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Aman M, Bergmeister KD, Festin C, Sporer ME, Russold MF, Gstoettner C, Podesser BK, Gail A, Farina D, Cederna P, Aszmann OC. Experimental Testing of Bionic Peripheral Nerve and Muscle Interfaces: Animal Model Considerations. Front Neurosci 2020; 13:1442. [PMID: 32116485 PMCID: PMC7025572 DOI: 10.3389/fnins.2019.01442] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2019] [Accepted: 12/23/2019] [Indexed: 12/05/2022] Open
Abstract
Introduction: Man-machine interfacing remains the main challenge for accurate and reliable control of bionic prostheses. Implantable electrodes in nerves and muscles may overcome some of the limitations by significantly increasing the interface's reliability and bandwidth. Before human application, experimental preclinical testing is essential to assess chronic in-vivo biocompatibility and functionality. Here, we analyze available animal models, their costs and ethical challenges in special regards to simulating a potentially life-long application in a short period of time and in non-biped animals. Methods: We performed a literature analysis following the PRISMA guidelines including all animal models used to record neural or muscular activity via implantable electrodes, evaluating animal models, group size, duration, origin of publication as well as type of interface. Furthermore, behavioral, ethical, and economic considerations of these models were analyzed. Additionally, we discuss experience and surgical approaches with rat, sheep, and primate models and an approach for international standardized testing. Results: Overall, 343 studies matched the search terms, dominantly originating from the US (55%) and Europe (34%), using mainly small animal models (rat: 40%). Electrode placement was dominantly neural (77%) compared to muscular (23%). Large animal models had a mean duration of 135 ± 87.2 days, with a mean of 5.3 ± 3.4 animals per trial. Small animal models had a mean duration of 85 ± 11.2 days, with a mean of 12.4 ± 1.7 animals. Discussion: Only 37% animal models were by definition chronic tests (>3 months) and thus potentially provide information on long-term performance. Costs for large animals were up to 45 times higher than small animals. However, costs are relatively small compared to complication costs in human long-term applications. Overall, we believe a combination of small animals for preliminary primary electrode testing and large animals to investigate long-term biocompatibility, impedance, and tissue regeneration parameters provides sufficient data to ensure long-term human applications.
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Affiliation(s)
- Martin Aman
- Clinical Laboratory for Bionic Extremity Reconstruction, Department of Surgery, Medical University of Vienna, Vienna, Austria.,Division of Biomedical Research, Medical University of Vienna, Vienna, Austria
| | - Konstantin D Bergmeister
- Clinical Laboratory for Bionic Extremity Reconstruction, Department of Surgery, Medical University of Vienna, Vienna, Austria.,Division of Biomedical Research, Medical University of Vienna, Vienna, Austria
| | - Christopher Festin
- Clinical Laboratory for Bionic Extremity Reconstruction, Department of Surgery, Medical University of Vienna, Vienna, Austria
| | - Matthias E Sporer
- Clinical Laboratory for Bionic Extremity Reconstruction, Department of Surgery, Medical University of Vienna, Vienna, Austria.,Division of Biomedical Research, Medical University of Vienna, Vienna, Austria
| | | | - Clemens Gstoettner
- Clinical Laboratory for Bionic Extremity Reconstruction, Department of Surgery, Medical University of Vienna, Vienna, Austria
| | - Bruno K Podesser
- Division of Biomedical Research, Medical University of Vienna, Vienna, Austria
| | - Alexander Gail
- Cognitive Neuroscience Lab, German Primate Center, Göttingen, Germany
| | - Dario Farina
- Department of Bioengineering, Imperial College, London, United Kingdom
| | - Paul Cederna
- Section of Plastic and Reconstructive Surgery, Department of Surgery, University of Michigan, Ann Arbor, MI, United States
| | - Oskar C Aszmann
- Clinical Laboratory for Bionic Extremity Reconstruction, Department of Surgery, Medical University of Vienna, Vienna, Austria.,Division of Plastic and Reconstructive Surgery, Medical University of Vienna, Vienna, Austria
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Vu PP, Irwin ZT, Bullard AJ, Ambani SW, Sando IC, Urbanchek MG, Cederna PS, Chestek CA. Closed-Loop Continuous Hand Control via Chronic Recording of Regenerative Peripheral Nerve Interfaces. IEEE Trans Neural Syst Rehabil Eng 2019; 26:515-526. [PMID: 29432117 DOI: 10.1109/tnsre.2017.2772961] [Citation(s) in RCA: 35] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Loss of the upper limb imposes a devastating interruption to everyday life. Full restoration of natural arm control requires the ability to simultaneously control multiple degrees of freedom of the prosthetic arm and maintain that control over an extended period of time. Current clinically available myoelectric prostheses do not provide simultaneous control or consistency for transradial amputees. To address this issue, we have implemented a standard Kalman filter for continuous hand control using intramuscular electromyography (EMG) from both regenerative peripheral nerve interfaces (RPNI) and an intact muscle within non-human primates. Seven RPNIs and one intact muscle were implanted with indwelling bipolar intramuscular electrodes in two rhesus macaques. Following recuperations, function-specific EMG signals were recorded and then fed through the Kalman filter during a hand-movement behavioral task to continuously predict the monkey's finger position. We were able to reconstruct continuous finger movement offline with an average correlation of and a root mean squared error (RMSE) of 0.12 between actual and predicted position from two macaques. This finger movement prediction was also performed in real time to enable closed-loop neural control of a virtual hand. Compared with physical hand control, neural control performance was slightly slower but maintained an average target hit success rate of 96.70%. Recalibration longevity measurements maintained consistent average correlation over time but had a significant change in RMSE ( ). Additionally, extracted single units varied in amplitude by a factor of +18.65% and -25.85% compared with its mean. This is the first demonstration of chronic indwelling electrodes being used for continuous position control via the Kalman filter. Combining these analyses with our novel peripheral nerve interface, we believe that this demonstrates an important step in providing patients with more naturalistic control of their prosthetic limbs.
