Design and application of neural electrical stimulation system with time-varying parameters_Journal of Biomedical Engineering

Authors：

YANG Gangsheng ,  FENG Zhouyan , ZHENG Lupiao , WANG Zhaoxiang

Key Laboratory of Biomedical Engineering of Education Ministry, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou 310027, P.R.China;

Corresponding author：

FENG Zhouyan, Email: fengzhouyan@zju.edu.cn

Keywords：

neural electrical stimulation; time-varying stimulation; random sequence of pulses; rat hippocampal region

DOI：

10.7507/1001-5515.202102028

Video：

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Abstract Full text Figures/Tables Video References Cited by

Currently, commercial devices for electrical neural stimulations can only provide fixed stimulation paradigms with preset constant parameters, while the development of new stimulation paradigms with time-varying parameters has emerged as one of the important research directions for expanding clinical applications. To facilitate the performance of electrical stimulation paradigms with time-varying parameters in animal experiments, the present study developed a well-integrated stimulation system to output various pulse sequences by designing a LabVIEW software to control a general data acquisition card and an electrical stimulus isolator. The system was able to generate pulse sequences with inter-pulse-intervals (IPI) randomly varying in real time with specific distributions such as uniform distribution, normal distribution, gamma distribution and Poisson distribution. It was also able to generate pulse sequences with arbitrary time-varying IPIs. In addition, the pulse parameters, including pulse amplitude, pulse width, interphase delay of biphasic pulse and duration of pulse sequence, were adjustable. The results of performance tests of the stimulation system showed that the errors of the parameters of pulse sequences output by the system were all less than 1%. By utilizing the stimulation system, pulse sequences with IPI randomly varying in the range of 5~10 ms were generated and applied in rat hippocampal regions for animal experiments. The experimental results showed that, even with a same mean pulse frequency of ~130 Hz, for neuronal populations, the excitatory effect of stimulations with randomly varying IPIs was significantly greater than the effect of stimulations with fixed IPIs. In conclusion, the stimulation system designed here may provide a useful tool for the researches and the development of new paradigms of neural electrical stimulations.

Citation： YANG Gangsheng, FENG Zhouyan, ZHENG Lupiao, WANG Zhaoxiang. Design and application of neural electrical stimulation system with time-varying parameters. Journal of Biomedical Engineering, 2021, 38(6): 1144-1153. doi: 10.7507/1001-5515.202102028 Copy

