Schizophrenia (Heidelberg, Germany)最新文献

Social dysfunction remains a core feature of schizophrenia (SCZ), particularly in individuals exhibiting prominent negative symptoms. The amygdala (AMYG), a key structure in emotional and social processing, may contribute to this dysfunction. This study investigated whether structural and functional alterations in the AMYG mediate the effects of negative symptoms on social functioning in SCZ. A total of 205 male participants were included: 53 with deficit schizophrenia (DS), 76 with non-deficit schizophrenia (NDS), and 76 matched healthy controls (HCs). Negative symptoms were assessed using the Scale for the Assessment of Negative Symptoms, and social functioning was evaluated with the Scale of Social Function in Psychosis Inpatients. Structural and resting-state functional MRI data were acquired. Amygdala volumes and region-of-interest-based functional connectivity (FC) were analyzed, and path analysis was used to test mediation effects. Patients with SCZ showed significantly reduced bilateral AMYG volumes compared to HCs. Within the SCZ group, the left amygdala (AMYG.L) was smaller than the right, with further reduction observed in DS compared to NDS. FC between the AMYG.L and the left superior temporal gyrus (STG.L) was also decreased in DS. Mediation analysis revealed that both AMYG.L volume and its FC with STG.L partially mediated the association between negative symptoms and poor social function. These findings suggest that AMYG.L abnormalities may involve social dysfunction in DS, offering potential targets for early intervention aimed at improving social outcomes in male patients with schizophrenia.

Cognitive impairments in schizophrenia significantly impact daily functioning and quality of life, posing a major therapeutic challenge. While both cognitive training and transcranial direct current stimulation (tDCS) have shown promise in improving cognitive function, their combined effects remain underexplored. This double-blind, sham-controlled, randomized clinical trial investigated whether adaptive working memory training (aWMT) paired with anodal tDCS to the right dorsolateral prefrontal cortex (DLPFC) enhances cognitive outcomes in schizophrenia. Twenty-eight individuals with schizophrenia or schizoaffective disorder completed ten sessions of aWMT with concurrent 2 mA anodal or sham tDCS. Cognitive improvements were assessed using the Brief Assessment of Cognition in Schizophrenia (BACS) at baseline, three days after training, and at follow-ups one month, and three months later. Clinical measures evaluated psychopathology, depression, and quality of life. Anodal tDCS significantly improved working memory performance during training, with gains partially sustained at follow-ups. Short-term transfer effects with large effect sizes were observed in the BACS, although clinical symptoms and quality of life remained unaffected. While preliminary, these findings indicate that tDCS could support cognitive training outcomes in schizophrenia. Larger randomized controlled trials are needed to confirm transfer effects and determine the long-term benefits of this approach. If proven effective, integrating tDCS into cognitive remediation therapies could represent an innovative strategy to address cognitive deficits in schizophrenia.

Cognitive impairments are generally observed in patients with schizophrenia. However, it is unclear whether neurocognitive dysfunction can predict the efficacy of antipsychotics for first-episode schizophrenia (FES). Machine learning methods provide a relatively unbiased approach when evaluating heterogeneous data, especially when building multifactor prediction models. This study conducted a secondary analysis based on the Chinese FES Trial (CNFEST), which was a 1-year study involving a randomized controlled trial for the first eight weeks followed by a 48-week open-label observation. The current study aimed to build a prediction model of eight-week antipsychotic response based on baseline clinical and demographic features. Six machine learning algorithms, including random forest, eXtreme gradient boosting (XGBoost), logistic regression, linear support vector machine (SVM), radial basis function SVM and poly SVM were applied and compared to draw the prediction model. The predictive effects were evaluated by balanced accuracy, sensitivity and specificity. The predictive factors were compared with F scores. A total of 450 qualified subjects contributed to the model. The prediction model constructed via XGBoost algorithm had the highest accuracy (68.8%) and prognostic certainty (44.3%) among all the algorithms. The baseline neurocognitive tests with strong predictive significance were the Grooved Pegboard Test, Trail Making Test Part A, Paced Auditory Serial Addition Test, Brief Visuospatial Learning Test, Hopkins Verbal Learning Test and Color Trails Test. This study emphasizes the importance of fine motor skills, verbal learning, visual learning, working memory and attention for the response of drug-naïve FES patients to antipsychotics. The model generated by XGBoost, which shows preferable accuracy, provides psychiatric practitioners with a possible way to predict efficacy for FES patients.

Positive symptoms are a prominent feature of schizophrenia. Despite antipsychotic treatment, ~30% of patients develop refractory positive symptoms (RPSs). Current research fails to elucidate the potential neurophysiological mechanisms underlying RPSs, thereby hindering the development of additional treatments. This study, which included 37 patients with RPSs and 40 with non-refractory positive symptoms (NRPSs), aimed to explore their underlying neural mechanisms. Outcome measures were relative power spectrum density and interregional synchronization across frequency bands and theta-gamma phase-amplitude coupling (θ-γ PAC). The single-frequency analysis indicated that RPSs exhibited elevated theta power and reduced lateralization in the left temporal lobe and temporo-parietal junction, along with enhanced functional connectivity in the left frontocentral region. The cross-frequency analysis revealed that RPSs exhibited slightly higher θ-γ coupling at the left temporo-parietal junction compared to NRPSs. Correlation analysis revealed significant associations among theta power, the lateralization index, functional connectivity, and the severity of positive symptoms. The aberrant activation of the theta rhythm in the left temporo-parietal region may lead to increased functional asymmetry in the brain, impeding interregional and inter-frequency information transmission and thus significantly impairing the normal processing of auditory information. These findings offer potential insights into the neurophysiological basis of positive symptoms in schizophrenia and may inform future clinical interventions.

