Gao Jia, Wang Wei
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2015 Oct;32(5):945-51.
Exploring the functional network during the interaction between emotion and cognition is an important way to reveal the underlying neural connections in the brain. Sparse Bayesian network (SBN) has been used to analyze causal characteristics of brain regions and has gradually been applied to the research of brain network. In this study, we got theta band and alpha band from emotion electroencephalogram (EEG) of 22 subjects, constructed effective networks of different arousal, and analyzed measurements of complex network including degree, average clustering coefficient and characteristic path length. We found that: (1) compared with EEG signal of low arousal, left middle temporal extensively interacted with other regions in high arousal, while right superior frontal interacted less; (2) average clustering coefficient was higher in high arousal and characteristic path length was shorter in low arousal.
探索情绪与认知相互作用过程中的功能网络是揭示大脑潜在神经连接的重要途径。稀疏贝叶斯网络（SBN）已被用于分析脑区的因果特征，并逐渐应用于脑网络研究。在本研究中，我们从22名受试者的情绪脑电图（EEG）中获取了θ波段和α波段，构建了不同唤醒水平下的有效网络，并分析了包括度、平均聚类系数和特征路径长度在内的复杂网络测量指标。我们发现：（1）与低唤醒水平的EEG信号相比，高唤醒水平下左颞中回与其他区域广泛相互作用，而右额上回相互作用较少；（2）高唤醒水平下平均聚类系数较高，低唤醒水平下特征路径长度较短。
