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胡一凡,周怡君,杨莉芳,李蒙蒙,尚志刚.2019.家鸽不同状态下脑电节律特异性分析.动物学杂志,54(6):860-866.
家鸽不同状态下脑电节律特异性分析
Analysis of Neural Signal Specificity in Pigeons Under Different Conditions
投稿时间:2019-07-15  修订日期:2019-10-22
DOI:10.13859/j.cjz.201906012
中文关键词:  局部场电位  节律特异性  意识状态  时频分析  样本熵
英文关键词:Local field potential (LFP)  Rhythm specificity  State of consciousness  Time-frequency analysis  Sample entropy
基金项目:国家自然科学基金项目(No. U1304602)
作者单位E-mail
胡一凡 ① 郑州大学电气工程学院 郑州 450001 ② 河南省脑科学与脑机接口技术重点实验室 郑州 450001 huuyifan@163.com 
周怡君 ① 郑州大学电气工程学院 郑州 450001 ② 河南省脑科学与脑机接口技术重点实验室 郑州 450001 1598904542@qq.com 
杨莉芳 ① 郑州大学电气工程学院 郑州 450001 ② 河南省脑科学与脑机接口技术重点实验室 郑州 450001 flyer1014@163.com 
李蒙蒙 ① 郑州大学电气工程学院 郑州 450001 ② 河南省脑科学与脑机接口技术重点实验室 郑州 450001 limengmeng1014@163.com 
尚志刚 ① 郑州大学电气工程学院 郑州 450001 ② 河南省脑科学与脑机接口技术重点实验室 郑州 450001 zhigang_shang@zzu.edu.cn 
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中文摘要:
      人类大脑神经电活动的不同节律与不同的状态有关,而其他物种如鸟类不同状态下脑信号的节律特异性尚不明确。本文通过分析家鸽(Columba livia domestica)在麻醉昏迷、清醒安静、自由探索三种典型状态下的局部场电位(LFP)信号,研究家鸽不同意识状态下神经电活动的节律特异性。首先采集不同状态下的LFP信号,提取δ(1 ~ 4 Hz)、θ(4 ~ 8 Hz)、α(8 ~ 12 Hz)、β(15 ~ 30 Hz)、γ(30 ~ 60 Hz)五个节律;然后使用小波变换进行时频分析,通过统计时频图的定性观察和小波能量的统计分析,使用Friedman检验进行统计假设检验,研究各状态不同节律的特异性,并基于样本熵分析信号复杂度,探索产生这种节律特异性的可能原因。结果表明,随着意识越来越清晰,较低频的δ、θ、α节律受到明显抑制(P < 0.001),而较高频的γ节律活动明显增强(P < 0.001);样本熵的分析表明,这可能是由于节律频带越高,信号样本熵越大,对应了从麻醉、清醒到自由探索意识清晰程度的提高。家鸽不同状态下神经电活动节律特异性的研究,有助于增进对不同物种脑信号节律编码机制的理解。
英文摘要:
      Different rhythms of brain electrical activity in humans are related to different consciousness states, while the specificity of different rhythms of neural signals in different states of other species such as birds is not yet clear. In this paper, we studied the rhythm specificity of neural activity in pigeons (Columba livia domestica) under different states of consciousness: anesthetic coma, consciously quiet, and freely exploring, by analyzing the local field potential (LPF) signals. Firstly, LPF signals in different states were collected. Then, five rhythms including delta (1﹣4 Hz), theta (4﹣8 Hz), alpha (8﹣12 Hz), beta (15﹣30 Hz) and gamma (30﹣60 Hz) were extracted.. Finally, time-frequency analysis was carried out by using wavelet transform, studying the characteristics of different rhythms by observation of statistical time-frequency diagram and statistical analysis of wavelet energy. We also analyzed the complexity of signals based on the Sample Entropy to explore the possible reasons for this rhythm specificity. The statistical hypothesis testing was carried out by Friedman test. Results showed that as the brain became clear and clear, the low-frequency rhythms delta, theta, and alpha, were significantly inhibited (P < 0.001, Fig. 3), while the activity of high-frequency rhythm, gamma was significantly enhanced (P < 0.001, Fig. 3). We then did statistical hypothesis testing for sample entropy of neural signal in different rhythms, and the results of Friedman test showed that the higher the rhythm frequency band, the greater the signal sample entropy (P < 0.001, Fig. 4), corresponding to the improvement of consciousness clarity from anesthesia, awakening to free exploring. The study on the rhythm specificity of neural electrical activity in pigeons under different conditions will make contribution to the understanding of the encoding mechanism of neural rhythm in different species.
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