Effective suppression of beta oscillation in Parkinsonian state via a noisy direct delayed feedback control scheme

doi:10.1088/1674-1056/abd395

Abstract This work explores the function of the noisy direct delayed feedback (NDDF) control strategy in suppressing the pathological oscillations in the basal ganglia (BG) with Parkinson's disease (PD). Deep brain stimulation (DBS) alleviates the PD state fantastically. However, due to its unclear mechanism and open-loop characteristic, it is challenging to further improve its effects with lower energy expenditure. The noise stimulus performs competitively in alleviating the PD state theoretically, but it cannot adapt to the neural condition timely and automatically due to its open-loop control scheme. The direct delayed feedback (DDF) control strategy is able to disturb excessive synchronous effectively. Therefore, the NDDF control strategy is proposed and researched based on a BG computational model, which can reflect the intrinsic properties of the BG neurons and their connections with thalamic neurons. Simulation results show that the NDDF control strategy with optimal parameters is effective in removing the pathological beta oscillations. By comparison, we find the NDDF control strategy performs more excellent than DDF in alleviating PD state. Additionally, we define the multiple-NDDF control strategy and find that the multiple-NDDF with appropriate parameters performs better than NDDF. The obtained results contribute to the cure for PD symptoms by optimizing the noise-induced improvement of the BG dysfunction.

Keywords: basal ganglia neural networks Parkinsonian state noise delayed feedback

Received: 29 August 2020
Revised: 20 October 2020
Accepted manuscript online: 15 December 2020

PACS:	87.19.lc	(Noise in the nervous system)
	87.19.lm	(Synchronization in the nervous system)
	87.19.X-	(Diseases)
	87.19.lr	(Control theory and feedback)

Fund: Project supported by Tianjin Natural Science Foundation, China (Grant No. 19JCYBJC18800), Tangshan Science and Technology Project, China (Grant No. 18130208A), and Hebei Science and Technology Project, China (Grant No. 18277773D).

Corresponding Authors: ^†Corresponding author. E-mail: jiangwang@tju.edu.cn

Cite this article:

Hai-Tao Yu(于海涛), Zi-Han Meng(孟紫寒), Chen Liu(刘晨), Jiang Wang(王江), and Jing Liu(刘静) Effective suppression of beta oscillation in Parkinsonian state via a noisy direct delayed feedback control scheme 2021 Chin. Phys. B 30 038703

