Direct and indirect effects of error monitoring on social functioning in a cohort with high-risk and first-episode psychosis.

Error monitoring plays a key role in people's adjustment to social life. This study aimed to examine the direct (DE) and indirect effects (IDE) of error monitoring, as indicated by error-related negativity (ERN), on social functioning in a clinical cohort from high-risk (APS) to first-episode psychosis (FEP). This study recruited 100 outpatients and 49 healthy controls (HC). ERN was recorded during a modified flanker task; social functioning was evaluated using the social scale of global functioning. The path analysis was executed using the "lavaan" package. When controlling for age and education, the clinical cohort had a smaller ERN than the HC group (F1, 145 = 19.58, p < 0.001, partial η2 = 0.12, 95%CI: 0.04-0.22). ERN demonstrated no substantial direct impact on current social functioning; however, it manifested indirect influences on social functioning via the disorganization factor of the Positive and Negative Syndrome Scale, both with (standardized IDE: -0.139, p = 0.009) and without (standardized IDE: -0.087, p = 0.018) accounting for the diagnosis, defined as a dummy variable (FEP = 1 and APS = 0) and included as a covariate. These findings suggest that error monitoring, as indicated by ERN, may serve as a potential prognostic indicator of social functioning in patients with psychosis.

Impaired insight and error-monitoring deficits among outpatients with attenuated psychosis syndrome and first-episode psychosis.

We aimed to determine the relationship between electrophysiological signatures of error monitoring and clinical insight among outpatients with attenuated psychosis syndrome (APS) and first-episode psychosis (FEP). Error-related negativity (ERN), error positivity (Pe), and correct response negativity (CRN) were recorded during a modified flanker task for patients with FEP (n = 32), APS individuals (n = 58), and healthy controls (HC, n = 49). Clinical insight was measured using the Schedule of Assessment of Insight (SAI) and included awareness of illness (SAI-illness), relabeling of specific symptoms (SAI-symptoms), and treatment compliance (SAI-treatment). Compared with HC, patients with FEP showed smaller ERN (p < 0.001) and Pe (p = 0.011) amplitudes and individuals with APS showed smaller ERN amplitude (p = 0.009). No significant difference in CRN amplitude was observed among the groups. A smaller negative amplitude of ERN correlated with a lower score on SAI-symptoms (b = -0.032, 95% CI: 0.062 to -0.002, p = 0.035) and a decreased total score of SAI (b = -0.096, 95% CI: 0.182 to -0.010, p = 0.029). This links were adjusted for age, education, and diagnosis (a dummy variable with FEP = 1 and APS = 0), and was independent of positive symptoms. SAI-illness was predominantly influenced by diagnosis, whereas SAI-treatment was additionally affected by disorganized communications. Neither Pe nor CRN amplitude exhibited an association with clinical insight. Unconscious error detection, as indicated by ERN, may aid individuals at the preliminary stage of psychosis in recognizing the unusual symptoms.

Development of early prediction model of in-hospital cardiac arrest based on laboratory parameters.

BACKGROUND: In-hospital cardiac arrest (IHCA) is an acute disease with a high fatality rate that burdens individuals, society, and the economy. This study aimed to develop a machine learning (ML) model using routine laboratory parameters to predict the risk of IHCA in rescue-treated patients. METHODS: This retrospective cohort study examined all rescue-treated patients hospitalized at the First Medical Center of the PLA General Hospital in Beijing, China, from January 2016 to December 2020. Five machine learning algorithms, including support vector machine, random forest, extra trees classifier (ETC), decision tree, and logistic regression algorithms, were trained to develop models for predicting IHCA. We included blood counts, biochemical markers, and coagulation markers in the model development. We validated model performance using fivefold cross-validation and used the SHapley Additive exPlanation (SHAP) for model interpretation. RESULTS: A total of 11,308 participants were included in the study, of which 7779 patients remained. Among these patients, 1796 (23.09%) cases of IHCA occurred. Among five machine learning models for predicting IHCA, the ETC algorithm exhibited better performance, with an AUC of 0.920, compared with the other four machine learning models in the fivefold cross-validation. The SHAP showed that the top ten factors accounting for cardiac arrest in rescue-treated patients are prothrombin activity, platelets, hemoglobin, N-terminal pro-brain natriuretic peptide, neutrophils, prothrombin time, serum albumin, sodium, activated partial thromboplastin time, and potassium. CONCLUSIONS: We developed a reliable machine learning-derived model that integrates readily available laboratory parameters to predict IHCA in patients treated with rescue therapy.

