不同模式经颅磁刺激对大鼠工作记忆中局部场电位因果网络连接的影响

doi:10.3969/j.issn.0258-8021.2023.02.004

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Abstract In recent years, non-invasive clinical neuromodulation methods have attracted intensive research interests. Among the methods, transcranial magnetic stimulation can non-invasively induce an induced electric field in the cerebral cortex, thereby regulating the function of the nervous system, and its regulatory effect on cognitive function is particularly significant. In this paper, the rats in stimulation group are treated in 5 Hz repetitive transcranial magnetic stimulation (rTMS), continuous theta burst stimulation (cTBS), and intermittent theta burst stimulation (iTBS). There were 6 rats in each group. The local field potentials of rats during the working memory task were recorded. The causal connection network of local field potential in the θ band and γ band was constructed based on the directional transfer function, and the characteristic parameters of the causal network such as the DTF value, average connection density, and global efficiency of each group of rats were calculated. Furthermore, the effects of different protocols of transcranial magnetic stimulation on behavioral ability was discussed. The results showed that rats in the rTMS[(4.83±1.84) d)] and iTBS[(5.00±1.55) d] groups learned more quickly than those in the control[(7.83±2.40) d) and cTBS[(9.33±2.07) d] groups(P<0.05) during working memory behavior tasks. There was no statistical difference in the DTF values of the LFPs signal in θ band between the groups (P >0.05). The DTF value, connection density, and global network efficiency of the γ band in the rTMS and iTBS groups were higher than those in the control and cTBS groups (P<0.05). The results indicated that 5 Hz rTMS and iTBS protocol enhanced the information interaction of gamma oscillations among the neuron clusters in the prefrontal cortex, and improved the working memory ability of rats. These findings can provide references for the research of TMS in clinical application and brain cognitive function.

Key words： repetitive transcranial magnetic stimulation working memory local field potentials directional transfer function

Received: 08 December 2021

PACS:

R318

Corresponding Authors: ^*E-mail: gmm@hebut.edu.cn

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	Guo Miaomiao
	Ji Lihui
	Zhang Tianheng
	Wang Zhonghao
	Xu Guizhi

Cite this article:

Guo Miaomiao,Ji Lihui,Zhang Tianheng, et al. Effects of Transcranial Magnetic Stimulation in Different Protocols on Causal Network Connection of Local Field Potential during Working Memory Task in Rats[J]. Chinese Journal of Biomedical Engineering, 2023, 42(2): 158-167.

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http://cjbme.csbme.org/EN/10.3969/j.issn.0258-8021.2023.02.004 OR http://cjbme.csbme.org/EN/Y2023/V42/I2/158

