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Malik RN, Samejima S, Shackleton C, Miller T, Pedrocchi ALG, Rabchevsky AG, Moritz CT, Darrow D, Field-Fote EC, Guanziroli E, Ambrosini E, Molteni F, Gad P, Mushahwar VK, Sachdeva R, Krassioukov AV. REPORT-SCS: minimum reporting standards for spinal cord stimulation studies in spinal cord injury. J Neural Eng 2024; 21:016019. [PMID: 38271712 DOI: 10.1088/1741-2552/ad2290] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Accepted: 01/25/2024] [Indexed: 01/27/2024]
Abstract
Objective.Electrical spinal cord stimulation (SCS) has emerged as a promising therapy for recovery of motor and autonomic dysfunctions following spinal cord injury (SCI). Despite the rise in studies using SCS for SCI complications, there are no standard guidelines for reporting SCS parameters in research publications, making it challenging to compare, interpret or reproduce reported effects across experimental studies.Approach.To develop guidelines for minimum reporting standards for SCS parameters in pre-clinical and clinical SCI research, we gathered an international panel of expert clinicians and scientists. Using a Delphi approach, we developed guideline items and surveyed the panel on their level of agreement for each item.Main results.There was strong agreement on 26 of the 29 items identified for establishing minimum reporting standards for SCS studies. The guidelines encompass three major SCS categories: hardware, configuration and current parameters, and the intervention.Significance.Standardized reporting of stimulation parameters will ensure that SCS studies can be easily analyzed, replicated, and interpreted by the scientific community, thereby expanding the SCS knowledge base and fostering transparency in reporting.
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Affiliation(s)
- Raza N Malik
- International Collaboration on Repair Discoveries, Department of Medicine, University of British Columbia, Vancouver, British Columbia, Canada
- Division of Physical Medicine and Rehabilitation, Department of Medicine, University of British Columbia, Vancouver, British Columbia, Canada
| | - Soshi Samejima
- International Collaboration on Repair Discoveries, Department of Medicine, University of British Columbia, Vancouver, British Columbia, Canada
- Division of Physical Medicine and Rehabilitation, Department of Medicine, University of British Columbia, Vancouver, British Columbia, Canada
| | - Claire Shackleton
- International Collaboration on Repair Discoveries, Department of Medicine, University of British Columbia, Vancouver, British Columbia, Canada
- Division of Physical Medicine and Rehabilitation, Department of Medicine, University of British Columbia, Vancouver, British Columbia, Canada
| | - Tiev Miller
- International Collaboration on Repair Discoveries, Department of Medicine, University of British Columbia, Vancouver, British Columbia, Canada
- Division of Physical Medicine and Rehabilitation, Department of Medicine, University of British Columbia, Vancouver, British Columbia, Canada
| | - Alessandra Laura Giulia Pedrocchi
- Nearlab, Department di Electronics, Information and Bioengineering, and We-Cobot Laboratory, Polo Territoriale di Lecco, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milan, Italy
| | - Alexander G Rabchevsky
- Spinal Cord & Brain Injury Research Center, Department of Physiology, University of Kentucky, Lexington, KY, United States of America
| | - Chet T Moritz
- Departments of Electrical & Computer Engineering, Rehabilitation Medicine, and Physiology & Biophysics, and the Center for Neurotechnology, University of Washington, Seattle, WA, United States of America
| | - David Darrow
- Department of Neurosurgery, University of Minnesota, Minneapolis, MN, United States of America
- Department of Neurosurgery, Hennepin County Medical Center, Minneapolis, MN, United States of America
| | - Edelle C Field-Fote
- Shepherd Center, Crawford Research Institute, Atlanta, Georgia, United States of America
- Emory University School of Medicine, Division of Physical Therapy, Atlanta, Georgia, United States of America
- Georgia Institute of Technology, School of Biological Sciences, Program in Applied Physiology, Atlanta, Georgia, United States of America
| | - Eleonora Guanziroli
