparative Study of Frontal and Cerebellar Lobe Volumes Between Patients With First-Episode Schizophrenia and Healthy Controls and Its Association With Psychopathology and Neurological Soft Signs in Patients.

Objectives Our study aimed to investigate the fronto-cerebellar volumes in both patients and controls, as well as explore their relationship with symptomatology. Our primary objectives were to compare the frontal and cerebellar lobe volumetric measurements between patients with first-episode schizophrenia (FES) and healthy controlsand to assess the relationship of these volumes with psychopathology, cognition, and neurological soft signs in FES patients. The secondary objective was to explore the association of fronto-cerebellar lobe volumes with socio-demographic factors among patients and controls, as well as the duration of untreated illness (DUI) among patients. Materials and methods This was a cross-sectional, case-control study involving 60 participants, including 30 antipsychotic-naïve FES patients and 30 healthy controls. Participants underwent MRI scanning to measure frontal and cerebellar lobe volumes using the volBrain platform. Additionally, FES patients were assessedusing the Positive and Negative Syndrome Scale (PANSS), Montreal Cognitive Assessment-Basic (MoCA-B), and Brief Motor Scale (BMS). Pearson's correlation, independent sample t-tests, multivariate linear regression, and binomial logistic regression were used to analyze the relationships between brain volumes, clinical assessments, and socio-demographic factors. Results No significant differences in frontal volumes were found between the two groups, while cerebellar volumes were significantly smaller in FES patients (p=0.004), particularly in younger males (p=0.026). Frontal volumes were negatively correlated with age in both groups (p=0.012), which remained robust in patients even after controlling for their gender, education, and DUI (p=0.012, aR2=0.221). Cerebellar volume reduction was associated with a higher likelihood of being classified as a patient (p=0.029). BMS was significantly correlated with frontal lobe volumes, especially in motor sequencing (MoSe), after adjusting for age, gender, and education (p=0.009). BMS MoSe scores were also significantly positively correlated with the DUI (r=0.415, p=0.023) and PANSS-General Psychopathology (GP) (r=0.494, p=0.005). MoCA-B scores were significantly lower in females than males (p=0.016), while PANSS-GP was significantly negatively correlated with age (r=-0.432, p=0.017). Conclusion Frontal and cerebellar volumes were differentially impacted in FES, with cerebellar atrophy being a significant distinguishing feature of the disorder. Frontal atrophy was associated with motor dysfunction but did not appear to influence psychopathology or cognition significantly in the early stages. The independent effects of frontal and cerebellar volumes, as shown by the lack of correlation between them, may suggest that these brain regions undergo separate pathological processes in schizophrenia, with frontal functional changes impacting motor function and cerebellar structural changes contributing to broader psychiatric symptoms,thereby warranting further exploration involving larger sample size.

The risk of diabetes and HbA1c deterioration during antipsychotic drug treatment: A Danish two-cohort study among patients with first-episode schizophrenia.

Antipsychotics increase the risk of developing diabetes, but clinical trials are not generalizable with short follow-up, while observational studies often lack important information, particularly hemoglobin A1c (HbA1c). We followed two Danish cohorts with schizophrenia. First, using Danish nationwide registers, we identified all individuals diagnosed with first-episode schizophrenia (FES) between 1999 and 2019 (n = 31,856). Exposure was a redeemed prescription for an antipsychotic, and the outcome was diabetes, defined via hospital-based diagnosis and redeemed prescriptions for glucose-lowering drugs. Adjusted Cox regression calculated hazard rate ratios (HRR). Second, using data from the Central Denmark Region, we identified all individuals diagnosed with FES from October 2016 to September 2022 (n = 2671). Using a within-subject design, we analyzed the change in HbA1c during the 2 years after initiation of specific antipsychotics compared to the 2 years before. In the nationwide cohort, 2543 (8.0%) individuals developed diabetes (incidence rate = 9.39 [95% CI = 9.03-9.76] per 1000 person-years). Antipsychotics, compared to periods without, were associated with an increased risk of developing diabetes (HRR = 2.04, 95% CI = 1.75-2.38). We found a dose-response association, particularly for second-generation antipsychotics, and different risk rates for specific antipsychotics. In the Central Denmark Region cohort, a total of 9.2% developed diabetes but mean HbA1c levels remained stable at 37 mmol/mol during the 2 years after initiation of antipsychotic medication. This comprehensive real-world two-cohort study emphasizes that diabetes affects almost 10% of patients with FES. Antipsychotics increase this risk, while HbA1c deterioration requires longer treatment. These findings are important for clinicians and young patients with FES.

