Literature DB >> 1487287

Modeling the effects of electric fields on nerve fibers: determination of excitation thresholds.

E N Warman1, W M Grill, D Durand.   

Abstract

We have developed a method to predict excitation of axons based on the response of passive models. An expression describing the transmembrane potential induced in passive models to an applied electric field is presented. Two terms were found to drive the polarization of each node. The first was a source term described by the activating function at the node, and the other was an ohmic term resulting from redistribution of current from sources at other nodes. A total equivalent driving function including both terms was then defined. We found that the total equivalent driving function can be used to provide accurate predictions of excitation thresholds for any applied field. The method requires only knowledge of the intracellular strength-duration relationship of the axon, the passive step response of the axon to an intracellular current, and the values of the extracellular potentials. Excitation thresholds for any given applied field can then be calculated using a simple algebraic expression. This method eliminates the errors associated with use of the activating function alone, and greatly reduces the computation required to determine fiber response to applied extracellular fields.

Mesh:

Year:  1992        PMID: 1487287     DOI: 10.1109/10.184700

Source DB:  PubMed          Journal:  IEEE Trans Biomed Eng        ISSN: 0018-9294            Impact factor:   4.538


  71 in total

1.  Modelling the effects of electric fields on nerve fibres: influence of the myelin sheath.

Authors:  A G Richardson; C C McIntyre; W M Grill
Journal:  Med Biol Eng Comput       Date:  2000-07       Impact factor: 2.602

2.  Nerve conduction block utilising high-frequency alternating current.

Authors:  K L Kilgore; N Bhadra
Journal:  Med Biol Eng Comput       Date:  2004-05       Impact factor: 2.602

3.  Effects of uniform extracellular DC electric fields on excitability in rat hippocampal slices in vitro.

Authors:  Marom Bikson; Masashi Inoue; Hiroki Akiyama; Jackie K Deans; John E Fox; Hiroyoshi Miyakawa; John G R Jefferys
Journal:  J Physiol       Date:  2004-02-20       Impact factor: 5.182

4.  Determining which mechanisms lead to activation in the motor cortex: a modeling study of transcranial magnetic stimulation using realistic stimulus waveforms and sulcal geometry.

Authors:  R Salvador; S Silva; P J Basser; P C Miranda
Journal:  Clin Neurophysiol       Date:  2010-10-28       Impact factor: 3.708

5.  Topographic spread of inferior colliculus activation in response to acoustic and intracochlear electric stimulation.

Authors:  Russell L Snyder; Julie A Bierer; John C Middlebrooks
Journal:  J Assoc Res Otolaryngol       Date:  2004-08-12

6.  Tissue and electrode capacitance reduce neural activation volumes during deep brain stimulation.

Authors:  Christopher R Butson; Cameron C McIntyre
Journal:  Clin Neurophysiol       Date:  2005-10       Impact factor: 3.708

7.  Simulation of high-frequency sinusoidal electrical block of mammalian myelinated axons.

Authors:  Niloy Bhadra; Emily A Lahowetz; Stephen T Foldes; Kevin L Kilgore
Journal:  J Comput Neurosci       Date:  2007-01-03       Impact factor: 1.621

8.  Predicting myelinated axon activation using spatial characteristics of the extracellular field.

Authors:  E J Peterson; O Izad; D J Tyler
Journal:  J Neural Eng       Date:  2011-07-13       Impact factor: 5.379

9.  Role of electrode design on the volume of tissue activated during deep brain stimulation.

Authors:  Christopher R Butson; Cameron C McIntyre
Journal:  J Neural Eng       Date:  2005-12-19       Impact factor: 5.379

10.  Modelling the response of scalp sensory receptors to transcranial electrical stimulation.

Authors:  V Suihko
Journal:  Med Biol Eng Comput       Date:  2002-07       Impact factor: 2.602

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