- Villa Beretta Rehabilitation Center, Valduce Hospital, Costa Masnaga, Lecco, Italy
| | - Emilia Ambrosini
- Nearlab, Department di Electronics, Information and Bioengineering, and We-Cobot Laboratory, Polo Territoriale di Lecco, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milan, Italy
| | - Franco Molteni
- Villa Beretta Rehabilitation Center, Valduce Hospital, Costa Masnaga, Lecco, Italy
| | - Parag Gad
- SpineX Inc., Los Angeles, Los Angeles, CA, United States of America
| | - Vivian K Mushahwar
- Department of Medicine and Sensory Motor Adaptive Rehabilitation Technology (SMART) Network, University of Alberta, Edmonton, Alberta, Canada
| | - Rahul Sachdeva
- International Collaboration on Repair Discoveries, Department of Medicine, University of British Columbia, Vancouver, British Columbia, Canada
- Division of Physical Medicine and Rehabilitation, Department of Medicine, University of British Columbia, Vancouver, British Columbia, Canada
| | - Andrei V Krassioukov
- International Collaboration on Repair Discoveries, Department of Medicine, University of British Columbia, Vancouver, British Columbia, Canada
- Division of Physical Medicine and Rehabilitation, Department of Medicine, University of British Columbia, Vancouver, British Columbia, Canada
- Spinal Cord Research Program, G.F. Strong Rehabilitation Centre, Vancouver Coastal Health, Vancouver, British Columbia, Canada
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Spieker EL, Dvorani A, Salchow-Hömmen C, Otto C, Ruprecht K, Wenger N, Schauer T. Targeting Transcutaneous Spinal Cord Stimulation Using a Supervised Machine Learning Approach Based on Mechanomyography. SENSORS (BASEL, SWITZERLAND) 2024; 24:634. [PMID: 38276326 PMCID: PMC10818383 DOI: 10.3390/s24020634] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/18/2023] [Revised: 01/14/2024] [Accepted: 01/17/2024] [Indexed: 01/27/2024]
Abstract
Transcutaneous spinal cord stimulation (tSCS) provides a promising therapy option for individuals with injured spinal cords and multiple sclerosis patients with spasticity and gait deficits. Before the therapy, the examiner determines a suitable electrode position and stimulation current for a controlled application. For that, amplitude characteristics of posterior root muscle (PRM) responses in the electromyography (EMG) of the legs to double pulses are examined. This laborious procedure holds potential for simplification due to time-consuming skin preparation, sensor placement, and required expert knowledge. Here, we investigate mechanomyography (MMG) that employs accelerometers instead of EMGs to assess muscle activity. A supervised machine-learning classification approach was implemented to classify the acceleration data into no activity and muscular/reflex responses, considering the EMG responses as ground truth. The acceleration-based calibration procedure achieved a mean accuracy of up to 87% relative to the classical EMG approach as ground truth on a combined cohort of 11 healthy subjects and 11 patients. Based on this classification, the identified current amplitude for the tSCS therapy was in 85%, comparable to the EMG-based ground truth. In healthy subjects, where both therapy current and position have been identified, 91% of the outcome matched well with the EMG approach. We conclude that MMG has the potential to make the tuning of tSCS feasible in clinical practice and even in home use.
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Affiliation(s)
- Eira Lotta Spieker
- Department of Neurology, Charité–Universitätsmedizin Berlin, Charitéplatz 1, 10117 Berlin, Germany; (E.L.S.); (C.S.-H.); (C.O.); (K.R.); (N.W.)
- Control Systems Group, Technische Universität Berlin, Einsteinufer 17, 10587 Berlin, Germany;
- SensorStim Neurotechnology GmbH, c/o TU Berlin, Einsteinufer 17, 10587 Berlin, Germany
| | - Ardit Dvorani
- Control Systems Group, Technische Universität Berlin, Einsteinufer 17, 10587 Berlin, Germany;
- SensorStim Neurotechnology GmbH, c/o TU Berlin, Einsteinufer 17, 10587 Berlin, Germany
| | - Christina Salchow-Hömmen
- Department of Neurology, Charité–Universitätsmedizin Berlin, Charitéplatz 1, 10117 Berlin, Germany; (E.L.S.); (C.S.-H.); (C.O.); (K.R.); (N.W.)
| | - Carolin Otto
- Department of Neurology, Charité–Universitätsmedizin Berlin, Charitéplatz 1, 10117 Berlin, Germany; (E.L.S.); (C.S.-H.); (C.O.); (K.R.); (N.W.)