The effect of initial antipsychotic treatment on hippocampal and amygdalar volume in first-episode schizophrenia is influenced by age.

BACKGROUND: Antipsychotic treatment has been shown to yield hippocampal and amygdalar volumetric changes in first-episode schizophrenia (FES). However, whether antipsychotic induced volumetric changes interact with age remains unclear. METHODS: The current study includes data from 120 medication naïve FES patients and 110 matched healthy controls (HC). Patients underwent MRI scans before (T1) and after (T2) antipsychotic treatment. HCs underwent MRI scans at baseline only. The hippocampus and amygdala were segmented via Freesurfer 7. General linear models were conducted to investigate the effect of age by diagnosis interaction on baseline volume. Linear mixed models (LMM) were used to detect the effect of age on volumetric changes from pre to post treatment in FES. RESULTS: GLM revealed a trending effect (F = 3.758, p = 0.054) of age by diagnosis interaction on the baseline volume of the left (whole) hippocampus, with older FES patients showing smaller hippocampal volumes, relative to HC, when controlled sex, education years, and ICV. LMM showed a significant age by time-point interaction effect (F = 4.194, estimate effect = -1.964, p = 0.043) on left hippocampal volume in all FES and significant time effect(F = 6.608,T1-T2(estimate effect) = 62.486, p = 0.011), whereby younger patients showed greater hippocampal volumetric decreases following treatment. At the subfield level, a significant time effect emerged in left molecular_layer_HP (F = 4.509,T1-T2(estimate effect) = 12.424, p = 0.032, FDR corrected) and left cornu ammonis(CA)4 (F = 4.800,T1-T2(estimate effect) = 7.527, p = 0.046, FDR corrected), implying volumetric reduction after treatment in these subfields. CONCLUSIONS: Our findings suggest that age plays an important role in the neuroplastic mechanisms of initial antipsychotics on the hippocampus and amygdala of schizophrenia.

Symptom-circuit mappings of the schizophrenia connectome.

OBJECTIVE: This paper aims to model the anatomical circuits underlying schizophrenia symptoms, and to explore patterns of abnormal connectivity among brain networks affected by psychopathology. METHODS: T1 magnetic resonance imaging (MRI), diffusion weighted imaging (DWI), and resting-state functional MRI (rsfMRI) were obtained from a total of 126 patients with schizophrenia who were recruited for the study. The images were processed using the Omniscient software (https://www.o8t. com). We further apply the use of the Hollow-tree Super (HoTS) method to gain insights into what brain regions had abnormal connectivity that might be linked to the symptoms of schizophrenia. RESULTS: The Positive and Negative Symptom Scale is characterised into 6 factors. Each symptom is mapped with specific anatomical abnormalities and circuits. Comparison between factors reveals co-occurrence in parcels in Factor 1 and Factor 2. Multiple large-scale networks are involved in SCZ symptomatology, with functional connectivity within Default Mode Network (DMN) and Central Executive Network (CEN) regions most frequently associated with measures of psychopathology. CONCLUSION: We present a summary of the relevant anatomy for regions of the cortical areas as part of a larger effort to understand its contribution in schizophrenia. This unique machine learning-type approach maps symptoms to specific brain regions and circuits by bridging the diagnostic subtypes and analysing the features of the connectome.

Effects of polygenic risk of schizophrenia on interhemispheric callosal white matter integrity and frontotemporal functional connectivity in first-episode schizophrenia.

