J Appl Biomed 18:33-40, 2020 | DOI: 10.32725/jab.2020.009

Application of a neural interface for restoration of leg movements: Intra-spinal stimulation using the brain electrical activity in spinally injured rabbits

Mohamad Amin Younessi Heravi1, Keivan Maghooli1,*, Fereidoun Nowshiravan Rahatabad1, Ramin Rezaee2,3
1 Islamic Azad University, Science and Research Branch, Department of Biomedical Engineering, Tehran, Iran
2 Mashhad University of Medical Sciences, Faculty of Medicine, Clinical Research Unit, Mashhad, Iran
3 Mashhad University of Medical Sciences, Neurogenic Inflammation Research Center, Mashhad, Iran

This study aimed to design a neural interface that extracts movement commands from the brain to generate appropriate intra-spinal stimulation to restore leg movement. This study comprised four steps: (1) Recording electrocorticographic (ECoG) signals and corresponding leg movements in different trials. (2) Partial laminectomy to induce spinal cord injury (SCI) and detect motor modules in the spinal cord. (3) Delivering appropriate intra-spinal stimulation to the motor modules for restoration of the movements to those documented before SCI. (4) Development of a neural interface created by sparse linear regression (SLiR) model to detect movement commands transmitted from the brain to the modules. Correlation coefficient (CC) and normalized root mean square (NRMS) error was calculated to evaluate the neural interface effectiveness. It was found that by stimulating detected spinal cord modules, joint angle evaluated before SCI was not significantly different from that of post-SCI (P > 0.05). Based on results of SLiR model, overall CC and NRMS values were 0.63 ± 0.14 and 0.34 ± 0.16 (mean ± SD), respectively. These results indicated that ECoG data contained information about intra-spinal stimulations and the developed neural interface could produce intra-spinal stimulation based on ECoG data, for restoration of leg movements after SCI.

Keywords: Brain; Laminectomy; Linear models; Spinal cord stimulation
Conflicts of interest:

The authors have no conflict of interests to declare.

Received: May 1, 2020; Revised: June 10, 2020; Accepted: June 12, 2020; Prepublished online: June 26, 2020; Published: August 27, 2020  Show citation

ACS AIP APA ASA Harvard Chicago IEEE ISO690 MLA NLM Turabian Vancouver
Heravi MAY, Maghooli K, Nowshiravan Rahatabad F, Rezaee R. Application of a neural interface for restoration of leg movements: Intra-spinal stimulation using the brain electrical activity in spinally injured rabbits. J Appl Biomed. 2020;18(2-3):33-40. doi: 10.32725/jab.2020.009. PubMed PMID: 34907723.
Share...
Download citation
  • BibTeX (.bib)
  • Bookends (.ris)
  • EasyBib (.ris)
  • EndNote (.enw)
  • EndNote 8 (.xml)
  • ISI WoS (.isi)
  • Medlars (.medlars)
  • Mendeley (.ris)
  • MODS (.xml)
  • Papers (.ris)
  • RefWorks (.txt)
  • RefManager (.ris)
  • RIS (.ris)
  • MS Word (.xml)
  • Zotero (.ris)
    • Open full article

