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Objective: We aimed to evaluate the functional network properties in first-episode schizophrenia (SZ) patients at baseline and after 4-months treatment with second-generation antipsychotic drugs., Methods: Resting-state functional magnetic resonance imaging and graph theory approaches were utilized to evaluate the functional integration and segregation of brain networks in 36 first-episode patients (20 male/16 female) with SZ and 36 age and sex matched healthy controls (20 male/16 female)., Results: Compared with healthy controls, SZ at baseline showed lower clustering coefficient (C p ) and local network efficiency (E loc ), and this abnormal pattern was modulated with treatment of antipsychotic drugs at follow-up. Longitudinally, the increase of C p was associated with the improvement of negative symptom. We found that the strength of functional connectivity between brain regions were significantly increased in three connections after treatment, mainly involving the frontal, parietal and occipital lobes., Conclusion: The current study suggested that antipsychotic drugs could modulate the faulty local clustering of the functional connectome in SZ. Furthermore, C p , the parameter that reflects local clustering of topological organization, demonstrated the potential to be a connectome-based biomarker of treatment response to second-generation antipsychotics in patients with SZ., (Copyright © 2019 Elsevier B.V. All rights reserved.)

Introduction: Treatment response at an early stage of schizophrenia is of considerable value with regard to future management of the disorder; however, there are currently no biomarkers that can inform physicians about the likelihood of response., Objects: We aim to develop and validate regional brain activity derived from functional magnetic resonance imaging (fMRI) as a potential signature to predict early treatment response in schizophrenia., Methods: Amplitude of low-frequency fluctuation (ALFF) was measured at the start of the first/single episode resulting in hospitalization. Inpatients were included in a principal dataset (n = 79) and a replication dataset (n = 44). Two groups of healthy controls (n = 87; n = 106) were also recruited for each dataset. The clinical response was assessed at discharge from the hospital. The predictive capacity of normalized ALFF in patients by healthy controls, ALFF ratio , was evaluated based on diagnostic tests and clinical correlates., Results: In the principal dataset, responders exhibited increased baseline ALFF in the left postcentral gyrus/inferior parietal lobule relative to non-responders. ALFF ratio of responders before treatment was significantly higher than that of non-responders (p < 0.001). The area under the receiver operating characteristic curve was 0.746 for baseline ALFF ratio to distinguish responders from non-responders, and the sensitivity, specificity, and accuracy were 72.7%, 68.6%, and 70.9%, respectively. Similar results were found in the independent replication dataset., Conclusions: Baseline regional activity of the brain seems to be predictive of early response to treatment for schizophrenia. This study shows that psycho-neuroimaging holds promise for influencing the clinical treatment and management of schizophrenia., (© 2019 The Authors. Brain and Behavior published by Wiley Periodicals, Inc.)

Specific biomarker reflecting neurobiological substrates of schizophrenia (SZ) is required for its diagnosis and treatment selection of SZ. Evidence from neuroimaging has implicated disrupted functional connectivity in the pathophysiology. We aimed to develop and validate a method of disease definition for SZ by resting-state functional connectivity using radiomics strategy. This study included 2 data sets collected with different scanners. A total of 108 first-episode SZ patients and 121 healthy controls (HCs) participated in the current study, among which 80% patients and HCs (n = 183) and 20% (n = 46) were selected for training and testing in intra-data set validation and 1 of the 2 data sets was selected for training and the other for testing in inter-data set validation, respectively. Functional connectivity was calculated for both groups, features were selected by Least Absolute Shrinkage and Selection Operator (LASSO) method, and the clinical utility of its features and the generalizability of effects across samples were assessed using machine learning by training and validating multivariate classifiers in the independent samples. We found that the accuracy of intra-data set training was 87.09% for diagnosing SZ patients by applying functional connectivity features, with a validation in the independent replication data set (accuracy = 82.61%). The inter-data set validation further confirmed the disease definition by functional connectivity features (accuracy = 83.15% for training and 80.07% for testing). Our findings demonstrate a valid radiomics approach by functional connectivity to diagnose SZ, which is helpful to facilitate objective SZ individualized diagnosis using quantitative and specific functional connectivity biomarker.

