Tianruo Guo
Yao-chuan Chang2,3
Luming Li
Socrates Dokos
Liming Li- 1Graduate School of Biomedical Engineering, The University of New South Wales Sydney, Sydney, NSW, Australia
- 2Institute of Bioelectronic Medicine, Feinstein Institutes for Medical Research, Manhasset, NY, United States
- 3Medtronic PLC, Minneapolis, MN, United States
- 4National Engineering Research Center of Neuromodulation, School of Aerospace Engineering, Tsinghua University, Beijing, China
- 5Precision Medicine and Healthcare Research Center, Tsinghua-Berkeley Shenzhen Institute, Tsinghua University, Shenzhen, China
- 6IDG/McGovern Institute for Brain Research, Tsinghua University, Beijing, China
- 7School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
Editorial on the Research Topic
Advances in bioelectronics and stimulation strategies for next generation neuroprosthetics
Recent technological advances have expanded our understanding of how artificial stimulation interacts with the living nervous system (Riva and Micera, 2021; Saha et al., 2021; Walter, 2021; Ahmed et al., 2022; Chen et al., 2022; Yao et al., 2022). This Research Topic, contributed to by electrophysiologists, biomedical engineers, computational neuroscientists, and neuropsychologists, provides a state-of-the-art overview of advances in neural stimulation technologies, ranging from recent progress in functional electrical stimulation (FES) to new understanding of how weak electric field (EF) stimulation affects cellular properties. The submissions to this Research Topic can be grouped into four key areas: (A) non-invasive stimulation, (B) optimal implantation location, (C) advances in stimulus parameter identification, and (D) the influence of neural ultrastructures under EF. It is helpful to provide four quotes from Sun Tzu's The Art of War1 to represent the theme of each section:
“The greatest victory requires no bleeding” — Chapter. Strategic attack 《孙子兵法·谋攻篇》
Significant research efforts have focused largely on developing non-invasive or minimally invasive stimulation techniques (Boes et al., 2018; Sun et al., 2018; Guo et al., 2020; Su et al., 2021, 2022; Lu et al., 2022; Ren et al., 2022). For example, transcranial direct current stimulation (tDCS) allows reversible region-specific modulation (Filmer et al., 2014). Liu S. et al. offer valuable insights into how prefrontal tDCS affects the attention bias by detecting electroencephalographic characteristics in response to rest and emotional oddball tasks. In this clinical study, tDCS caused increased brain neural activities related to emotion regulation and distinguished electrical signatures following positive targets and negative distracters, indicating great potential for tDCS in the treatment of depression. Another non-invasive neuromodulatory technique is that of trans-spinal direct current stimulation (tsDCS). Song and Martin found that cathodal tsDCS can selectively target voltage-dependent calcium channels to modulate motoneuron activity, informing therapeutic treatment strategies to achieve rehabilitation goals after injury; in particular, to increase muscle force.
In addition, motor imaginary (MI)-based brain-computer interface (BCI) must overcome multiple issues to be commercially usable, especially related to signal quality and subject-variation (Singh et al., 2021). Sensory threshold somatosensory electrical stimulation (st-SES) has been recently used to guide participants in motor imaginary tasks (Corbet et al., 2018; Vidaurre et al., 2019; Zhang et al., 2022). Chen et al. suggest that st-SES can only improve brain-switch BCI performance in those subjects with higher classification accuracy (high performers) in discriminating the MI condition from rest. Moreover, they showed that st-SES influences functional connectivity of the fronto-parietal network, but through different frequency bands for different subjects. These findings can potentially help to optimize guidance strategies to adapt to different types of MI-BCI users.
