额叶α不对称神经反馈学习效能的多模式和半球图理论脑网络预测因子

IF 3.1 3区 工程技术 Q2 NEUROSCIENCES
Cognitive Neurodynamics Pub Date : 2024-06-01 Epub Date: 2023-02-19 DOI:10.1007/s11571-023-09939-x
Linling Li, Yutong Li, Zhaoxun Li, Gan Huang, Zhen Liang, Li Zhang, Feng Wan, Manjun Shen, Xue Han, Zhiguo Zhang
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引用次数: 0

摘要

利用额叶阿尔法不对称性(FAA)进行脑电图神经反馈已被广泛用于情绪调节,但其有效性尚存争议。研究表明,神经反馈训练的个体差异可追溯到神经解剖和神经功能特征。然而,这些研究只关注区域性大脑结构或功能,而忽略了大脑网络可能存在的神经相关性。此外,迄今为止还没有关于 FAA 神经反馈方案的神经影像预测指标的报道。我们设计了一个单盲伪对照 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)和休息时(L2)诱发脑电图调制的学习成绩,并基于多模态脑网络和图论特征的综合分析,研究了与成绩相关的预测因素。本研究的主要发现如下。首先,真实组和假组在训练期间都能增加 FAA,但只有真实组在休息时 FAA 有显著增加。其次,训练时和休息时的预测因子不同:L1 与右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 神经反馈学习的个体差异可以用结构/功能架构的个体差异来解释,而学习成绩指数的相关图论指标则显示了半脑网络的不同侧向性。这些结果有助于深入了解神经反馈学习中个体间差异的神经相关性:在线版本包含补充材料,可在10.1007/s11571-023-09939-x上获取。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Multimodal and hemispheric graph-theoretical brain network predictors of learning efficacy for frontal alpha asymmetry neurofeedback.

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.

Supplementary information: The online version contains supplementary material available at 10.1007/s11571-023-09939-x.

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来源期刊
Cognitive Neurodynamics
Cognitive Neurodynamics 医学-神经科学
CiteScore
6.90
自引率
18.90%
发文量
140
审稿时长
12 months
期刊介绍: Cognitive Neurodynamics provides a unique forum of communication and cooperation for scientists and engineers working in the field of cognitive neurodynamics, intelligent science and applications, bridging the gap between theory and application, without any preference for pure theoretical, experimental or computational models. The emphasis is to publish original models of cognitive neurodynamics, novel computational theories and experimental results. In particular, intelligent science inspired by cognitive neuroscience and neurodynamics is also very welcome. The scope of Cognitive Neurodynamics covers cognitive neuroscience, neural computation based on dynamics, computer science, intelligent science as well as their interdisciplinary applications in the natural and engineering sciences. Papers that are appropriate for non-specialist readers are encouraged. 1. There is no page limit for manuscripts submitted to Cognitive Neurodynamics. Research papers should clearly represent an important advance of especially broad interest to researchers and technologists in neuroscience, biophysics, BCI, neural computer and intelligent robotics. 2. Cognitive Neurodynamics also welcomes brief communications: short papers reporting results that are of genuinely broad interest but that for one reason and another do not make a sufficiently complete story to justify a full article publication. Brief Communications should consist of approximately four manuscript pages. 3. Cognitive Neurodynamics publishes review articles in which a specific field is reviewed through an exhaustive literature survey. There are no restrictions on the number of pages. Review articles are usually invited, but submitted reviews will also be considered.
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