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A systematic review of computational models for the design of spinal cord stimulation therapies: from neural circuits to patient-specific simulations.
Liang, Lucy; Damiani, Arianna; Del Brocco, Matteo; Rogers, Evan R; Jantz, Maria K; Fisher, Lee E; Gaunt, Robert A; Capogrosso, Marco; Lempka, Scott F; Pirondini, Elvira.
Affiliation
  • Liang L; Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA.
  • Damiani A; Rehab Neural Engineering Labs, University of Pittsburgh, Pittsburgh, PA, USA.
  • Del Brocco M; Center for the Neural Basis of Cognition, Pittsburgh, PA, USA.
  • Rogers ER; Rehab Neural Engineering Labs, University of Pittsburgh, Pittsburgh, PA, USA.
  • Jantz MK; Department of Physical Medicine and Rehabilitation, University of Pittsburgh, Pittsburgh, PA, USA.
  • Fisher LE; Rehab Neural Engineering Labs, University of Pittsburgh, Pittsburgh, PA, USA.
  • Gaunt RA; Department of Physical Medicine and Rehabilitation, University of Pittsburgh, Pittsburgh, PA, USA.
  • Capogrosso M; Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA.
  • Lempka SF; Biointerfaces Institute, University of Michigan, Ann Arbor, MI, USA.
  • Pirondini E; Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA.
J Physiol ; 601(15): 3103-3121, 2023 08.
Article in En | MEDLINE | ID: mdl-36409303
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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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Chronic Pain / Spinal Cord Stimulation Type of study: Guideline / Qualitative_research / Systematic_reviews Limits: Humans Language: En Journal: J Physiol Year: 2023 Document type: Article Affiliation country: United States Country of publication: United kingdom

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Chronic Pain / Spinal Cord Stimulation Type of study: Guideline / Qualitative_research / Systematic_reviews Limits: Humans Language: En Journal: J Physiol Year: 2023 Document type: Article Affiliation country: United States Country of publication: United kingdom