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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]

Background and Purpose: Conventional magnetic resonance imaging (MRI) is sometimes difficult to distinguish primary central nervous system lymphoma (PCNSL) from other malignant brain tumors effectively. The study aimed to evaluate the diagnostic performance of arterial spin labeling (ASL) and dynamic contrast-enhanced (DCE)-derived permeability parameters to differentiate PCNSL from high-grade glioma (HGG) and brain metastasis.Materials and Methods: Eight patients with PCNSL, twenty one patients with HGG and six brain metastasis underwent preoperative 3.0-T MR imaging including conventional, ASL and DCE. Quantitative parameters including relative cerebral blood flow (rCBF), extravascular extracellular volume fraction (Ve) and the volume transfer constant (Ktrans) among PCNSL, HGG and metastasis were compared with a one-way analysis of variance. In addition, the area under the receiver-operating characteristic (ROC) curve (AUC) was constructed to evaluate the differentiation diagnostic performance of each parameter and the combination.Results: The PCNSL demonstrated significantly lower rCBF, higher Ktrans and Ve compared with HGG and metastasis. For the ROC analyses, both Ktrans and rCBF had good diagnostic performance for discriminating PCNSL from HGG and metastasis, with the AUC of 0.880 and 0.889. With the combination of rCBF and Ktrans, the diagnostic ability for PCNSL was improved with AUC of 0.986.Conclusion: rCBF and Ktrans are useful parameters for differentiating PCNSL from HGG and brain metastasis. The combination of rCBF and Ktrans further helps to improve the diagnostic performance of PCNSL. [ABSTRACT FROM AUTHOR]