辐射与模拟失重对大鼠脑电信号的影响规律及损伤机制
doi: 10.11728/cjss2025.01.2023-0149 cstr: 32142.14.cjss.2023-0149
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丰俊东 女, 1978年12月出生于黑龙江省哈尔滨市, 现为南京航空航天大学核科学与技术系副教授, 硕士生导师, 主要研究方向为辐射风险评估与防护技术研究等. E-mail: jundongfeng@nuaa.edu.cn
作者简介:
Effects of Radiation and Simulated Weightlessness on Rat EEG and Its Mechanism
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摘要: 通过生物电信号评估辐射与失重对脑的影响, 并揭示其影响规律与损伤机制, 为空间环境风险评估与防护技术研究提供参考. 以SD大鼠为对象, 设立不同实验组. 采集并分析大鼠脑电信号频谱变化, 利用神经网络模型识别脑电信号异常. 同时检测大鼠脑部特定区域的蛋白质表达量变化, 以探讨损伤机制. 辐射组与失重辐射复合组大鼠脑电信号出现慢波化, 复合作用影响显著, 神经网络模型能有效识别异常信号. 辐射与失重导致大鼠脑部髓鞘受损, 相关蛋白表达量出现变化, 提示胶质细胞激活. 辐射与失重对大鼠脑电信号有明显影响, 复合作用效果更为显著, 这可能与髓鞘受损及胶质细胞激活有关. 本研究为空间环境下的风险评估与防护技术提供了重要参考.Abstract: This article aims to assess the impact of radiation and weightlessness on the brain, specifically through the analysis of bioelectrical signals. Our goal is to elucidate the patterns and mechanisms underlying the effects of radiation, weightlessness, and their combined influence on Electroencephalogram (EEG) signals. This understanding will serve as a crucial reference for risk assessment and protective technology research in space environments. A comprehensive study was conducted utilizing SD rats as the experimental subjects. The eight different experimental groups were established to analyze EEG signals and automatically detect abnormal brain signals caused by radiation or weightlessness exposure.To delve deeper into the underlying biological mechanisms, the expression levels of Myelin Basic Protein (MBP), Glial Fibrillary Acidic Protein (GFAP), and ionized calcium-binding adapter molecule 1 (IBA1) were examined in specific brain regions, such as the frontal lobe, temporal lobe, and hippocampus. The study found that rats exposed to radiation alone or in combination with weightlessness showed a marked slow-wave EEG pattern, with the combined effect being more significant, indicating an enhanced effect. The neural network model accurately distinguished normal from abnormal EEG signals. The decrease of MBP and increase of GFAP and IBA1 were observed in the combined radiation-weightlessness groups, suggesting myelin damage and activation of astrocytes and microglias. The study reveals the effects of radiation and weightlessness on brain signals, showing that radiation alone induces slow-wave EEG patterns, and the combination with weightlessness exacerbates these patterns. The mechanism involves myelin sheath damage and glial cell activation. This understanding is crucial for assessing risks and developing protective measures in space, laying the groundwork for future research to improve astronaut safety and well-being.
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Key words:
- Gamma ray /
- Weightlessness /
- Electroencephalogram (EEG) /
- Neural network /
- Proteins
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图 4 通道 1 EEG 信号各波段占比变化趋势
Figure 4. Ratio trend of EEG signals of each band in Channel 1
图 7 通道 4 EEG 信号各波段占比变化趋势
Figure 7. Ratio trend of EEG signals of each band in Channel 4
图 5 通道 2 EEG 信号各波段占比变化趋势
Figure 5. Ratio trend of EEG signals of each band in Channel 2
图 6 通道 3 EEG 信号各波段占比变化趋势
Figure 6. Ratio trend of EEG signals of each band in Channel 3
图 8 神经网络回归预测脑电信号特征值的学习曲线
Figure 8. Learning curves of neural network regression to predict EEG eigenvalues
图 9 大鼠额叶髓鞘碱性蛋白免疫荧光检测结果
Figure 9. Immunofluorescence analysis of myelin basic protein in rat frontal cortex
图 10 大鼠颞叶髓鞘碱性蛋白免疫荧光检测结果
Figure 10. Immunofluorescence analysis of myelin basic protein in rat temporal lobe
图 11 大鼠海马髓鞘碱性蛋白免疫荧光检测结果
Figure 11. Immunofluorescence analysis of myelin basic protein in rat hippocampus
图 13 大鼠额叶胶质纤维酸性蛋白免疫荧光检测结果
Figure 13. Immunofluorescence analysis of glial fibrillary acidic protein in rat frontal cortex
图 14 大鼠颞叶胶质纤维酸性蛋白免疫荧光检测结果
Figure 14. Immunofluorescence analysis of glial fibrillary acidic protein in rat temporal lobe
图 15 大鼠海马胶质纤维酸性蛋白免疫荧光检测结果
Figure 15. Immunofluorescence analysis of glial fibrillary acidic protein in rat hippocampus
图 17 大鼠额叶小胶质细胞IBA-1免疫荧光检测结果
Figure 17. Immunofluorescence analysis of IBA-1 in rat frontal cortex
图 18 大鼠颞叶小胶质细胞IBA-1免疫荧光检测结果
Figure 18. Immunofluorescence analysis of IBA-1 in rat hippocampus
图 19 大鼠海马小胶质细胞IBA-1免疫荧光检测结果
Figure 19. Immunofluorescence analysis of IBA-1 in rat hippocampus
表 1 不同模型对大鼠脑电自动识别的准确度
Table 1. Accuracy of automatic recognition of rat EEG with different models
Machine learning model Recognition accuracy K-nearest neighbors 0.6166 Logistic regression 0.2195 Random forest 0.8502 Neural networks 0.9029 
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