“Choose the favorable terrain before the war starts” — Chapter. Terrain 《孙子兵法·地形篇》
An optimal implantation region improves not only stimulation performance but also the long-term stability of implantable microelectrodes, as well as reducing side effects (Wang et al., 2020; Song et al., 2022; Zhao et al., 2022). Urdaneta et al. describe a somatosensory cortex layer-dependent long-term stability in intracortical microstimulation. Their results suggest a more consistent stimulation efficiency and less foreign body response when the electrodes were implanted in L4 and L5 of the somatosensory cortex, indicating the critical role of interface depth in the design of chronic implants. Another example of optimizing electrode placement is for electroconvulsive therapy (ECT) for severe treatment-resistant depression. Steele et al. proposed a fronto-medial ECT electrode placement that would maximize the EF in specific sagittal brain regions, whilst minimizing EF in sub-regions of the bilateral hippocampi. Such outcomes suggest electrode location can significantly reduce cognitive and non-cognitive side-effects.
“Fight smarter not harder” — Chapter. Military dispositions 《孙子兵法·军形篇》
Programming nerve stimulation setting is challenging and time consuming due to the huge number of possible stimulus parameter combinations (O'Doherty et al., 2011; Li et al., 2013; Yan et al., 2016; Jia et al., 2018; Guo et al., 2019; Muralidharan et al., 2020; Song et al., 2020; Zhang et al., 2020; Chang et al., 2022). In terms of FES, several “smart” strategies have been used to improve its effectiveness and acceptability. Xu et al. have introduced a control strategy for FES parameter selection, based on a direct transfer function using surface electromyography (sEMG) features and joint angles as inputs. A similar idea has been used historically in other neuromodulation fields since various stimulation parameters have been shown to evoke distinct neurological and physiological responses. Conversely, elicited physiological effects, both for targeted and untargeted neurons, can guide stimulus parameter tuning for many neural systems, including the brain (Qian et al., 2016; Chen et al., 2020, 2022), spinal cord (Verrills et al., 2016), vagus nerve (Chang et al., 2020), and the retina (Guo et al., 2018). In another example, Dong et al. have proposed a walking assistance system with adaptive algorithm to support FES therapy. As the stimulation sequence is tailored to the individual need based on real-time gait phase, healthy subjects are able to achieve better treadmill performance for various speed conditions. A similar adaptive idea has also been successfully adopted in clinical neuromodulation therapies to improve effectiveness, such as deep brain stimulation (Bocci et al., 2021) and spinal cord stimulation (Schultz et al., 2012), utilizing chosen physiological indices.
“Know the enemy, know yourself” — Chapter. Strategic attack 《孙子兵法·谋攻篇》
Electrical stimulation performance cannot be significantly improved by only optimizing the device in isolation without considering the biophysical complexity of the target nerve system (Abbasi and Rizzo, 2021; Ahmed et al., 2022; Italiano et al., 2022). Sophisticated computational models have been widely used in predicting the role of tissue or neural or ultra-structures under EF (Guo et al., 2014, 2016; Yang et al., 2018; Bai et al., 2019a; Dokos and Guo, 2020). Liu(a) et al. have proposed a new biophysical model of myelin ultra-structures by simulating cytoplasmic channels in the myelin sheath as a low-impedance route, while previous models approximate the myelin sheath as an insulation layer (Schwarz and Reid, 1995; Bean, 2007; Ge et al., 2020). Using this model, Liu(b) et al. further investigated how cytoplasmic channels affect EF across the myelin sheaths, concluding that the externally applied EF can control myelin growth. These new findings indicate the possibility of using electrical modulation to treat degenerative neural diseases. Neurodegenerative progression can affect the neuroprosthetic performance (Rattay et al., 2001; Hilker et al., 2005; Loizos et al., 2018; Ly et al., 2022). Croner et al. have investigated the differential performance of cochlear stimulation in a cochlea with intact and degenerating spiral ganglion neurons (SGNs) using a biophysically detailed computational model of the human cochlea (Bai et al., 2019b). Their study identified the increased activation of neurons in unintended areas, and an insensitive neural response to various apical electrode settings when degenerating SGNs were stimulated. This study also suggested that stimulation thresholds are unlikely to be a good indicator of neural health, since degenerating SGNs showed both an increase and decrease in current threshold depending on the initial stimulation site.
Author's note
The Chinese characters shown in the Editorial refer to the origin of each subtitle in the ancient literature The Art of War by Sun Tzu.
Author contributions
All authors listed have made a substantial, direct, and intellectual contribution to the work and approved it for publication.