1.	Lozano A M, Lipsman N, Bergman H, et al. Deep brain stimulation: current challenges and future directions. Nat Rev Neurol, 2019, 15(3): 148-160.
2.	Mckinnon C, Gros P, Lee D J, et al. Deep brain stimulation: potential for neuroprotection. Ann Clin Transl Neurol, 2019, 6(1): 174-185.
3.	Grill W M. Temporal pattern of electrical stimulation is a new dimension of therapeutic innovation. Curr Opin Biomed Eng, 2018, 8: 1-6.
4.	Cota V R, Drabowski B M, de Oliveira J C, et al. The epileptic amygdala: toward the development of a neural prosthesis by temporally coded electrical stimulation. J Neurosci Res, 2016, 94(6): 463-485.
5.	Karamintziou S D, Deligiannis N G, Piallat B, et al. Dominant efficiency of nonregular patterns of subthalamic nucleus deep brain stimulation for Parkinson's disease and obsessive-compulsive disorder in a data-driven computational model. J Neural Eng, 2016, 13(1): 016013.
6.	Wyckhuys T, Boon P, Raedt R, et al. Suppression of hippocampal epileptic seizures in the kainate rat by Poisson distributed stimulation. Epilepsia, 2010, 51(11): 2297-2304.
7.	Dorval A D, Kuncel A M, Birdno M J, et al. Deep brain stimulation alleviates parkinsonian bradykinesia by regularizing pallidal activity. J Neurophysiol, 2010, 104(2): 911-921.
8.	Mcconnell G C, So R Q, Grill W M. Failure to suppress low-frequency neuronal oscillatory activity underlies the reduced effectiveness of random patterns of deep brain stimulation. J Neurophysiol, 2016, 115(6): 2791-2802.
9.	Hodgkin A L, Huxley A F. A quantitative description of membrane current and its application to conduction and excitation in nerve. Bull Math Biol, 1990, 52(1-2): 25-71.
10.	Birdno M J, Kuncel A M, Dorval A D, et al. Tremor varies as a function of the temporal regularity of deep brain stimulation. Neuroreport, 2008, 19(5): 599-602.
11.	Feng Z, Ma W, Wang Z, et al. Small changes in inter-pulse-intervals can cause synchronized neuronal firing during high-frequency stimulations in rat hippocampus. Front Neurosci, 2019, 13: 36.
12.	Breen P P, Serrador J M, Gargiulo G D. Arbitrary waveform constant current stimulator for long-term wearable applications. Med Eng Phys, 2019, 68: 108-115.
13.	Mottaghi S, Afshari N, Buchholz O, et al. Modular current stimulation system for pre-clinical studies. Front Neurosci, 2020, 14: 408.
14.	Merrill D R, Bikson M, Jefferys J G. Electrical stimulation of excitable tissue: design of efficacious and safe protocols. J Neurosci Methods, 2005, 141(2): 171-198.
15.	Koeglsperger T, Palleis C, Hell F, et al. Deep brain stimulation programming for movement disorders: current concepts and evidence-based strategies. Front Neurol, 2019, 10: 410.
16.	Feng Zhouyan, Zheng Xiaojing, Yu Ying, et al. Functional disconnection of axonal fibers generated by high frequency stimulation in the hippocampal CA1 region in-vivo. Brain Res, 2013, 1509: 32-42.
17.	Theoret Y, Brown A, Fleming S P, et al. Hippocampal field potential: a microcomputer aided comparison of amplitude and integral. Brain Res Bull, 1984, 12(5): 589-595.
18.	Yu Ying, Feng Zhouyan, Cao Jiayue, et al. Modulation of local field potentials by high-frequency stimulation of afferent axons in the hippocampal CA1 region. J Integr Neurosci, 2016, 15(1): 1-17.
19.	Cai Z, Feng Z, Hu H, et al. Design of a novel stimulation system with time-varying paradigms for investigating new modes of high frequency stimulation in brain. Biomed Eng Online, 2018, 17(1): 90.
20.	Jensen A L, Durand D M. High frequency stimulation can block axonal conduction. Exp Neurol, 2009, 220(1): 57-70.
21.	Feng Zhouyan, Wang Zhaoxiang, Guo Zheshan, et al. High frequency stimulation of afferent fibers generates asynchronous firing in the downstream neurons in hippocampus through partial block of axonal conduction. Brain Res, 2017, 1661: 67-78.
22.	Wang Z, Feng Z, Wei X. Axonal stimulations with a higher frequency generate more randomness in neuronal firing rather than increase firing rates in rat hippocampus. Front Neurosci, 2018, 12: 783.
23.	Zheng L, Feng Z, Hu H, et al. The appearance order of varying intervals introduces extra modulation effects on neuronal firing through non-linear dynamics of Sodium channels during High-Frequency stimulations. Front Neurosci, 2020, 14: 397.
24.	Brocker D T, Swan B D, Turner D A, et al. Improved efficacy of temporally non-regular deep brain stimulation in Parkinson's disease. Exp Neurol, 2013, 239: 60-67.
25.	Diéguez-Pérez I, Leirós-Rodríguez R. Effectiveness of different application parameters of neuromuscular electrical stimulation for the treatment of dysphagia after a stroke: a systematic review. J Clin Med, 2020, 9(8): 2618.
26.	Urits I, Markel M, Vij N, et al. Use of spinal cord stimulation for the treatment of post total knee arthroplasty pain. Best Pract Res Clin Anaesthesiol, 2020, 34(3): 633-642.