Patients with schizophrenia (SCZ) face multiple health challenges due to the complication of chronic diseases and psychiatric disorders. Among these, cardiovascular comorbidities are the leading cause of their life expectancy being 15-20 years shorter than that of the general population. Identifying comorbidity patterns and uncovering differences in immune and metabolic function are crucial steps toward improving prevention and management strategies. A retrospective cross-sectional study was conducted using electronic medical records of inpatients discharged between 2015 and 2024 from a municipal psychiatric hospital in China. The study included patients diagnosed with Schizophrenia, Schizotypal, and Delusional Disorders (SSDs) (ICD-10: F20-F29). Comorbidity patterns were identified through latent class analysis (LCA) based on the 20 most common comorbid conditions among SSD patients. To investigate differences in peripheral blood metabolic and immune function, linear regression or generalized linear models were applied to 44 laboratory test indicators collected during the acute episode. The Benjamini-Hochberg method was used for p-value correction, and the false discovery rate (FDR) was calculated, with statistical significance set at FDR < 0.05. Among 3,697 inpatients with SSDs, four distinct comorbidity clusters were identified: SSDs only (Class 1), High-Risk Metabolic Multisystem Disorders (Class 2, n = 39), Low-Risk Metabolic Multisystem Disorders (Class 3, n = 573), and Sleep Disorders (Class 4, n = 205). Compared to Class 1, Class 2 exhibited significantly elevated levels of apolipoprotein A (ApoA; β = 90.62), apolipoprotein B (ApoB; β = 0.181), mean platelet volume (MPV; β = 0.994), red cell distribution width-coefficient of variation (RDW-CV; β = 1.182), antistreptolysin O (ASO; β = 276.80), and absolute lymphocyte count (ALC; β = 0.306), along with reduced apolipoprotein AI (ApoAI; β = -0.173) and hematocrit (HCT; β = -35.13). Class 3 showed moderate increases in low-density lipoprotein cholesterol (LDL-C; β = 0.113), MPV (β = 0.267), white blood cell count (WBC; β = 0.476), and absolute neutrophil count (ANC; β = 0.272), with decreased HCT (β = -9.81). Class 4 was characterized by elevated aggregate index of systemic inflammation (AISI; β = 81.07), neutrophil-to-lymphocyte ratio (NLR; β = 0.465), and systemic inflammation response index (SIRI; β = 0.346), indicating a heightened inflammatory state. The comorbidity patterns of patients with SCZ can be distinctly classified. During the acute episode, those with comorbid metabolic disorders exhibit a higher risk of cardiovascular diseases and immune system abnormalities, while patients with comorbid sleep disorders present a pronounced systemic inflammatory state and immune dysfunction. This study provides a basis for the chronic disease management and anti-inflammatory treatment, while also offering objective biomarker insights for transdiagnostic research.

Language impairments are core symptoms of both schizophrenia and autism spectrum disorders and have been linked to deficits in predictive language processing. While altered use of semantic predictions have been reported in both conditions, little is known whether semantic predictions are stable over time. The goal of this study was therefore to investigate the temporal stability of semantic prior beliefs focusing on individuals with schizotypal and autistic traits. 115 participants, assessed for subclinical schizotypal (SPQ_5ls; mean = 77.99, SD = 39.31) and autistic traits (AQ; mean = 15.67, SD = 6.01), completed an auditory stability paradigm at two timepoints to investigate the temporal stability of semantic predictions. At timepoint one, consisting of one session, participants listened to 240 sentence beginnings varying in predictability (e.g., high: "The swimmer jumped into the…"; low: "The child hid the toy under the…") and provided a prediction for each sentence-final word. Timepoint two, consisting of two sessions, each session comprising of 120 old and 120 new sentences. In addition to final-word predictions, sentence recall was assessed to examine the influence of memory on prediction stability. Generalized linear mixed models revealed that higher predictability led to greater temporal stability of semantic predictions. Importantly, increasing schizotypal and autistic traits were associated with reduced stability, particularly in highly predictable contexts where stable predictions typically facilitate efficient language processing. While poorer sentence recall was linked to greater instability, especially in medium- and low-predictability contexts, it did not account for the reduced stability observed in relation to schizotypal and autistic traits. These findings suggest that individuals with higher schizotypal and autistic traits struggle to form stable, lasting semantic predictions, which may contribute to difficulties in efficient language processing.