Multilayer network analysis reveals instability of brain dynamics in untreated first-episode schizophrenia.

Although aberrant static functional brain network activity has been reported in schizophrenia, little is known about how the dynamics of neural function are altered in first-episode schizophrenia and are modulated by antipsychotic treatment. The baseline resting-state functional magnetic resonance imaging data were acquired from 122 first-episode drug-naïve schizophrenia patients and 128 healthy controls (HCs), and 44 patients were rescanned after 1-year of antipsychotic treatment. Multilayer network analysis was applied to calculate the network switching rates between brain states. Compared to HCs, schizophrenia patients at baseline showed significantly increased network switching rates. This effect was observed mainly in the sensorimotor (SMN) and dorsal attention networks (DAN), and in temporal and parietal regions at the nodal level. Switching rates were reduced after 1-year of antipsychotic treatment at the global level and in DAN. Switching rates at baseline at the global level and in the inferior parietal lobule were correlated with the treatment-related reduction of negative symptoms. These findings suggest that instability of functional network activity plays an important role in the pathophysiology of acute psychosis in early-stage schizophrenia. The normalization of network stability after antipsychotic medication suggests that this effect may represent a systems-level mechanism for their therapeutic efficacy.

Cognitive improvements linked to lysophosphatidylethanolamine after olanzapine treatment in drug-naïve first-episode schizophrenia.

Cognitive impairments are a hallmark symptom of schizophrenia (SCZ). Phosphatidylethanolamine (PE) is the second most abundant phospholipid in mammalian cells, yet its role in cognitive deficits remains unexplored. The aim of this study was to investigate the association between plasma LysoPE and cognitive improvements following olanzapine monotherapy in drug-naïve first-episode (DNFE) SCZ patients. Twenty-five female DNFE SCZ patients were treated with olanzapine for four weeks, and cognitive function was assessed using the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS) at baseline and after the 4-week follow-up. Utilizing an untargeted ultra-performance liquid chromatography-mass spectrometry (UPLC-MS)-based metabolomics approach, we measured LysoPE concentrations. Significant improvements in immediate and delayed memory domains were observed post-treatment. We identified nine differential LysoPE species after olanzapine monotherapy, with increased concentrations for all LysoPE except LysoPE (22:6). Elevated LysoPE (22:1) concentration positively correlated with cognitive improvement in patients. Baseline LysoPE (16:1) emerged as a predictive factor for cognitive improvement following olanzapine monotherapy. This study offers preliminary evidence for the involvement of LysoPE in cognitive improvements observed in drug-naïve first-episode SCZ patients after olanzapine treatment.

Profiling expressing features of surface proteins on single-exosome in first-episode Schizophrenia patients: a preliminary study

Proximity barcoding assay, a high-throughput method for single-exosome analysis, was employed to profile surface proteins on individual exosomes of SCZ patients. This analysis identified five differentially expressed proteins (DEPs) between SCZ patients and healthy controls (HC) and six DEPs between antipsychotic responders and non-responders. Furthermore, two exosome clusters were found to be associated with SCZ, and certain DEPs were correlated with cognitive functions.

The dimensional structure of the Positive and Negative Syndrome Scale in first-episode schizophrenia spectrum disorders: an Exploratory Graph Analysis from the OPTiMiSE trial

The Positive and Negative Syndrome Scale (PANSS) is the most widely used rating scale to assess psychotic symptoms in patients with schizophrenia and other primary psychoses. However, a definitive consensus regarding its dimensional structure remains elusive. The present work aims to determine the number of dimensions of the scale through a network analysis approach in a sample of individuals experiencing first-episode schizophrenia spectrum disorder (FE-SSD) with minimal or no prior exposure to antipsychotic treatment. Baseline data of 446 participants (age 25.96 ± 5.99 years, 70% males) enrolled in the OPTiMiSE trial were analysed. Exploratory Graph Analysis (EGA) was conducted to evaluate the dimensionality of the PANSS, and a bootstrap approach (bootEGA) was employed to assess model stability. The analysis was replicated, excluding unstable items with stability values below 0.75, until a stable model was achieved. The analysis of the 30 items of the PANSS revealed inadequate structural consistency, resulting in the exclusion of 9 unstable items. The final model comprised 21 symptoms distributed across four communities (Positive, Cognitive/Disorganised, Excited/Aggressive and Negative) but lacked a depressive domain. In conclusion, we propose a concise version of the PANSS, incorporating 21 items, to better assess the core symptoms of the first episode of SSD. This revised version provides clinicians with a robust psychometric tool with reduced administration time, but the complementary administration of a dedicated instrument for evaluating affective symptoms is advisable.