Funding
This work was supported by the National Natural Science Foundation of China (61971280 and 61671300), Tsinghua University Initiative Scientific Research Program (20191080597), University of Toronto-Tsinghua Joint Research Fund (20193080065), Tsinghua Precision Medicine Foundation (LC201906), and the National Key Research and Development Program of China (2021YFC2400200).
Acknowledgments
The editorial team would like to thank Dr. Chen Zhang from Tsinghua University, China, for her excellent work in communication and coordination during compilation of this Research Topic.
Conflict of interest
Y-cC was employed by Medtronic PLC.
The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Publisher's note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
Footnotes
1. ^The Art of War is an ancient Chinese military treatise written in 5th century BC. It remains the most influential strategy text used in military thinking, business tactics, legal strategy, politics, sports, lifestyles and beyond.
References
Abbasi, B., and Rizzo, J. F. (2021). Advances in neuroscience, not devices, will determine the effectiveness of visual prostheses. Semin Ophthalmol. 36, 168–175. doi: 10.1080/08820538.2021.1887902
Ahmed, U., Chang, Y.-C., Zafeiropoulos, S., Nassrallah, Z., Miller, L., et al. (2022). Strategies for precision vagus neuromodulation. Bioelectron. Med. 8, 9. doi: 10.1186/s42234-022-00091-1
Bai, S., Encke, J., Obando-Leitón, M., Wei,ß, R., Schäfer, F., Eberharter, J., et al. (2019b). Electrical stimulation in the human cochlea: A computational study based on high-resolution micro-CT scans. Front. Neurosci. 13, 1312. doi: 10.3389/fnins.2019.01312
Bai, S., Martin, D., Guo, T., Dokos, S., and Loo, C. (2019a). Computational comparison of conventional and novel electroconvulsive therapy electrode placements for the treatment of depression. Eur. Psychiat. 60, 71–78. doi: 10.1016/j.eurpsy.2019.05.006
Bean, B. P. (2007). The action potential in mammalian central neurons. Nat. Rev. Neurosci. 8, 451–465. doi: 10.1038/nrn2148
Bocci, T., Prenassi, M., Arlotti, M., Cogiamanian, F. M., Borellini, L., Moro, E., et al. (2021). Eight-hours conventional versus adaptive deebrain stimulation of the subthalamic nucleus in Parkinson's disease. NPJ Parkinsons Dis 7, 88. doi: 10.1038/s41531-021-00229-z
Boes, A. D., Kelly, M. S., Trapp, N. T., Stern, A. P., Press, D. Z., and Pascual-Leone, A. (2018). Noninvasive brain stimulation: challenges and opportunities for a new clinical specialty. J Neuropsychiat. Clin. Neurosci. 30, 173–179. doi: 10.1176/appi.neuropsych.17110262
Chang, Y.-C., Ahmed, U., Jayaprakash, N., Mughrabi, I., Lin, Q., Wu, et al. (2022). kHz-frequency electrical stimulation selectively activates small, unmyelinated vagus afferents. Brain Stimul. 15, 1389–1404. doi: 10.1016/j.brs.2022.09.015
Chang, Y.-C., Cracchiolo, M., Ahmed, U., Mughrabi, I., Gabalski, A., et al. (2020). Quantitative estimation of nerve fiber engagement by vagus nerve stimulation using physiological markers. Brain Stimul. 13, 1617–1630. doi: 10.1016/j.brs.2020.09.002
Chen, Y., Gong, C., Tian, Y., Orlov, N., Zhang, J., Guo, Y., et al. (2020). Neuromodulation effects of deebrain stimulation on beta rhythm: A longitudinal local field potential study. Brain Stimul. 13, 1784–1792. doi: 10.1016/j.brs.2020.09.027
