基于脑电溯源的顿悟状态脑功能网络研究

Study on brain functional network of insight based on EEG source analysis

利用脑电(EEG)溯源方法,将头皮记录的EEG信号重建大脑皮层源信号的分布模式,以大脑皮层源信号构建顿悟状态脑网络并计算网络的属性参数,探讨顿悟的神经机制。选取14名被试进行中文远距离联想任务(CRAT),同步记录64导EEG,分别对beta(14～30Hz)和高gamma(50～80Hz)节律的EEG溯源数据进行同步似然分析,选择合适阈值构建脑网络并计算网络的节点度和聚类系数。结果显示,顿悟状态beta节律和gamma节律的脑网络的节点度、聚类系数均高于非顿悟状态,且其gamma节律的脑网络属性参数高于beta节律,提示顿悟的神经机制与beta节律、尤其与高gamma节律的脑功能连接增强及脑信息传输能力提高有关。相较于头皮EEG信号,大脑皮层源信号更准确地体现皮层区域间连接特性,本研究为揭示认知的神经机制提供了新视角。

Signal from the cerebral cortex can be reconstructed based on electroencephalography（ EEG） by source analysis. The brain network of insight is constructed based on the cortical sourced signal and the attribute parameters of the network are calculated to explore the neural mechanism underlying insight. 64-channel EEGs are recorded from14 volunteers when they finish Chinese remote association tasks（ CRAT）,the synchronization likelihood analysis are performed on the source analyzed EEGs in beta（ 14 ～ 30 Hz） and high gamma rhythm（ 50 ～ 80 Hz）,the appropriate threshold is selected to construct the brain network and calculate the node degree and clustering coefficient of the network. The results show that the node degree and clustering coefficient of insight brain network are larger than that of non-insight in both beta and gamma rhythm,the brain network in gamma band shows higher values of these parameters than that in beta rhythm. The result suggests that the neural mechanism underlying insight involves increased functional connectivity and information transmission efficiency in beta rhythm,especially in gamma rhythm.Compared with the scalp EEG signal,the cortical sourced signal more accurately reflects the characteristics of cortical interregional connection. This study provides a new perspective for revealing the cognitive neural mechanism.