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Page DM, George JA, Kluger DT, Duncan C, Wendelken S, Davis T, Hutchinson DT, Clark GA. Motor Control and Sensory Feedback Enhance Prosthesis Embodiment and Reduce Phantom Pain After Long-Term Hand Amputation. Front Hum Neurosci 2018; 12:352. [PMID: 30319374 PMCID: PMC6166773 DOI: 10.3389/fnhum.2018.00352] [Citation(s) in RCA: 93] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2018] [Accepted: 08/17/2018] [Indexed: 12/29/2022] Open
Abstract
We quantified prosthesis embodiment and phantom pain reduction associated with motor control and sensory feedback from a prosthetic hand in one human with a long-term transradial amputation. Microelectrode arrays were implanted in the residual median and ulnar arm nerves and intramuscular electromyography recording leads were implanted in residual limb muscles to enable sensory feedback and motor control. Objective measures (proprioceptive drift) and subjective measures (survey answers) were used to assess prosthesis embodiment. For both measures, there was a significant level of embodiment of the physical prosthetic limb after open-loop motor control of the prosthesis (i.e., without sensory feedback), open-loop sensation from the prosthesis (i.e., without motor control), and closed-loop control of the prosthesis (i.e., motor control with sensory feedback). There was also a statistically significant reduction in reported phantom pain after experimental sessions that included open-loop nerve microstimulation, open-loop prosthesis motor control, or closed-loop prosthesis motor control. The closed-loop condition provided no additional significant improvements in phantom pain reduction or prosthesis embodiment relative to the open-loop sensory condition or the open-loop motor condition. This study represents the first long-term (14-month), systematic report of phantom pain reduction and prosthesis embodiment in a human amputee across a variety of prosthesis use cases.
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Affiliation(s)
- David M. Page
- Department of Bioengineering, University of Utah, Salt Lake City, UT, United States
| | - Jacob A. George
- Department of Bioengineering, University of Utah, Salt Lake City, UT, United States
| | - David T. Kluger
- Department of Bioengineering, University of Utah, Salt Lake City, UT, United States
| | - Christopher Duncan
- Division of Physical Medicine and Rehabilitation, University of Utah, Salt Lake City, UT, United States
| | - Suzanne Wendelken
- Department of Bioengineering, University of Utah, Salt Lake City, UT, United States
| | - Tyler Davis
- Department of Neurosurgery, University of Utah, Salt Lake City, UT, United States
| | | | - Gregory A. Clark
- Department of Bioengineering, University of Utah, Salt Lake City, UT, United States
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Adewole DO, Serruya MD, Harris JP, Burrell JC, Petrov D, Chen HI, Wolf JA, Cullen DK. The Evolution of Neuroprosthetic Interfaces. Crit Rev Biomed Eng 2016; 44:123-52. [PMID: 27652455 PMCID: PMC5541680 DOI: 10.1615/critrevbiomedeng.2016017198] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
The ideal neuroprosthetic interface permits high-quality neural recording and stimulation of the nervous system while reliably providing clinical benefits over chronic periods. Although current technologies have made notable strides in this direction, significant improvements must be made to better achieve these design goals and satisfy clinical needs. This article provides an overview of the state of neuroprosthetic interfaces, starting with the design and placement of these interfaces before exploring the stimulation and recording platforms yielded from contemporary research. Finally, we outline emerging research trends in an effort to explore the potential next generation of neuroprosthetic interfaces.
