Wang Zhijie, Zhang Fengrui, Yue Lupeng, Hu Li, Li Xiaoli, Xu Bo, Liang Zhenhu
Institute of Electrical Engineering, Yanshan University, Qinhuangdao 066004, People's Republic of China.
Key Laboratory of Intelligent Rehabilitation and Neuromodulation, Hebei Province, Qinhuangdao 066004, People's Republic of China.
J Neural Eng. 2022 May 12;19(3). doi: 10.1088/1741-2552/ac6a7b.
The investigation of neurophysiologic mechanisms of anesthetic drug-induced loss of consciousness (LOC) by using the entropy, complexity, and information integration theories at the mesoscopic level has been a hot topic in recent years. However, systematic research is still lacking.We analyzed electrocorticography (ECoG) data recorded from nine rats during isoflurane-induced unconsciousness. To characterize the complexity and connectivity changes, we investigated ECoG power, symbolic dynamic-based entropy (i.e. permutation entropy (PE)), complexity (i.e. permutation Lempel-Ziv complexity (PLZC)), information integration (i.e. permutation cross mutual information (PCMI)), and PCMI-based cortical brain networks in the frontal, parietal, and occipital cortical regions.Firstly, LOC was accompanied by a raised power in the ECoG beta (12-30 Hz) but a decreased power in the high gamma (55-95 Hz) frequency band in all three brain regions. Secondly, PE and PLZC showed similar change trends in the lower frequency band (0.1-45 Hz), declining after LOC (< 0.05) and increasing after recovery of consciousness (< 0.001). Thirdly, intra-frontal and inter-frontal-parietal PCMI declined after LOC, in both lower (0.1-45 Hz) and higher frequency bands (55-95 Hz) (< 0.001). Finally, the local network parameters of the nodal clustering coefficient and nodal efficiency in the frontal region decreased after LOC, in both the lower and higher frequency bands (< 0.05). Moreover, global network parameters of the normalized average clustering coefficient and small world index increased slightly after LOC in the lower frequency band. However, this increase was not statistically significant.. The PE, PLZC, PCMI and PCMI-based brain networks are effective metrics for qualifying the effects of isoflurane.
近年来，运用介观层面的熵、复杂性和信息整合理论来研究麻醉药物诱导意识丧失（LOC）的神经生理机制一直是热门话题。然而，系统性研究仍然匮乏。我们分析了九只大鼠在异氟烷诱导昏迷期间记录的皮质脑电图（ECoG）数据。为了表征复杂性和连通性变化，我们研究了ECoG功率、基于符号动力学的熵（即排列熵（PE））、复杂性（即排列莱姆尔 - 齐夫复杂性（PLZC））、信息整合（即排列交叉互信息（PCMI））以及额叶、顶叶和枕叶皮质区域基于PCMI的皮质脑网络。首先，在所有三个脑区中，LOC伴随着ECoGβ频段（12 - 30Hz）功率升高，但高γ频段（55 - 95Hz）功率降低。其次，PE和PLZC在较低频段（0.1 - 45Hz）呈现相似的变化趋势，LOC后下降（<0.05），意识恢复后上升（<0.001）。第三，额叶内以及额叶 - 顶叶间的PCMI在LOC后下降，在低频段（0.1 - 45Hz）和高频段（55 - 95Hz）均如此（<0.001）。最后，额叶区域节点聚类系数和节点效率的局部网络参数在LOC后下降，在低频段和高频段均如此（<0.05）。此外，低频段LOC后归一化平均聚类系数和小世界指数的全局网络参数略有增加。然而，这种增加无统计学意义。PE、PLZC、PCMI以及基于PCMI的脑网络是用于量化异氟烷作用的有效指标。
