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Abstract Machine learning can be used to define subtypes of psychiatric conditions based on shared biological foundations of mental disorders. Here we analyzed cross-sectional brain images from 4,222 individuals with schizophrenia and 7038 healthy subjects pooled across 41 international cohorts from the ENIGMA, non-ENIGMA cohorts and public datasets. Using the Subtype and Stage Inference (SuStaIn) algorithm, we identify two distinct neurostructural subgroups by mapping the spatial and temporal ‘trajectory’ of gray matter change in schizophrenia. Subgroup 1 was characterized by an early cortical-predominant loss with enlarged striatum, whereas subgroup 2 displayed an early subcortical-predominant loss in the hippocampus, striatum and other subcortical regions. We confirmed the reproducibility of the two neurostructural subtypes across various sample sites, including Europe, North America and East Asia. This imaging-based taxonomy holds the potential to identify individuals with shared neurobiological attributes, thereby suggesting the viability of redefining existing disorder constructs based on biological factors.

Abstract Background Ischemic cardio-cerebrovascular disease is the leading cause of mortality worldwide. However, studies focusing on elderly and very elderly patients are scarce. Hence, our study aimed to characterize and investigate the long-term prognostic implications of ischemic cardio-cerebrovascular diseases in elderly Chinese patients. Methods This retrospective cohort study included 1026 patients aged ≥ 65 years who were categorized into the mono ischemic cardio-cerebrovascular disease (MICCD) (either coronary artery disease or ischemic stroke/transient ischemic attack) (n = 912) and the comorbidity of ischemic cardio-cerebrovascular disease (CICCD) (diagnosed with both coronary artery disease and ischemic stroke/transient ischemic attack at admission) (n = 114). The primary outcome was all-cause death. The mortality risk was evaluated using the Cox proportional hazards risk model with multiple adjustments by conventional and propensity-score-based approaches. Results Of the 2494 consecutive elderly patients admitted to the hospital, 1026 (median age 83 years [interquartile range]: 76.5–86.4; 94.4% men) met the inclusion criteria. Patients with CICCD consisted mostly of very elderly (79.2% vs. 66.1%, P

Schizophrenia is among top disability causes worldwide; however, its pathological mechanism is still unclear. In order to advance the research progress on schizophrenia, the Schizophrenia Imaging Lab has been dedicated to the diagnosis, treatment, and prevention of schizophrenia and conducted systematic studies. One of the recent studies involved the Allen Human Brain Atlas in the imaging study of schizophrenia. The combination of the two methods mitigated the gap between the human brain imaging and genetic mechanism, and enabled us to observe the transcriptome alterations and brain changes at the same time. This review concluded the studies that combined the imaging method and the Allen Human Brain Atlas application in schizophrenia. We aim to provide a holistic view of the brain changes in schizophrenia at the micro level.

Abstract High suicide risk represents a serious problem in patients with major depressive disorder (MDD), yet treatment options that could safely and rapidly ameliorate suicidal ideation remain elusive. Here, we tested the feasibility and preliminary efficacy of the Stanford Accelerated Intelligent Neuromodulation Therapy (SAINT) in reducing suicidal ideation in patients with MDD. Thirty-two MDD patients with moderate to severe suicidal ideation participated in the current study. Suicidal ideation and depression symptoms were assessed before and after 5 days of open-label SAINT. The neural pathways supporting rapid-acting antidepressant and suicide prevention effects were identified with dynamic causal modelling based on resting-state functional magnetic resonance imaging. We found that 5 days of SAINT effectively alleviated suicidal ideation in patients with MDD with a high response rate of 65.63%. Moreover, the response rates achieved 78.13% and 90.63% with 2 weeks and 4 weeks after SAINT, respectively. In addition, we found that the suicide prevention effects of SAINT were associated with the effective connectivity involving the insula and hippocampus, while the antidepressant effects were related to connections of the subgenual anterior cingulate cortex (sgACC). These results show that SAINT is a rapid-acting and effective way to reduce suicidal ideation. Our findings further suggest that distinct neural mechanisms may contribute to the rapid-acting effects on the relief of suicidal ideation and depression, respectively.

