Wei Mengting, Wang Qingyun, Jiang Xiang, Guo Yiyun, Fan Hui, Wang Haixian, Lu Xuesong
Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China.
Key Laboratory of Child Development and Learning Science of Ministry of Education, School of Biological Science & Medical Engineering, Southeast University, Nanjing 210096, Jiangsu, China.
Comput Intell Neurosci. 2020 Aug 28;2020:4209321. doi: 10.1155/2020/4209321. eCollection 2020.
The neurocognitive characteristics of mathematically gifted adolescents are characterized by highly developed functional interactions between the right hemisphere and excellent cognitive control of the prefrontal cortex, enhanced frontoparietal cortex, and posterior parietal cortex. However, it is still unclear when and how these cortical interactions occur. In this paper, we used directional coherence analysis based on Granger causality to study the interactions between the frontal brain area and the posterior brain area in the mathematical frontoparietal network system during deductive reasoning tasks. Specifically, the scalp electroencephalography (EEG) signal was first converted into a cortical dipole source signal to construct a Granger causality network over the -band and -band ranges. We constructed the binary Granger causality network at the 40 pairs of cortical nodes in the frontal lobe and parietal lobe across the -band and the -band, which were selected as regions of interest (ROI). We then used graph theory to analyze the network differences. It was found that, in the process of reasoning tasks, the frontoparietal regions of the mathematically gifted show stronger working memory information processing at the -band. Additionally, in the middle and late stages of the conclusion period, the mathematically talented individuals have less information flow in the anterior and posterior parietal regions of the brain than the normal subjects. We draw the conclusion that the mathematically gifted brain frontoparietal network appears to have more "automated" information processing during reasoning tasks.
数学天赋青少年的神经认知特征表现为右半球之间高度发达的功能交互以及前额叶皮层、增强的额顶叶皮层和顶叶后皮层出色的认知控制。然而，这些皮层交互何时以及如何发生仍不清楚。在本文中，我们使用基于格兰杰因果关系的方向相干分析来研究演绎推理任务期间数学额顶叶网络系统中额叶脑区与后脑区之间的交互。具体而言，首先将头皮脑电图（EEG）信号转换为皮层偶极子源信号，以在 - 频段和 - 频段范围内构建格兰杰因果关系网络。我们在额叶和顶叶的40对皮层节点处跨 - 频段和 - 频段构建二元格兰杰因果关系网络，这些节点被选为感兴趣区域（ROI）。然后我们使用图论来分析网络差异。结果发现，在推理任务过程中，数学天赋者的额顶叶区域在 - 频段表现出更强的工作记忆信息处理能力。此外，在结论期的中后期，数学天赋者大脑前后顶叶区域的信息流比正常受试者少。我们得出结论，数学天赋者的大脑额顶叶网络在推理任务期间似乎具有更多“自动化”的信息处理能力。
