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Ho JS, Poon C, North R, Grubb W, Lempka S, Bikson M. A Visual and Narrative Timeline Review of Spinal Cord Stimulation Technology and US Food and Drug Administration Milestones. Neuromodulation 2024; 27:1020-1025. [PMID: 38970616 DOI: 10.1016/j.neurom.2024.05.006] [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: 03/26/2024] [Revised: 05/03/2024] [Accepted: 05/13/2024] [Indexed: 07/08/2024]
Abstract
OBJECTIVES The aim of this study was to present key technologic and regulatory milestones in spinal cord stimulation (SCS) for managing chronic pain on a narrative timeline with visual representation, relying on original sources to the extent possible. MATERIALS AND METHODS We identified technical advances in SCS that facilitated and enhanced treatment on the basis of scientific publications and approvals from the United States (US) Food and Drug Administration (FDA). We presented milestones limited to first use in key indications and in the context of new technology validation. We focused primarily on pain management, but other indications (eg, motor disorder in multiple sclerosis) were included when they affected technology development. RESULTS We developed a comprehensive visual and narrative timeline of SCS technology and US FDA milestones. Since its conception in the 1960s, the science and technology of SCS neuromodulation have continuously evolved. Advances span lead design (from paddle-type to percutaneous, and increased electrode contacts) and stimulator technology (from wireless power to internally powered and rechargeable, with miniaturized components, and programmable multichannel devices), with expanding stimulation program flexibility (such as burst and kilohertz stimulation frequencies), as well as usage features (such as remote programming and magnetic resonance imaging conditional compatibility). CONCLUSIONS This timeline represents the evolution of SCS technology alongside expanding FDA-approved indications for use.
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Affiliation(s)
- Johnson S Ho
- Department of Anesthesiology, Rutgers Robert Wood Johnson Medical School, New Brunswick, NJ, USA.
| | - Cynthia Poon
- Department of Biomedical Engineering, Columbia University, New York, NY, USA
| | - Richard North
- The Neuromodulation Foundation, Inc, Baltimore, MD, USA; Department of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - William Grubb
- Department of Anesthesiology, Rutgers Robert Wood Johnson Medical School, New Brunswick, NJ, USA
| | - Scott Lempka
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA; Department of Anesthesiology, University of Michigan, Ann Arbor, MI, USA; Biointerfaces Institute, University of Michigan, Ann Arbor, MI, USA
| | - Marom Bikson
- Department of Biomedical Engineering, The City College of New York, New York, NY, USA
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Wang B, Peterchev AV, Gaugain G, Ilmoniemi RJ, Grill WM, Bikson M, Nikolayev D. Quasistatic approximation in neuromodulation. J Neural Eng 2024; 21:10.1088/1741-2552/ad625e. [PMID: 38994790 PMCID: PMC11370654 DOI: 10.1088/1741-2552/ad625e] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2024] [Accepted: 06/28/2024] [Indexed: 07/13/2024]
Abstract
We define and explain the quasistatic approximation (QSA) as applied to field modeling for electrical and magnetic stimulation. Neuromodulation analysis pipelines include discrete stages, and QSA is applied specifically when calculating the electric and magnetic fields generated in tissues by a given stimulation dose. QSA simplifies the modeling equations to support tractable analysis, enhanced understanding, and computational efficiency. The application of QSA in neuromodulation is based on four underlying assumptions: (A1) no wave propagation or self-induction in tissue, (A2) linear tissue properties, (A3) purely resistive tissue, and (A4) non-dispersive tissue. As a consequence of these assumptions, each tissue is assigned a fixed conductivity, and the simplified equations (e.g. Laplace's equation) are solved for the spatial distribution of the field, which is separated from the field's temporal waveform. Recognizing that electrical tissue properties may be more complex, we explain how QSA can be embedded in parallel or iterative pipelines to model frequency dependence or nonlinearity of conductivity. We survey the history and validity of QSA across specific applications, such as microstimulation, deep brain stimulation, spinal cord stimulation, transcranial electrical stimulation, and transcranial magnetic stimulation. The precise definition and explanation of QSA in neuromodulation are essential for rigor when using QSA models or testing their limits.