In the face of the unprecedented public health crisis caused by the novel coronavirus pneumonia epidemic, front-line health workers are under enormous mental pressure. This paper aims to explore the mental health challenges faced by front-line health workers in the early stages of a public health emergency, such as stress, anxiety, and depression. At the same time, the factors that increase their mental stress are analyzed, and practical measures are put forward to prevent and manage mental health problems, aiming at improving the quality of medical treatment during public health emergencies. This paper has some reference value for people engaged in mental health prevention.

Abstract Background Inflammation has been implicated in the pathology of schizophrenia and may cause neuronal cell death and dendrite loss. Neuroimaging studies have highlighted longitudinal brain structural changes in patients with schizophrenia, yet it is unclear whether this is related to inflammation. We aim to address this question, by relating brain structural changes with the transcriptional profile of inflammation markers in the early stage of schizophrenia. Methods Thirty-eight patients with first-episode schizophrenia and 51 healthy controls were included. High-resolution T1-weighted magnetic resonance imaging (MRI) and clinical assessments were performed at baseline and 2 ~ 6 months follow-up for all subjects. Changes in the brain structure were analyzed using surface-based morphological analysis and correlated with the expression of immune cells-related gene sets of interest reported by previous reviews. Transcriptional data were retrieved from the Allen Human Brain Atlas. Furthermore, we examined the brain structural changes and peripheral inflammation markers in association with behavioral symptoms and cognitive functioning in patients. Results Patients exhibited accelerated cortical thickness decrease in the left frontal cortices, less decrease or an increase in the superior parietal lobule and right lateral occipital lobe, and increased volume in the bilateral pallidum, compared with controls. Changes in cortical thickness correlated with the transcriptional level of monocyte across cortical regions in patients (r = 0.54, p

Abstract Background Cognitive impairment is the main factor in the poor prognosis of schizophrenia, but its mechanism remains unclear. The inferior parietal lobule (IPL) is related to various clinical symptoms and cognitive impairment in schizophrenia. We aimed to explore the relationship between IPL-related functions and cognitive impairment in schizophrenia. Methods 136 schizophrenia patients and 146 demographically matched healthy controls were enrolled for a cross-sectional study. High-spatial-resolution structural and resting-state functional images were acquired to demonstrate the alternations of brain structure and function. At the same time, the digit span and digit symbol coding tasks of the Chinese Wechsler Adult Intelligence Test Revised (WAIS-RC) were utilized in assessing the subjects’ cognitive function. Patients were divided into cognitive impairment and normal cognitive groups according to their cognitive score and then compared whether there were differences between the three groups in fractional amplitude of low-frequency fluctuation (fALFF). In addition, we did a correlation analysis between cognitive function and the fALFF for the left IPL of patients and healthy controls. Based on the Allen Human Brain Atlas, we obtained genes expressed in the left IPL, which were then intersected with the transcriptome-wide association study results and differentially expressed genes in schizophrenia. Results Grouping of patients by the backward digit span task and the digit symbol coding task showed differences in fALFF values between healthy controls and cognitive impairment patients (P 0.017). In addition, we found that diacylglycerol kinase ζ (DGKζ) is differentially expressed in the left IPL and associated with schizophrenia. Conclusion Our study demonstrates that the left IPL plays a vital role in cognitive impairment in schizophrenia. DGKζ may act as an essential regulator in the left IPL of schizophrenia patients with cognitive impairment.

Cognitive impairments in schizophrenia are pivotal clinical issues that need to be solved urgently. However, the mechanism remains unknown. It has been suggested that cognitive impairments in schizophrenia are associated with connectome damage, and are especially relevant to the disrupted hub nodes in the frontal and parietal lobes. Activating the dorsolateral prefrontal cortex (DLPFC) via repetitive transcranial magnetic stimulation (rTMS) could result in improved cognition. Based on several previous magnetic resonance imaging (MRI) studies on schizophrenia, we found that the first-episode patients showed connectome damage, as well as abnormal activation and connectivity of the DLPFC and inferior parietal lobule (IPL). Accordingly, we proposed that DLPFC-IPL pathway destruction might mediate connectome damage of cognitive impairments in schizophrenia. In the meantime, with the help of multimodal MRI and noninvasive neuromodulation tool, we may not only validate the hypothesis, but also find IPL as the potential intervention target for cognitive impairments in schizophrenia.