Peak width of skeletonized mean diffusivity as a neuroimaging biomarker in first-episode schizophrenia.

Peak width of skeletonized mean diffusivity (PSMD), a fully automated diffusion tensor imaging (DTI) biomarker of white matter (WM) microstructure damage, has been shown to be associated with cognition in various WM pathologies. However, its application in schizophrenic disease remains unexplored. This study aims to investigate PSMD along with other DTI markers in first-episode schizophrenia patients compared to healthy controls (HCs), and explore the correlations between these metrics and clinical characteristics. A total of 56 first-episode drug-naive schizophrenia patients and 64 HCs were recruited for this study. Participants underwent structural imaging and DTI, followed by comprehensive clinical assessments, including the Positive and Negative Syndrome Scale (PANSS) for patients and cognitive function tests for all participants. We calculated PSMD, peak width of skeletonized fractional anisotropy (PSFA), axial diffusivity (PSAD), radial diffusivity (PSRD) values, skeletonized average mean diffusivity (MD), average fractional anisotropy (FA), average axial diffusivity (AD), and average radial diffusivity (RD) values as well as structural network global topological parameters, and examined between-group differences in these WM metrics. Furthermore, we investigated associations between abnormal metrics and clinical characteristics. Compared to HCs, patients exhibited significantly increased PSMD values (t = 2.467, p = 0.015), decreased global efficiency (Z = -2.188, p = 0.029), and increased normalized characteristic path length (lambda) (t = 2.270, p = 0.025). No significant differences were observed between the groups in the remaining metrics, including PSFA, PSAD, PSRD, average MD, FA, AD, RD, local efficiency, normalized cluster coefficient, small-worldness, assortativity, modularity, or hierarchy (p > 0.05). After adjusting for relevant variables, both PSMD and lambda values exhibited a significant negative correlation with reasoning and problem-solving scores (PSMD: r = -0.409, p = 0.038; lambda: r = -0.520, p = 0.006). No statistically significant correlations were observed between each PANSS score and the aforementioned metrics in the patient group (p > 0.05). Multivariate linear regression analysis revealed that increased PSMD (β = -0.426, t = -2.260, p = 0.034) and increased lambda (β = -0.490, t = -2.994, p = 0.007) were independently associated with decreased reasoning and problem-solving scores respectively ( = 0.295, F = 2.951, p = 0.029). But these significant correlations did not withstand FDR correction (p_FDR > 0.05). PSMD can be considered as a valuable neuroimaging biomarker that complements conventional diffusion measurements for investigating abnormalities in WM microstructural integrity and cognitive functions in schizophrenia.

Incremental predictive value of genetic risk and functional brain connectivity in determining antipsychotic response in schizophrenia

We aimed to assess the incremental value of schizophrenia polygenic risk score (PgRS) and resting-state functional brain connectivity (rsFC) when added to clinical data in predicting the six-week response to oral risperidone (Risperdal) in schizophrenia. Fifty-seven, 54, and 43 individuals in a group of never-before-treated first-episode schizophrenia had good quality whole-genome sequencing (10x), rsFC, and both genomic and rsFC data, respectively, at baseline. Symptom severity ratings were obtained at baseline and six-weeks of oral risperidone (Risperdal) treatment. The primary outcome was the percentage change in the Positive and Negative Syndrome Scale Total scores after risperidone (Risperdal) treatment. Clinical, PgRS, and rsFC determinants of treatment response were first evaluated independently. Subsequently, three blocks of hierarchical multiple regression analyses with leave-one-out cross-validation (n = 43), were implemented to study clinical, clinical + PgRS and clinical + PgRS + rsFC determinants of treatment response. While the combined clinical variables did not show a statistically significant prediction of treatment response, adding PgRS (9 % R2 change) and rsFC (26 % R2 change) in hierarchical steps, significantly improved the overall proportion of variance explained in treatment response. This proof-of-concept investigation underscores the incremental benefits offered by genetic and neuroimaging metrics over clinical measures in determining prospectively-ascertained short-term treatment response in first-episode schizophrenia.

