Long term power frequency electromagnetic fields exposure influences the causal network connection pattern of local field potentials during working memory_Journal of Biomedical Engineering

Authors：

LI Shuangyan ^1,2 , WEN Xuehan ^1,2 , SANG Haojun ^1,2 ,  XU Guizhi ^1,2

1. State Key Laboratory of Reliability and Intelligence of Electrical Equipment, School of Electrical Engineering, Hebei University of Technology, Tianjin 300130, P.R.China;
2. Key Laboratory of Electromagnetic Field and Electrical Apparatus Reliability of Hebei Province, School of Electrical Engineering, Hebei University of Technology, Tianjin 300130, P.R.China;

Corresponding author：

XU Guizhi, Email: gzxu@hebut.edu.cn

Keywords：

power frequency electromagnetic field; long term exposure; working memory; local field potentials; connection pattern of causal network

DOI：

10.7507/1001-5515.201806004

Video：

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The possible influence of electromagnetic field (EMF) on the function of neural systems has been widely concerned. In this article, we intend to investigate the effects of long term power frequency EMF exposure on brain cognitive functions and it’s mechanism. The Sprague-Dawley (SD) rats were randomly divided into 3 groups: the rats in EMF Ⅰ group were placed in the 2 mT power frequency EMF for 24 days. The rats in EMF Ⅱ group were placed in the 2 mT power frequency EMF for 48 days. The rats in control group were not exposed to the EMF. Then, the 16 channel local field potentials (LFPs) were recorded from rats’ prefrontal cortex (PFC) in each group during the working memory (WM) tasks. The causal networks of LFPs were also established by applying the directed transfer function (DTF). Based on that, the differences of behavior and the LFPs network connection patterns between different groups were compared in order to investigate the influence of long term power frequency EMF exposure on working memory. The results showed the rats in the EMF Ⅱ group needed more training to reach the task correction criterion (over 80%). Moreover, the causal network connection strength and the global efficiency of the rats in EMF Ⅰ and EMF Ⅱ groups were significantly lower than the corresponding values of the control group. Meanwhile, significant differences of causal density values were found between EMF Ⅱ group and the other two groups. These results indicate that long term exposure to 2 mT power frequency EMF will reduce the connection strength and the information transfer efficiency of the LFPs causal network in the PFC, as well as the behavior performance of the rats. These results may explain the effect of EMF exposure on working memory from the view of neural network connectivity and provide a support for further studies on the mechanism of the effect of EMF on cognition.

Citation： LI Shuangyan, WEN Xuehan, SANG Haojun, XU Guizhi. Long term power frequency electromagnetic fields exposure influences the causal network connection pattern of local field potentials during working memory. Journal of Biomedical Engineering, 2018, 35(6): 829-836. doi: 10.7507/1001-5515.201806004 Copy

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18.	Seth A K. A MATLAB toolbox for Granger causal connectivity analysis. J Neurosci Methods, 2010, 186(2): 262-273.
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1. de Bruyn L, de Jager L. Effect of long-term exposure to a randomly varied 50 Hz power frequency magnetic field on the fertility of the mouse. Electromagn Biol Med, 2010, 29(1/2): 52-61.
2. Hardell L, Sage C. Biological effects from electromagnetic field exposure and public exposure standards. Biomedicine & Pharmacotherapy, 2008, 62(2): 104-109.
3. Foroozandeh E, Derakhshan-Barjoei P, Jadidi M. Toxic effects of 50 Hz electromagnetic field on memory consolidation in male and female mice. Toxicol Ind Health, 2013, 29(3): 293-299.
4. Wang Xiusong, Zhao Ke, Wang Dong, et al. Effects of exposure to a 50 Hz sinusoidal magnetic field during the early adolescent period on spatial memory in mice. Bioelectromagnetics, 2013, 34(4): 275-284.
5. 李妮, 邬雄, 裴春明. 工频电磁场长期曝露健康风险的预防性政策分析. 高电压技术, 2011, 37(12): 2930-2936.
6. 王东, 郭键锋, 孔令丰, 等. 极低频电磁场暴露对大鼠学习记忆功能影响的研究. 辐射防护, 2012, 32(5): 296-300.
7. Baddeley A. Working memory. Science, 1992, 255(5044): 556-559.
8. Baddeley A. Working memory: the interface between memory and cognition. Cogn Neurosci, 1992, 4(3): 281-288.
9. Wimmer K, Nykamp D Q, Constantinidis C, et al. Bump attractor dynamics in prefrontal cortex explains behavioral precision in spatial working memory. Nat Neurosci, 2014, 17(3): 431-439.
10. Stokes M G, Kusunoki M, Sigala N, et al. Dynamic coding for cognitive control in prefrontal cortex. Neuron, 2013, 78(2): 364-375.
11. Benchenane K, Peyrache A, Khamassi M, et al. Coherent theta oscillations and reorganization of spike timing in the hippocampal-prefrontal network upon learning. Neuron, 2010, 66(6): 921-936.
12. Li Shuangyan, Bai Wenwen, Liu Tiaotiao, et al. Increases of theta-low gamma coupling in rat medial prefrontal cortex during working memory task. Brain Res Bull, 2012, 89(3/4): 115-123.
13. Liu Tiaotiao, Bai Wenwen, Yi Hu, et al. Functional connectivity in a rat model of alzheimer's disease during a working memory task. Curr Alzheimer Res, 2014, 11(10): 981-991.
14. Xu Xinyu, Tian Yu, Li Shuangyan, et al. Inhibition of propofol anesthesia on functional connectivity between LFPs in PFC during rat working memory task. PLoS One, 2013, 8(12): e83653.
15. Pesaran B, Musallam S, Andersen RA, et al. Cognitive neural prosthetics. Current Biology, 2006, 16(3): R77-R80.
16. 易虎. 工作记忆 LFP 网络和 spike 网络协同机制的研究. 天津: 天津医科大学, 2015.
17. Rubinov M, Sporns O. Complex network measures of brain connectivity: uses and interpretations. Neuroimage, 2010, 52(3): 1059-1069.
18. Seth A K. A MATLAB toolbox for Granger causal connectivity analysis. J Neurosci Methods, 2010, 186(2): 262-273.
19. Fairhall A L, Lewen G D, Bialek W, et al. Efficiency and ambiguity in an adaptive neural code. Nature, 2001, 412(6849): 787-792.
20. Achard S, Bullmore E. Efficiency and cost of economical brain functional networks. PLoS Comput Biol, 2007, 3(2): e17.

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