Abstract This study focuses on the prevalence and characteristics of anxiety in patients with pulmonary nodules that was assessed by Hamilton Anxiety Scale (HAMA) scores. A total of 890 patients were enrolled in this study, including incidence of absence of anxiety n = 343 (38.54%), mild or probable anxiety n = 459 (51.57%) and moderate or definite anxiety n = 79 (8.88%) and obvious anxiety n = 9 (1.01%), respectively. According to the definition of anxiety, 88 (9.89%) patients were enrolled in anxiety group. The incidence of anxiety in females was significantly higher than male (11.98% vs. 7.20%, p = 0.018), patients with respiratory symptoms were significantly higher than without respiratory symptoms (13.33% vs. 8.50%, p = 0.029) and diameter of pulmonary nodules >8 mm is significantly higher than ≤8 mm (13.35% vs. 7.10%, p = 0.002). Regression analysis showed that female (OR = 0.548, 95% CI: 0.340–0.884), family history of malignant tumour (OR = 1.691, 95% CI: 1.067–2.678), respiratory symptoms (OR = 1.713, 95% CI: 1.073–2.733) and diameter >8 mm (OR = 2.135, 95% CI: 1.350–3.375) were independent risk factors of anxiety. Further analysis of 88 patients with anxiety showed the sum of psychic anxiety was significantly higher than somatic anxiety (16.66 ± 2.46 vs. 0.97 ± 1.10, p

Electroconvulsive therapy (ECT) is an important treatment for depression. Although it is known as the most effective acute treatment for severe mood disorders, its therapeutic mechanism is still unclear. With the rapid development of neuroimaging technology, various neuroimaging techniques have been available to explore the alterations of the brain by ECT, such as structural magnetic resonance imaging, functional magnetic resonance imaging, magnetic resonance spectroscopy, positron emission tomography, single photon emission computed tomography, arterial spin labeling, etc. This article reviews studies in neuroimaging on ECT for depression. These findings suggest that the neurobiological mechanism of ECT may regulate the brain functional activity, and neural structural plasticity, as well as balance the brain’s neurotransmitters, which finally achieves a therapeutic effect.

Major psychiatric disorders create a significant public health burden, and mental disorders such as major depressive disorder, bipolar disorder, and schizophrenia are major contributors to the national disease burden. The search for biomarkers has been a leading endeavor in the field of biological psychiatry in recent decades. And the application of cross-scale and multi-omics approaches combining genes and imaging in major psychiatric studies has facilitated the elucidation of gene-related pathogenesis and the exploration of potential biomarkers. In this article, we summarize the results of using combined transcriptomics and magnetic resonance imaging to understand structural and functional brain changes associated with major psychiatric disorders in the last decade, demonstrating the neurobiological mechanisms of genetically related structural and functional brain alterations in multiple directions, and providing new avenues for the development of quantifiable objective biomarkers, as well as clinical diagnostic and prognostic indicators.

ObjectiveThis study aims to investigate novel clinical risk factors for cognitive impairment (CI) in elderly.MethodsA total of 3221 patients (259 patients with CI and 2,962 subjects without CI) were recruited into this nested case-control study who underwent cerebral magnetic resonance angiography (MRA) from 2007 to 2021. All of the clinical data with MRA imaging were recorded followed by standardization processing blindly. The maximum stenosis score of the posterior circulatory artery, including the basilar artery, and bilateral posterior cerebral artery (PCA), was calculated by the cerebral MRA automatic quantitative analysis method. Logistic regression (LR) analysis was used to evaluate the relationship between risk factors and CI. Four machine learning approaches, including LR, decision tree (DT), random forest (RF), and support vector machine (SVM), employing 5-fold cross-validation were used to establish CI predictive models.ResultsAfter matching with age and gender, 208 CI patients and 208 control subjects were finalized the follow-up (3.46 ± 3.19 years) with mean age at 84.47 ± 6.50 years old. Pulse pressure (PP) in first tertile (