Slow negative potentials in the pre-stimulus period in norm and patients with the first episode of schizophrenia

Behavioral data (correct response latency and errors number increase) indicate a significant decrease in the task performance efficiency in patients with the first episode of schizophrenia, compared to the norm. At the same time the SNP 1, 2, 3 amplitudes were found to enhance in the patients group, compared to the norm, which may reflect a compensatory activation of predictive attention and inhibition cortical networks for increase the response efficiency. Intergroup differences in the topography of the SNP1 and SNP2 peaks amplitude suggest “disorganization” of the left hemisphere cognitive control cortical networks at the early stage of schizophrenia. It is assumed that the SNP 1, 2, 3 components can be considered as potentially significant clinically markers of cognitive control disorders in schizophrenia.

Psychological interventions for early-phase schizophrenia: protocol for a systematic review and network meta-analysis

Introduction Treating the early phase of schizophrenia is crucial for preventing further episodes and improving quality of life, functioning, and social inclusion. Pharmacotherapies are first-line treatments, but have limitations. There is consensus on the need for non-pharmacological interventions for individuals in the early phase of schizophrenia. Several psychological interventions have shown promising effects; however, their comparative effectiveness remains largely unknown. To address this issue, a network meta-analysis will be performed. We aim to develop a hierarchy of existing psychological treatments concerning their efficacy and tolerability, which will inform treatment guidelines. Protocol Randomized controlled trials (RCTs) investigating psychological interventions for first-episode psychosis, first-episode schizophrenia, or early phase schizophrenia will be included. The primary outcome will be overall schizophrenia symptoms (measured up to 6 and 12 months, and at the longest follow-up) and relapse as a co-primary outcome. Secondary outcomes are premature discontinuation; change in positive, negative, and depressive symptoms of schizophrenia; response; quality of life; overall functioning; satisfaction with care; adherence; adverse events; and mortality. The study selection and data extraction are performed by two independent reviewers. We will assess the risk of bias of each study using the Cochrane Risk of Bias tool 2 and evaluate the confidence in the results using Confidence in Network Meta-Analysis (CINeMA). Subgroup and sensitivity analyses will be conducted to explore heterogeneity and assess the robustness of our findings. Discussion This systematic review and network meta-analysis aims to compare multiple existing psychological interventions, establishing which are best for symptom reduction, relapse prevention, and other important outcomes in early phase schizophrenia. Our results may provide practical guidance concerning the most effective psychological intervention to reduce symptom severity and the societal burden associated with the disorder.

Neuroimaging markers of aberrant brain activity and treatment response in schizophrenia patients based on brain complexity

The complexity of brain activity reflects its ability to process information, adapt to environmental changes, and transition between states. However, it remains unclear how schizophrenia (SZ) affects brain activity complexity, particularly its dynamic changes. This study aimed to investigate the abnormal patterns of brain activity complexity in SZ, their relationship with cognitive deficits, and the impact of antipsychotic medication. Forty-four drug-naive first-episode (DNFE) SZ patients and thirty demographically matched healthy controls (HC) were included. Functional MRI-based sliding window analysis was utilized for the first time to calculate weighted permutation entropy to characterize complex patterns of brain activity in SZ patients before and after 12 weeks of risperidone treatment. Results revealed reduced complexity in the caudate, putamen, and pallidum at baseline in SZ patients compared to HC, with reduced complexity in the left caudate positively correlated with Continuous Performance Test (CPT) and Category Fluency Test scores. After treatment, the complexity of the left caudate increased. Regions with abnormal complexity showed decreased functional connectivity, with complexity positively correlated with connectivity strength. We observed that the dynamic complexity of the brain exhibited the characteristic of spontaneous, recurring “complexity drop”, potentially reflecting transient state transitions in the resting brain. Compared to HC, patients exhibited reduced scope, intensity, and duration of complexity drop, all of which improved after treatment. Reduced duration was negatively correlated with CPT scores and positively with clinical symptoms. The results suggest that abnormalities in brain activity complexity and its dynamic changes may underlie cognitive deficits and clinical symptoms in SZ patients. Antipsychotic treatment partially restores these abnormalities, highlighting their potential as indicators of treatment efficacy and biomarkers for personalized therapy.