    References

    1. Aasdi A, Erfanian Omidvar A (2013). Restoring the Stepping-Like Movement in Spinal Rat by Electrical Micro-Stimulation of Motor Primitive Blocks. J Isfahan Med School 31: 1324-1328.
    2. Alam M, Rodrigues W, Pham BN, Thakor NV (2016). Brain-machine interface facilitated neurorehabilitation via spinal stimulation after spinal cord injury: Recent progress and future perspectives. Brain Res 1646: 25-33. DOI: 10.1016/j.brainres.2016.05.039. Go to original source... Go to PubMed...
    3. Bizzi E, Cheung VCK, d'Avella A, Saltiel P, Tresch M (2008). Combining modules for movement. Brain Res Rev 57: 125-133. DOI: 10.1016/j.brainresrev.2007.08.004. Go to original source... Go to PubMed...
    4. Caldwell DJ, Ojemann JG, Rao RP (2019). Direct Electrical Stimulation in Electrocorticographic Brain-Computer Interfaces: Enabling Technologies for Input to Cortex. Front Neurosc 13: 804. DOI: 10.3389/fnins.2019.00804. Go to original source... Go to PubMed...
    5. Chao ZC, Nagasaka Y, Fujii N (2010). Long-term asynchronous decoding of arm motion using electrocorticographic signals in monkey. Front Neuroeng 3: 3. DOI: 10.3389/fneng.2010.00003. Go to original source... Go to PubMed...
    6. Chen C, Shin D, Watanabe H, Nakanishi Y, Kambara H, Yoshimura N, et al. (2013). Prediction of hand trajectory from electrocorticography signals in primary motor cortex. PloS One 8. DOI: 10.1371/journal.pone.0083534. Go to original source... Go to PubMed...
    7. Cho N, Squair JW, Bloch J, Courtine G (2019). Neurorestorative interventions involving bioelectronic implants after spinal cord injury. Bioelectron Med 5: 10. DOI: 10.1186/s42234-019-0027-x. Go to original source... Go to PubMed...
    8. Duggan C, Wilson C, Diponio L, Trumpower B, Meade MA (2016). Resilience and happiness after spinal cord injury: a qualitative study. Top Spinal Cord Inj Rehabil 22: 99-110. DOI: 10.1310/sci2202-99. Go to original source... Go to PubMed...
    9. Filli L, Schwab ME (2012). The rocky road to translation in spinal cord repair. Ann Neurol 72: 491-501. DOI: 10.1002/ana.23630. Go to original source... Go to PubMed...
    10. Freund P, Weiskopf N, Ward NS, Hutton C, Gall A, Ciccarelli O, et al. (2011). Disability, atrophy and cortical reorganization following spinal cord injury. Brain 134: 1610-1622. DOI: 10.1093/brain/awr093. Go to original source... Go to PubMed...
    11. Gaines BR, Kim J, Zhou H (2018). Algorithms for fitting the constrained lasso. J Comput Graph Stat 27: 861-871. DOI: 10.1080/10618600.2018.1473777. Go to original source... Go to PubMed...
    12. Grahn PJ, Mallory GW, Berry BM, Hachmann JT, Lobel DA, Lujan JL (2014). Restoration of motor function following spinal cord injury via optimal control of intraspinal microstimulation: toward a next generation closed-loop neural prosthesis. Front Neurosci 8: 296. DOI: 10.3389/fnins.2014.00296. Go to original source... Go to PubMed...
    13. Heravi MAY, Maghooli K, Rahatabad FN, Rezaee R (2019). Intra-Spinal Stimulation Leads to Activation of Motor Modules and Restoration of Monitored Movements in Spinally-Injured Rabbits. NeuroQuantology 17: 71-79. DOI: 10.14704/nq.2019.17.6.2456. Go to original source...
    14. Hiebert G, Khodarahmi K, McGraw J, Steeves J, Tetzlaff W (2002). Brain-derived neurotrophic factor applied to the motor cortex promotes sprouting of corticospinal fibers but not regeneration into a peripheral nerve transplant. J Neurosci Res 69: 160-168. DOI: 10.1002/jnr.10275. Go to original source... Go to PubMed...
    15. Jackson A, Zimmermann JB (2012). Neural interfaces for the brain and spinal cord - restoring motor function. Nat Rev Neurol 8: 690. DOI: 10.1038/nrneurol.2012.219. Go to original source... Go to PubMed...
    16. Lobel DA, Lee KH (2014). Brain machine interface and limb reanimation technologies: Restoring function after spinal cord injury through development of a bypass system. Mayo Clin Proc 89: 708-714. DOI: 10.1016/j.mayocp.2014.02.003. Go to original source... Go to PubMed...
    17. Ludwig KA, Miriani RM, Langhals NB, Joseph MD, Anderson DJ, Kipke DR (2009). Using a common average reference to improve cortical neuron recordings from microelectrode arrays. J Neurophysiol 101: 1679-1689. DOI: 10.1152/jn.90989.2008. Go to original source... Go to PubMed...
    18. McFarland DJ, McCane LM, David SV, Wolpaw JR (1997). Spatial filter selection for EEG-based communication. Electroencephalogr Clin Neurophysiol 103: 386-394. DOI: 10.1016/s0013-4694(97)00022-2. Go to original source... Go to PubMed...