[1] Tremblay S, Rogasch NC, Premoli I, et al. Clinical utility and prospective of TMS-EEG[J]. Clinical Neurophysiology, 2019, 130(5): 802-844.
[2] Simonetta-Moreau M. Non-invasive brain stimulation (NIBS) and motor recovery after stroke[J]. Annals of Physical and Rehabilitation Medicine, 2014, 57(8):530-542.
[3] Iramina K, Maeno T, Ueno S. Topography of EEG responses evoked by transcranial magnetic stimulation to the cerebellum[J]. IEEE Transactions on Magnetics, 2004, 40(4): 2982-2984.
[4] Klomjai W, Katz R. Basic principles of transcranial magnetic stimulation (TMS) and repetitive TMS (rTMS)[J]. Annals of Physical and Rehabilitation Medicine, 2015, 58(4): 208-213.
[5] Chang CW, Lo YC, Lin SH, et al. Modulation of theta-band local field potential oscillations across brain networks with central thalamic deep brain stimulation to enhance spatial working memory[J]. Frontiers in Neuroscience, 2019, 13(1): 1-11.
[6] Yu Chunxiu, Fan D, Lopez A, et al. Dynamic changes in single unit activity and gamma oscillations in a thalamocortical circuit during rapid instrumental learning[J]. The Public Library of Science, 2012, 7(11): 1-10.
[7] 孙钰烨,王敬灵,江文宇.重复经颅磁刺激在阿尔茨海默症认知功能治疗中的应用进展[J].国际老年医学杂志,2021,42(5):309-312.
[8] Kumar M. A primer on TMS with a focus on its use in treatment-resistant depression[J]. The Brown University Child and Adolescent Behavior Letter, 2022, 38(1):1-5.
[9] 许毅, 李达, 谭立文, 等. 重复经颅磁刺激治疗专家共识[J]. 转化医学杂志, 2018, 7(1): 4-9.
[10] Takuya, Sasaki, Satoshi, et al. The intensity of continuous theta burst stimulation, but not the waveform used to elicit motor evoked potentials, influences its outcome in the human motor cortex[J]. Brain Stimulation, 2018, 11(2): 400-410.
[11] Blumberger DM, Vila-Rodriguez F, Thorpe KE, et al. Effectiveness of theta burst versus high-frequency repetitive transcranial magnetic stimulation in patients with depression (THREE-D): a randomised non-inferiority trial[J]. The Lancet, 2018, 391(10131): 1683-1692.
[12] Ahmed MA, Da Rwish ES, Khedr EM, et al. Effects of low versus high frequencies of repetitive transcranial magnetic stimulation on cognitive function and cortical excitability in Alzheimer′s dementia[J]. Journal of Neurology, 2012, 259(1): 83-92.
[13] Huang Yingzu, Edwards MJ, Rounis E, et al. Theta burst stimulation of the human motor cortex[J]. Neuron, 2005, 45(2): 201-206.
[14] Gamboa OL, Antal A, Moliadze V, et al. Simply longer is not better: reversal of theta burst after-effect with prolonged stimulation[J]. Experimental Brain Research, 2010, 204(2): 181-187.
[15] Reinhard G, Katharina W, Claus R, et al. Depression of human corticospinal excitability induced by magnetic theta-burst stimulation: evidence of rapid polarity-reversing metaplasticity[J]. Cerebral Cortex, 2008, 18(9): 2046-2053.
[16] Lazzaro VD, Pilato F, Saturno E, et al. Theta-burst repetitive transcranial magnetic stimulation suppresses specific excitatory circuits in the human motor cortex[J]. Journal of Physiology, 2010, 565(18): 945-950.
[17] Witte SD, Baeken C, Pulopulos MM, et al. The effect of neurostimulation applied to the left dorsolateral prefrontal cortex on post-stress adaptation as a function of depressive brooding[J]. Progress in Neuro-Psychopharmacology and Biological Psychiatry, 2019, 96: 109687.
[18] 王帅帅, 李颖, 李继鹏,等. 基于皮层脑网络的背景音乐对空间认知工作记忆影响的实验研究[J]. 生物医学工程学杂志, 2020, 37(4): 587-595.
[19] Wimmer K, Nykamp DQ, Constantinidis C, et al. Bump attractor dynamics in prefrontal cortex explains behavioral precision in spatial working memory[J]. Nature Neuroscience, 2014, 17(3): 431-439.
