Anatomically based lower limb nerve model for electrical stimulation

Kim, Juliana H. K.; Davidson, John B.; Röhrle, Oliver; Soboleva, T. K.; Pullan, Andrew J.

doi:10.1186/1475-925x-6-48

Cited by 11 publications

(6 citation statements)

References 32 publications

(42 reference statements)

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“…The resulting model is a multiscale, multiphysics skeletal muscle model that can be linked to a motor neuron model; for example the model of Fuglevand et al () has been used to drive the simulated muscle contractions by assuming a one‐by‐one relation between α MN APs and muscle fiber APs (Röhrle et al, ). In order to simulate functional electrical stimulation, Kim, Davidson, Röhrle, Soboleva, and Pullan () established an anatomical detailed model of the nerve trunk connecting the MNs and the muscle fibers, and which considers the spatial propagation of APs along the nerve. While the original model of Röhrle and co‐workers was limited to isometric contractions, subsequent works established a fully coupled and flexible modeling framework (Heidlauf & Röhrle, ; Heidlauf & Röhrle, ) embracing neural inputs, feedback mechanisms, and force generation and EMG generation during nonisometric conditions.…”

Section: Review On State‐of‐the‐art Approaches In Modeling the Neurommentioning

confidence: 99%

Multiscale modeling of the neuromuscular system: Coupling neurophysiology and skeletal muscle mechanics

Röhrle

Yavuz

Klotz

et al. 2019

WIREs Mechanisms of Disease

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Mathematical models and computer simulations have the great potential to substantially increase our understanding of the biophysical behavior of the neuromuscular system. This, however, requires detailed multiscale, and multiphysics models. Once validated, such models allow systematic in silico investigations that are not necessarily feasible within experiments and, therefore, have the ability to provide valuable insights into the complex interrelations within the healthy system and for pathological conditions. Most of the existing models focus on individual parts of the neuromuscular system and do not consider the neuromuscular system as an integrated physiological system. Hence, the aim of this advanced review is to facilitate the prospective development of detailed biophysical models of the entire neuromuscular system. For this purpose, this review is subdivided into three parts. The first part introduces the key anatomical and physiological aspects of the healthy neuromuscular system necessary for modeling the neuromuscular system. The second part provides an overview on state‐of‐the‐art modeling approaches representing all major components of the neuromuscular system on different time and length scales. Within the last part, a specific multiscale neuromuscular system model is introduced. The integrated system model combines existing models of the motor neuron pool, of the sensory system and of a multiscale model describing the mechanical behavior of skeletal muscles. Since many sub‐models are based on strictly biophysical modeling approaches, it closely represents the underlying physiological system and thus could be employed as starting point for further improvements and future developments. This article is categorized under: Physiology > Mammalian Physiology in Health and Disease Analytical and Computational Methods > Computational Methods Models of Systems Properties and Processes > Organ, Tissue, and Physiological Models

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Section: Review On State‐of‐the‐art Approaches In Modeling the Neurommentioning

confidence: 99%

Multiscale modeling of the neuromuscular system: Coupling neurophysiology and skeletal muscle mechanics

Röhrle

Yavuz

Klotz

et al. 2019

WIREs Mechanisms of Disease

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“…The extracted nerve sample was placed inside a container with the predefined thickness embedded into two glass slides. The measured T and R values were then used to approximate the first guess for Newton's iterative method to find the values of k and n by using equations (8) and (9). After a few iterations, the method converges towards the solution with user defined error tolerance.…”

Section: B Spectrophotometric Set-upmentioning

confidence: 99%

Assessment of the Complex Refractive Indices of Xenopus Laevis Sciatic Nerve for the Optimization of Optical (NIR) Neurostimulation

Rahman

Powner

Kyriacou

et al. 2018

IEEE Trans. Neural Syst. Rehabil. Eng.

