Gene ontology analysis revealed that the effect of negative symptoms was related to autistic, aggressive and violent behavior the effect of positive symptoms was associated with hyperammonemia and acidosis and the interaction effect was correlated with abnormal motor function. Multivariate analysis confirmed that positive and negative symptoms had opposite effects on dynamic segregation and integration of brain networks. In a machine-learning model, NSP-based features outperformed graph measures at predicting positive and negative symptoms. Hallucinations were associated with higher integration in attention systems, and avolition was related to a more variable segregating process in default-mode network (DMN) and control systems. Schizophrenia brains showed a more stable integrating process and a more variable segregating process, thus maintaining higher segregation, especially in the limbic system. Here, we utilized a nested-spectral partition (NSP) approach to detect hierarchical modules in resting-state brain functional networks in schizophrenia patients and healthy controls, and we studied dynamic transitions of segregation and integration as well as their relationships with clinical symptoms. Schizophrenia is a severe psychotic disorder characterized by positive and negative symptoms, but their neural bases remain poorly understood.
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