BackgroundThe relations between depression and intolerance of uncertainty (IU) have been extensively investigated during the COVID-19 pandemic. However, there is a lack of understanding on how each component of IU may differentially affect depression symptoms and vice versa. The current study used a network approach to reveal the component-to-symptom interplay between IU and depression and identify intervention targets for depression during the COVID-19 pandemic.MethodsA total of 624 college students participated in the current study. An IU-Depression network was estimated using items from the 12-item Intolerance of Uncertainty Scale and the Patient Health Questionnaire-9. We examined the network structure, node centrality, and node bridge centrality to identify component-to-symptom pathways, central nodes, and bridge nodes within the IU-Depression network.ResultsSeveral distinct pathways (e.g., “Frustration when facing uncertainty” and “Feelings of worthlessness”) emerged between IU and Depression. “Fatigue” and “Frustration when facing uncertainty” were identified as the central nodes in the estimated network. “Frustration when facing uncertainty,” “Psychomotor agitation/retardation,” and “Depressed or sad mood” were identified as bridging nodes between the IU and Depression communities.ConclusionBy delineating specific pathways between IU and depression and highlighting the influential role of “Frustration when facing uncertainty” in maintaining the IU-Depression co-occurrence, current findings may inform targeted prevention and interventions for depression during the COVID-19 pandemic.

Abstract Background Intolerance of uncertainty (IU) is considered as a specific risk factor in the development and maintenance of generalized anxiety disorder (GAD). Yet, researches have investigated the relations between IU and GAD (or worry) using total scores on self-report measures. This ignores that there are different components exist in IU and the heterogeneity of GAD symptoms. In the present study, we explored the relations among different components of IU and symptoms of GAD. Methods A dimensional approach which take individual differences into consideration in different components of IU along a full range of normal to abnormal symptom severity levels of GAD were used in this study. Components of IU were measured by 12-item Intolerance of Uncertainty Scale and symptoms of GAD were measured by Generalized Anxiety Disorder 7-Item Questionnaire. Regularized partial-correlation network was estimated using cross-sectional data from 624 university students. Results Four strongest edges are between components of IU, like “Unforeseen events upset me greatly” and “It frustrates me not having all the information I need”. Two strongest edges are between symptoms of GAD, like “Being so restless that it is hard to sit still” and “Feeling afraid as if something awful might happen”. Symptom “Worrying too much about different things” and component “It frustrates me not having all the information I need” have the highest expected influences in the present network. In the community of IU, component “It frustrates me not having all the information I need” has the highest bridge expected influence. And in the community of GAD, symptoms “Worrying too much about different things” and “Not being able to stop or control worrying” have the highest bridge expected influence. Conclusions This study reveals potential pathways between different components of IU and various symptoms of GAD. Understanding how putative risk factors such as different components of IU are related to symptoms of GAD may provide some references for related preventions and interventions, such as targeting component “It frustrates me not having all the information I need” may be more effective at reducing symptoms of GAD than targeting other components of IU.

Abstract Background Comorbidity between depressive and anxiety disorders is common. From network perspective, mental disorders arise from direct interactions between symptoms and comorbidity is due to direct interactions between depression and anxiety symptoms. The current study investigates the network structure of depression and anxiety symptoms in Chinese female nursing students and identifies the central and bridge symptoms as well as how other symptoms in present network are related to depression symptom “thoughts of death”. Methods To understand the full spectrum of depression and anxiety, we recruited 776 Chinese female nursing students with symptoms of depression and anxiety that span the full range of normal to abnormal. Depression symptoms were measured by Patient Health Questionnaire-9 while anxiety symptoms were measured by Generalized Anxiety Disorder 7-Item Questionnaire. Network analysis was used to construct networks. Specifically, we computed the predictability, expected influence and bridge expected influence for each symptom and showed a flow network of “thoughts of death”. Results Nine strongest edges existed in network were from the same disorder. Four were between depression symptoms, like “sleep difficulties” and “fatigue”, and “anhedonia” and “fatigue”. Five were between anxiety symptoms, like “nervousness or anxiety” and “worry too much”, and “restlessness” and “afraid something will happen”. The symptom “fatigue”, “feeling of worthlessness” and “irritable” had the highest expected influence centrality. Results also revealed two bridge symptoms: “depressed or sad mood” and “irritable”. As to “thoughts of death”, the direct relations between it and “psychomotor agitation/retardation” and “feeling of worthlessness” were the strongest direct relations. Conclusions The current study highlighted critical central symptoms “fatigue”, “feeling of worthlessness” and “irritable” and critical bridge symptoms “depressed or sad mood” and “irritable”. Particularly, “psychomotor agitation/retardation” and “feeling of worthlessness” were identified as key priorities due to their strongest associations with suicide ideation. Implications for clinical prevention and intervention based on these symptoms are discussed.

