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Logo of nihpaAbout Author manuscriptsSubmit a manuscriptHHS Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
 
Expert Rev Neurother. Author manuscript; available in PMC 2010 April 1.
Published in final edited form as:
PMCID: PMC2685465
NIHMSID: NIHMS105196

Neuroprosthetic devices: How far are we from recovering movement in paralyzed patients?

In October, 2008, Eberhard Fetz’s group from the University of Washington demonstrated that a direct artificial connection from a region of the brain associated with the control of movement to muscles in the limb could restore movement induced by electrical stimulation in monkeys whose arms had been temporarily anesthetized. The report of this progress created tremendous public interest as a perceived milestone in the development of a seamless neurotechnological solution for restoring function in paralysis. This work relied on the combination of an implanted electrode array in the motor cortex and functional electrical stimulation (FES) to muscles in the paralyzed wrist. Indeed, the combination of FES, a technique which has achieved a track record of clinical utility, with brain machine interfaces (BMIs) has become the subject of several research groups. Based on progress from these groups, we suggest that the technology has reached sufficient maturation that the proof-of-concept clinical demonstration could be accomplished within the next five years. In this paper, we provide a brief overview of the key technologies, several key issues that are being explored, and discuss the importance of supporting translational neuroprosthetics research and development to achieve this goal.

Paralysis resulting from neurological injury or disease presents a significant burden on patients, their families, and caregivers. Neuroprostheses offer the promise of restoring movement to paralyzed patients by bypassing damaged regions of the nervous system, ultimately allowing patients the ability to live with greater independence and improving their quality of life. Indeed, the coordinated stimulation of paralyzed muscles through FES has been shown to be effective and safe for restoring hand grasp (1,2,3), bladder control (4,5,6), respiration (7,8), and clinical feasibility has been shown for standing and walking (9,10,11,12), and coughing (13,14).

These neural interfaces function by delivering small levels of pulsed electrical current to intact presynaptic nerve innervating target muscles to initiate stimulus-secretion coupling. Although no longer receiving meaningful input from neural structures caudal to the lesion, the paralyzed muscles can be induced to contract through FES to restore purposeful movements for the patient. Several research participants have received multiple implantable devices, enabling them to achieve multiple functions such as use of both hands, standing and hand control, postural control and hand control.

Implantable devices used to provide these functions range from the BION (15), a small single channel microstimulator that is readily implantable through a cannula, to multichannel implantable stimulator/telemeter systems (16), to a modular networked and wirelessly controlled system for stimulation and sensing (17,18 19). An implantable system for control of hand grasp achieved commercialization as the Freehand system in 1997, and has been successfully used by over 250 C5/C6 spinal cord injured persons throughout the world (2). Freehand recipients control hand grasp through operation of an external joystick, controlled by the movement of the opposing non-paralyzed shoulder, which through a radio frequency powered and controlled implanted stimulator, delivers electrical stimulation (20,21). Claims of utility of medical devices, like other therapeutic interventions, are ultimately subject to validation through clinical trials. Importantly, a multi-center trial of the Freehand system based on 51 C5/C6 patients quantitatively demonstrated the efficacy of the system. Building from this success, the implantable FES technology is undergoing significant design improvements, enabling the recording of neural and muscle potentials and using those as command signal sources, the use of implanted rechargeable power and wireless telemetry, allowing the use to be free of encumbering external aspects of the technology and improving his/her control. However, the control of FES devices is currently rudimentary, relying on either the movement or underlying muscle activation from a non-paralyzed body part to trigger the coordinated electrical stimulation of muscles in the paralyzed limb.

One of the most exciting recent advances in the neuroprosthetics field has been the emergence of BMI. Over 20 years ago, Georgopoulos and colleagues showed that the firing of individual neurons in the motor cortex could be correlated with specific movements (22). With the use of microelectrode arrays in various brain regions responsible for movement planning or execution, populations of neurons could be recorded simultaneously. Based on the instantaneous firing rates across the population, limb kinematics or muscle activation patterns responsible for limb movements could be decoded and predicted (23,24). Advances in real time computation have allowed the implementation of closed loop control of cursor movement and robotic limb control based on real time recording and analysis of spike rates or field potentials (25-28).

