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为了更有效地评估抑郁症患者治疗前后的改善效果,使用动态模块化算法探测抑郁症患者静息态脑网络的灵活度属性.使用独立成分分析获得每个被试的特定脑网络分区信号,通过滑动窗口和L1范数计算动态功能连接矩阵,然后运用社区探测算法计算功能连接的动态社区结构.最终获得的模块化分配结构具有大脑活动随时间推移的一般特征.灵活性指标是动态社区结构的特征之一,表征区域变化的次数.本次研究中,有16名患者实现临床缓解并治疗前后各扫描一次.计算得到的所有患者治疗前后全脑6个网络的灵活度指标组间置换检验结果显示,患者治疗前和治疗后的默认网络和认知控制网络灵活性度分布存在下降趋势,且该趋势具有统计学差异.因此这2个网络的灵活度指标可用于抑郁病人治疗效果评估的客观参考.
In order to evaluate the improvement effect of depression patients before and after treatment more effectively, the dynamic modular algorithm was used to explore the flexibility properties of resting brain network in patients with depression.Using independent component analysis to obtain the specific brain network partition signal of each subject, Sliding window and L1 norm to calculate the dynamic functional connection matrix and then use the community detection algorithm to calculate the dynamic community structure of the functional connection.The resulting modular distribution structure has the general characteristics of brain activity over time.The flexibility index is the dynamic community structure Characteristics of the number of changes in the characterization of the region in this study, 16 patients achieved clinical remission and before and after treatment of each scan.All of the calculated patients before and after treatment of the brain flexibility index of six groups between the replacement test results Showed that there was a downward trend in the distribution of flexibility of the default network and the cognitive control network before and after treatment, and the trend was statistically different, so the flexibility index of these two networks could be used to objectively evaluate the treatment effect of depression patients reference.