OBJECTIVES: The most widely used and studied neurostimulation procedure for medically refractory epilepsy is vagus nerve stimulation (VNS) Therapy. The goal of this study was to develop a computational model for improved understanding of the anatomy and neurophysiology of the vagus nerve as it pertains to the principles of electrical stimulation, aiming to provide clinicians with a systematic and rational understanding of VNS Therapy. MATERIALS AND METHODS: Computational modeling allows the study of electrical stimulation of peripheral nerves. We used finite element electric field models of the vagus nerve with VNS Therapy electrodes to calculate the voltage field for several output currents and studied the effects of two programmable parameters (output current and pulse width) on optimal fiber activation. RESULTS: The mathematical models correlated well with strength-duration curves constructed from actual patient data. In addition, digital constructs of chronic versus acute implant models demonstrated that at a given pulse width and current combination, presence of a 110-μm fibrotic tissue can decrease fiber activation by 50%. Based on our findings, a range of output current settings between 0.75 and 1.75 mA with pulse width settings of 250 or 500 μs may result in optimal stimulation. CONCLUSIONS: The modeling illustrates how to achieve full or nearly full activation of the myelinated fibers of the vagus nerve through output current and pulse width settings. This knowledge will enable clinicians to apply these principles for optimal vagus nerve activation and proceed to adjust duty cycle and frequency to achieve effectiveness.
OBJECTIVES: The most widely used and studied neurostimulation procedure for medically refractory epilepsy is vagus nerve stimulation (VNS) Therapy. The goal of this study was to develop a computational model for improved understanding of the anatomy and neurophysiology of the vagus nerve as it pertains to the principles of electrical stimulation, aiming to provide clinicians with a systematic and rational understanding of VNS Therapy. MATERIALS AND METHODS: Computational modeling allows the study of electrical stimulation of peripheral nerves. We used finite element electric field models of the vagus nerve with VNS Therapy electrodes to calculate the voltage field for several output currents and studied the effects of two programmable parameters (output current and pulse width) on optimal fiber activation. RESULTS: The mathematical models correlated well with strength-duration curves constructed from actual patient data. In addition, digital constructs of chronic versus acute implant models demonstrated that at a given pulse width and current combination, presence of a 110-μm fibrotic tissue can decrease fiber activation by 50%. Based on our findings, a range of output current settings between 0.75 and 1.75 mA with pulse width settings of 250 or 500 μs may result in optimal stimulation. CONCLUSIONS: The modeling illustrates how to achieve full or nearly full activation of the myelinated fibers of the vagus nerve through output current and pulse width settings. This knowledge will enable clinicians to apply these principles for optimal vagus nerve activation and proceed to adjust duty cycle and frequency to achieve effectiveness.
Authors: D Val-Laillet; E Aarts; B Weber; M Ferrari; V Quaresima; L E Stoeckel; M Alonso-Alonso; M Audette; C H Malbert; E Stice Journal: Neuroimage Clin Date: 2015-03-24 Impact factor: 4.881
Authors: Nil Z Gurel; Asim H Gazi; Kristine L Scott; Matthew T Wittbrodt; Amit J Shah; Viola Vaccarino; J Douglas Bremner; Omer T Inan Journal: AMIA Annu Symp Proc Date: 2020-03-04
Authors: Chaitanya Kolluru; Ananya Subramaniam; Yehe Liu; Aniruddha Upadhye; Monty Khela; Lindsey Druschel; Farzad Fereidouni; Richard Levenson; Andrew Shoffstall; Michael Jenkins; David L Wilson Journal: Proc SPIE Int Soc Opt Eng Date: 2021-03-05
Authors: Robert S Fisher; Pegah Afra; Micheal Macken; Daniela N Minecan; Anto Bagić; Selim R Benbadis; Sandra L Helmers; Saurabh R Sinha; Jeremy Slater; David Treiman; Jason Begnaud; Pradheep Raman; Bita Najimipour Journal: Neuromodulation Date: 2015-12-13