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Liang L, Damiani A, Del Brocco M, Rogers ER, Jantz MK, Fisher LE, Gaunt RA, Capogrosso M, Lempka SF, Pirondini E. A systematic review of computational models for the design of spinal cord stimulation therapies: from neural circuits to patient-specific simulations. J Physiol 2023; 601:3103-3121. [PMID: 36409303 PMCID: PMC10259770 DOI: 10.1113/jp282884] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Accepted: 11/08/2022] [Indexed: 08/02/2023] Open
Abstract
Seventy years ago, Hodgkin and Huxley published the first mathematical model to describe action potential generation, laying the foundation for modern computational neuroscience. Since then, the field has evolved enormously, with studies spanning from basic neuroscience to clinical applications for neuromodulation. Computer models of neuromodulation have evolved in complexity and personalization, advancing clinical practice and novel neurostimulation therapies, such as spinal cord stimulation. Spinal cord stimulation is a therapy widely used to treat chronic pain, with rapidly expanding indications, such as restoring motor function. In general, simulations contributed dramatically to improve lead designs, stimulation configurations, waveform parameters and programming procedures and provided insight into potential mechanisms of action of electrical stimulation. Although the implementation of neural models are relentlessly increasing in number and complexity, it is reasonable to ask whether this observed increase in complexity is necessary for improved accuracy and, ultimately, for clinical efficacy. With this aim, we performed a systematic literature review and a qualitative meta-synthesis of the evolution of computational models, with a focus on complexity, personalization and the use of medical imaging to capture realistic anatomy. Our review showed that increased model complexity and personalization improved both mechanistic and translational studies. More specifically, the use of medical imaging enabled the development of patient-specific models that can help to transform clinical practice in spinal cord stimulation. Finally, we combined our results to provide clear guidelines for standardization and expansion of computational models for spinal cord stimulation.
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Affiliation(s)
- Lucy Liang
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA
- Rehab Neural Engineering Labs, University of Pittsburgh, Pittsburgh, PA, USA
- Center for the Neural Basis of Cognition, Pittsburgh, PA, USA
| | - Arianna Damiani
- Rehab Neural Engineering Labs, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Physical Medicine and Rehabilitation, University of Pittsburgh, Pittsburgh, PA, USA
| | - Matteo Del Brocco
- Rehab Neural Engineering Labs, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Physical Medicine and Rehabilitation, University of Pittsburgh, Pittsburgh, PA, USA
| | - Evan R Rogers
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI, USA
| | - Maria K Jantz
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA
- Rehab Neural Engineering Labs, University of Pittsburgh, Pittsburgh, PA, USA
- Center for the Neural Basis of Cognition, Pittsburgh, PA, USA
| | - Lee E Fisher
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA
- Rehab Neural Engineering Labs, University of Pittsburgh, Pittsburgh, PA, USA
- Center for the Neural Basis of Cognition, Pittsburgh, PA, USA
- Department of Physical Medicine and Rehabilitation, University of Pittsburgh, Pittsburgh, PA, USA
| | - Robert A Gaunt
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA
- Rehab Neural Engineering Labs, University of Pittsburgh, Pittsburgh, PA, USA
- Center for the Neural Basis of Cognition, Pittsburgh, PA, USA
- Department of Physical Medicine and Rehabilitation, University of Pittsburgh, Pittsburgh, PA, USA
| | - Marco Capogrosso
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA
- Rehab Neural Engineering Labs, University of Pittsburgh, Pittsburgh, PA, USA
- Center for the Neural Basis of Cognition, Pittsburgh, PA, USA
- Department of Neurological Surgery, University of Pittsburgh, Pittsburgh, PA, USA
| | - Scott F Lempka
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI, USA
- Department of Anesthesiology, University of Michigan, Ann Arbor, MI, USA
| | - Elvira Pirondini
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA
- Rehab Neural Engineering Labs, University of Pittsburgh, Pittsburgh, PA, USA
- Center for the Neural Basis of Cognition, Pittsburgh, PA, USA
- Department of Physical Medicine and Rehabilitation, University of Pittsburgh, Pittsburgh, PA, USA
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Khadka N, Truong DQ, Williams P, Martin JH, Bikson M. The Quasi-uniform assumption for Spinal Cord Stimulation translational research. J Neurosci Methods 2019; 328:108446. [PMID: 31589892 DOI: 10.1016/j.jneumeth.2019.108446] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2019] [Revised: 09/23/2019] [Accepted: 09/25/2019] [Indexed: 12/18/2022]
Abstract
BACKGROUND Quasi-uniform assumption is a general theory that postulates local electric field predicts neuronal activation. Computational current flow model of spinal cord stimulation (SCS) of humans and animal models inform how the quasi-uniform assumption can support scaling neuromodulation dose between humans and translational animal. NEW METHOD Here we developed finite element models of cat and rat SCS, and brain slice, alongside SCS models. Boundary conditions related to species specific electrode dimensions applied, and electric fields per unit current (mA) predicted. RESULTS Clinically and across animal, electric fields change abruptly over small distance compared to the neuronal morphology, such that each neuron is exposed to multiple electric fields. Per unit current, electric fields generally decrease with body mass, but not necessarily and proportionally across tissues. Peak electric field in dorsal column rat and cat were ∼17x and ∼1x of clinical values, for scaled electrodes and equal current. Within the spinal cord, the electric field for rat, cat, and human decreased to 50% of peak value caudo-rostrally (C5-C6) at 0.48 mm, 3.2 mm, and 8 mm, and mediolaterally at 0.14 mm, 2.3 mm, and 3.1 mm. Because these space constants are different, electric field across species cannot be matched without selecting a region of interest (ROI). COMPARISON WITH EXISTING METHOD This is the first computational model to support scaling neuromodulation dose between humans and translational animal. CONCLUSIONS Inter-species reproduction of the electric field profile across the entire surface of neuron populations is intractable. Approximating quasi-uniform electric field in a ROI is a rational step to translational scaling.
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Affiliation(s)
- Niranjan Khadka
- Department of Biomedical Engineering, The City College of New York, New York, NY, USA.
| | - Dennis Q Truong
- Department of Biomedical Engineering, The City College of New York, New York, NY, USA
| | - Preston Williams
- Department of Molecular, Cellular, and Biomedical Sciences, City University of NY School of Medicine, New York, NY, 10031, USA
| | - John H Martin
- CUNY Graduate Center, New York, NY, 10031, USA; Department of Molecular, Cellular, and Biomedical Sciences, City University of NY School of Medicine, New York, NY, 10031, USA
| | - Marom Bikson
- Department of Biomedical Engineering, The City College of New York, New York, NY, USA.
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