BACKGROUND Previous studies have shown that regulating the target region by real-time fMRI-based neurofeedback training can influence the activation of other regions and the functional connectivity between them. However, it is not clear whether the training effect of neurofeedback, especially in emotion regulation, is manifested in local network specialization or global network integration. In the current study, we chose the left amygdala (LA) as the target region to regulate positive emotion through real-time fMRI training. Average-linkage hierarchical clustering was employed to cluster the fMRI data recorded during the training to construct whole-brain networks and the LA network to which the LA belongs. RESULTS The activation in the LA and those in some other regions were significantly up-regulated during the training. The clustering analysis at group level showed that the feedback training did not affect the number of networks in the whole brain but altered the distribution and functional connectivity in the LA network. CONCLUSION These findings suggested that the feedback training effects in emotion regulation pattern reflected by the activity of the target brain network and the connections within the network were robustly embodied in local network specialization instead of in global network integration.

中文翻译：

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使用层次聚类评估神经反馈训练诱导的杏仁核网络用于情绪调节。

背景 以往的研究表明，通过基于 fMRI 的实时神经反馈训练来调节目标区域可以影响其他区域的激活以及它们之间的功能连接。然而，目前尚不清楚神经反馈的训练效果，尤其是在情绪调节方面，是表现在局部网络专业化还是全局网络整合上。在目前的研究中，我们选择左侧杏仁核 (LA) 作为目标区域，通过实时 fMRI 训练来调节积极情绪。采用平均连锁层次聚类对训练过程中记录的 fMRI 数据进行聚类，构建全脑网络和 LA 所属的 LA 网络。结果 LA 和其他一些地区的激活在训练过程中显着上调。组级聚类分析表明，反馈训练不影响整个大脑中的网络数量，但改变了 LA 网络中的分布和功能连接。结论这些发现表明，由目标大脑网络的活动和网络内的连接所反映的情绪调节模式的反馈训练效果强烈地体现在局部网络专业化而不是全局网络集成中。