Chen, Y., Zhang, G., Guan, L., Gong, C., Ma, B., Hao, H., et al. (2022). Progress in the development of a fully implantable brain-computer interface: the potential of sensing-enabled neurostimulators. Nat. Sci. Rev. 9, 99. doi: 10.1093/nsr/nwac099
Corbet, T., Iturrate, I., Pereira, M., Perdikis, S., and Millán, J. D. R. (2018). Sensory threshold neuromuscular electrical stimulation fosters motor imagery performance. Neuroimage 176, 268–276. doi: 10.1016/j.neuroimage.2018.04.005
Dokos, S., and Guo, T. (2020). Computational models of neural retina. In Encyclopedia of Computational Neuroscience, eds. D. Jaeger and R. Jung (Berlin: Springer). doi: 10.1007/978-1-4614-7320-6_652-2
Filmer, H. L., Dux, P. E., and Mattingley, J. B. (2014). Applications of transcranial direct current stimulation for understanding brain function. Trends Neurosci. 37, 742–753. doi: 10.1016/j.tins.2014.08.003
Ge, Y., Ye, S., Zhu, K., Guo, T., Su, D., Zhang, D., et al. (2020). Mediating different-diameter A beta nerve fibers using a biomimetic 3D TENS computational model. J. Neurosci. Methods 346, 108891. doi: 10.1016/j.jneumeth.2020.108891
Guo, T., Shivdasani, M. N., Tsai, D., Ayton, L. N., and Rathbun, D. L. (2020). Visual prostheses: neuroengineering handbook. in Handbook of Neuroengineering, eds. N. V. Thakor (Singapore: Springer Singapore). doi: 10.1007/978-981-15-2848-4_31-2
Guo, T., Tsai, D., Bai, S., Morley, J. W., Suaning, G. J., Lovell, N. H., et al. (2014). Understanding the retina: a review of computational models of the retina from the single cell to the network level. Crit. Rev. Biomed. Eng. 42, 419–436. doi: 10.1615/CritRevBiomedEng.2014011732
Guo, T., Tsai, D., Morley, J. W., Suaning, G. J., Kameneva, T., Lovell, N. H., et al. (2016). Electrical activity of ON and OFF retinal ganglion cells: a modelling study. J. Neural. Eng. 13, 025005. doi: 10.1088/1741-2560/13/2/025005
Guo, T., Tsai, D., Yang, C. Y., Abed, A. A., Twyford, P., Fried, S. I., et al. (2019). Mediating retinal ganglion cell spike rates using high-frequency electrical stimulation. Front. Neurosci 13, 413. doi: 10.3389/fnins.2019.00413
Guo, T., Yang, C. Y., Tsai, D., Muralidharan, M., Suaning, G. J., Morley, J. W., et al. (2018). Closed-looefficient searching of optimal electrical stimulation parameters for preferential excitation of retinal ganglion cells. Front. Neurosci. 12, 168. doi: 10.3389/fnins.2018.00168
Hilker, R., Portman, A. T., Voges, J., Staal, M. J., Burghaus, L., van Laar, T., et al. (2005). Disease progression continues in patients with advanced Parkinson's disease and effective subthalamic nucleus stimulation. J. Neurol. Neurosurg. Psychiat. 76, 1217–1221. doi: 10.1136/jnnp.2004.057893
Italiano, M. L., Guo, T., and Lovell, N. H. (2022). Improving the spatial resolution of artificial vision using midget retinal ganglion cell populations modeled at the human fovea. J. Neur. Eng. 19, 035002. doi: 10.1088/1741-2552/ac72c2
Jia, F., Shukla, A. W., Hu, W., Almeida, L., Holanda, V., Zhang, J., et al. (2018). DeeBrain stimulation at variable frequency to improve motor outcomes in parkinson's disease. Movem. Diso. Clin. Pract. 5, 538–541. doi: 10.1002/mdc3.12658
Li, M., Yan, Y., Wang, Q., Zhao, H., Chai, X., Sui, X., et al. (2013). A simulation of current focusing and steering with penetrating optic nerve electrodes. J. Neural. Eng. 10, 066007. doi: 10.1088/1741-2560/10/6/066007
