Aberrant functional connectivity for diagnosis of major depressive disorder: A discriminant analysis
Aberrant functional connectivity for diagnosis of major depressive disorder: A discriminant analysis
复制标题
用于诊断重度抑郁症的异常功能连接：判别分析
DOI：
10.1111/pcn.12106
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发表时间：
2014-02-01
影响因子：
11.9
通讯作者：
Liu, Zhening
中科院分区：
文献类型：
--
作者：
Cao, Longlong;Guo, Shuixia;Liu, Zhening
目的：已有关于重度抑郁障碍(MDD)患者异常脑功能连接模式的报道。目前尚不清楚它们是否可以用于MDD的判别分析。方法：基于静息状态功能磁共振成像数据，采用一种新的方法研究39例MDD患者和37例正常对照的功能连接性改变。通过使用所提出的特征选择方法，我们确定了患者中显著改变的功能连接。随后使用支持向量机分类方法将它们作为判别特征应用于我们的分析。结果：在对高维特征进行子集选择后，采用留一法训练的支持向量机分类器在识别过程中达到了约84%的精度。通过总结功能连接性的分类贡献，我们得到了四个明显的贡献模块：眶前下部模块、缘上回模块、顶下小叶-后扣带回模块和颞中回-颞下回模块。结论：实验结果表明，该方法能有效地区分MDD患者和正常对照组。功能连接性可作为新的生物标志物，辅助临床医生进行MDD的计算机辅助诊断。
Aim: Aberrant brain functional connectivity patterns have been reported in major depressive disorder (MDD). It is unknown whether they can be used in discriminant analysis for diagnosis of MDD. In the present study we examined the efficiency of discriminant analysis of MDD by individualized computer-assisted diagnosis.Methods: Based on resting-state functional magnetic resonance imaging data, a new approach was adopted to investigate functional connectivity changes in 39 MDD patients and 37 well-matched healthy controls. By using the proposed feature selection method, we identified significant altered functional connections in patients. They were subsequently applied to our analysis as discriminant features using a support vector machine classification method. Furthermore, the relative contribution of functional connectivity was estimated.Results: After subset selection of high-dimension features, the support vector machine classifier reached up to approximately 84% with leave-one-out training during the discrimination process. Through summarizing the classification contribution of functional connectivities, we obtained four obvious contribution modules: inferior orbitofrontal module, supramarginal gyrus module, inferior parietal lobule-posterior cingulated gyrus module and middle temporal gyrus-inferior temporal gyrus module.Conclusion: The experimental results demonstrated that the proposed method is effective in discriminating MDD patients from healthy controls. Functional connectivities might be useful as new biomarkers to assist clinicians in computer auxiliary diagnosis of MDD.