1. Lozano A M, Lipsman N, Bergman H, et al. Deep brain stimulation: current challenges and future directions. Nat Rev Neurol, 2019, 15(3): 148-160.
2. Mckinnon C, Gros P, Lee D J, et al. Deep brain stimulation: potential for neuroprotection. Ann Clin Transl Neurol, 2019, 6(1): 174-185.
3. Grill W M. Temporal pattern of electrical stimulation is a new dimension of therapeutic innovation. Curr Opin Biomed Eng, 2018, 8: 1-6.
4. Cota V R, Drabowski B M, de Oliveira J C, et al. The epileptic amygdala: toward the development of a neural prosthesis by temporally coded electrical stimulation. J Neurosci Res, 2016, 94(6): 463-485.
5. Karamintziou S D, Deligiannis N G, Piallat B, et al. Dominant efficiency of nonregular patterns of subthalamic nucleus deep brain stimulation for Parkinson's disease and obsessive-compulsive disorder in a data-driven computational model. J Neural Eng, 2016, 13(1): 016013.
6. Wyckhuys T, Boon P, Raedt R, et al. Suppression of hippocampal epileptic seizures in the kainate rat by Poisson distributed stimulation. Epilepsia, 2010, 51(11): 2297-2304.
7. Dorval A D, Kuncel A M, Birdno M J, et al. Deep brain stimulation alleviates parkinsonian bradykinesia by regularizing pallidal activity. J Neurophysiol, 2010, 104(2): 911-921.
8. Mcconnell G C, So R Q, Grill W M. Failure to suppress low-frequency neuronal oscillatory activity underlies the reduced effectiveness of random patterns of deep brain stimulation. J Neurophysiol, 2016, 115(6): 2791-2802.
9. Hodgkin A L, Huxley A F. A quantitative description of membrane current and its application to conduction and excitation in nerve. Bull Math Biol, 1990, 52(1-2): 25-71.
10. Birdno M J, Kuncel A M, Dorval A D, et al. Tremor varies as a function of the temporal regularity of deep brain stimulation. Neuroreport, 2008, 19(5): 599-602.
11. Feng Z, Ma W, Wang Z, et al. Small changes in inter-pulse-intervals can cause synchronized neuronal firing during high-frequency stimulations in rat hippocampus. Front Neurosci, 2019, 13: 36.
12. Breen P P, Serrador J M, Gargiulo G D. Arbitrary waveform constant current stimulator for long-term wearable applications. Med Eng Phys, 2019, 68: 108-115.
13. Mottaghi S, Afshari N, Buchholz O, et al. Modular current stimulation system for pre-clinical studies. Front Neurosci, 2020, 14: 408.
14. Merrill D R, Bikson M, Jefferys J G. Electrical stimulation of excitable tissue: design of efficacious and safe protocols. J Neurosci Methods, 2005, 141(2): 171-198.
15. Koeglsperger T, Palleis C, Hell F, et al. Deep brain stimulation programming for movement disorders: current concepts and evidence-based strategies. Front Neurol, 2019, 10: 410.
16. Feng Zhouyan, Zheng Xiaojing, Yu Ying, et al. Functional disconnection of axonal fibers generated by high frequency stimulation in the hippocampal CA1 region in-vivo. Brain Res, 2013, 1509: 32-42.
17. Theoret Y, Brown A, Fleming S P, et al. Hippocampal field potential: a microcomputer aided comparison of amplitude and integral. Brain Res Bull, 1984, 12(5): 589-595.
18. Yu Ying, Feng Zhouyan, Cao Jiayue, et al. Modulation of local field potentials by high-frequency stimulation of afferent axons in the hippocampal CA1 region. J Integr Neurosci, 2016, 15(1): 1-17.
19. Cai Z, Feng Z, Hu H, et al. Design of a novel stimulation system with time-varying paradigms for investigating new modes of high frequency stimulation in brain. Biomed Eng Online, 2018, 17(1): 90.
20. Jensen A L, Durand D M. High frequency stimulation can block axonal conduction. Exp Neurol, 2009, 220(1): 57-70.
21. Feng Zhouyan, Wang Zhaoxiang, Guo Zheshan, et al. High frequency stimulation of afferent fibers generates asynchronous firing in the downstream neurons in hippocampus through partial block of axonal conduction. Brain Res, 2017, 1661: 67-78.
22. Wang Z, Feng Z, Wei X. Axonal stimulations with a higher frequency generate more randomness in neuronal firing rather than increase firing rates in rat hippocampus. Front Neurosci, 2018, 12: 783.
23. Zheng L, Feng Z, Hu H, et al. The appearance order of varying intervals introduces extra modulation effects on neuronal firing through non-linear dynamics of Sodium channels during High-Frequency stimulations. Front Neurosci, 2020, 14: 397.
24. Brocker D T, Swan B D, Turner D A, et al. Improved efficacy of temporally non-regular deep brain stimulation in Parkinson's disease. Exp Neurol, 2013, 239: 60-67.
25. Diéguez-Pérez I, Leirós-Rodríguez R. Effectiveness of different application parameters of neuromuscular electrical stimulation for the treatment of dysphagia after a stroke: a systematic review. J Clin Med, 2020, 9(8): 2618.
26. Urits I, Markel M, Vij N, et al. Use of spinal cord stimulation for the treatment of post total knee arthroplasty pain. Best Pract Res Clin Anaesthesiol, 2020, 34(3): 633-642.

Journal of Biomedical Engineering

Design and application of neural electrical stimulation system with time-varying parameters

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