Purpose To investigate auditory verbal hallucination (AVH)-specific patterns of brain activity within the resting-state networks (RSNs) that have been proposed to underpin the neural mechanisms of schizophrenia (SZ). Materials and Methods This cross-sectional study was approved by the local ethics committee, and written informed consent was obtained from all participants prospectively recruited. Independent component analysis was used to investigate RSNs in 17 patients with first-episode untreated SZ with AVHs, 15 patients with SZ without AVHs, and 19 healthy control subjects who underwent resting-state functional magnetic resonance imaging. Dual regression was implemented to perform between-group analysis. Regional brain function was then explored within RSNs by using the amplitude of low-frequency fluctuation. Two-sample t tests were used to compare regional brain function between the two patient groups, and Pearson correlation analysis was used to characterize the relationship between imaging findings and severity of AVHs. Receiver operating characteristic analysis was used to evaluate the diagnostic performance of these brain function measures. Results Independent component analysis demonstrated symptom-specific abnormal disrupted coactivation within the auditory, default mode, executive, motor, and frontoparietal networks and was pronounced in the auditory cortex, supramarginal gyrus, insula, putamen, dorsolateral prefrontal cortex, angular gyrus, precuneus, and thalamus (P < .05 with false discovery rate correction). Amplitude of low-frequency fluctuation analysis demonstrated similar patterns within these RSNs (P < .05 with false discovery rate correction). Furthermore, a positive correlation between the degree of coactivation within the motor network and the severity of AVHs was observed in patients with SZ with AVHs (r = 0.67, P = .003). The area under the receiver operating characteristic curve was 0.76-0.90 for all RSNs. Conclusion These findings indicate that dysfunctional brain regions are involved in auditory processing, language production and monitoring, and sensory information filtering in patients with SZ with AVHs, which may be helpful in furthering the understanding of pathophysiological correlates of AVHs in SZ. Online supplemental material is available for this article.

Multimodal neuroimaging features provide opportunities for accurate classification and personalized treatment options in the psychiatric domain. This study aimed to investigate whether brain features predict responses to the overall treatment of schizophrenia at the end of the first or a single hospitalization. Structural and functional magnetic resonance imaging (MRI) data from two independent samples (N = 85 and 63, separately) of schizophrenia patients at baseline were included. After treatment, patients were classified as responders and non‐responders. Radiomics features of gray matter morphology and functional connectivity were extracted using Least Absolute Shrinkage and Selection Operator. Support vector machine was used to explore the predictive performance. Prediction models were based on structural features (cortical thickness, surface area, gray matter regional volume, mean curvature, metric distortion, and sulcal depth), functional features (functional connectivity), and combined features. There were 12 features after dimensionality reduction. The structural features involved the right precuneus, cuneus, and inferior parietal lobule. The functional features predominately included inter‐hemispheric connectivity. We observed a prediction accuracy of 80.38% (sensitivity: 87.28%; specificity 82.47%) for the model using functional features, and 69.68% (sensitivity: 83.96%; specificity: 72.41%) for the one using structural features. Our model combining both structural and functional features achieved a higher accuracy of 85.03%, with 92.04% responder and 80.23% non‐responders to the overall treatment to be correctly predicted. These results highlight the power of structural and functional MRI‐derived radiomics features to predict early response to treatment in schizophrenia. Prediction models of the very early treatment response in schizophrenia could augment effective therapeutic strategies. [ABSTRACT FROM AUTHOR]

