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Emotion regulation (ER) refers to the “implementation of a conscious or non-conscious goal to start, stop or otherwise modulate the trajectory of an emotion” (Etkin et al., 2015). Whereas multiple brain areas have been found to be involved in ER, relatively little is known about whether and how ER is associated with the global functioning of brain networks. Recent advances in brain connectivity research using graph-theory based analysis have shown that the brain can be organized into complex networks composed of functionally or structurally connected brain areas. Global efficiency is one graphic metric indicating the efficiency of information exchange among brain areas and is utilized to measure global functioning of brain networks. The present study examined the relationship between trait measures of ER (expressive suppression (ES) and cognitive reappraisal (CR)) and global efficiency in resting-state functional brain networks (the whole brain network and ten predefined networks) using structural equation modeling (SEM). The results showed that ES was reliably associated with efficiency in the fronto-parietal network and default-mode network. The finding advances the understanding of neural substrates of ER, revealing the relationship between ES and efficient organization of brain networks.

情绪调节（Emotion Regulation, ER）指"启动、终止或调控情绪发展轨迹的有意识或无意识目标执行过程"（Etkin等，2015）。尽管已有研究发现多个脑区参与情绪调节，但目前对情绪调节是否以及如何与脑网络的全局功能相关的认知仍相对匮乏。近年来，基于图论分析的脑连接研究取得进展，研究表明大脑可被划分为由功能或结构上相互连接的脑区构成的复杂网络。全局效率是一项用于表征脑区间信息交换效率的图论指标，常被用于衡量脑网络的全局功能。本研究采用结构方程模型（Structural Equation Modeling, SEM），探究了情绪调节的两类特质性测量指标——表达抑制（Expressive Suppression, ES）与认知重评（Cognitive Reappraisal, CR）——与静息态功能脑网络（包括全脑网络及十个预设脑网络）的全局效率之间的关联。结果显示，表达抑制与额顶网络（fronto-parietal network）及默认模式网络（default-mode network）的效率存在显著相关。该发现推进了对情绪调节神经基础的认知，揭示了表达抑制与脑网络高效组织之间的关联。