Abstract Background Improving the psychotherapies for generalized anxiety disorder (GAD) is dependent on a deeper understanding of the relations between GAD and its associated cognitive factors. In the present study, we investigate how the core feature of GAD (i.e., worry) and its associated cognitive factors, such as meta-worry, intolerance of uncertainty, and attention bias towards threat, relate to each other in men at high risk for GAD. Methods We used network analysis to explore the relations among these variables in a cross-sectional sample of 122 men at high risk for generalized anxiety disorder. Specifically, we computed the expected influence and predictability of each variable. Results In the final network, we found that worry and meta-worry had the highest expected influence and predictability. In contrast, attention bias towards threat showed the lowest expected influence and predictability. The estimates of the expected influence of the nodes were stable (correlation stability coefficient = 0.52). Conclusions The present study is the first to investigate the relations among worry, meta-worry, intolerance of uncertainty, and attention bias towards threat in men at high risk for generalized anxiety disorder. These findings indicate that worry and meta-worry may play important roles in the present network. The implications for clinical interventions and future studies are discussed.

Introduction: Cognitive decline is the core schizophrenia symptom, which is now well accepted. Holding a role in various aspects of cognition, lentiform nucleus (putamen and globus pallidus) dysfunction contributes to the psychopathology of this disease. However, the effects of lentiform nucleus function on cognitive impairments in schizophrenia are yet to be investigated.Objectives: We aim to detect the fractional amplitude of low-frequency fluctuation (fALFF) alterations in patients with schizophrenia, and examine how their behavior correlates in relation to the cognitive impairments of the patients.Methods: All participants underwent magnetic resonance imaging (MRI) and cognitive assessment (digit span and digit symbol coding tests). Screening of brain regions with significant changes in fALFF values was based on analysis of the whole brain. The data were analyzed between Jun 2020 and Mar 2021. There were no interventions beyond the routine therapy determined by their clinicians on the basis of standard clinical practice.Results: There were 136 patients (75 men and 61 women, 24.1 ± 7.4 years old) and 146 healthy controls (82 men and 64 women, 24.2 ± 5.2 years old) involved in the experiments seriatim. Patients with schizophrenia exhibited decreased raw scores in cognitive tests (p < 0.001) and increased fALFF in the bilateral lentiform nuclei (left: 67 voxels; x = −24, y = −6, z = 3; peak t-value = 6.90; right: 16 voxels; x = 18, y = 0, z = 3; peak t-value = 6.36). The fALFF values in the bilateral lentiform nuclei were positively correlated with digit span-backward test scores (left: r = 0.193, p = 0.027; right: r = 0.190, p = 0.030), and the right lentiform nucleus was positively correlated with digit symbol coding scores (r = 0.209, p = 0.016).Conclusion: This study demonstrates that cognitive impairments in schizophrenia are associated with lentiform nucleus function as revealed by MRI, involving working memory and processing speed.

