基于fMRI动态功能连接的抑郁症患者分类研究

Classifying patients with depression based on f MRI dynamic functional connectivity

针对当前抑郁症诊断正确率偏低、误诊率偏高的问题,利用f MRI动态功能连接研究了抑郁症辅助诊断问题。采用滑动时间窗技术研究功能连接及其网络拓扑特性的动态变化,然后基于这些动态特征应用多元模式分析方法对22名抑郁症患者和27名健康被试者进行分类。采用动态分析方法能够增加样本数量,从而更加有利于一些分类算法的应用。实验结果表明以动态功能连接和网络拓扑特性为特征的分类正确率均为93.88%,明显优于对应非动态特征81.63%和85.71%的结果。分析表明,具有高辨别力的特征所对应的脑区主要分布在默认网络、情感网络、视觉皮层区等,动态功能连接可能为抑郁症的辅助诊断提供新的手段。

This paper investigated a computer aided diagnosis model of depression using dynamic functional connectivity,the aim of which was to improve the accuracy of depression diagnosis. Firstly,it studied the dynamic change of topologic characteristic of f MRI and brain functional connectivity by sliding time window technique. Then,it employed a multivariate pattern analysis method to classify 22 patients with depression and 27 healthy volunteers. Dynamic functional connectivity analysis could increase the number of samples,which made more classification algorithms practicable for this problem. The best performance based on dynamic features was 93. 88%,which was much better than non-dynamic features based results 81. 63%and 85. 71%. Further analysis demonstrates that the brain areas corresponding to the most discriminating features are mainly located in default mode network,affective network and visual cortical areas. Dynamic functional connectivity may be a potential measure for the diagnosis of depression.