BACKGROUND: Schizophrenia is a severely debilitating psychiatric disorder with high heritability and polygenic architecture. A higher polygenic risk score for schizophrenia (SzPRS) has been associated with smaller gray matter volume, lower activation, and decreased functional connectivity (FC). However, the effect of polygenic inheritance on the brain white matter microstructure has only been sparsely reported. METHODS: Eighty-four patients with first-episode schizophrenia (FES) patients and ninety-three healthy controls (HC) with genetics, diffusion tensor imaging (DTI), and resting-state functional magnetic resonance imaging (rs-fMRI) data were included in our study. We investigated impaired white matter integrity as measured by fractional anisotropy (FA) in the FES group, further examined the effect of SzPRS on white matter FA and FC in the regions connected by SzPRS-related white matter tracts. RESULTS: Decreased FA was observed in FES in many commonly identified regions. Among these regions, we observed that in the FES group, but not the HC group, SzPRS was negatively associated with the mean FA in the genu and body of corpus callosum, right anterior corona radiata, and right superior corona radiata. Higher SzPRS was also associated with lower FCs between the left inferior frontal gyrus (IFG)-left inferior temporal gyrus (ITG), right IFG-left ITG, right IFG-left middle frontal gyrus (MFG), and right IFG-right MFG in the FES group. CONCLUSION: Higher polygenic risks are linked with disrupted white matter integrity and FC in patients with schizophrenia. These correlations are strongly driven by the interhemispheric callosal fibers and the connections between frontotemporal regions.

Relationships between self-reflectiveness and clinical symptoms in individuals during pre-morbid and early clinical stages of psychosis.

Background: Self-reflectiveness, one dimension of cognitive insight, plays a protective role in an individual's mental state. Both high and low levels of self-reflectiveness have been reported in patients with schizophrenia and individuals at clinical high risk for the illness. Aims: This study aimed to explore the relationship patterns between self-reflectiveness and clinical symptoms in individuals during the pre-morbid and early clinical stages of psychosis. Methods: A total of 181 subjects, including individuals with attenuated positive symptoms (APS, n=122) and patients with first-episode psychosis (FEP, n=59), completed the Beck Cognitive Insight Scale and were evaluated using the Schedule of Assessment of Insight and Positive and Negative Syndrome Scale. All subjects were classified into three groups according to their level of self-reflectiveness: low level (LSR, n=59), medium level (MSR, n=67) and high level (HSR, n=55). Both linear and non-linear relationships between self-reflectiveness and clinical symptoms were explored. Results: More individuals with APS were classified into the MSR group, while more patients with FEP were classified into the LSR group. The LSR group demonstrated less awareness of illness than the MSR and HSR groups, more stereotyped thinking and poorer impulse control but less anxiety than the MSR group, and lower levels of blunted affect and guilt feelings than the HSR group. The MSR group demonstrated lower stereotyped thinking than the HSR group. Compared to the LSR group, the MSR group had increased self-reflectiveness, improved awareness of illness, decreased stereotyped thinking, and better impulse control, but increased feelings of guilt. The HSR group showed increased stereotyped thinking when compared to the MSR group, but the other variables did not change significantly between these two groups. Overall, self-reflectiveness demonstrated an approximately inverse S-shaped relationship with the awareness of illness, a U-shaped relationship with stereotyped thinking and poor impulse control, and an almost linear relationship with anxiety and guilt feelings. Conclusions: Self-reflectiveness demonstrates complex relationships with clinical symptoms and fails to exert significant positive effects when reaching a certain high level.

Effect of cognitive insight on clinical insight from pre-morbid to early psychosis stages.

Poor cognitive insight, including low self-reflectiveness and high self-certainty, contributes to poor clinical insight, which includes awareness of illness, relabelling of specific symptoms, and treatment compliance. However, inconsistent results regarding cognitive insight among individuals at clinical high risk of psychosis (CHR) have been reported. This study investigated the difference in cognitive insight among groups with different severity of positive symptoms and analysed the effect of cognitive insight on clinical insight in each group. All participants, including CHR individuals with 3 or 4 points (L-Pitem, n = 85) and 5 points (H-Pitem, n = 37) on any positive-symptom item of the Scale of Prodromal Syndromes, and patients with first-episode psychosis (FEP, n = 59), were measured cognitive and clinical insight using the Beck Cognitive Insight Scale and the Schedule of Assessment of Insight, respectively. The self-reflectiveness of cognitive insight was highest in the L-Pitem group and lowest in the FEP group. Self-reflectiveness was positively associated with awareness of illness in the L-Pitem and FEP groups; both self-reflectiveness and self-certainty was positively associated with treatment compliance in the L-Pitem group. Improving self-reflectiveness of cognitive insight may conduce to good clinical insight. Self-certainty may have different implication to individuals with mild prodromal symptoms.

