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Left-right asymmetry is an important organizing feature of the healthy brain that may be altered in schizophrenia, but most studies have used relatively small samples and heterogeneous approaches, resulting in equivocal findings. We carried out the largest case-control study of structural brain asymmetries in schizophrenia, with MRI data from 5,080 affected individuals and 6,015 controls across 46 datasets, using a single image analysis protocol. Asymmetry indexes were calculated for global and regional cortical thickness, surface area, and subcortical volume measures. Differences of asymmetry were calculated between affected individuals and controls per dataset, and effect sizes were meta-analyzed across datasets. Small average case-control differences were observed for thickness asymmetries of the rostral anterior cingulate and the middle temporal gyrus, both driven by thinner left-hemispheric cortices in schizophrenia. Analyses of these asymmetries with respect to the use of antipsychotic medication and other clinical variables did not show any significant associations. Assessment of age- and sex-specific effects revealed a stronger average leftward asymmetry of pallidum volume between older cases and controls. Case-control differences in a multivariate context were assessed in a subset of the data (N = 2,029), which revealed that 7% of the variance across all structural asymmetries was explained by case-control status. Subtle case-control differences of brain macrostructural asymmetry may reflect differences at the molecular, cytoarchitectonic, or circuit levels that have functional relevance for the disorder. Reduced left middle temporal cortical thickness is consistent with altered left-hemisphere language network organization in schizophrenia.

Additional file 5. Table S5: Plasma miR320a/miR-483 levels compared between different groups in patients

Additional file 8. Table S8: Target analysis for miR-320a and miR-483

Additional file 4. Table S4: Risk estimates of 3 h miRNA levels for rivaroxaban response by univariate logistic analysis in healthy volunteers

Additional file 1. Table S1: The inclusion criteria of healthy volunteers and patients

Additional file 2. Table S2: Analytics for the assessment of rivaroxaban plasma concentrations in healthy volunteers in sub-centers

Additional file 7. Table S7: Comparison of plasma miR-320a and miR-483 levels between 3 and 0 h in healthy volunteers

Additional file 3. Table S3: Comparison of plasma miR-320a and miR-483 levels between the case and control groups in healthy volunteers

Additional file 6. Table S6: Risk estimates of 3 h miRNA levels for rivaroxaban response by univariate logistic analysis in patients

ObjectiveTo explore differences and similarities in relationships between subcortical structure volumes and neurocognition among the four subject groups, including first-episode schizophrenia (FES), bipolar disorder (BD), major depression disorder (MDD), and healthy controls (HCs).MethodsWe presented findings from subcortical volumes and neurocognitive analyses of 244 subjects (109 patients with FES; 63 patients with BD, 30 patients with MDD, and 42 HCs). Using the FreeSurfer software, volumes of 16 selected subcortical structures were automatically segmented and analyzed for relationships with results from seven neurocognitive tests from the MATRICS (Measurement and Treatment Research to Improve Cognition in Schizophrenia) Cognitive Consensus Battery (MCCB).ResultsLarger left lateral ventricle volumes in FES and BD, reduced bilateral hippocampus and amygdala volumes in FES, and lower bilateral amygdala volumes in BD and MDD were presented compared with HCs, and both FES and BD had a lower bilateral amygdala volume than MDD; there were seven cognitive dimension, five cognitive dimension, and two cognitive dimension impairments in FES, BD, and MDD, respectively; significant relationships were found between subcortical volumes and neurocognition in FES and BD but not in MDD and HCs; besides age and years of education, some subcortical volumes can predict neurocognitive performances variance.ConclusionThe different degrees of subcortical volume lessening may contribute to the differences in cognitive impairment among the three psychiatric disorders.

Additional file 1. Spirit Checklist.

PurposeTo explore the differences in facial emotion recognition among patients with unipolar depression (UD), bipolar depression (BD), and normal controls.MethodsThirty patients with UD and 30 patients with BD, respectively, were recruited in Zhumadian Second People’s Hospital from July 2018 to August 2019. Fifteen groups of facial expressions including happiness, sadness, anger, surprise, fear, and disgust were identified.ResultsA single-factor ANOVA was used to analyze the facial expression recognition results of the three groups, and the differences were found in the happy-sad (P = 0.009), happy-angry (P = 0.001), happy-surprised (P = 0.034), and disgust-surprised (P = 0.038) facial expression groups. The independent sample T-test analysis showed that compared with the normal control group, there were differences in the happy-sad (P = 0.009) and happy-angry (P = 0.009) groups in patients with BD, and the accuracy of facial expression recognition was lower than the normal control group. Compared with patients with UD, there were differences between the happy-sad (P = 0.005) and happy-angry (P = 0.002) groups, and the identification accuracy of patients with UD was higher than that of patients with BD. The time of facial expression recognition in the normal control group was shorter than that in the patient group. Using happiness-sadness to distinguish unipolar and BDs, the area under the ROC curve (AUC) is 0.933, the specificity is 0.889, and the sensitivity is 0.667. Using happiness-anger to distinguish unipolar and BD, the AUC was 0.733, the specificity was 0.778, and the sensitivity was 0.600.ConclusionPatients with UD had lower performance in recognizing negative expressions and had longer recognition times. Those with BD had lower accuracy in recognizing positive expressions and longer recognition times. Rapid facial expression recognition performance may be as a potential endophenotype for early identification of unipolar and BD.