1 Leventhal D K, Gage G J, Schmidt R, Pettibone J R, Case A C and Berke J D 2012 Neuron 73 523
2 Schiff S J 2010 Philos. Trans. A Math. Phys. Eng. Sci. 368 2269
3 Guo Y and Rubin J E 2011 Neural Netw. 24 602
4 Holt A B and Netoff T I 2014 Comput. Neurosci. 37 505
5 Daneshzand M, Faezipour M and Barkana B D 2018 PloS one 13 6165
6 Liu C, Wang J, Li H, Fietkiewicz C and Loparo K A 2018 IEEE Transactions on Neural Networks and Learning Systems 29 1864
7 Liu C, Wang J, Deng B, Li H, Fietkiewicz C and Loparo K A 2018 IEEE Transactions on Cybernetics 49 3655
8 Yu Y, Hao Y and Wang Q 2020 Neural Networks 122 308
9 Stefani A, Trendafilov V, Liguori C, Fedele E and Galati S 2017 Prog. Neurobiol. 151 157
10 Dorval A D and Grill W M 2014 Neurophysiol. 111 1949
11 Vitek J L, Zhang J, Hashimoto T, Russo G S and Baker K B 2012 Exp. Neurol. 233 581
12 Zhang J, Wang Z I, Baker K B and Vitek J L 2012 Exp. Neurol. 233 575
13 Lin W, Pu Y, Guo Y and Kurths J 2013 Europhys Lett. 102 20003
14 Pikovsky A and Rosenblum M 2015 Chaos 25 097616
15 Zhou S, Ji P, Zhou Q, Feng J, Kurths J and Lin W 2017 New J. Phys. 19 083004
16 Popovych O V, Lysyansky B, Rosenblum M, Pikovsky A and Tass P A 2017 PLoS one 3 1
17 Popovych O V and Tass P A 2018 Front. Physiol. 9 46
18 Pyragas K 1992 Phys. Lett. A 170 421
19 Pal S, Rosengren S M and Colebatch J G 2009 J. Vestib. Res. 19 137
20 Yu H, Wang J and Liu Q 2013 Neurocomputing 99 178
21 Liu C, Zhou C, Wang J, et al. \hrefhttps://doi.org/10.1109/TCYB.2019.2923317 2019 IEEE Transactions on Cybernetics 99 1
22 Funke K, Kerscher N J and W\'org\"otter F 2007 Eur. J. Neurosci. 26 1322
23 Yu H, Wang J, Du J, Deng B, Wei X and Liu C 2013 Phys. Rev. E 87 052917
24 Wang Q, Perc M, Duan Z and Chen G 2009 Chaos 19 023112
25 Terman D, Rubin J E, Yew A C and Wilson C J2002 Neurosci. 22 2963
26 Izhikevich E M 2003 IEEE Trans. Neural. Netw. 14 1569
27 Thibeault C M and Srinivasa N 2013 Front. Comput. Neurosci. 7 88
28 Izhikevich E M and Hoppensteadt F 2004 Chaos 14 3847
29 So R Q, Kent A R and Grill W M 2012 Comput. Neurosci. 32 499
30 Izhikevich E M 2004 IEEE Trans. Neural. Netw. 15 1063
31 Wichmann T and Soares J 2005 J. Neurosci. 95 2120
32 Steigerwald F, Potter M, Herzog J, Pinsker M, Kopper F and Mehdorn H 2008 J. Neurophysiol. 100 2515
33 Samoudi G, Nissbrandt H, Dutia M B and Bergquist F 2012 PLoS ONE 7 e29308
34 Rosenblum M and Pikovsky A 2004 Phys. Rev. E. 70 041904
35 Buzsaki G and Draguhn A 2004 Science 304 1926
36 Uhlhaas P J 2010 Trends Cogn. Sci. 14 72
37 Yu H, Wang J, Deng B, Wei X, Wong Y K, Chan W L, Tsang K M and Yu Z 2011 Chaos 21 013127
38 Yu H, Wang J, Liu Q, Wen J, Deng B and Wei X 2011 Chaos 21 043125
39 Wang R, Zhang Z, Qu J and Cao J 2011 IEEE Transactions on Neural Networks 22 1097
40 Yan C and Wang R 2012 Chin. Phys. Lett. 29 090501
41 Hauptmann, Popovych C O and Tass P A 2005 Neurocomputing 65-66 759
42 Hauptmann, Popovych C O and Tass P A 2007 Computing and Visualization in Science 10 71
43 Yu Y and Wang Q 2019 Nonlinear Dynamics 98 1065
44 Wang J, Niebur E, Hu J and Li X 2016 Sci. Rep. 6 27344
45 Schiff S J, So P, Chang T, Burke R E and Sauer T 1996 Phys. Rev. E 54 6708
46 Guo Y, Park C, Worth R M and Rubchinsky L L 2013 Front. Comput. Neurosc. 7 214
47 Chiken S and Nambu A 2014 Front. Syst. Neurosci. 8 33
48 Wang Z and Wang Q 2019 Nonlinear Dynamics 96 1649
49 Klinger N V and Mittal S 2016 Clin. Neurol. Neurosurg. 140 11
50 Hess C W, Vaillancourt D E and Okun M S 2013 Exp. Neurol. 247 296
51 Popovych O V and Tass P A 2014 Front. Neurol. 5