Corrigendum: Relationship Between Cognitive and Clinical Insight at Different Durations of Untreated Attenuated Psychotic Symptoms in High-Risk Individuals.

[This corrects the article DOI: 10.3389/fpsyt.2021.753130.].

Antipsychotics Effects on Network-Level Reconfiguration of Cortical Morphometry in First-Episode Schizophrenia.

Cortical thickness reductions are evident in schizophrenia (SZ). Associations between antipsychotic medications (APMs) and cortical morphometry have been explored in SZ patients. This raises the question of whether the reconfiguration of morphological architecture by APM plays potential compensatory roles for abnormalities in the cerebral cortex. Structural magnetic resonance imaging was obtained from 127 medication-naive first-episode SZ patients and 133 matched healthy controls. Patients received 12 weeks of APM and were categorized as responders (n = 75) or nonresponders (NRs, n = 52) at follow-up. Using surface-based morphometry and structural covariance (SC) analysis, this study investigated the short-term effects of antipsychotics on cortical thickness and cortico-cortical covariance. Global efficiency was computed to characterize network integration of the large-scale structural connectome. The relationship between covariance and cortical thinning was examined by SC analysis among the top-n regions with thickness reduction. Widespread cortical thickness reductions were observed in pre-APM patients. Post-APM patients showed more reductions in cortical thickness, even in the frontotemporal regions without baseline reductions. Covariance analysis revealed strong cortico-cortical covariance and higher network integration in responders than in NRs. For the NRs, some of the prefrontal and temporal nodes were not covariant between the top-n regions with cortical thickness reduction. Antipsychotic effects are not restricted to a single brain region but rather exhibit a network-level covariance pattern. Neuroimaging connectomics highlights the positive effects of antipsychotics on the reconfiguration of brain architecture, suggesting that abnormalities in regional morphology may be compensated by increasing interregional covariance when symptoms are controlled by antipsychotics.

Atypical Antipsychotics Mediate Dynamics of Intrinsic Brain Activity in Early-Stage Schizophrenia? A Preliminary Study.

OBJECTIVE: Abnormalities of static brain activity have been reported in schizophrenia, but it remains to be clarified the temporal variability of intrinsic brain activities in schizophrenia and how atypical antipsychotics affect it. METHODS: We employed a resting-state functional magnetic resonance imaging (rs-fMRI) and a sliding-window analysis of dynamic amplitude of low-frequency fluctuation (dALFF) to evaluate the dynamic brain activities in schizophrenia (SZ) patients before and after 8-week antipsychotic treatment. Twenty-six schizophrenia individuals and 26 matched healthy controls (HC) were included in this study. RESULTS: Compared with HC, SZ showed stronger dALFF in the right inferior temporal gyrus (ITG.R) at baseline. After medication, the SZ group exhibited reduced dALFF in the right middle occipital gyrus (MOG.R) and increased dALFF in the left superior frontal gyrus (SFG.L), right middle frontal gyrus (MFG.R), and right inferior parietal lobule (IPL.R). Dynamic ALFF in IPL.R was found to significant negative correlate with the Scale for the Assessment of Negative Symptoms (SANS) scores at baseline. CONCLUSION: Our results showed dynamic intrinsic brain activities altered in schizophrenia after short term antipsychotic treatment. The findings of this study support and expand the application of dALFF method in the study of the pathological mechanism in psychosis in the future.

Relationship Between Cognitive and Clinical Insight at Different Durations of Untreated Attenuated Psychotic Symptoms in High-Risk Individuals.

