Extracellular potentials from active myelinated fibers inside insulated and noninsulated peripheral nerve

Meier, J.H.; Rutten, Wim; Boom, H.B.K.

doi:10.1109/10.709558

Cited by 18 publications

(8 citation statements)

References 17 publications

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“…Time steps of 1 s were used in the model. The membrane current waveform obtained with this model agrees with previous experimental [34] and modeling [9], [20], [28] results. The current sources simulating the waveform were moved along the endoneurium of the finite-element model at discrete steps that corresponded with the conduction velocity (100 m/s) of the propagating action potential.…”

Section: ) Selectivity Index (Si): Let Vectorsupporting

confidence: 88%

Modeling study of peripheral nerve recording selectivity

Pérez-Orive

Durund²

2000

IEEE Trans. Rehab. Eng.

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Recording of sensory information from afferent fibers can be used as feedback for the closed-loop control of neural prostheses. Clinical applications suggest that recording selectively from various nerve fascicles is important. Current nerve cuff electrodes are generally circular in shape and use a tripolar recording configuration. Preliminary experiments suggest that slowly changing the shape of the nerve to a flatter cross section can improve its selectivity. The objective of this work is to determine the effects of nerve reshaping and other cuff design parameters on the fascicular recording selectivity of a nerve cuff. A finite-element computer model of a multifasciculated nerve with different cuff electrodes was implemented to simulate the recordings. The model included the inhomogeneous and anisotropic properties of peripheral nerves. The recording selectivity was quantified with the use of a Selectivity Index. The results from the model provided information regarding the effect of using monopolar versus tripolar recording configurations, the length of the tripoles in tripolar recordings, the number of contacts that maximize the selectivity index, and the cuff length. Nerve reshaping was found to cause important recording selectivity improvements (106% average). These results provide specific criteria for the design of selectively recording nerve cuff electrodes.

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Section: ) Selectivity Index (Si): Let Vectorsupporting

confidence: 88%

Modeling study of peripheral nerve recording selectivity

Pérez-Orive

Durund²

2000

IEEE Trans. Rehab. Eng.

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“…Computer models can facilitate the evaluation and optimization of nerve cuff electrode designs. They have been used to predict fascicular selectivity of stimulation [2], [9]–[17] and recording [18]–[22] using various electrode configurations.…”

Section: Introductionmentioning

confidence: 99%

Fascicular Perineurium Thickness, Size, and Position Affect Model Predictions of Neural Excitation

Grinberg

Schiefer

Tyler

et al. 2008

IEEE Trans. Neural Syst. Rehabil. Eng.

121

107

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The number of applications using neural prosthetic interfaces is expanding. Computer models are a valuable tool to evaluate stimulation techniques and electrode designs. Although our understanding of neural anatomy has improved, its impact on the effects of neural stimulation is not well understood. This study evaluated the effects of fascicle perineurial thickness, diameter, and position on axonal excitation thresholds and population recruitment using finite element models and NEURON simulations. The perineurial thickness of human fascicles was found to be 3.0% ± 1.0% of the fascicle diameter. Increased perineurial thickness and fascicle diameter increased activation thresholds. The presence of a large neighboring fascicle caused a significant change in activation of a smaller target fascicle by as much as 80% ± 11% of the total axon population. Smaller fascicles were recruited at lower amplitudes than neighboring larger fascicles. These effects were further illustrated in a realistic model of a human femoral nerve surrounded by a nerve cuff electrode. The data suggest that fascicular selectivity is strongly dependent upon the anatomy of the nerve being stimulated. Therefore, accurate representations of nerve anatomy are required to develop more accurate computer models to evaluate and optimize nerve electrode designs for neural prosthesis applications.

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“…1,29,50 The model of the spinal cord ( Fig. 2(A)) consisted of two concentric cylinders (infinite length) in a bath, an inner cylinder that represented the gray matter and an outer annulus that represented the white matter.…”

Section: Volume Conductor Model Of the Spinal Cordmentioning

confidence: 99%

“…We used previously established methods 1,29,50 to derive the voltage generated in a volume-conductor by an ideal point current source located within the volume-conductor. The volume-conductor model (Fig.…”

Section: Appendixmentioning

confidence: 99%

Electrical Localization of Neural Activity in the Dorsal Horn of the Spinal Cord: A Modeling Study

Moffitt

Grill

2004

Ann Biomed Eng

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Intraspinal microstimulation is a means of eliciting coordinated motor responses for restoration of function. However, detailed maps of the neuroanatomy of the human spinal cord are lacking, and it is not clear where electrodes should be implanted. We developed an electrical approach to localize active neurons in the spinal cord using potentials recorded from the surface of the spinal cord. We evaluated this localization method using an analytical model of the spinal cord and two previously developed inverse algorithms (standardized low resolution brain electromagnetic tomography (sLORETA) and a locally optimal source (LOS) method). The results support electrical source localization as a feasible imaging approach for localizing (within 300 microm) active neurons in the spinal cord. The LOS method could localize the source when 16 recording electrodes were placed on the dorsolateral aspect of the cord and the noise level was 2%. When recording electrodes were positioned around the entire circumference of the spinal cord, either localization method could localize the source, even at 15% noise. Finally, localization error was not sensitive to inaccuracies in the expected electrode positions or the electrical parameters of the forward model, but was sensitive to a geometrical modification of the forward model in one case.

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Extracellular potentials from active myelinated fibers inside insulated and noninsulated peripheral nerve

Cited by 18 publications

References 17 publications

Modeling study of peripheral nerve recording selectivity

Modeling study of peripheral nerve recording selectivity

Fascicular Perineurium Thickness, Size, and Position Affect Model Predictions of Neural Excitation

Electrical Localization of Neural Activity in the Dorsal Horn of the Spinal Cord: A Modeling Study

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