Modulation of motor responses and neural activities with transcranial ultrasound stimulation based on closed-loop control_Journal of Biomedical Engineering

Authors：

DONG Shuxun , XIE Zhenyu , WANG Xingran ,  YUAN Yi

School of Electrical Engineering, Yanshan University, Qinhuangdao, Hebei 066004, P. R. China;

Corresponding author：

YUAN Yi, Email: yuanyi513@ysu.edu.cn

Keywords：

Transcranial ultrasound stimulation; Closed-loop control; PID neural network control; Generalized minimum variance control

DOI：

10.7507/1001-5515.202205052

Video：

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Abstract Full text Figures/Tables Video References Cited by

Closed-loop transcranial ultrasound stimulation technology is based on real-time feedback signals, and has the potential for precise regulation of neural activity. In this paper, firstly the local field potential (LFP) and electromyogram (EMG) signals of mice under different intensities of ultrasound stimulation were recorded, then the mathematical model of ultrasound intensity and mouse LFP peak/EMG mean was established offline based on the data, and the closed-loop control system of LFP peak and EMG mean based on PID neural network control algorithm was simulated and built to realize closed-loop control of LFP peak and EMG mean of mice. In addition, using the generalized minimum variance control algorithm, the closed-loop control of theta oscillation power was realized. There was no significant difference between the LFP peak, EMG mean and theta power under closed-loop ultrasound control and the given value, indicating a significant control effect on the LFP peak, EMG mean and theta power of mice. Transcranial ultrasound stimulation based on closed-loop control algorithms provides a direct tool for precise modulation of electrophysiological signals in mice.

Citation： DONG Shuxun, XIE Zhenyu, WANG Xingran, YUAN Yi. Modulation of motor responses and neural activities with transcranial ultrasound stimulation based on closed-loop control. Journal of Biomedical Engineering, 2023, 40(2): 265-271. doi: 10.7507/1001-5515.202205052 Copy

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21.	Li Z, Xia Y, Su C Y, et al. Missile guidance law based on robust model predictive control using neural-network optimization. IEEE Trans Neural Networks Learn Syst, 2017, 26(8): 1803-1809.
22.	Wang T, Gao H, Qiu J. A combined adaptive neural network and nonlinear model predictive control for multirate networked industrial process control. IEEE Trans Neural Networks Learn Syst, 2017, 27(2): 416-425.
23.	Fei J, Wang Z. Fractional-order terminal sliding-mode control using self-evolving recurrent Chebyshev fuzzy neural network for MEMS gyroscope. IEEE Trans Fuzzy Syst, 2020, 30(7): 2747-2758.
24.	Shu H, Pi Y. PID neural networks for time-delay systems. Comput Chem Eng, 2000, 24(2): 859-862.
25.	黄建国, 武延祥, 杨世兴. 现代谱估计原理与应用. 北京: 科学出版社, 1994.
26.	徐湘元. 自适应控制理论和与应用. 北京: 电子工业出版社, 2007.