Clearly, BMI has enormous implications for individuals living with severe disabilities. By tapping into one or more regions of the brain responsible for the control of movement, BMI may provide natural and complex upper extremity control by relying on volitional signals recorded from brain regions integral to movement control. However, the state of the science is somewhat in flux from basic to the translational/clinical domain and as a result, there are a number of issues that will need to be resolved. First, there is the question of whether or not paralyzed individuals retain the ability to generate stable patterns that can be decoded to ascertain movement intent. Through a pilot clinical feasibility study initiated by Cyberkinetics Neurotechnology Systems, Inc., Hochberg and Donoghue demonstrated that an individual paralyzed for up to 3 years could modulate cortical activity to drive cursor movement with the BrainGate system (29). It remains to be determined how generalized these observations will be and importantly, how stable the recordings are in patients. Based on experimental studies involving healthy non-human primates, it is well known that quality recordings are typically obtained for no more than several months to 1 year, regardless of the microelectrode array platform employed (30). A recent preliminary report suggests that the clinical outlook for existing devices may be more optimistic (31). Second, the leadership in the BMI field has been largely driven by the fundamental question of how the brain controls movement. For example, does the brain encode limb kinematics (direction, velocity, etc) or the activity of muscles, which ultimately result in limb kinematics? How can proprioceptive and/or tactile information, perhaps delivered through microstimulation of the somatosensory cortex improve BMI performance (32)? These are undoubtedly important scientific questions, but to move forward with the clinical integration of FES and BMI, the field needs to develop design specifications for BMI as a source of control signals for FES systems. Musculoskeletal modeling, which captures the activation and dynamics of limb control in real time, may be used to simulate useful movements (33). By varying the BMI characteristics such as the number of recorded neurons, the stability of the recordings, and the decoding methodology, one may empirically derive the minimum performance requirements for a useful control signal source. Robert Kirsch’s group at Case Western Reserve University/Cleveland VA, working in conjunction with Leigh Hochberg at Brown University/Massachusetts General Hospital is actively pursuing this area by coupling their dynamic limb simulator to the data stream from BrainGate patient recordings (34). Third, BMI systems need to be fully implantable to minimize risk associated with infection and enhance the mobility of users. Several groups are working towards the development of a wireless system, and in at least two cases, they are leveraging against the Utah array architecture that underlies the BrainGate system (35-38). Fourth, funding sources that enable translational and pilot clinical feasibility studies for neural prosthetics need to be in place. The NIH Neural Prosthesis Program initiated in 1970s enabled targeted basic, translational, and clinical neural engineering projects through contract-based initiatives. With the wide-spread recognition of bioengineering as an academic discipline, translational neural prosthetics projects have attempted to compete for grant funding with basic science, ultimately with mixed results. Recognizing this gap, several Institutes at the NIH have partnered to offer a new program announcement on Neural Prosthetics encouraging translational and pilot clinical studies for neural prosthetics1. This program will enable support for milestone-driven projects for the design, development, and demonstration of clinically-useful neural prosthetic devices. Activities to be supported in this program include implementation of clinical prototype devices, preclinical safety and efficacy testing, design verification and validation activities, pursuit of regulatory approval for clinical study, and proof-of-concept or pilot clinical studies.

In summary, neuroprosthetics have already provided hundreds of individuals the capability to move and regain essential functions lost after their paralysis. FES-based devices have proven safe and effective in the body for decades without deterioration of function, and the users continue to use them on a nearly daily basis. However, control must be made even more natural to the user, enabling them to accomplish movement in a fluid and transparent way. With the recent advances in BMI, including the emergence of wireless systems, we suggest that these technologies are reaching sufficient maturation that the proof-of-concept clinical demonstration could be accomplished within the next five years. Finally, programs are in place to enable translation of findings from the research laboratory to the clinic, such that a neuroprosthetic solution can become a component of routine clinical care for persons who have become paralyzed.

Acknowledgments

Research conducted by PHP is supported by the National Institutes of Health–National Institute of Neurological Disorders and Stroke (R01-NS-29549), the Food and Drug Administration Orphan Products (FD-R-002389), the National Institutes of Health General Clinical Research Center at MetroHealth Medical Center (M01-RR00080), the Rehabilitation Research and Development Service of the Department of Veterans Affairs (Merit Review #A3707R), and the Cleveland Department of Veterans Affairs Functional Electrical Stimulation Center (#C3819C). The views expressed here are those of the authors and do not represent those of the National Institutes of Health or the US Government. No official support or endorsement by the National Institutes of Health is intended or should be inferred.