Abstract Background The dual-process theory is central to several models of addiction, implying the importance of automatic processes in the maintenance and development of addiction. Implicit beliefs are traces of previous experience which relate to the representation in cognition. Implicit behavioral tendencies are traces of previous experience which relate to the representation in behavioral tendencies. In this study, we aim to provide behavioral evidence for implicit beliefs and implicit behavioral tendencies towards smoking-related cues among Chinese male smokers and non-smokers. We also examine the relationships among implicit beliefs, implicit behavioral tendencies and smoking behaviors of smokers. Methods In order to achieve these goals, we used an implicit association test (IAT) to measure implicit beliefs and implicit behavioral tendencies simultaneously. Thirty-nine smokers and twenty-five non-smokers were tested, using smoking-related words and images, as well as neutral words and images as stimuli. Results Our analysis shows significant differences in smokers’ and non-smokers’ implicit beliefs and behavioral tendencies (t62 = 3.494, p

Schizophrenia is a complex mental illness with genetic heterogeneity, which is often accompanied by alterations in brain structure and function. The neurobiological mechanism of schizophrenia associated with heredity remains unknown. Recently, the development of trans-scale and multi-omics methods that integrate gene and imaging information sheds new light on the nature of schizophrenia. In this article, we summarized the results of brain structural and functional changes related to the specific single-nucleotide polymorphisms (SNPs) in the past decade, and the SNPs were divided into non-coding regions and coding regions, respectively. It is hoped that the relationship between SNPs and cerebral alterations can be displayed more clearly and intuitively, so as to provide fresh approaches for the discovery of potential biomarkers and the development of clinical accurate individualized treatment decision-making.

BackgroundEmerging evidence suggests structural and functional disruptions of the thalamus in schizophrenia, but whether thalamus abnormalities are able to be used for disease identification and prediction of early treatment response in schizophrenia remains to be determined. This study aims at developing and validating a method of disease identification and prediction of treatment response by multi-dimensional thalamic features derived from magnetic resonance imaging in schizophrenia patients using radiomics approaches.MethodsA total of 390 subjects, including patients with schizophrenia and healthy controls, participated in this study, among which 109 out of 191 patients had clinical characteristics of early outcome (61 responders and 48 non-responders). Thalamus-based radiomics features were extracted and selected. The diagnostic and predictive capacity of multi-dimensional thalamic features was evaluated using radiomics approach.ResultsUsing radiomics features, the classifier accurately discriminated patients from healthy controls, with an accuracy of 68%. The features were further confirmed in prediction and random forest of treatment response, with an accuracy of 75%.ConclusionOur study demonstrates a radiomics approach by multiple thalamic features to identify schizophrenia and predict early treatment response. Thalamus-based classification could be promising to apply in schizophrenia definition and treatment selection.

Resting-state functional magnetic resonance imaging (rs-fMRI) was used to investigate the effects of electroconvulsive therapy (ECT) causing brain function changes in adolescents who suffered from depression and suicidal ideation (SI). A total of 30 patients (MDDs) and 25 healthy controls (HCs) matched by gender, age, and education level were enrolled. The amplitude of low-frequency fluctuations (ALFF) and regional homogeneity (ReHo) were used to compare differences between HCs and MDDs at baseline, and differences in ALFF and ReHo pre/post ECT in MDDs. Pearson correlation analysis was used to evaluate the relationship between altered brain function and clinical symptoms. At baseline, MDDs showed decreased ALFF in the left inferior temporal gyrus and right amygdala, decreased ReHo in left inferior temporal gyrus, and increased ReHo in the right inferior frontal gyrus, opercular part and left middle occipital gyrus. After ECT, MDDs showed increased ALFF in the right middle occipital gyrus, decreased ALFF in left temporal pole, left inferior frontal gyrus, opercular part, and right frontal middle gyrus, increased ReHo in the right middle occipital gyrus, and left inferior temporal gyrus. Pearson correlation found HAMD scores at baseline were negatively correlated with ALFF in the left inferior temporal gyrus, and HAMD and BSSI scores after ECT were negatively correlated with ALFF in the right middle occipital gyrus. The abnormal activities of amygdala, inferior temporal gyrus and middle occipital gyrus might be related to depressive and suicidal symptoms in adolescents.

As a non-invasive detection method and an advanced imaging method, magnetic resonance imaging (MRI) has been widely used in the research of schizophrenia. Although a large number of neuroimaging studies have confirmed that MRI can display abnormal brain phenotypes in patients with schizophrenia, no valid uniform standard has been established for its clinical application. On the basis of previous evidence, we argue that MRI is an important tool throughout the whole clinical course of schizophrenia. The purpose of this commentary is to systematically describe the role of MRI in schizophrenia and to provide references for its clinical application.