Background: This study examines whether cognitive insight is impaired in high-risk individuals with attenuated psychotic symptoms (APS) and explores the relationship between cognitive and clinical insight at different durations of untreated attenuated psychotic symptoms (DUAPS). Methods: The Structured Interview for Psychosis high-risk Syndrome (SIPS) was used to identify APS individuals. APS (n = 121) and healthy control (HC, n = 87) subjects were asked to complete the Beck Cognitive Insight Scale (BCIS). Clinical insight of APS individuals was evaluated using the Schedule for Assessment of Insight (SAI). APS individuals were classified into four subgroups based on DUAPS, including 0-3, 4-6, 7-12, and >12 months. Power analysis for significant correlation was conducted using the WebPower package in R. Results: Compared with HC subjects, APS individuals showed poorer cognitive insight, with lower scores on BCIS self-reflectiveness and composite index (BCIS self-reflectiveness minus BCIS self-certainty). Only when DUAPS was longer than 12 months did the significant positive correlation between cognitive and clinical insight obtain the power about 0.8, including the associations between self-reflectiveness and awareness of illness, self-reflectiveness and the total clinical insight, and composite index and awareness of illness. The positive associations of composite index with awareness of illness within 0-3 months DUAPS and with the total score of SAI when DUAPS > 12 months were significant but failed to obtain satisfactory power. Conclusions: APS individuals may have impaired cognitive insight, demonstrating lower self-reflectiveness. The correlation between cognitive and clinical insight is associated with the duration of untreated attenuated psychotic symptoms.

The effects of antipsychotics on interactions of dynamic functional connectivity in the triple-network in first episode schizophrenia.

BACKGROUND: Brain dynamics abnormalities in the triple-network, which involves the salience network (SN), the default mode network (DMN) and the central executive network (CEN), have been reported in schizophrenia. However, it remains to be clarified how antipsychotics affect dynamic functional connectivity (DFC) within the triple-network and whether differences in clinical outcomes are associated with varying levels of network model dysfunction. METHODS: Resting-state functional magnetic resonance imaging scans were obtained from 64 first-episode schizophrenia patients (SZ) and 67 healthy controls (HC). All patients were scanned before and after 12-week antipsychotic treatment and the HC were scanned only at baseline. RESULTS: At baseline, SZ participants showed significantly reduced dynamic functional interactions across the triple-network compared to HC. The SZ group displayed a pattern of reduction in resting-state DFC among the triple-network compared with HC. After medication, the mean dynamic network interaction index (dNII) value was improved. A significant quadratic relation was observed between longitudinal change of mean dNII and the reduction ratio of PANSS total score within the SZ group. The DFC within inter-network (between DMN and SN, and between DMN and CEN) and intra-network connections of DMN were significantly higher relative to baseline. Intra-SN DFC, intra-DMN DFC and DFC between SN and DMN were found to be predictive of clinical features at baseline. Intra-CEN DFC and DFC between DMN and CEN were predictive of treatment response. CONCLUSIONS: Aberrant brain dynamics in the triple-network could be regulated with medication. DFC organization in the triple network was found to predict the clinical outcome.

Temporal Dynamics in Degree Centrality of Brain Functional Connectome in First-Episode Schizophrenia with Different Short-Term Treatment Responses: A Longitudinal Study.

PURPOSE: This study investigated temporal dynamics in degree centrality (DC) of the brain functional connectome in first-episode schizophrenia with different short-term treatment responses. METHODS: A total of 127 first-episode patients (FEPs) with schizophrenia and 133 healthy controls (HCs) were recruited in this study. All subjects underwent resting-state functional magnetic resonance imaging. FEPs were scanned at baseline (pretreatment) and at follow-up (posttreatment), while HCs were scanned only at baseline. The patients were exposed to naturalistic antipsychotic treatment for 12 weeks, and classified as schizophrenia responders (SRs) or nonresponders (NRs). Voxel-wise dynamic DC analyses were conducted among the SRs (n=75), NRs (n=52), and HCs (n=133) to assess temporal variability in functional connectivity across the entire neuronal network. RESULTS: The SRs and NRs showed dissimilar dynamic DC at baseline, with differences mainly involving the temporal lobe. Different DC alteration was observed in the left fusiform gyrus, right fusiform gyrus, left middle cingulate cortex, and left superior parietal gyrus in the SRs and NRs pre- and posttreatment. SRs group and NRs presented opposite changing patterns of dynamic DC in particular regions of the brain. CONCLUSION: These findings indicate that dynamic DC abnormalities exist in unmedicated patients with schizophrenia. The NRs differed from the SRs in dynamic DC not only at baseline but in the characteristics of changes before and after treatment as well. Our study may contribute to understanding pathophysiology in schizophrenia with different treatment responses.

A novel artificial intelligence-assisted triage tool to aid in the diagnosis of suspected COVID-19 pneumonia cases in fever clinics.

