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动态核磁共振社区结构灵活度指标在抑郁症疗效评估中的应用(英文)

发布时间:2018-05-09 19:14

  本文选题:动态社区结构 + 核磁共振成像 ; 参考:《Journal of Southeast University(English Edition)》2017年03期


【摘要】: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.
[Abstract]:In order to evaluate the improvement effect of depression patients before and after treatment, dynamic modularization algorithm was used to detect the flexibility of resting brain network in depression patients. Independent component analysis (ICA) was used to obtain the specific brain network partitioning signals of each subject. The dynamic functional connection matrix was calculated by sliding window and L1 norm, and then the dynamic community structure of functional connection was calculated by community detection algorithm. The resulting modular allocation structure has the general characteristics of brain activity over time. Flexibility index is one of the characteristics of dynamic community structure, representing the number of regional changes. In this study, 16 patients achieved clinical remission and one scan before and after treatment. The results of intergroup replacement test of all patients before and after treatment showed that the distribution of default network and cognitive control network flexibility decreased before and after treatment. And the trend is statistically different. Therefore, these two network flexibility indicators can be used as an objective reference for the evaluation of the therapeutic effect of depressive patients.
【作者单位】: 东南大学儿童发展与学习科学教育部重点实验室;南京医科大学附属脑科医院精神科;
【基金】:The National High Technology Research and Development Program of China(863 program)(No.2015AA020509) the National Natural Science Foundation of China(No.81571639,81371522,61372032)
【分类号】:R445.2;R749.4


本文编号:1867081

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