Loizos, K., Marc, R., Humayun, M., Anderson, J. R., Jones, B. W., Lazzi, G., et al. (2018). Increasing electrical stimulation efficacy in degenerated retina: stimulus waveform design in a multiscale computational model. IEEE Trans. Neur. Syst. Rehabil. Eng. 26, 1111–1120. doi: 10.1109/TNSRE.2018.2832055
Lu, Z., Zhou, M., Guo, T., Liang, J., Wu, W., Gao, Q., et al. (2022). An in-silico analysis of retinal electric field distribution induced by different electrode design of trans-corneal electrical stimulation. J. Neural Eng. 19, 055004. doi: 10.1088/1741-2552/ac8e32
Ly, K., Guo, T., Tsai, D., Muralidharan, M., Shivdasani, M. N., Lovell, N. H., et al. (2022). Simulating the impact of photoreceptor loss and inner retinal network changes on electrical activity of the retina. J. Neur. Eng. 19, 065002. doi: 10.1088/1741-2552/aca221
Muralidharan, M., Guo, T., Shivdasani, M. N., Tsai, D., Fried, S., Li, L., et al. (2020). Neural activity of functionally different retinal ganglion cells can be robustly modulated by high-rate electrical pulse trains. J. Neur. Eng. 17, 045013. doi: 10.1088/1741-2552/ab9a97
O'Doherty, J. E., Lebedev, M. A., Ifft, P. J., Zhuang, K. Z., Shokur, S., Bleuler, H., et al. (2011). Active tactile exploration using a brain-machine-brain interface. Nature 479, 228–231. doi: 10.1038/nature10489
Qian, X., Chen, Y., Ma, B., and Hao, H. (2016). Chronically monitoring the deebrain rhythms: from stimulation to recording. Sci. Bull. 61, 1522–1524. doi: 10.1007/s11434-016-1159-y
Rattay, F., Leao, R. N., and Felix, H. A. (2001). model of the electrically excited human cochlear neuron. I. Contribution of neural substructures to the generation and propagation of spikes. Hear. Res. 153, 43–63. doi: 10.1016/S0378-5955(00)00256-2
Ren, J., Chi, Q., Hubbard, C. S., Cui, W., Wang, D., Li, L., et al. (2022). Personalized functional imaging identifies brain stimulation target for a patient with trauma-induced functional disruption. Brain Stimul. 15, 53–56. doi: 10.1016/j.brs.2021.11.005
Riva, E. R., and Micera, S. (2021). Progress and challenges of implantable neural interfaces based on nature-derived materials. Bioelectron. Med. 7, 6. doi: 10.1186/s42234-021-00067-7
Saha, S., Mamun, K. A., Ahmed, K., Mostafa, R., Naik, G. R., Darvishi, S., et al. (2021). Progress in brain computer interface: challenges and opportunities. Front. Syst. Neurosci. 15. doi: 10.3389/fnsys.2021.578875
Schultz, D. M., Webster, L., Kosek, P., Dar, U., Tan, Y., Sun, M., et al. (2012). Sensor-driven position-adaptive spinal cord stimulation for chronic pain. Pain Phys. 15, 1–12. doi: 10.36076/ppj.2012/15/1
Schwarz, J. R., and Reid, G. (1995). Action potentials and membrane currents in the human node of Ranvier. Pflügers Archiv. 430, 283–292. doi: 10.1007/BF00374660
Singh, A., Hussain, A. A., and Lal, S. (2021). A comprehensive review on critical issues and possible solutions of motor imagery based electroencephalography brain-computer interface. Sensors 21, 2173. doi: 10.3390/s21062173
Song, X., Guo, T., Shivdasani, M. N., Dokos, S., Lovell, N. H., Li, X., et al. (2020). Creation of virtual channels in the retina using synchronous and asynchronous stimulation—a modelling study. J. Neur. Eng. 17, 065001. doi: 10.1088/1741-2552/abc3a9
Song, X., Qiu, S., Shivdasani, M. N., Zhou, F., Liu, Z., Ma, S., et al. (2022). An in-silico analysis of electrically evoked responses of midget and parasol retinal ganglion cells in different retinal regions. J. Neur. Eng. 19, 026018. doi: 10.1088/1741-2552/ac5b18