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Affiliation(s)
- Boshuo Wang
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC 27710, United States of America
| | - Angel V Peterchev
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC 27710, United States of America
- Department of Electrical and Computer Engineering, Duke University, Durham, NC 27708, United States of America
- Department of Biomedical Engineering, Duke University, Durham, NC 27708, United States of America
- Department of Neurosurgery, Duke University, Durham, NC 27710, United States of America
| | - Gabriel Gaugain
- Institut d’Électronique et des Technologies du numéRique (IETR UMR 6164), CNRS / University of Rennes, 35000 Rennes, France
| | - Risto J Ilmoniemi
- Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, Espoo, Finland
| | - Warren M Grill
- Department of Electrical and Computer Engineering, Duke University, Durham, NC 27708, United States of America
- Department of Biomedical Engineering, Duke University, Durham, NC 27708, United States of America
- Department of Neurosurgery, Duke University, Durham, NC 27710, United States of America
- Department of Neurobiology, Duke University, Durham, NC 27710, United States of America
| | - Marom Bikson
- The City College of New York, New York, NY 11238, United States of America
| | - Denys Nikolayev
- Institut d’Électronique et des Technologies du numéRique (IETR UMR 6164), CNRS / University of Rennes, 35000 Rennes, France
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Chitneni A, Jain E, Sahni S, Mavrocordatos P, Abd-Elsayed A. Spinal Cord Stimulation Waveforms for the Treatment of Chronic Pain. Curr Pain Headache Rep 2024; 28:595-605. [PMID: 38607547 DOI: 10.1007/s11916-024-01247-1] [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] [Accepted: 03/19/2024] [Indexed: 04/13/2024]
Abstract
PURPOSE OF REVIEW Since the advent of spinal cord stimulation (SCS), advances in technology have allowed for improvement and treatment of various conditions, especially chronic pain. Additionally, as the system has developed, the ability to provide different stimulation waveforms for patients to treat different conditions has improved. The purpose and objective of the paper is to discuss basics of waveforms and present the most up-to-date literature and research studies on the different types of waveforms that currently exist. During our literature search, we came across over sixty articles that discuss the various waveforms we intend to evaluate. RECENT FINDINGS There are several publications on several waveforms used in clinical practice, but to our knowledge, this is the only educational document teaching on waveforms which provides essential knowledge. There is a gap of knowledge related to understanding wave forms and how they work.
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Affiliation(s)
- Ahish Chitneni
- Department of Rehabilitation and Regenerative Medicine, New York-Presbyterian Hospital - Columbia and Cornell, New York, NY, USA
| | - Esha Jain
- Department of Rehabilitation and Human Performance, Icahn School of Medicine at Mount Sinai, Mount Sinai Hospital, New York, NY, USA
| | | | | | - Alaa Abd-Elsayed
- Department of Anesthesia, Division of Pain Medicine, School of Medicine and Public Health, University of Wisconsin, 600 Highland Avenue, Madison, WI, B6/319 CSC53792-3272, USA.
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Wang B, Peterchev AV, Gaugain G, Ilmoniemi RJ, Grill WM, Bikson M, Nikolayev D. Quasistatic approximation in neuromodulation. ARXIV 2024:arXiv:2402.00486v5. [PMID: 38351938 PMCID: PMC10862934] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/19/2024]
Abstract
We define and explain the quasistatic approximation (QSA) as applied to field modeling for electrical and magnetic stimulation. Neuromodulation analysis pipelines include discrete stages, and QSA is applied specifically when calculating the electric and magnetic fields generated in tissues by a given stimulation dose. QSA simplifies the modeling equations to support tractable analysis, enhanced understanding, and computational efficiency. The application of QSA in neuro-modulation is based on four underlying assumptions: (A1) no wave propagation or self-induction in tissue, (A2) linear tissue properties, (A3) purely resistive tissue, and (A4) non-dispersive tissue. As a consequence of these assumptions, each tissue is assigned a fixed conductivity, and the simplified equations (e.g., Laplace's equation) are solved for the spatial distribution of the field, which is separated from the field's temporal waveform. Recognizing that electrical tissue properties may be more complex, we explain how QSA can be embedded in parallel or iterative pipelines to model frequency dependence or nonlinearity of conductivity. We survey the history and validity of QSA across specific applications, such as microstimulation, deep brain stimulation, spinal cord stimulation, transcranial electrical stimulation, and transcranial magnetic stimulation. The precise definition and explanation of QSA in neuromodulation are essential for rigor when using QSA models or testing their limits.
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