Objective: Considerable evidence has shown that facial expression recognition ability and cognitive function are impaired in patients with depression. We aimed to investigate the relationship between facial expression recognition and cognitive function in patients with depression.Methods: A total of 51 participants (i.e., 31 patients with depression and 20 healthy control subjects) underwent facial expression recognition tests, measuring anger, fear, disgust, sadness, happiness, and surprise. The Chinese version of the MATRICS Consensus Cognitive Battery (MCCB), which assesses seven cognitive domains, was used.Results: When compared with a control group, there were differences in the recognition of the expressions of sadness (p = 0.036), happiness (p = 0.041), and disgust (p = 0.030) in a depression group. In terms of cognitive function, the scores of patients with depression in the Trail Making Test (TMT; p < 0.001), symbol coding (p < 0.001), spatial span (p < 0.001), mazes (p = 0.007), the Brief Visuospatial Memory Test (BVMT; p = 0.001), category fluency (p = 0.029), and continuous performance test (p = 0.001) were lower than those of the control group, and the difference was statistically significant. The accuracy of sadness and disgust expression recognition in patients with depression was significantly positively correlated with cognitive function scores. The deficits in sadness expression recognition were significantly correlated with the TMT (p = 0.001, r = 0.561), symbol coding (p = 0.001, r = 0.596), maze (p = 0.015, r = 0.439), and the BVMT (p = 0.044, r = 0.370). The deficits in disgust expression recognition were significantly correlated with impairments in the TMT (p = 0.005, r = 0.501) and symbol coding (p = 0.001, r = 0.560).Conclusion: Since cognitive function is impaired in patients with depression, the ability to recognize negative facial expressions declines, which is mainly reflected in processing speed, reasoning, problem-solving, and memory.

BackgroundThe profile of cortical neuroanatomical abnormalities in schizophrenia is not fully understood, despite hundreds of published structural brain imaging studies. This study presents the first meta-analysis of cortical thickness and surface area abnormalities in schizophrenia conducted by the ENIGMA (Enhancing Neuro Imaging Genetics through Meta Analysis) Schizophrenia Working Group.MethodsThe study included data from 4474 individuals with schizophrenia (mean age, 32.3 years; range, 11-78 years; 66% male) and 5098 healthy volunteers (mean age, 32.8 years; range, 10-87 years; 53% male) assessed with standardized methods at 39 centers worldwide.ResultsCompared with healthy volunteers, individuals with schizophrenia have widespread thinner cortex (left/right hemisphere: Cohen's d=-0.530/-0.516) and smaller surface area (left/right hemisphere: Cohen's d=-0.251/-0.254), with the largest effect sizes for both in frontal and temporal lobe regions. Regional group differences in cortical thickness remained significant when statistically controlling for global cortical thickness, suggesting regional specificity. In contrast, effects for cortical surface area appear global. Case-control, negative, cortical thickness effect sizes were two to three times larger in individuals receiving antipsychotic medication relative to unmedicated individuals. Negative correlations between age and bilateral temporal pole thickness were stronger in individuals with schizophrenia than in healthy volunteers. Regional cortical thickness showed significant negative correlations with normalized medication dose, symptom severity, and duration of illness and positive correlations with age at onset.ConclusionsThe findings indicate that the ENIGMA meta-analysis approach can achieve robust findings in clinical neuroscience studies; also, medication effects should be taken into account in future genetic association studies of cortical thickness in schizophrenia.

In order to represent vehicles merging behavior accurately in urban expressway merging sections, and overcome the shortcomings of existing models, firstly, a typical merging section in Guangzhou is selected to investigate field data, and the video processing software VEVID is used to get larger amounts of vehicle trajectory data. Then a lot of factors that affect vehicle merging behaviors are analyzed, and a logistic model that is applied to estimate the probabilities that driver will merge into adjacent gaps is built. Finally, the microscopic traffic simulation system MTSS is taken as simulation platform to build merging simulation model, the real merging section traffic data in Guangzhou is used to calibrate and validate the merging probability model, the output results from the simulation system are compared with field data, the satisfactory results indicates that the built merging simulation model can be used to describe the complex driver behaviors of merging sections.

A common limitation of neuroimaging studies is their small sample sizes. To overcome this hurdle, the Enhancing Neuro Imaging Genetics through Meta-Analysis (ENIGMA) Consortium combines neuroimaging data from many institutions worldwide. However, this introduces heterogeneity due to different scanning devices and sequences. ENIGMA projects commonly address this heterogeneity with random-effects meta-analysis or mixed-effects mega -analysis. Here we tested whether the batch adjustment method, ComBat, can further reduce site-related het-erogeneity and thus increase statistical power. We conducted random-effects meta-analyses, mixed-effects mega -analyses and ComBat mega-analyses to compare cortical thickness, surface area and subcortical volumes between 2897 individuals with a diagnosis of schizophrenia and 3141 healthy controls from 33 sites. Specifically, we compared the imaging data between individuals with schizophrenia and healthy controls, covarying for age and sex. The use of ComBat substantially increased the statistical significance of the findings as compared to random - effects meta-analyses. The findings were more similar when comparing ComBat with mixed-effects mega-analysis, although ComBat still slightly increased the statistical significance. ComBat also showed increased statistical power when we repeated the analyses with fewer sites. Results were nearly identical when we applied the ComBat harmonization separately for cortical thickness, cortical surface area and subcortical volumes. Therefore, we recommend applying the ComBat function to attenuate potential effects of site in ENIGMA projects and other multi-site structural imaging work. We provide easy-to-use functions in R that work even if imaging data are partially missing in some brain regions, and they can be trained with one data set and then applied to another (a requirement for some analyses such as machine learning).