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Affiliation(s)
- Dayo O. Adewole
- Center for Brain Injury and Repair, Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Corporal Michael J. Crescenz Veterans Affairs Medical Center, Philadelphia, PA, USA
- Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA
- Penn Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA, USA
| | - Mijail D. Serruya
- Department of Neurology, Jefferson University, Philadelphia, PA, USA
| | - James P. Harris
- Center for Brain Injury and Repair, Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Corporal Michael J. Crescenz Veterans Affairs Medical Center, Philadelphia, PA, USA
| | - Justin C. Burrell
- Center for Brain Injury and Repair, Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Corporal Michael J. Crescenz Veterans Affairs Medical Center, Philadelphia, PA, USA
| | - Dmitriy Petrov
- Center for Brain Injury and Repair, Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Corporal Michael J. Crescenz Veterans Affairs Medical Center, Philadelphia, PA, USA
| | - H. Isaac Chen
- Center for Brain Injury and Repair, Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Corporal Michael J. Crescenz Veterans Affairs Medical Center, Philadelphia, PA, USA
- Penn Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA, USA
| | - John A. Wolf
- Center for Brain Injury and Repair, Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Corporal Michael J. Crescenz Veterans Affairs Medical Center, Philadelphia, PA, USA
| | - D. Kacy Cullen
- Center for Brain Injury and Repair, Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Corporal Michael J. Crescenz Veterans Affairs Medical Center, Philadelphia, PA, USA
- Penn Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA, USA
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Morel P, Ferrea E, Taghizadeh-Sarshouri B, Audí JMC, Ruff R, Hoffmann KP, Lewis S, Russold M, Dietl H, Abu-Saleh L, Schroeder D, Krautschneider W, Meiners T, Gail A. Long-term decoding of movement force and direction with a wireless myoelectric implant. J Neural Eng 2015; 13:016002. [DOI: 10.1088/1741-2560/13/1/016002] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
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Baker J, Bishop W, Kellis S, Levy T, House P, Greger B. Multi-scale recordings for neuroprosthetic control of finger movements. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2010; 2009:4573-7. [PMID: 19963841 DOI: 10.1109/iembs.2009.5332692] [Citation(s) in RCA: 11] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
We trained a rhesus monkey to perform individuated and combined finger flexions and extensions of the thumb, index, and middle finger. A Utah Electrode Array (UEA) was implanted into the hand region of the motor cortex contralateral to the monkey's trained hand. We also implanted a microwire electrocorticography grid (microECoG) epidurally so that it covered the UEA. The microECoG grid spanned the arm and hand regions of both the primary motor and somatosensory cortices. Previously this monkey had Implantable MyoElectric Sensors (IMES) surgically implanted into the finger muscles of the monkey's forearm. Action potentials (APs), local field potentials (LFPs), and microECoG signals were recorded from wired head-stage connectors for the UEA and microECoG grids, while EMG was recorded wirelessly. The monkey performed a finger flexion/extension task while neural and EMG data were acquired. We wrote an algorithm that uses the spike data from the UEA to perform a real-time decode of the monkey's finger movements. Also, analyses of the LFP and microECoG data indicate that these data show trial-averaged differences between different finger movements, indicating the data are potentially decodeable.
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Affiliation(s)
- Justin Baker
- University of Utah, Salt Lake City, UT, 84112 USA
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Baker JJ, Scheme E, Englehart K, Hutchinson DT, Greger B. Continuous detection and decoding of dexterous finger flexions with implantable myoelectric sensors. IEEE Trans Neural Syst Rehabil Eng 2010; 18:424-32. [PMID: 20378481 DOI: 10.1109/tnsre.2010.2047590] [Citation(s) in RCA: 44] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
A rhesus monkey was trained to perform individuated and combined finger flexions of the thumb, index, and middle finger. Nine implantable myoelectric sensors (IMES) were then surgically implanted into the finger muscles of the monkey's forearm, without any adverse effects over two years postimplantation. Using an inductive link, EMG was wirelessly recorded from the IMES as the monkey performed a finger flexion task. The EMG from the different IMES implants showed very little cross correlation. An offline parallel linear discriminant analysis (LDA) based algorithm was used to decode finger activity based on features extracted from continuously presented frames of recorded EMG. The offline parallel LDA was run on intraday sessions as well as on sessions where the algorithm was trained on one day and tested on following days. The performance of the algorithm was evaluated continuously by comparing classification output by the algorithm to the current state of the finger switches. The algorithm detected and classified seven different finger movements, including individual and combined finger flexions, and a no-movement state (chance performance = 12.5%) . When the algorithm was trained and tested on data collected the same day, the average performance was 43.8+/-3.6% n=10. When the training-testing separation period was five months, the average performance of the algorithm was 46.5+/-3.4% n=8. These results demonstrated that using EMG recorded and wirelessly transmitted by IMES offers a promising approach for providing intuitive, dexterous control of artificial limbs where human patients have sufficient, functional residual muscle following amputation.
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Affiliation(s)
- Justin J Baker
- Bioengineering Laboratory, University of Utah, Salt Lake City, UT 84602, USA
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