Footnotes

1PA-09-063 - Advanced Neural Prosthetics Research and Development (U01); http://grants.nih.gov/grants/guide/pa-files/PA-09-063.html

References

1. Bryden AM, Kilgore KL, Kirsch RF, Memberg WD, Peckham PH, Keith MW. An implanted neuroprosthesis for high tetraplegia. Top Spinal Cord Inj Rehabil. 2005;10(3):38–52.
2. Peckham PH, Keith MW, Kilgore KL, Grill JH, Wuolle KS, Thrope GB, Gorman P, Hobby J, Mulcahey MJ, Carroll S, Hentz V, Wiegner A. Efficacy of an Implanted Neuroprosthesis for Restoring Hand Grasp in Tetraplegia: A Multicenter Study, Arch. Physical Medicine and Rehabilitation. 2001;82:1380–8. [PubMed]
3. Kilgore KL, Peckham PH, Keith MW, Thrope GB, Wuolle KS, Bryden AS, Hart RL. An implanted upper extremity neuroprosthesis: A five patient review. J B Joint Surg. 1997;79A(4):533–541. [PubMed]
4. Brindley GS. History of the sacral anterior root stimulator, 1969-1982. Neurol Urodyn. 1993;12:481–83. [PubMed]
5. Brindley GS, Polkey CE, Rushton DN. Sacral anterior root stimulators for bladder control in paraplegia. Paraplegia. 20:365–81. [PubMed]
6. Brindley GS. The First 500 Sacral Anterior Root Stimulators: Implant Failures and Their Repair. Paraplegia. 1995;33:5–9. [PubMed]
7. Baer GA, Talonen PP, Shneerson JM, et al. Phrenic Nerve Stimulation For Central Ventilatory Failure With Bipolar And Four-Pole Electrode Systems. PACE. 1990;19:1061. [PubMed]
8. Glenn WW, Phelps ML, Elefteriades JA, Dentz B, Hogan JF. Twenty Years Experience in Phrenic Nerve Stimulation to Pace The Diaphragm. PACE. 1986;9:781. [PubMed]
9. Davis JA, Triolo RJ, Uhlir JP, Bhadra N, Lissy DA, Nandurkar S, Marsolais EB. Surgical Technique for Installing an 8-Channel Neuroprosthesis for Standing. Clinical Orthopaedics and Related Research. 2001;2001(4):237–252. [PubMed]
10. Triolo R, Bieri C, Uhlir J, Kobetic R, Scheiner A, Marsolais EB. Implanted FNS Systems For Assisted Standing And Transfers For Individuals With Cervical Spinal Cord Injuries. Phys Med & Rehab. 1996;7(11):1119–28. [PubMed]
11. Kobetic R, Triolo RJ, Uhlir J, Bieri C, Wibowo M, Polando G, Marsolais EB, Davis JA, Ferguson K, Sharma M. Implanted Functional Electrical Stimulation System for Mobility in Paraplegia: A Follow-up Case Report. IEEE Trans Rehab Eng. 1999;7(4):390–398. [PubMed]
12. Davis R, MacFarland WC, Emmons SE. Initial results of the Nucleus FES-22-implanted system for limb movement in paraplegia. Stereotactic & Func Neurosurg. 1994;63:192–97. [PubMed]
13. Linder SH. Functional electrical stimulation to enhance cough in quadriplegia. Chest. 1993 Jan;103(1):166–9. [PubMed]
14. DiMarco AF, Onders RP, Ignagni A, Kowalski KE, Mortimer JT. Phrenic nerve pacing via intramuscular diaphragm electrodes in tetraplegic subjects. Chest. 2005;127(2):671–678. [PubMed]
15. Loeb GE, Peck RA, Moore WH, Hood K. BION systemfor distributed neural prosthetic interfaces. Med Eng Phys. 2001;23:9–18. [PubMed]
16. Smith B, Tang Z, Johnson MW, Pourmehdi S, Gazdik MM, et al. An externally powered, multichannel, implantable stimulator-telemeter for control of paralyzed muscle. IEEE Trans Biomed Eng. 1998;45:463–75. [PubMed]
17. Snoek GJ, Ijzerman MJ, in’t Groen FACG, Stofferrs TS, Zilvold G. Use of the NESS Handmaster to restore hand function in tetraplegia: clinical experiences in ten patients. Spinal Cord. 2000;398:244–49. [PubMed]
18. Alon G, McBride K. Persons with C5 or C6 tetraplegia achieve selected functional gains using a neuroprosthesis. Arch Phys Med Rehabil. 2003;84:119–24. [PubMed]
19. Wheeler CA, Peckham PH. A Wireless Wearable Controller for an Upper Extremity Neuroprosthesis. Journal of Rehabilitation Research and Development. 2008 May; Accepted for Publication. [PubMed]
20. Keith MW, Peckham PH, thrope GB, Stroh KC, Smith B, et al. Implantable functional neuromuscular stimulataion in the tetraplegic hand. J Hand surg Am. 1989;14:524–30. [PubMed]