[1]	Meshfree-based physics-informed neural networks for the unsteady Oseen equations Keyi Peng(彭珂依), Jing Yue(岳靖), Wen Zhang(张文), and Jian Li(李剑). Chin. Phys. B, 2023, 32(4): 040208.
[2]	Precision measurement and suppression of low-frequency noise in a current source with double-resonance alignment magnetometers Jintao Zheng(郑锦韬), Yang Zhang(张洋), Zaiyang Yu(鱼在洋), Zhiqiang Xiong(熊志强), Hui Luo(罗晖), and Zhiguo Wang(汪之国). Chin. Phys. B, 2023, 32(4): 040601.
[3]	A cladding-pumping based power-scaled noise-like and dissipative soliton pulse fiber laser Zhiguo Lv(吕志国), Hao Teng(滕浩), and Zhiyi Wei(魏志义). Chin. Phys. B, 2023, 32(2): 024207.
[4]	Inhibitory effect induced by fractional Gaussian noise in neuronal system Zhi-Kun Li(李智坤) and Dong-Xi Li(李东喜). Chin. Phys. B, 2023, 32(1): 010203.
[5]	Exploring fundamental laws of classical mechanics via predicting the orbits of planets based on neural networks Jian Zhang(张健), Yiming Liu(刘一鸣), and Zhanchun Tu(涂展春). Chin. Phys. B, 2022, 31(9): 094502.
[6]	Nonvanishing optimal noise in cellular automaton model of self-propelled particles Guang-Le Du(杜光乐) and Fang-Fu Ye(叶方富). Chin. Phys. B, 2022, 31(8): 086401.
[7]	Characteristics of piecewise linear symmetric tri-stable stochastic resonance system and its application under different noises Gang Zhang(张刚), Yu-Jie Zeng(曾玉洁), and Zhong-Jun Jiang(蒋忠均). Chin. Phys. B, 2022, 31(8): 080502.
[8]	Hyperparameter on-line learning of stochastic resonance based threshold networks Weijin Li(李伟进), Yuhao Ren(任昱昊), and Fabing Duan(段法兵). Chin. Phys. B, 2022, 31(8): 080503.
[9]	Fast prediction of aerodynamic noise induced by the flow around a cylinder based on deep neural network Hai-Yang Meng(孟海洋), Zi-Xiang Xu(徐自翔), Jing Yang(杨京), Bin Liang(梁彬), and Jian-Chun Cheng(程建春). Chin. Phys. B, 2022, 31(6): 064305.
[10]	Nano-friction phenomenon of Frenkel—Kontorova model under Gaussian colored noise Yi-Wei Li(李毅伟), Peng-Fei Xu(许鹏飞), and Yong-Ge Yang(杨勇歌). Chin. Phys. B, 2022, 31(5): 050501.
[11]	Effects of colored noise on the dynamics of quantum entanglement of a one-parameter qubit—qutrit system Odette Melachio Tiokang, Fridolin Nya Tchangnwa, Jaures Diffo Tchinda,Arthur Tsamouo Tsokeng, and Martin Tchoffo. Chin. Phys. B, 2022, 31(5): 050306.
[12]	Acoustic multipath structure in direct zone of deep water and bearing estimation of tow ship noise of towed line array Zhi-Bin Han(韩志斌), Zhao-Hui Peng (彭朝晖), Jun Song(宋俊), Lei Meng(孟雷), Xiu-Ting Yang(杨秀庭), and Bing Su(苏冰). Chin. Phys. B, 2022, 31(5): 054301.
[13]	Deterministic remote state preparation of arbitrary three-qubit state through noisy cluster-GHZ channel Zhihang Xu(许智航), Yuzhen Wei(魏玉震), Cong Jiang(江聪), and Min Jiang(姜敏). Chin. Phys. B, 2022, 31(4): 040304.
[14]	Dynamics and near-optimal control in a stochastic rumor propagation model incorporating media coverage and Lévy noise Liang'an Huo(霍良安) and Yafang Dong(董雅芳). Chin. Phys. B, 2022, 31(3): 030202.
[15]	Development of series SQUID array with on-chip filter for TES detector Wentao Wu(伍文涛), Zhirong Lin(林志荣), Zhi Ni(倪志), Peizhan Li(李佩展), Tiantian Liang(梁恬恬), Guofeng Zhang(张国峰), Yongliang Wang(王永良), Liliang Ying(应利良), Wei Peng(彭炜), Wen Zhang(张文), Shengcai Shi(史生才), Lixing You(尤立星), and Zhen Wang(王镇). Chin. Phys. B, 2022, 31(2): 028504.

No Suggested Reading articles found!

Viewed

Full text

Abstract

Cited

Metrics
Related Articles

Effective suppression of beta oscillation in Parkinsonian state via a noisy direct delayed feedback control scheme

Cite this article:

Online attention