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Chin. Phys. B, 2021, Vol. 30(3): 038703    DOI: 10.1088/1674-1056/abd395
INTERDISCIPLINARY PHYSICS AND RELATED AREAS OF SCIENCE AND TECHNOLOGY Prev   Next  

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

Hai-Tao Yu(于海涛)1, Zi-Han Meng(孟紫寒)1, Chen Liu(刘晨)1, Jiang Wang(王江)1,†, and Jing Liu(刘静)2
1 School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China; 2 Department of Neurology, Tangshan Gongren Hospital, Tangshan 063000, China
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  
Published:  22 February 2021
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
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