21. Smith B, Peckham PH, Keith MW, Roscoe DD. An externally powered, multichannel, implantable stimulator for versatile control of paralyzed muscle. IEEE Trans Biomed Eng. 1987;34:499–508. [PubMed]
22. Georgopoulos AP, Kalaska JF, Caminiti R, Massey JT. On the relations between the direction of two-dimensional arm movements and cell discharge in primate motor cortex. J Neurosci. 1982;2(11):1527–37. [PubMed]
23. Georgopoulos AP, Schwartz AB, Kettner RE. Neuronal population coding of movement direction. Science. 1986;233:1357–440. [PubMed]
24. Wessberg J, Stambaugh CR, Kralik JD, Beck PD, Laubach M, Chapin JK, Kim J, Biggs SJ, Srinivasan MA, Nicolelis MAL. Real-time prediction of hand trajectory by ensembles of cortical neurons in primates. Nature. 2000;408:361–365. [PubMed]
25. Taylor DM, Tillery SI, Schwartz AB. Direct cortical control of 3D neuroprosthetic devices. Science. 2002;296:1829–1832. [PubMed]
26. Serruya MD, Hatsopoulos NG, Paninski L, Fellows MR, Donoghue JP. Brain-machine interface: Instant neural control of a movement signal. Nature. 2002;416:141–142. [PubMed]
27. Carmena JM, Lebedev MA, Crist RE, O’Doherty JE, Santucci DM, Dimitrov DF, Patil PG, Henriquez CS, Nicolelis MAL. Learning to control a brain-machine interface for reaching and grasping by primates. PLOS BIOLOGY. 2003;1(2):193–208. [PMC free article] [PubMed]
28. Musallam S, Corneil BD, Greger B, Scherberger H, Andersen RA. Cognitive control signals for neural prosthetics. Science. 2004;305(5681):258–262. [PubMed]
29. Hochberg LR, Serruya MD, Friehs GM, Mukand JA, Saleh M, Caplan AH, Branner A, Chen D, Penn RD, Donoghue JP. Neuronal ensemble control of prosthetic devices by a human with tetraplegia. Nature. 2006;442(7099):164–171. [PubMed]
30. Kipke DR, Shain W, Buzsáki G, Fetz E, Henderson JM, Hetke JF, Schalk G. Advanced neurotechnologies for chronic neural interfaces: New horizons and clinical opportunities. J Neurosci. 2008;28(46):11830–11838. [PMC free article] [PubMed]
31. Hochberg LR, Simeral JD, Kim SP, Stein J, Friehs GM, Black MJ, Donoghue JP. More than two years of intracortically-based cursor control via a neural interface system. Society for Neuroscience Abstract 673.15. 2008
32. Fagg AH, Hatsopoulos NG, de Lafuente V, Moxon KA, Nemati S, Rebesco JM, Romo R, Solla SA, Reimer J, Tkach D, Pohlmeyer EA, Miller LE. Biomimetic brain machine interfaces for the control of movement. J Neurosci. 2007;27(44):11842–11846. [PMC free article] [PubMed]
33. Blana D, Hincapie JG, Chadwick EK, Kirsch RF. A musculoskeletal model of the upper extremity for use in the development of neuroprosthetic systems. J Biomech. 2008;41(8):1714–1721. [PMC free article] [PubMed]
34. Ajiboye B, Taylor DM, Simeral JD, Lambrecht JM, Hochberg LR, Donoghue JP, Kirsch RF. Neural signal modulation during single versus multi-degree-of-freedom imagined arm movements in individuals with high tetraplegia. Society for Neuroscience Abstract 673.17. 2008
35. Harrison RR, Watkins PT, Kier RJ, Lovejoy RO, Black DJ, Greger B, Solzbacher F. A low-power integrated circuit for a wireless 100-electrode neural recording system. IEEE J Solid-State Circ. 2007;42(1):123–133.
36. Rizk M, Obeid I, Callender SH, Wolf PD. A single-chip signal processing and telemetry engine for an implantable 96-channel neural data acquisition system. J Neural Engin. 2007;4(3):309–321. [PubMed]
37. Donoghue JP, Nurmikko A, Black M, Hochberg LR. Assistive technology and robotic control using motor cortex ensemble-based neural interface systems in humans with tetraplegia. J Physiol (Lond.) 2007;579(3):603–611. [PubMed]
38. Wise KD, Sodagar AM, Yao Y, Gulari MN, Perlin GE, Najafi K. Microelectrodes, microelectronics, and implantable neural microsystems. Proc IEEE. 2008;96(7):1184–1202.