Department of Biomedical Engineering, School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran.
Department of Neuroimaging, School of Cognitive Sciences, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran.
Brain Connect. 2020 Aug;10(6):302-315. doi: 10.1089/brain.2019.0734. Epub 2020 Jul 7.
Emotion regulation by neurofeedback involves interactions among multiple brain regions, including prefrontal cortex and subcortical regions. Previous studies focused on connections of specific brain regions such as amygdala with other brain regions. Electroencephalography (EEG) neurofeedback is used to upregulate positive emotion by retrieving positive autobiographical memories and functional magnetic resonance imaging (fMRI) data acquired simultaneously. A global data-driven approach, group independent component analysis, is applied to the fMRI data and functional network connectivity (FNC) estimated. The proposed approach identified all functional networks engaged in positive autobiographical memories and evaluated effects of neurofeedback. The results revealed two pairs of networks with significantly different functional connectivity among emotion regulation blocks (relative to other blocks of the experiment) and between experimental and control groups (false discovery rate corrected for multiple comparisons, = 0.05). FNC distribution showed significant connectivity differences between neurofeedback blocks and other blocks, revealing more synchronized brain networks during neurofeedback. Although the results are consistent with those of previous model-based studies, some of the connections found in this study were not found previously. These connections are between (a) occipital and other regions including limbic system/sublobar, prefrontal/frontal cortex, inferior parietal, and middle temporal gyrus and (b) posterior cingulate cortex and hippocampus. This study provided a global insight into brain connectivity for emotion regulation. The brain network interactions may be used to develop connectivity-based neurofeedback methods and alternative therapeutic approaches, which may be more effective than the traditional activity-based neurofeedback methods.
神经反馈中的情绪调节涉及多个脑区之间的相互作用，包括前额叶皮层和皮质下区域。以前的研究主要集中在特定脑区（如杏仁核）与其他脑区的连接上。脑电图（EEG）神经反馈通过检索积极的自传体记忆和同时获得的功能磁共振成像（fMRI）数据来上调积极情绪。采用全局数据驱动方法——组独立成分分析（group independent component analysis），对 fMRI 数据进行分析，并估计功能网络连接（functional network connectivity，FNC）。所提出的方法确定了参与积极自传体记忆的所有功能网络，并评估了神经反馈的效果。结果显示，在情绪调节块（相对于实验的其他块）和实验组与对照组之间（经多重比较校正的错误发现率，p = 0.05），有两对功能网络的功能连接存在显著差异。FNC 分布显示神经反馈块和其他块之间存在显著的连接差异，表明在神经反馈过程中大脑网络更加同步。尽管这些结果与之前基于模型的研究结果一致，但本研究中发现的一些连接以前并未发现。这些连接包括：（a）枕叶与包括边缘系统/亚叶、前额叶/额叶皮层、下顶叶和中颞叶在内的其他区域之间；（b）后扣带回皮质与海马体之间。这项研究提供了对情绪调节中大脑连接的全局认识。大脑网络的相互作用可用于开发基于连接的神经反馈方法和替代治疗方法，这些方法可能比传统的基于活动的神经反馈方法更有效。
