Luo Qian, Liu Weixiang, Jin Lili, Chang Chunqi, Peng Ziwen
School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China.
Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, Shenzhen University, Shenzhen, China.
Front Neuroinform. 2021 Oct 20;15:676491. doi: 10.3389/fninf.2021.676491. eCollection 2021.
Both the Pearson correlation and partial correlation methods have been widely used in the resting-state functional MRI (rs-fMRI) studies. However, they can only measure linear relationship, although partial correlation excludes some indirect effects. Recent distance correlation can discover both the linear and non-linear dependencies. Our goal was to use the multivariate pattern analysis to compare the ability of such three correlation methods to distinguish between the patients with obsessive-compulsive disorder (OCD) and healthy control subjects (HCSs), so as to find optimal correlation method. The main process includes four steps. First, the regions of interest are defined by automated anatomical labeling (AAL). Second, functional connectivity (FC) matrices are constructed by the three correlation methods. Third, the best discriminative features are selected by support vector machine recursive feature elimination (SVM-RFE) with a stratified N-fold cross-validation strategy. Finally, these discriminative features are used to train a classifier. We had a total of 128 subjects out of which 61 subjects had OCD and 67 subjects were normal. All the three correlation methods with SVM have achieved good results, among which distance correlation is the best [accuracy = 93.01%, specificity = 89.71%, sensitivity = 95.08%, and area under the receiver-operating characteristic curve (AUC) = 0.94], followed by Pearson correlation and partial correlation is the last. The most discriminative regions of the brain for distance correlation are right dorsolateral superior frontal gyrus, orbital part of left superior frontal gyrus, orbital part of right middle frontal gyrus, right anterior cingulate and paracingulate gyri, left the supplementary motor area, and right precuneus, which are the promising biomarkers of OCD.
皮尔逊相关分析和偏相关分析方法在静息态功能磁共振成像（rs-fMRI）研究中都得到了广泛应用。然而，尽管偏相关排除了一些间接效应，但这两种方法都只能测量线性关系。最近提出的距离相关分析能够发现线性和非线性依赖关系。我们的目标是使用多变量模式分析来比较这三种相关分析方法区分强迫症（OCD）患者和健康对照者（HCSs）的能力，从而找到最佳的相关分析方法。主要过程包括四个步骤。首先，通过自动解剖标记（AAL）定义感兴趣区域。其次，用这三种相关分析方法构建功能连接（FC）矩阵。第三，采用支持向量机递归特征消除（SVM-RFE）和分层N折交叉验证策略选择最佳判别特征。最后，使用这些判别特征训练分类器。我们共有128名受试者，其中61名患有强迫症，67名是正常对照。所有三种相关分析方法结合支持向量机都取得了良好的结果，其中距离相关分析效果最佳[准确率=93.01%，特异性=89.71%，敏感性=95.08%，受试者工作特征曲线下面积（AUC）=0.94]，其次是皮尔逊相关分析，偏相关分析效果最差。距离相关分析在大脑中最具判别力的区域是右侧背外侧额上回、左侧额上回眶部、右侧额中回眶部、右侧前扣带回和旁扣带回、左侧辅助运动区以及右侧楔前叶，这些都是有前景的强迫症生物标志物。
