Li Linling, Li Yutong, Li Zhaoxun, Huang Gan, Liang Zhen, Zhang Li, Wan Feng, Shen Manjun, Han Xue, Zhang Zhiguo
School of Biomedical Engineering, Medical School, Shenzhen University, Shenzhen 518060, China.
Guangdong Provincial Key Laboratory of Biomedical Measurements and Ultrasound Imaging, Shenzhen 518060, China.
Cogn Neurodyn. 2024 Jun;18(3):847-862. doi: 10.1007/s11571-023-09939-x. Epub 2023 Feb 19.
EEG neurofeedback using frontal alpha asymmetry (FAA) has been widely used for emotion regulation, but its effectiveness is controversial. Studies indicated that individual differences in neurofeedback training can be traced to neuroanatomical and neurofunctional features. However, they only focused on regional brain structure or function and overlooked possible neural correlates of the brain network. Besides, no neuroimaging predictors for FAA neurofeedback protocol have been reported so far. We designed a single-blind pseudo-controlled FAA neurofeedback experiment and collected multimodal neuroimaging data from healthy participants before training. We assessed the learning performance for evoked EEG modulations during training (L1) and at rest (L2), and investigated performance-related predictors based on a combined analysis of multimodal brain networks and graph-theoretical features. The main findings of this study are described below. First, both real and sham groups could increase their FAA during training, but only the real group showed a significant increase in FAA at rest. Second, the predictors during training blocks and at rests were different: L1 was correlated with the graph-theoretical metrics (clustering coefficient and local efficiency) of the right hemispheric gray matter and functional networks, while L2 was correlated with the graph-theoretical metrics (local and global efficiency) of the whole-brain and left the hemispheric functional network. Therefore, the individual differences in FAA neurofeedback learning could be explained by individual variations in structural/functional architecture, and the correlated graph-theoretical metrics of learning performance indices showed different laterality of hemispheric networks. These results provided insight into the neural correlates of inter-individual differences in neurofeedback learning.
The online version contains supplementary material available at 10.1007/s11571-023-09939-x.
使用额叶α波不对称性(FAA)的脑电图神经反馈已被广泛用于情绪调节,但其有效性存在争议。研究表明,神经反馈训练中的个体差异可追溯到神经解剖学和神经功能特征。然而,这些研究仅关注局部脑结构或功能,而忽略了脑网络可能的神经关联。此外,迄今为止尚未有关于FAA神经反馈方案的神经影像学预测指标的报道。我们设计了一项单盲伪对照FAA神经反馈实验,并在训练前收集了健康参与者的多模态神经影像学数据。我们评估了训练期间(L1)和静息时(L2)诱发脑电图调制的学习表现,并基于多模态脑网络和图论特征的综合分析研究了与表现相关的预测指标。本研究的主要发现如下。首先,真实组和伪刺激组在训练期间均可提高其FAA,但只有真实组在静息时FAA有显著增加。其次,训练阶段和静息时的预测指标不同:L1与右侧半球灰质和功能网络的图论指标(聚类系数和局部效率)相关,而L2与全脑和左侧半球功能网络的图论指标(局部和全局效率)相关。因此,FAA神经反馈学习中的个体差异可由结构/功能结构的个体差异来解释,且学习表现指标的相关图论指标显示出半球网络的不同偏侧性。这些结果为神经反馈学习中个体差异的神经关联提供了见解。
在线版本包含可在10.1007/s11571-023-09939-x获取的补充材料。