Li Yanling, Dai Xin, Wu Huawang, Wang Lijie
School of Electrical Engineering and Electronic Information, Xihua University, Chengdu, China.
Key Laboratory of Fluid and Power Machinery, Ministry of Education, Xihua University, Chengdu, China.
Front Neurosci. 2021 Sep 9;15:729958. doi: 10.3389/fnins.2021.729958. eCollection 2021.
Major depressive disorder (MDD) is a severe mental disorder and is lacking in biomarkers for clinical diagnosis. Previous studies have demonstrated that functional abnormalities of the unifying triple networks are the underlying basis of the neuropathology of depression. However, whether the functional properties of the triple network are effective biomarkers for the diagnosis of depression remains unclear. In our study, we used independent component analysis to define the triple networks, and resting-state functional connectivities (RSFCs), effective connectivities (EC) measured with dynamic causal modeling (DCM), and dynamic functional connectivity (dFC) measured with the sliding window method were applied to map the functional interactions between subcomponents of triple networks. Two-sample -tests with < 0.05 with Bonferroni correction were used to identify the significant differences between healthy controls (HCs) and MDD. Compared with HCs, the MDD showed significantly increased intrinsic FC between the left central executive network (CEN) and salience network (SAL), increased EC from the right CEN to left CEN, decreased EC from the right CEN to the default mode network (DMN), and decreased dFC between the right CEN and SAL, DMN. Moreover, by fusion of the changed RSFC, EC, and dFC as features, support vector classification could effectively distinguish the MDD from HCs. Our results demonstrated that fusion of the multiple functional connectivities measures of the triple networks is an effective way to reveal functional disruptions for MDD, which may facilitate establishing the clinical diagnosis biomarkers for depression.
重度抑郁症（MDD）是一种严重的精神障碍，缺乏用于临床诊断的生物标志物。先前的研究表明，统一三重网络的功能异常是抑郁症神经病理学的潜在基础。然而，三重网络的功能特性是否为抑郁症诊断的有效生物标志物仍不清楚。在我们的研究中，我们使用独立成分分析来定义三重网络，并应用静息态功能连接（RSFC）、用动态因果模型（DCM）测量的有效连接（EC）以及用滑动窗口法测量的动态功能连接（dFC）来描绘三重网络子成分之间的功能相互作用。采用双样本检验，经Bonferroni校正后P<0.05，以确定健康对照（HC）与MDD之间的显著差异。与HC相比，MDD患者左中央执行网络（CEN）与突显网络（SAL）之间的内在功能连接显著增加，从右侧CEN到左侧CEN的有效连接增加，从右侧CEN到默认模式网络（DMN）的有效连接减少，右侧CEN与SAL、DMN之间的动态功能连接减少。此外，通过将改变的RSFC、EC和dFC作为特征进行融合，支持向量分类可以有效地区分MDD和HC。我们的结果表明，三重网络的多种功能连接测量的融合是揭示MDD功能破坏的有效方法，这可能有助于建立抑郁症的临床诊断生物标志物。