BACKGROUND: Currently, the need to prevent and control the spread of the 2019 novel coronavirus disease (COVID-19) outside of Hubei province in China and internationally has become increasingly critical. We developed and validated a diagnostic model that does not rely on computed tomography (CT) images to aid in the early identification of suspected COVID-19 pneumonia (S-COVID-19-P) patients admitted to adult fever clinics and made the validated model available via an online triage calculator. METHODS: Patients admitted from January 14 to February 26, 2020 with an epidemiological history of exposure to COVID-19 were included in the study [model development group (n=132) and validation group (n=32)]. Candidate features included clinical symptoms, routine laboratory tests, and other clinical information on admission. The features selection and model development were based on the least absolute shrinkage and selection operator (LASSO) regression. The primary outcome was the development and validation of a diagnostic aid model for the early identification of S-COVID-19-P on admission. RESULTS: The development cohort contained 26 cases of S-COVID-19-P and seven cases of confirmed COVID-19 pneumonia (C-COVID-19-P). The final selected features included one demographic variable, four vital signs, five routine blood values, seven clinical signs and symptoms, and one infection-related biomarker. The model's performance in the testing set and the validation group resulted in area under the receiver operating characteristic (ROC) curves (AUCs) of 0.841 and 0.938, F1 scores of 0.571 and 0.667, recall of 1.000 and 1.000, specificity of 0.727 and 0.778, and precision of 0.400 and 0.500, respectively. The top five most important features were age, interleukin-6 (IL-6), systolic blood pressure (SYS_BP), monocyte ratio (MONO%), and fever classification (FC). Based on this model, an optimized strategy for the early identification of S-COVID-19-P in fever clinics has also been designed. CONCLUSIONS: A machine-learning model based solely on clinical information and not on CT images was able to perform the early identification of S-COVID-19-P on admission in fever clinics with a 100% recall score. This high-performing and validated model has been deployed as an online triage tool, which is available at https://intensivecare.shinyapps.io/COVID19/.

Neurochemical and brain functional changes in the ventromedial prefrontal cortex of first-episode psychosis patients: A combined functional magnetic resonance imaging-proton magnetic resonance spectroscopy study.

OBJECTIVE: Previous studies showed alterations of brain function in the ventromedial prefrontal cortex of schizophrenia patients. Also, neurochemical changes, especially GABA level alteration, have been found in the medial prefrontal cortex of schizophrenia patients. However, the relationship between GABA level in the ventromedial prefrontal cortex and brain functional activity in schizophrenia patients remains unexplored. METHODS: In total, 23 drug-naïve, first-episode psychosis patients and 26 matched healthy controls completed the study. The single voxel proton magnetic resonance spectroscopy data were acquired in ventromedial prefrontal cortex region, which was used as the seed region for resting-state functional connectivity analysis. The proton magnetic resonance spectroscopy data were processed to quantify the concentrations of GABA+, glutamine and glutamate, and N-acetylaspartate in ventromedial prefrontal cortex. Spearman correlation analysis was used to examine the relationship between metabolite concentration, functional connectivity and clinical variables. Pearson correlation analysis was used to examine the relationship between GABA+ concentration and functional connectivity value. RESULTS: In first-episode psychosis patients, GABA+ level in ventromedial prefrontal cortex was higher and was positively correlated with ventromedial prefrontal cortex-left middle orbital frontal cortex functional connectivity. N-acetylaspartate level was positively correlated with positive symptoms, and the functional connectivity between ventromedial prefrontal cortex and left precuneus was negatively associated with negative symptoms of first-episode psychosis patients. CONCLUSION: Our results indicated that ventromedial prefrontal cortex functional connectivity changes were positively correlated with higher local GABA+ level in first-episode psychosis patients. The altered neurochemical concentration and functional connectivity provide insights into the pathology of schizophrenia.

Repetitive transcranial magnetic stimulation as an adjunctive treatment for negative symptoms and cognitive impairment in patients with schizophrenia: a randomized, double-blind, sham-controlled trial.