Su, X., Guo, J., Zhou, M., Chen, J., Li, L., Chen, Y., et al. (2021). Computational modeling of spatially selective retinal stimulation with temporally interfering electric fields. IEEE Trans. Neural. Syst. Rehabil. Eng. 29, 418–428. doi: 10.1109/TNSRE.2021.3055203
Su, X., Zhou, M., Di, L., Chen, J., Zhai, Z., Liang, J., et al. (2022). The visual cortical responses to sinusoidal transcorneal electrical stimulation. Brain Res. 1785, 147875. doi: 10.1016/j.brainres.2022.147875
Sun, P., Li, H., Lu, Z., Su, X., Ma, Z., Chen, J., et al. (2018). Comparison of cortical responses to the activation of retina by visual stimulation and transcorneal electrical stimulation. Brain Stimul. 11, 667–675. doi: 10.1016/j.brs.2018.02.009
Verrills, P., Sinclair, C., and Barnard, A. (2016). A review of spinal cord stimulation systems for chronic pain. J. Pain Res. 9, 481–492. doi: 10.2147/JPR.S108884
Vidaurre, C., Murguialday, A. R., Haufe, S., Gómez, M., Müller, K.-R., and Nikulin, V. V. (2019). Enhancing sensorimotor BCI performance with assistive afferent activity: An online evaluation. Neuroimage 199, 375–386. doi: 10.1016/j.neuroimage.2019.05.074
Walter, P. (2021). Visual Prostheses. in Neuroengineering Handbook, eds. N. V. Thakor (Singapore: Springer).
Wang, Z., Cai, X., Qiu, R., Yao, C., Tian, Y., Gong, C., et al. (2020). Case report: Lateral habenula deebrain stimulation for treatment-resistant depression. Front. Psychiat. 11, 616501. doi: 10.3389/fpsyt.2020.616501
Yan, Y., Lu, Y., Li, M., Ma, Z., Cao, P., Chen, Y., et al. (2016). Electrically evoked responses in the rabbit cortex induced by current steering with penetrating optic nerve electrodes. Invest. Ophthalmol. Vis. Sci. 57, 6327–6338. doi: 10.1167/iovs.15-17543
Yang, C. Y., Tsai, D., Guo, T., Dokos, S., Suaning, G. J., Morley, J. W., et al. (2018). Differential electrical responses in retinal ganglion cell subtypes: effects of synaptic blockade and stimulating electrode location. J. Neural. Eng. 15, 046020. doi: 10.1088/1741-2552/aac315
Yao, X., Liu, H., Si, J., Ding, X., Zhao, Y., Zheng, Y., et al. (2022). Research status and future development of cochlear reimplantation. Front. Neurosci. 16, 824389. doi: 10.3389/fnins.2022.824389
Zhang, J., Hu, W., Chen, H., Meng, F., Li, L., Okun, M. S., et al. (2020). Implementation of a novel bluetooth technology for remote deebrain stimulation programming: the pre- and post-COVID-19 Beijing experience. Movement Diso. 35, 909–910. doi: 10.1002/mds.28098
Zhang, L., Chen, L., Wang, Z., Zhang, X., and Liu, X. (2022). “Enhancing Visual-Guided Motor Imagery Performance Via Sensory Threshold Somatosensory Electrical Stimulation Training,” in IEEE Transactions on Biomedical Engineering. doi: 10.1109/TBME.2022.3202189
Keywords: non-invasive stimulation, optimal implantation location, stimulus parameter identification, neural ultrastructure, neuromodulation
Citation: Guo T, Chang Y-c, Li L, Dokos S and Li L (2023) Editorial: Advances in bioelectronics and stimulation strategies for next generation neuroprosthetics. Front. Neurosci. 16:1116900. doi: 10.3389/fnins.2022.1116900
Received: 05 December 2022; Accepted: 13 December 2022;
Published: 10 January 2023.
Edited and reviewed by: Laura Ballerini, International School for Advanced Studies (SISSA), Italy
Copyright © 2023 Guo, Chang, Li, Dokos and Li. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Liming Li, 
lilm@sjtu.edu.cn