Schizophrenia is a disorder of still unknown aetiology characterized by positive, negative and cognitive symptoms. The first evident signs emerge at the end of adolescence and the beginning of adulthood as a psychotic episode. Patients are then treated with antipsychotics to ameliorate positive symptoms. However, this pharmacological approach is ineffective for negative and cognitive ones. Schizophrenia patients also exhibit metabolic and immune alterations, regardless of antipsychotic treatment. Clinical research in this field is challenging, as there is no way to identify people at risk before the first psychotic episode, and once it emerges, antipsychotic treatment is applied, worsening metabolic and immune profiles which may be detrimental for cognitive and negative symptoms. A faithful animal model of schizophrenia may be valuable to understand molecular events and brain regions involved in each of the symptoms, evaluate novel pharmacological compounds for unattended symptoms and explore objective diagnostic strategies. Here, we show that the selective dopamine D2 receptor deletion from parvalbumin interneurons, a mutation that results in schizophrenia-like phenotypes, causes intrinsic metabolic and immune defects in mice, in a similar way to what is described in schizophrenia patients. Mutant animals show dysglycaemia and dyslipidaemia, abnormal white blood cell counts, increased neutrophil-to-lymphocyte ratio, CD4/CD8 ratio imbalances, increased circulating C-reactive protein levels and reactive microglia. Therefore, selective dopamine D2 receptor deletion causes a wide spectrum of phenotypes resembling those described in patients. This animal line may be a useful research tool to expand our knowledge on the aetiology of schizophrenia.

Cognitive impairment is a core characteristic of schizophrenia. Immunosenescence has been consistently implicated in the cognitive dysfunction observed in neurodegenerative diseases, but how it may relate to cognitive deficits in schizophrenia is still unclear. We explored the associations between immunosenescence and cognitive impairment in patients with schizophrenia (SCZ, n = 65) and healthy controls (HCs, n = 39). Immunosenescence markers were assessed by flow cytometry and included the percentage of naïve or memory T cell subsets labeled by CD4+/CD8+, CD45RA+(naïve)/CD45RO (memory), or CD95+(memory), as well as the intracellular levels of selected cytokines (IL-1β, IL-6, TNF-α, and IFN-γ) in T cell subsets. T1-weighted magnetic resonance imaging was performed to assess the subcortical volume and cortical thickness. Participants were evaluated using the Positive and Negative Syndrome Scale and the Chinese version of the MATRICS Consensus Cognitive Battery.The results indicated that (1) Compared with HCs, SCZ patients were characterized by fewer naïve and more memory T cell subsets, accompanied by altered intracellular cytokine levels, indicating immunosenescence phenotypes. (2) The intracellular IL-1β level in naïve CD8+CD45RA+CD95+ T cells was associated with working memory deficit in SCZ patients. (3) In a moderated mediation model, the effect of the IL-1β level on the working memory score was mediated by the thickness of the right inferior parietal lobule (IPL_R), and the volume of the right choroid plexus (CP) moderated the indirect pathway between the IL-1β level and IPL_R thickness. Our findings highlighted immunosenescence-related T cell phenotypes and the CP as potential biomarkers of cognitive deficit in SCZ.

Formal thought disorder (FTD) is a core symptom of schizophrenia. The pathophysiology of FTD is still unclear. We focus on multiple cortical measures to capture the exact nature of brain alterations (e.g., plasticity, early brain development) in FTD dimensions. We included 70 schizophrenia patients. We assessed FTD, acquired structural neuroimaging scans, and analyzed cortical thickness, volume, surface area, and local gyrification (IGI). Results reveal negative FTD to be associated with different structural brain correlates compared to the positive and linguistic control FTD dimensions most prominent in markers of early brain development. Severity of positive and linguistic control FTD dimensions correlated positively with IGI of core language regions including temporal, Heschl's, and inferior frontal gyri. Severity of negative FTD dimension was inversely correlated with lGI of occipital and parietal regions. Findings propose distinguishable changes most prominent in markers of early brain development associated with FTD dimensions suggesting a distinct pathophysiology.

Mental illnesses often manifest through behavioral changes, with speech serving as a key medium for expressing thoughts and emotions. The use of computational linguistics on speech data in mental illnesses is a promising approach to uncover objective biomarkers for the early detection of mental illnesses. This study analyzed speech transcripts from 80 youths at ultra-high risk of psychosis (UHR) and 329 healthy controls, examining text features such as sentiment variability, cohesion, lexical sophistication, morphology, syntactic sophistication, and lexical diversity. Factor analysis revealed five key linguistic themes: Sentiment Intensity and Variability, Linguistic Register Alignment, Phonographic Uniqueness and Recognizability, Morphological Complexity and Imageability, and Lexical Richness and Typicalness. Regression analysis indicated UHR speech is characterized by diminished sentiment variability (β = -0.07), deviation from linguistic registers (β = -0.16), fewer phonographic neighbors (β = -0.11), lower morphological complexity (β = -0.36), and more predictable lexical structures (β = 0.05). Optimized machine learning (ML) models trained on Boruta-selected features achieved a mean AUC of 0.70. Our findings highlight the potential of sentiment and linguistic analyses in speech for training ML models to aid in early detection and monitoring of mental health conditions.