[20] Stokes MG, Kusunoki M, Sigala N, et al. Dynamic coding for cognitive control in prefrontal cortex[J]. Neuron, 2013, 78(2): 364-375.
[21] Liu Tiaotiao, Bai Wenwen, Yi Hu, et al. Functional connectivity in a rat model of Alzheimer′s disease during a working memory task[J]. Current Alzheimer Research, 2014, 11(10): 981-991.
[22] Xu Xinyu, Tian Yu, Li Shuangyan, et al. Inhibition of propofol anesthesia on functional connectivity between LFPs in PFC during rat working memory task[J]. The Public Library of Science, 2013, 8(12): e83653.
[23] Pernia-Andrade AJ, Jonas P. Theta-gamma-modulated synaptic currents in hippocampal granule cells in vivo define a mechanism for network oscillations[J]. Neuron, 2014, 81(1): 140-152.
[24] Roux F, Uhlhaas PJ. Working memory and neural oscillations: alpha-gamma versus theta-gamma codes for distinct WM information?[J]. Trends in Cognitive Sciences, 2014, 18(1): 16-25.
[25] 陈忠, 张世红. 深部脑刺激在神经精神疾病治疗中的应用研究进展[J]. 浙江大学学报, 2009, 38(6): 549-558.
[26] Ashdown-Franks G, Firth J, Carney R, et al. Exercise as medicine for mental and substance use disorders: a meta-review of the benefits for neuropsychiatric and cognitive outcomes[J]. Sports Medicine, 2020, 50(2): 1-20.
[27] 白杨, 杨佳佳, 郑晨光. Gamma节律:认知障碍疾病的潜在诊断靶点[J]. 生物化学与生物物理进展, 2020, 47(6): 62-76.
[28] Bai Wenwen, Liu Tiaotiao, Dou Mengmeng, et al. Repetitive transcranial magnetic stimulation reverses aβ1-42-induced dysfunction in gamma oscillation during working memory[J]. Current Alzheimer Research, 2018, 15(6): 570-577.
[29] Ziemann U. Thirty years of transcranial magnetic stimulation: where do we stand?[J]. Experimental Brain Research, 2017, 235(4): 973-984.
[30] Curtin A, Ayaz H, Tang Y, et al. Enhancing neural efficiency of cognitive processing speed via training and neurostimulation: an fNIRS and TMS study[J]. Neuroimage, 2019, 198(1): 73-82.
[31] Park HJ, Friston K. Structural and functional brain networks: from connections to cognition[J]. Science, 2013, 342(6158): 1238411.
[32] 杨硕, 江文涛, 王磊,等. 基于脑网络的脑疲劳对不同工作记忆负荷的影响[J]. 中国生物医学工程学报, 2021, 40(4): 145-153.
[33] Xia Mi, Liu Tiaotiao, Bai Wenwen, et al. Information transmission in HPC-PFC network for spatial working memory in rat[J]. Behavioural Brain Research, 2019, 356(1): 170-178.
[34] 李双燕, 温雪晗, 桑浩钧,等. 工频电磁场长期作用影响工作记忆中局部场电位因果网络连接特征[J]. 生物医学工程学杂志, 2018,35(6):829-836.
[35] Noda Y. Toward the establishment of neurophysiological indicators for neuropsychiatric disorders using transcranial magnetic stimulation-evoked potentials: a systematic review[J]. Psychiatry and Clinical Neurosciences, 2020, 74(1): 1-57.
[36] Seth AK. A MATLAB toolbox for Granger causal connectivity analysis[J]. Journal of Neuroscience Methods, 2010, 186(2): 262-273.
[37] Fairhall AL, Lewen GD, Bialek W, et al. Efficiency and ambiguity in an adaptive neural code[J]. Nature, 2001, 412(6849): 787-792.
[38] Achard S, Bullmore E. Efficiency and cost of economical brain functional networks[J]. PLoS Computational Biology, 2007, 3(2): e17.
[39] Splittgerber M, Suwelack JH, Kadish NE, et al. The effects of 1 mA tACS and tRNS on children/adolescents and adults: investigating age and sensitivity to sham stimulation[J]. Neural Plasticity, 2020, 2020: 1-14.
[40] Sung WC, Nigel CR, Kate EH, et al. Impact of different intensities of intermittent theta burst stimulation on the cortical properties during TMS-EEG and working memory performance[J]. Human Brain Mapping, 2018, 39(2): 1-20.
[41] Beynel L, Davis SW, Crowell C A, et al. Online repetitive transcranial magnetic stimulation during working memory in younger and older adults: A randomized within-subject comparison[J]. The Public Library of Science, 2019, 14(3): 1-19.