1. 佟祯, 丁萌, 李小俚, 等. 低频重复经颅磁刺激对孤独症儿童脑电节律的影响. 生物医学工程学杂志, 2018, 35(3): 337-342.
2. 付蕊, 徐桂芝, 朱海军, 等. 经颅磁刺激对学习记忆及大脑神经突触可塑性影响的研究进展. 生物医学工程学杂志, 2021, 38(4): 783-789.
3. 张帅, 武健康, 许家悦, 等. 经颅磁声电刺激对前额叶皮层神经集群钙信号的影响. 生物医学工程学杂志, 2022, 39(1): 19-27.
4. 袁毅, 庞娜, 陈玉东, 等. 经颅磁声刺激作用下神经元放电频率适应性的研究. 生物医学工程学杂志, 2017, 34(6): 934-941.
5. Bikmullina R, Kii D, Carlson S, et al. Electrophysiological correlates of short-latency afferent inhibition: a combined EEG and TMS study. Exp Brain Res, 2009, 194(4): 517-526.
6. Kim C K, Adhikari A, Deisseroth K. Integration of optogenetics with complementary methodologies in systems neuroscience. Nat Rev Neurosci, 2017, 18(4): 222.
7. Tyler W J, Tufail Y, Finsterwald M, et al. Remote excitation of neuronal circuits using low-intensity, low-frequency ultrasound. PLoS ONE, 2008, 3(10): e3511.
8. Muratore R, Lamanna J, Szulman E, et al. Bioeffective ultrasound at very low doses: reversible manipulation of neuronal cell morphology and Function in vitro// 8th international symposium on therapeutic ultrasound. Minneapolis: AIP, 2009: 25-29.
9. Tufail Y, Yoshihiro A, Pati S, et al. Ultrasonic neuromodulation by brain stimulation with transcranial ultrasound. Nat Protoc, 2011, 6(9): 1453.
10. Tufail Y, Matyushov A, Baldwin N, et al. Transcranial pulsed ultrasound stimulates intact brain circuits. Neuron, 2010, 66(5): 681-694.
11. Lee W, Lee S D, Park M Y, et al. Image-guided focused ultrasound-mediated regional brain stimulation in sheep. Ultrasound Med Biol, 2016, 42(2): 459-470.
12. Deffieux T, Younan Y, Wattiez N, et al. Low-intensity focused ultrasound modulates monkey visuomotor behavior. Curr Biol, 2013, 23(23): 2430-2433.
13. Brinker S T, Preiswerk F, White P J, et al. Focused ultrasound platform for investigating therapeutic neuromodulation across the human hippocampus. Ultrasound Med Biol, 2020, 46(5): 1270-1274.
14. Lee C C, Chou C C, Hsiao F J, et al. Pilot study of focused ultrasound for drug-resistant epilepsy. Epilepsia, 2022, 63(1): 162-175.
15. Gill B C, Pizarro-Berdichevsky J, Bhattacharyya P K, et al. Real-time changes in brain activity during sacral neuromodulation for overactive bladder. J Urol, 2017, 198(6): 1379-1385.
16. Rosin B, Slovik M, Mitelman R, et al. Closed-loop deep brain stimulation is superior in ameliorating parkinsonism. Neuron, 2011, 72(2): 197-198.
17. Liu X, Zhang M, Richardson A G, et al. Design of a closed-loop, bidirectional brain machine interface system with energy efficient neural feature extraction and PID control. IEEE Trans Biomed Circuits Syst, 2017, 11(4): 729-742.
18. Soltan A, Xia L, Jackson A, et al. Fractional order PID system for suppressing epileptic activities// 2018 IEEE International Conference on Applied System Innovation (ICASI). Chiba: IEEE, 2018: 338-341.
19. Coronel-Escamilla A, Gomez-Aguilar J F, Stamova I, et al. Fractional order controllers increase the robustness of closed-loop deep brain stimulation systems. Chaos Soliton Fract, 2020, 140: 110149.
20. Ran W, Zhou Z, Qu G. Fuzzy neural network PID control based on RBF neural network for variable configuration spacecraft// IEEE 3rd Advanced Information Technology, Electronic and Automation Control Conference (IAEAC). Chongqing: IEEE, 2018: 1203-1207.
21. Li Z, Xia Y, Su C Y, et al. Missile guidance law based on robust model predictive control using neural-network optimization. IEEE Trans Neural Networks Learn Syst, 2017, 26(8): 1803-1809.
22. Wang T, Gao H, Qiu J. A combined adaptive neural network and nonlinear model predictive control for multirate networked industrial process control. IEEE Trans Neural Networks Learn Syst, 2017, 27(2): 416-425.
23. Fei J, Wang Z. Fractional-order terminal sliding-mode control using self-evolving recurrent Chebyshev fuzzy neural network for MEMS gyroscope. IEEE Trans Fuzzy Syst, 2020, 30(7): 2747-2758.
24. Shu H, Pi Y. PID neural networks for time-delay systems. Comput Chem Eng, 2000, 24(2): 859-862.
25. 黄建国, 武延祥, 杨世兴. 现代谱估计原理与应用. 北京: 科学出版社, 1994.
26. 徐湘元. 自适应控制理论和与应用. 北京: 电子工业出版社, 2007.

Journal of Biomedical Engineering

Modulation of motor responses and neural activities with transcranial ultrasound stimulation based on closed-loop control

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