Objective: The symptom-related neurobiology characteristic of schizophrenia in the brain from a network perspective is still poorly understood, leading to a lack of potential biologically-based markers and difficulty identifying therapeutic targets. We aim to test the dysregulated cross-network interactions among the Salience Network (SN), Central Executive Network (CEN) and Default Mode Network (DMN) and how they contributed to different symptoms in schizophrenia patients.Methods: We examined network interactions among the SN, CEN and DMN in 76 patients with schizophrenia vs. 80 well-matched controls using dynamic causal modeling (DCM). We further analyzed the relation between network dynamics and Positive and Negative Syndrome Scale (PANSS).Results: We observed that the DMN, CEN and SN across healthy controls and schizophrenia patients showed several similarities within or between-network pattern in the resting state. Comparing schizophrenia to controls, SN-centered cross-network interactions were most significantly reduced. Crucially, the strength of connections from CEN subnetwork 1 to DMN subnetwork 1 was positively correlated with the Positive Score of PANSS. The connection from the DMN subnetwork 2 to CEN subnetwork 2 was negatively correlated with the Negative Score of PANSS.Conclusions: Our study provides strong evidence for the dysregulation among SN, CEN and DMN in a triple-network perspective in schizophrenia. The connection between DMN and CEN could be clinically-relevant neurobiological signature of schizophrenia symptoms. Our study indicated that the description of brain triple network hypothesis could be a novel and possible bio-marker for schizophrenia. [ABSTRACT FROM AUTHOR]

Despite evidence of structural brain abnormalities in schizophrenia, the current study aimed to explore the effects of antipsychotic treatment on gray matter (GM) volume using structural magnetic resonance imaging (MRI) and investigate the relationship between brain structure and treatment response. The GM volumes of 33 patients with first-episode schizophrenia were calculated with voxel-based morphometry (VBM), with 33 matched healthy controls. Longitudinal volume changes within subjects after 4-month antipsychotic treatment were also evaluated. Correlation between volumetric changes and clinical symptoms derived from the Positive and Negative Syndrome Scale (PANSS) were further investigated. Compared with healthy controls, decreased GM volumes in the frontal gyrus were observed in schizophrenia patients. After 4-month treatment, patients showed significantly decreased GM volume primarily in the bilateral frontal, temporal and left parietal brain regions. In addition, the GM volume changes of the left postcentral gyrus was positively correlated with negative symptoms improvement, and the correlation analysis revealed the total PANSS scores changes were associated with GM volume changes in the right inferior frontal gyrus and the right superior temporal gyrus. Besides, non-responders had reduced GM volume in the bilateral middle frontal gyrus and the right superior frontal gyrus compared with responders and healthy controls. Our results suggest that the abnormality in the right frontal gyrus exists in the early stage of schizophrenia. Moreover, the relationship between antipsychotics and structural changes was identified. The GM volume might have the potential to reflect the symptom improvement in schizophrenia patients. And MRI may assist in predicting the antipsychotic treatment response in first-episode schizophrenia patients. [ABSTRACT FROM AUTHOR]

Objective: Auditory verbal hallucinations (AVHs) are one of the cardinal symptoms of schizophrenia (SZ). Cerebral dysfunction may represent pathophysiological underpinnings behind AVHs in SZ. However, regional and network functional deficits for AVHs in SZ remain to be identified. Methods: Seventeen medication-naïve first-episode SZ patients with AVHs, 15 without AVHs, and 19 healthy controls (HCs) were studied using resting-state functional magnetic resonance imaging. We compared the amplitude of low-frequency fluctuation (ALFF) and regional homogeneity (ReHo) among these subjects. Areas with both ALFF and ReHo alterations were used as seeds in functional connectivity (FC) analysis. Then we performed correlation analysis between image measures and symptoms and receiver operating characteristic analysis. Results: One-way analysis of variance showed significant differences of ALFF and ReHo in the bilateral putamen, thereby being used as seeds. SZ patients with AVHs showed decreased ALFF in the left putamen, increased ReHo in the right dorsolateral prefrontal cortex (DLPFC), and increased right putamen-seeded FC with the left DLPFC and Broca's area relative to those without AVHs. Furthermore, the increased strength of the connectivity between the right putamen and left Broca's area correlated with the severity of SZ symptoms. Both patient groups demonstrated hypoconnectivity within frontal/parietal/temporal cortico-striatal-cerebellar networks compared with HCs. Conclusion: AVHs in SZ may be caused by abnormal regional function in the putamen and prefrontal cortex, as well as hyperconnectivity between them. The putamen-related regional and network functional deficits may reflect imbalance in neuromodulation of AVHs in SZ. Furthermore, dysconnectivity within cortico-striatal-cerebellar networks might subserve the pathogenesis of SZ. [ABSTRACT FROM AUTHOR]