Background: Evidence from neuroimaging has implicated abnormal cerebral cortical patterns in schizophrenia. Application of machine learning techniques is required for identifying structural signature reflecting neurobiological substrates of schizophrenia at the individual level. We aimed to develop a method to identify patients with schizophrenia from healthy individuals via the features of cerebral cortex using high-resolution magnetic resonance imaging (MRI). Method: In this study, cortical features were measured, including volumetric (cortical thickness, surface area, and gray matter volume) and geometric (mean curvature, metric distortion, and sulcal depth) features. Patients with first-episode schizophrenia (n = 52, ranging 17–45 years old) and healthy controls (n = 66, ranging 18–46 years old) were included from the Department of Psychiatry at Xijing Hospital. Multivariate computation was used to examine the abnormalities of cortical features in schizophrenia. Features were selected by least absolute shrinkage and selection operator (LASSO) method. The diagnostic capacity of multi-dimensional neuroanatomical patterns-based classification was evaluated based on receiver operating characteristic (ROC) analysis. Results: Mean curvature (left insula and left inferior frontal gyrus), cortical thickness (left fusiform gyrus), and metric distortion (left cuneus and right superior temporal gyrus) revealed both group differences and diagnostic capacity. Area under ROC curve was 0.88, and the sensitivity, specificity, and accuracy were 94 %, 82 %, and 88 %, respectively. Confirming these findings, similar results were observed in the independent validation (sensitivity of 91 %, specificity of 78 %, and accuracy of 85 %). There was a positive association between index score derived from the multi-dimensional patterns and the severity of symptoms (r = 0.33, P < .05) for patients. Discussion: Our findings demonstrate a view of cortical differences with capacity to discriminate between patients with schizophrenia and healthy population. Structural neuroimaging-based measurements hold great promise of paving the road for their clinical utility in schizophrenia.

ObjectiveNeuroimaging-based brain signatures may be informative in identifying patients with psychosis who will respond to antipsychotics. However, signatures that inform the electroconvulsive therapy (ECT) health care professional about the response likelihood remain unclear in psychosis with radiomics strategy. This study investigated whether brain structure-based signature in the prediction of ECT response in a sample of schizophrenia patients using radiomics approach.MethodsThis high-resolution structural magnetic resonance imaging study included 57 patients at baseline. After ECT combined with antipsychotics, 28 and 29 patients were classified as responders and non-responders. Features of gray matter were extracted and compared. The logistic regression model/support vector machine (LRM/SVM) analysis was used to explore the predictive performance.ResultsThe regularized multivariate LRM accurately discriminated responders from non-responders, with an accuracy of 90.91%. The structural features were further confirmed in the validating data set, resulting in an accuracy of 87.59%. The accuracy of the SVM in the training set was 90.91%, and the accuracy in the validation set was 91.78%.ConclusionOur results support the possible use of structural brain feature-based radiomics as a potential tool for predicting ECT response in patients with schizophrenia undergoing antipsychotics, paving the way for utilization of markers in psychosis.

Many studies have focused on patients with schizophrenia with or without auditory verbal hallucinations (AVHs), but due to the complexity of schizophrenia, biologically based diagnosis of patients with schizophrenia remains unsolved. The objectives of this study are to classify between first-episode drug-naïve patients with schizophrenia and healthy controls, and to classify between patients with and without AVHs. Resting state fMRI data from 41 patients with schizophrenia (22 with and 19 without AVHs) and 23 normal controls (NC) were included to compute functional connectivity between brain regions. Classifiers based on support vector machine (SVM) were developed to classify patients with schizophrenia from NC, as well as between the two subgroups of patients. The classification accuracy was evaluated with a leave-one-out cross-validation (LOOCV) strategy. The accuracy in discriminating both subgroups of patients from NC was 81.3%, with 92.0% (sensitivity) and 65.2% (specificity) for the patients and NC, respectively. The classification accuracy in discriminating patients with and without AVHs was 75.6%, with 77.3% (sensitivity) and 73.9% (specificity) for patients with and without AVHs, respectively. The results suggest that functional connectivity provided good discriminative power not only for identifying patients with schizophrenia among NC, but also in discriminating patients with schizophrenia with and without AVHs. Keywords: First episode schizophrenia, Auditory verbal hallucinations, Functional connectivity, Support vector machine

