Objective:To explore the differences in clustering coefficient of brain structure networks between bipolar disorder and schizophrenia patients,and evaluate the predictive ability of clustering coefficient in identifying diseases.Method:A total of 37 participants were enrolled in the psychiatric department of Nanjing brain hospital from December 2012 to December 2014 during a follow-up period of ≥9 years.Eighteen patients with bipolar disorder,nineteen patients with schizophrenia,who had no changes in the review examination diagnosis,and 30 healthy controls were included in the statistical analysis.In SPSS 26.0,the variances between three groups and receiver operation characteristic(ROC)were plotted for general clinical data and brain structural network clustering attribute values.Results:Compared with the healthy control group,the clustering coefficient of the patient group increased in the left inferior frontal gyrus and right superior temporal gyrus;compared between patient groups,the clustering coefficient of schizophrenia patients in the right temporal gyrus was higher than that in the bipolar depression group,and the difference was statistically significant(P＜0.05,family wise error correction).Using the clustering coefficient of the right middle temporal gyrus network as the depiction of the ROC curve,the recognition rate of the two groups of diseases reached 95％.Conclusion:The clustering coefficient of brain networks can serve as a potential imaging biomarker to identify bipolar disorder and schizophrenia.
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