Objective . It is important to improve identification accuracy for possible early intervention of major depressive disorder (MDD). Recently, effective connectivity (EC), defined as the directed influence of spatially distant brain regions on each other, has been used to find the dysfunctional organization of brain networks in MDD. However, little is known about the ability of whole-brain resting-state EC features in identification of MDD. Here, we employed EC by whole-brain analysis to perform MDD diagnosis. Approach . In this study, we proposed a high-order EC network capturing high-level relationship among multiple brain regions to discriminate 57 patients with MDD from 60 normal controls (NC). In high-order EC networks and traditional low-order EC networks, we utilized the network properties and connection strength for classification. Meanwhile, the support vector machine (SVM) was employed for model training. Generalization of the results was supported by 10-fol...

中文翻译：

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使用源自静息状态功能 MRI 的全脑有效连接网络诊断重度抑郁症

客观的 。提高识别准确性对于可能的重度抑郁症（MDD）的早期干预非常重要。最近，有效连接（EC）被定义为空间上遥远的大脑区域对彼此的直接影响，已被用于发现 MDD 中大脑网络的功能失调组织。然而，关于全脑静息状态 EC 特征在识别 MDD 中的能力知之甚少。在这里，我们通过全脑分析使用 EC 来进行 MDD 诊断。方法 。在这项研究中，我们提出了一个高阶 EC 网络，该网络捕获多个大脑区域之间的高级关系，以区分 57 名 MDD 患者和 60 名正常对照 (NC)。在高阶 EC 网络和传统的低阶 EC 网络中，我们利用网络属性和连接强度进行分类。同时，支持向量机（SVM）用于模型训练。结果的推广得到了 10-fol ... 的支持。