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Despite an increasing interest in the use of light for neural stimulation there is little information on how it interacts with neural tissue. The choice of wavelength in most of the optical stimulation literature is based on already available light sources designed for other applications. This paper is the first one to report the complex refractive index of the Sciatic nerve of Xenopus laevis, which is a crucial parameter for identifying the optimal wavelength of optical stimuli. The Xenopus laevis neural tissue is the most widely used tissue type in peripheral neurostimulation studies. In this work, the Reflectance (R) and the Transmittance (T) of the Sciatic nerve were measured over a wavelength range of 860 nm to 2250 nm, and the corresponding real (n) and the imaginary (k) refractive indices were calculated using appropriate formulae in a novel way. The reported n values were between 1.3-1.44 and the k values are of the order of 10 −5 over the full wavelength range. The absorption coefficient, α was found to be 100-500 cm −1. Several localised wavelength ranges were identified that can offer a maximised power coupling between potential optical stimuli and the neural tissue (1150-1200 nm, 1500-1700 nm and 1900-2050 nm). The narrower regions of 1400-1600 nm and 1850-2150 nm were found to exhibit maximised absorbance. Separately, three regions were identified, where the penetration depths are the greatest (950-1000 nm, 1050-1350 nm and 1600-1900 nm). This paper provides, for the first time, the fundamental specifications for optimising the parameters of optical neurostimulation systems.

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“…The initial calculated values of the membrane potential, ionic currents and their respective ODEs obtained from the CellML based electrophysiological nerve models were passed to the equally space single grid points of this 1D SSEM. In finite element modeling (FEM), bidomain model (Tung, 1978) was selected to characterize the APP along the single grid point of the designed 1D SSEM by applying extracellular stimulus as it was formerly used to model neural tissue electrical activity (Kim et al, 2007). External stimulation was applied at time t = 0 and the membrane voltage value returned to the CellML based electrophysiological nerve model which computed updated values of membrane potential, ionic currents and their particular ODEs and this parameters value were once more reassigned to the bidomain model for computing newer values of the action potential.…”

Section: Functional Nerve Modelmentioning

confidence: 99%

“…Formerly, the electrical stimulation in the retinal ganglion cell and human cochlear neuron was done by employing HH model respectively (Greenberg et al, 1999;Rattay et al, 2001). Whereas, the electrical stimulation in the sciatic nerve of the lower limb was achieved by implementing the CRRSS model (Kim et al, 2007).…”

Section: Introductionmentioning

confidence: 99%

A mathematical framework simulating nerve fiber physiology

Haque

2017

Int. j. adv. appl. sci.

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Nerves are an important part in human body which not only controls the movement and locomotion of the body but also but also contains sensory receptors from other parts of the body which provide continuous feedback to the brain and spinal cord. Various diagnostic methods are used to detect the specific damage response of the nerve but they do not identify the precise location of the nerve damage. A computational nerve model may help to identify the exact location of nerve damage. Therefore, in this study the organization of a one-dimensional (1D) synthetic single element structural and functional model which typify the anatomy and physiology of the nerve is proposed. The geometrical model was developed using 1D linear Lagrange basis function while the functional model was developed by applying external stimulus and solving the bidomain model. The unmyelinated and myelinated nerve electrophysiological models were used to generate and propagate the action potential in this 1D synthetic single element model (SSEM). The nerve conduction velocity (NCV) was also computed in this proposed model and found that the myelinated nerve model has a higher NCV in contrast to unmyelinated model. This model will provide a platform for the development of the complete anatomical and functional model of the nerves in the various location of the body and may be helpful for clinician and physiologists in the evaluation and diagnosis of the structural as well as functional consequences of diabetic neuropathy in its initial stages.

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Anatomically based lower limb nerve model for electrical stimulation

Cited by 11 publications

References 32 publications

Multiscale modeling of the neuromuscular system: Coupling neurophysiology and skeletal muscle mechanics

Multiscale modeling of the neuromuscular system: Coupling neurophysiology and skeletal muscle mechanics

Assessment of the Complex Refractive Indices of Xenopus Laevis Sciatic Nerve for the Optimization of Optical (NIR) Neurostimulation

A mathematical framework simulating nerve fiber physiology

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