| | - Klemens Ruprecht
- Department of Neurology, Charité–Universitätsmedizin Berlin, Charitéplatz 1, 10117 Berlin, Germany; (E.L.S.); (C.S.-H.); (C.O.); (K.R.); (N.W.)
| | - Nikolaus Wenger
- Department of Neurology, Charité–Universitätsmedizin Berlin, Charitéplatz 1, 10117 Berlin, Germany; (E.L.S.); (C.S.-H.); (C.O.); (K.R.); (N.W.)
| | - Thomas Schauer
- Control Systems Group, Technische Universität Berlin, Einsteinufer 17, 10587 Berlin, Germany;
- SensorStim Neurotechnology GmbH, c/o TU Berlin, Einsteinufer 17, 10587 Berlin, Germany
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Bryson N, Lombardi L, Hawthorn R, Fei J, Keesey R, Peiffer J, Seáñez I. Enhanced selectivity of transcutaneous spinal cord stimulation by multielectrode configuration. J Neural Eng 2023; 20: 10.1088/1741-2552/ace552. [PMID: 37419109 PMCID: PMC10481387 DOI: 10.1088/1741-2552/ace552] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2023] [Accepted: 07/07/2023] [Indexed: 07/09/2023]
Abstract
Objective.Transcutaneous spinal cord stimulation (tSCS) has been gaining momentum as a non-invasive rehabilitation approach to restore movement to paralyzed muscles after spinal cord injury (SCI). However, its low selectivity limits the types of movements that can be enabled and, thus, its potential applications in rehabilitation.Approach.In this cross-over study design, we investigated whether muscle recruitment selectivity of individual muscles could be enhanced by multielectrode configurations of tSCS in 16 neurologically intact individuals. We hypothesized that due to the segmental innervation of lower limb muscles, we could identify muscle-specific optimal stimulation locations that would enable improved recruitment selectivity over conventional tSCS. We elicited leg muscle responses by delivering biphasic pulses of electrical stimulation to the lumbosacral enlargement using conventional and multielectrode tSCS.Results.Analysis of recruitment curve responses confirmed that multielectrode configurations could improve the rostrocaudal and lateral selectivity of tSCS. To investigate whether motor responses elicited by spatially selective tSCS were mediated by posterior root-muscle reflexes, each stimulation event was a paired pulse with a conditioning-test interval of 33.3 ms. Muscle responses to the second stimulation pulse were significantly suppressed, a characteristic of post-activation depression suggesting that spatially selective tSCS recruits proprioceptive fibers that reflexively activate muscle-specific motor neurons in the spinal cord. Moreover, the combination of leg muscle recruitment probability and segmental innervation maps revealed a stereotypical spinal activation map in congruence with each electrode's position.Significance. Improvements in muscle recruitment selectivity could be essential for the effective translation into stimulation protocols that selectively enhance single-joint movements in neurorehabilitation.
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Affiliation(s)
- Noah Bryson
- Biomedical Engineering, Washington University in St. Louis
- Division of Neurotechnology, Washington University School of Medicine in St. Louis
| | - Lorenzo Lombardi
- Biomedical Engineering, Washington University in St. Louis
- Division of Neurotechnology, Washington University School of Medicine in St. Louis
| | - Rachel Hawthorn
- Biomedical Engineering, Washington University in St. Louis
- Division of Neurotechnology, Washington University School of Medicine in St. Louis
| | - Jie Fei
- Biomedical Engineering, Washington University in St. Louis
- Division of Neurotechnology, Washington University School of Medicine in St. Louis
| | - Rodolfo Keesey
- Biomedical Engineering, Washington University in St. Louis
- Division of Neurotechnology, Washington University School of Medicine in St. Louis
| | - J.D. Peiffer
- Biomedical Engineering, Washington University in St. Louis
- Division of Neurotechnology, Washington University School of Medicine in St. Louis
- Biomedical Engineering, Northwestern University
| | - Ismael Seáñez
- Biomedical Engineering, Washington University in St. Louis
- Division of Neurotechnology, Washington University School of Medicine in St. Louis
- Neurosurgery, Washington University School of Medicine in St. Louis
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