Dynamics of symptom network in patients with first-episode schizophrenia: Insight from the CNFEST project

BackgroundSchizophrenia is a heterogeneous psychotic disorder. Recent theories have emphasized the importance of interactions among psychiatric symptoms in understanding the pathological mechanisms of schizophrenia. In the current study, we examined the symptom network in patients with first-episode schizophrenia (FES) at four time points during a six-month follow-up period. MethodsIn total, 565 patients with FES were recruited from the Chinese First-Episode Schizophrenia Trial (CNFEST) project. Clinical symptoms were measured using the Positive and Negative Syndrome Scale (PANSS) at baseline and follow-up (514 patients at one month, 429 at three months, and 392 at six months). We used a network analysis approach to estimate symptom networks with individual symptoms as nodes and partial correlation coefficients between symptoms as edges. A cross-lagged panel network (CLPN) model was used to identify predictive pathways for clinical symptoms. ResultsWe found stable and strongly connected edges in patients across the time points, such as links between delusions and suspiciousness/persecution (P1:P6), and emotional withdrawal and passive/apathetic social withdrawal (N2:N4). Emotional withdrawal (N2), poor rapport (N3), and passive/apathetic social withdrawal (N4) had high centrality estimates across all four time points. CLPN analysis showed that negative symptoms, including emotional withdrawal (N2), poor rapport (N3), and passive/apathetic social withdrawal (N4), and stereotyped thinking (N7) may have predictive effects for negative and general symptoms at follow-ups. ConclusionsThe symptom network of schizophrenia may be dynamic as treatment progresses. Negative symptoms remain the central and stable symptoms of schizophrenia. Negative symptoms may be potential therapeutic targets that predict other symptoms.

The potential predictive value and relationship of blood-based inflammatory markers with the clinical symptoms of Han Chinese patients with first-episode adolescent-onset schizophrenia.

Inflammation is associated with the pathophysiology of schizophrenia. The blood markers for systemic inflammation include neutrophil-lymphocyte ratio (NLR), systemic immune-inflammation index (SII), lymphocyte-monocyte ratio (LMR), system inflammation response index (SIRI), and platelet-lymphocyte ratio (PLR). However, these inflammation markers and their relationships with clinical phenotypes among Han Chinese patients with first-episode adolescent-onset schizophrenia (AOS) is unclear. This investigation aimed to elucidate the impact of inflammation on Han Chinese AOS patients as well as the association of blood-based inflammation markers with clinical symptoms. Altogether, 203 Han Chinese individuals participated in this study, 102 first-episode AOS patients and 101 healthy controls. The assessment of inflammatory indices was based on complete blood cell count. Furthermore, schizophrenia-related clinical symptoms were evaluated using the five-factor model of the Positive and Negative Syndrome Scale (PANSS). In Han Chinese first-episode AOS patients, levels of SIRI, PLR, SII, and NLR were significantly increased (p < 0.001), while LMR decreased (p < 0.001) compared to healthy controls. Furthermore, multivariate logistic regression showed that LMR, NLR, SII, and SIRI (all p < 0.05) were independently associated with AOS. Moreover, Receiver operating characteristics assessment indicated that NLR, SIRI, LMR, and SII could effectively distinguish AOS patients from healthy controls. Their areas under the curves were 0.734, 0.701, 0.715, and 0.730 (all p < 0.001). In addition, Correlation analysis revealed that LMR was negatively correlated with the PANSS total, negative, and cognitive factor scores (all p < 0.05); NLR was positively correlated with the cognitive factor score (p < 0.01); SII was negatively correlated with the positive factor score and positively with the negative and cognitive factor scores (all p < 0.05); SIRI was positively correlated with the PANSS total and cognitive factor scores (all p < 0.01). This research established the involvement of peripheral blood inflammatory markers (LMR, NLR, SII, and SIRI) with the clinical manifestations and pathophysiology of schizophrenia, and these can serve as screening tools or potential indices of the inflammatory state and AOS symptoms severity.