    19. Nakanishi Y, Yanagisawa T, Shin D, Chen C, Kambara H, Yoshimura N, et al. (2014). Decoding fingertip trajectory from electrocorticographic signals in humans. Neurosci Res 85: 20-27. DOI: 10.1016/j.neures.2014.05.005. Go to original source... Go to PubMed...
    20. Nakanishi Y, Yanagisawa T, Shin D, Fukuma R, Chen C, Kambara H, et al. (2013). Prediction of three-dimensional arm trajectories based on ECoG signals recorded from human sensorimotor cortex. PloS One 8. DOI: 10.1371/journal.pone.0072085. Go to original source... Go to PubMed...
    21. Nakanishi Y, Yanagisawa T, Shin D, Kambara H, Yoshimura N, Tanaka M, et al. (2017). Mapping ECoG channel contributions to trajectory and muscle activity prediction in human sensorimotor cortex. Sci Rep 7: 45486. DOI: 10.1038/srep45486. Go to original source... Go to PubMed...
    22. Nicolas-Alonso LF, Gomez-Gil J (2012). Brain computer interfaces, a review. Sensors 12: 1211-1279. DOI: 10.3390/s120201211. Go to original source... Go to PubMed...
    23. Pistohl T, Ball T, Schulze-Bonhage A, Aertsen A, Mehring C (2008). Prediction of arm movement trajectories from ECoG-recordings in humans. J Neurosci Methods 167: 105-114. DOI: 10.1016/j.jneumeth.2007.10.001. Go to original source... Go to PubMed...
    24. Rezaee R, Abdollahi M (2017). The importance of translatability in drug discovery. Expert Opin Drug Discov 12: 237-239. DOI: 10.1080/17460441.2017.1281245. Go to original source... Go to PubMed...
    25. Righetti L, Ijspeert AJ (2006). Programmable central pattern generators: an application to biped locomotion control. Proceedings IEEE International Conference on Robotics and Automation, 2006. ICRA 2006. IEEE 1585-1590. DOI: 10.1109/ROBOT.2006.1641933. Go to original source...
    26. Roh J, Cheung VC, Bizzi E (2011). Modules in the brain stem and spinal cord underlying motor behaviors. J Neurophysiol 106: 1363-1378. DOI: 10.1152/jn.00842.2010. Go to original source... Go to PubMed...
    27. Sezer N, Akkuş S, Uğurlu FG (2015). Chronic complications of spinal cord injury. World J Orthop 16: 24-33. DOI: 10.5312/wjo.v6.i1.24. Go to original source... Go to PubMed...
    28. Sharma G, Friedenberg DA, Nicholas A, Bradley G, Bockbrader M, Majstorovic C, et al. (2016). Using an artificial neural bypass to restore cortical control of rhythmic movements in a human with quadriplegia. Sci Rep 6: 33807. DOI: 10.1038/srep33807. Go to original source... Go to PubMed...
    29. Shimoda K, Nagasaka Y, Chao ZC, Fujii N (2012). Decoding continuous three-dimensional hand trajectories from epidural electrocorticographic signals in Japanese macaques. J Neural Eng 9: 036015. DOI: 10.1088/1741-2560/9/3/036015. Go to original source... Go to PubMed...
    30. Shin D, Kambara H, Yoshimura N, Koike Y (2018). Control of a Robot Arm Using Decoded Joint Angles from Electrocorticograms in Primate. Comput Intel Neurosc 2018: 1-8. DOI: 10.1155/2018/2580165. Go to original source... Go to PubMed...
    31. Shin D, Watanabe H, Kambara H, Nambu A, Isa T, Nishimura Y, Koike Y (2012). Prediction of muscle activities from electrocorticograms in primary motor cortex of primates. PloS One 7(10): e47992. DOI: 10.1371/journal.pone.0047992. Go to original source... Go to PubMed...
    32. Ting LH, Chiel HJ, Trumbower RD, Allen JL, McKay JL, Hackney ME, Kesar TM (2015). Neuromechanical principles underlying movement modularity and their implications for rehabilitation. Neuron 86: 38-54. DOI: 10.1016/j.neuron.2015.02.042. Go to original source... Go to PubMed...
    33. Tresch MC, Saltiel P, d'Avella A, Bizzi E (2002). Coordination and localization in spinal motor systems. Brain Res Rev 40: 66-79. DOI: 10.1016/s0165-0173(02)00189-3. Go to original source... Go to PubMed...
    34. Yanagisawa T, Hirata M, Saitoh Y, Kishima H, Matsushita K, Goto T, et al. (2012). Electrocorticographic control of a prosthetic arm in paralyzed patients. Ann Neurol 71: 353-361. DOI: 10.1002/ana.22613. Go to original source... Go to PubMed...

    This is an open access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0), which permits non-comercial use, distribution, and reproduction in any medium, provided the original publication is properly cited. No use, distribution or reproduction is permitted which does not comply with these terms.


    Return to the content