Purpose: Effective treatment options for negative symptoms and cognitive impairment in patients with schizophrenia are still to be developed. The present study was to examine potential benefits of repetitive transcranial magnetic stimulation (rTMS) to improve negative symptoms and cognition in this patient population. Methods: The study was a 4-week, randomized, double-blind sham-controlled trial. Patients with schizophrenia were treated with adjunctive 20-Hz rTMS for 4 weeks or sham condition to the left dorsolateral prefrontal cortex (DLPFC). Negative symptoms were measured using the Scale for the Assessment of Negative Symptoms (SANS) and the Positive and Negative symptom scale (PANSS) negative subscale at baseline and week 4. Cognitive function was measured using the MATRICS Consensus Cognitive Battery (MCCB) at the same two time points. In addition, possible moderators for rTMS treatment efficacy were explored. Results: Sixty patients (33 in the treatment group, 27 in the sham group) completed the study. There was a significant decrease in negative symptoms after 4-week rTMS treatment as measured by the SANS total score and the PANSS negative symptom subscale score. However, there was no significant improvement in cognition with rTMS treatment. Stepwise multiple linear regression analysis suggested that the baseline severity of positive symptoms may predict poorer improvement in negative symptoms at week 4. Conclusion: Twenty-Hz rTMS stimulation over left DLPFC as an adjunctive treatment might be beneficial in improving negative symptoms of schizophrenia. Future studies with a longer treatment duration and a larger sample size are needed. Clinical trial ID: NCT01940939.

Modular Functional-Metabolic Coupling Alterations of Frontoparietal Network in Schizophrenia Patients.

Background: Brain functional dysconnectivity, as well as altered network organization, have been demonstrated to occur in schizophrenia. Brain networks are increasingly understood to exhibit modular community structures, which provides advantages in robustness and functional adaptivity. The frontoparietal network (FPN) serves as an important functional module, and metabolic and functional alterations in the FPN are associated with the pathophysiology of schizophrenia. However, how intra-modular biochemical disruptions lead to inter-modular dysfunction of the FPN, remains unclear. In this study, we aim to investigate alterations in the modular functional-metabolic coupling of the FPN, in patients with schizophrenia. Methods: We combined resting-state functional magnetic resonance imaging (rs-fMRI) and magnetic resonance spectroscopy (MRS) technology and acquired multimodal neuroimaging data in 20 patients with schizophrenia and 26 healthy controls. For the MRS, the dorsolateral prefrontal cortex (DLPFC) region within the FPN was explored. Metabolites including gamma aminobutyric acid (GABA), N-aspart-acetyl (NAA) and glutamate + glutamine (Glx) were quantified, using LCModel software. A graph theoretical approach was applied for functional modular parcellation. The relationship between inter/intra-modular connectivity and metabolic concentration was examined using the Pearson correlation analysis. Moreover, correlations with schizophrenia symptomatology were investigated by the Spearman correlation analysis. Results: The functional topological network consisted of six modules in both subject groups, namely, the default mode, frontoparietal, central, hippocampus, occipital, and subcortical modules. Inter-modular connectivity between the frontoparietal and central modules, and the frontoparietal and the hippocampus modules was decreased in the patient group compared to the healthy controls, while the connectivity within the frontoparietal modular increased in the patient group. Moreover, a positive correlation between the frontoparietal and central module functional connectivity and the NAA in the DLPFC was found in the healthy control group (r = 0.614, p = 0.001), but not in the patient group. Significant functional dysconnectivity between the frontoparietal and limbic modules was correlated with the clinical symptoms of patients. Conclusions: This study examined the links between functional connectivity and the neuronal metabolic level in the DLPFC of SCZ. Impaired functional connectivity of the frontoparietal areas in SCZ, may be partially explained by a neurochemical-functional connectivity decoupling effect. This disconnection pattern can further provide useful insights in the cognitive and perceptual impairments of schizophrenia in future studies.

Association between the T6459C point mutation of the mitochondrial MT-CO1 gene and susceptibility to sepsis among Chinese Han people.