Background: Hippocampal dysconnectivity has been detected in schizophrenia patients with auditory verbal hallucinations (AVHs). Neuroanatomical evidence has indicated distinct sub-regions in the hippocampus, but which sub-regions within the hippocampus may emerge dysfunction in the brain network, and the relationship between connection strength and the severity of this debilitating disorder have yet to be revealed. Masked independent component analysis (mICA), i.e., ICA restricted to a defined region of interest, can provide insight into observing local functional connectivity in a particular brain region. We aim to map out the sub-regions in the hippocampus with dysconnectivity linked to AVHs in schizophrenia.Methods: In this functional magnetic resonance imaging study of schizophrenia patients with (n = 57) and without (n = 83) AVHs, and 71 healthy controls, we first examined hippocampal connectivity using mICA, and then the correlation between connection metric and clinical severity was generated.Results: As compared with patients without AVHs, mICA showed a group of hyper-connections for the left middle part, as well as another group of hypo-connections for the bilateral antero-lateral and right antero-medial parts in patients with AVHs. Connectivity was linked to the clinical symptoms scores in the sample of patients with AVHs.Conclusion: These findings demonstrate that the left middle part is more densely connected, but the bilateral antero-lateral and right antero-medial parts are more sparsely connected in schizophrenia patients with AVHs. The findings in the present study show proof of precious location in the hippocampus mediating the neural mechanism behind AVHs in schizophrenia.

Familial risk plays a significant role in the etiology of schizophrenia (SZ). Many studies using neuroimaging have demonstrated structural and functional alterations in relatives of SZ patients, with significant results found in diverse brain regions involving the anterior cingulate cortex (ACC), caudate, dorsolateral prefrontal cortex (DLPFC), and hippocampus. This study investigated whether unaffected relatives of first episode SZ differ from healthy controls (HCs) in effective connectivity measures among these regions. Forty-six unaffected first-degree relatives of first episode SZ patients — according to the DSM-IV — were studied. Fifty HCs were included for comparison. All subjects underwent resting state functional magnetic resonance imaging (fMRI). We used stochastic dynamic causal modeling (sDCM) to estimate the directed connections between the left ACC, right ACC, left caudate, right caudate, left DLPFC, left hippocampus, and right hippocampus. We used Bayesian parameter averaging (BPA) to characterize the differences. The BPA results showed hyperconnectivity from the left ACC to right hippocampus and hypoconnectivity from the right ACC to right hippocampus in SZ relatives compared to HCs. The pattern of anterior cingulate cortico-hippocampal connectivity in SZ relatives may be a familial feature of SZ risk, appearing to reflect familial susceptibility for SZ.

In the face of the unprecedented public health crisis caused by the novel coronavirus pneumonia epidemic, front-line health workers are under enormous mental pressure. This paper aims to explore the mental health challenges faced by front-line health workers in the early stages of a public health emergency, such as stress, anxiety, and depression. At the same time, the factors that increase their mental stress are analyzed, and practical measures are put forward to prevent and manage mental health problems, aiming at improving the quality of medical treatment during public health emergencies. This paper has some reference value for people engaged in mental health prevention. [ABSTRACT FROM AUTHOR]

Schizophrenia is a severe mental disorder with a neurodevelopmental origin. Although schizophrenia results from changes in the brain, the underlying biological mechanisms are unknown. Transcriptomics studies quantitative expression changes or qualitative changes of all genes and isoforms, providing a more meaningful biological insight. Magnetic resonance imaging (MRI) techniques play roles in revealing brain structure and function. We give a narrative focused review on the current transcriptome combined with MRI studies related to schizophrenia and summarize the research methodology and content of these studies to identify the research commonalities as well as the implications for future research, in an attempt to provide new insights into the mechanism, clinical diagnosis, and treatments of schizophrenia. [ABSTRACT FROM AUTHOR]