To search for an association between sepsis and mitochondrial genetic basis, we began our study. In this study, a proband harbouring mitochondrial T6459C mutation with sepsis and his Chinese Han pedigree including 7 members of 3 generations were enrolled. General information, blood parameters and mitochondrial full sequence scanning of all members were performed, and cellular functions, including cellular reactive oxygen species (ROS) levels, mitochondrial membrane potential (MMP), degrees of cell apoptosis and adenosine triphosphate (ATP) concentrations, were measured in members with and without the T6459C mutation. Through mitochondrial full sequence scanning and analysis of all members we found, the maternal members (I-1, II-1, II-2 and II-4) in this Chinese Han pedigree all had the mitochondrial T6459C mutation and were used as the mutation group. The non-maternal members (II-3, III-1 and III-2) did not have this mutation and were used as the non-mutation group. The differences in all indicators, including the blood routine, blood biochemistry and coagulation function tests, between members in these two groups were not significant. Under the non-stimulation condition, the mutation group had higher ROS levels (4210.42 ± 1043.35 vs 3387.78 ± 489.66, P = .028) and apoptosis ratios (P = .004) and lower ATP concentrations (P = .049) and MMP levels (P = .047) than the non-mutation group. After 6 hours of simulated LPS stimulation, the mutation group had significantly increased ROS levels (5759.25 ± 2297.90 vs 3862.00 ± 1519.77, P = .045) compared with the non-mutation group, whereas the mutation group continued to demonstrate higher ROS levels (P = .045) and apoptosis ratios (P = .003) and lower MMP levels (P = .005) and ATP concentrations (P = .010). We speculated that the mtDNA T6459C mutation might be the basis for the genetic susceptibility to sepsis.

Abnormal Concentration of GABA and Glutamate in The Prefrontal Cortex in Schizophrenia.-An in Vivo 1H-MRS Study.

BACKGROUND: The etiology and pathomechanism of schizophrenia are unknown. The traditional dopamine (DA) hypothesis is unable to fully explain its pathology and therapeutics. The glutamate (Glu) and γ-aminobutyric acid (GABA) hypotheses suggest Glu or GABA concentrations are abnormal in the brains of patients with schizophrenia. Magnetic resonance spectroscopy (MRS) show glutamate level increases in the ventromedial prefrontal cortex (vmPFC) including the anterior cingulated cortex (ACC) in those with schizophrenia. AIMS: To investigate the function of the glutamate system (glutamate and γ-aminobutyric acid) in the etiology and pathomechanism of schizophrenia. METHODS: 24 drug naïve patients with schizophrenia and 24 healthy volunteers were matched by gender, age, and educational level. The Siemens 3T MRI system was used to collect the magnetic resonance spectroscopy (MRS) data of the subjects. The regions of interest included the left dorsolateral prefrontal cortex (IDLPFC), ventromedial prefrontal cortex (vmPFC), and anterior cingulate cortex (ACC). LCModel software was used to analyze the concentrations of γ-aminobutyric acid (GABA), glutamate (Glu), glutamine (Gln), N-acetylaspartate (NAA), and N-acetylaspartylglutamate (NAAG) in the region of interest. Meanwhile, the Positive and Negative Syndrome Scale (PANSS) and the Clinical Global Impression Scale (CGI) were used to assess the mental symptoms and severity of the disease. RESULTS: The median GABA concentrations in the anterior cingulate cortex of the schizophrenia group and the healthy control group were 1.90 (Q1=1.55, Q3=2.09) and 2.16 (Q1=1.87, Q3=2.59) respectively; the mean (sd) Glu concentrations were 6.07 (2.48) and 6.54 (1.99); the median Gln concentrations were 0.36 (Q1=0.00, Q3=0.74) and 0.29 (Q1=0.00, Q3=0.59); the between-group difference of the GABA concentrations was statistically significant (Z=-2.95, p=0.003); the between-group difference of the GABA/(NAA+NAAG) was statistically significant (Z=-2.72, p=0.012); the between-group difference of Glu and Gln was not statistically significant. The age of the schizophrenia group was negatively correlated with the GABA concentration in the anterior cingulate (R=-0.494, p=0.014), and negatively correlated with GABA/ (NAA+NAAG) (R=-0.473, p=0.020). Yet there was no such correlation in the control group. After calibration, no significant correlation was found between the clinical symptoms and the concentrations of the metabolites. CONCLUSIONS: The concentration of glutamate in the vemtromedial prefrontal cortex of patients with schizophrenia was abnormal, whereas the concentration of GABA in the anterior cingulate cortex decreased, supporting the hypothesis of abnormal glutamate -GABA in the brains of those individuals with schizophrenia. In patients with schizophrenia, the GABA in the anterior cingulate cortex had an accelerated decline with age. The clinical symptoms may be correlated to the metabolite concentration of the anterior cingulate cortex.