Elsevier

Clinical Neurophysiology

Volume 112, Issue 11, November 2001, Pages 2118-2132
Clinical Neurophysiology

The motor nerve simulator

https://doi.org/10.1016/S1388-2457(01)00672-1Get rights and content

Abstract

Objective: The aim of the study was to develop a mathematical model of the motor nerve and the action potentials generated from its axons, in order to simulate conditions seen in neurography. The model should be used for the detailed study of the relationship between various nerve characteristics and the electrophysiological recordings obtained.

Methods: The model was developed as a software tool. The signals from individual motor units were real recordings using conventional surface electrodes. There was good agreement between the constructed compound muscle action potential and the one recorded live for the subject from whom the individual signals were obtained.

Result: A number of physiological characteristics can be changed, including the number of axons, their conduction properties, excitability properties, degree of proximo-distal velocity slowing. F-waves and A-waves can be generated.

Conclusion: The model gives a good similarity to findings obtained in live recordings. A number of physiological characteristics can be studied individually, something that cannot be done in live recordings. The model can be used in teaching and in research studies of the relationship between nerve properties and neurography parameters.

Section snippets

Background

The myelinated nerve axon conducts impulses in a saltatory fashion with depolarization at the nodes (Tasaki and Takeuchi, 1942). The currents are prevented from penetrating the membrane between the nodes in the normal nerve due to an isolating myelin sheath. This means that the impulse propagation is much faster compared with continuous depolarization. The conduction velocity is also dependent on the axonal diameter and the properties of the membrane (Waxman, 1977). A normal axon conducts with

Procedures for motor conduction studies

The procedures for nerve conduction studies are standardized, and the principles for motor conduction studies (MCS) used in our laboratory, have been summarized elsewhere (Falck and Stålberg, 1995). Most of these are relatively similar for all neurophysiological laboratories world-wide. Standard measurements will not be summarized here but the reader is referred to the literature. Parameters that are quantified are indicated in Fig. 1. Calculations, briefly summarized below, are made from the

The model

An anatomical nerve model for motor conduction studies was constructed for alpha motor axons and their muscle fibres. A similar approach was described earlier (Lee et al., 1975). A number of nerve characteristics were incorporated in the model according to the description in the next section. The default parameters are summarized in Table 2.

Construction of the model

The model was constructed by summating a large number of surface recorded motor unit potentials, extracted by spike triggered averaging. In one subject, surface electrodes were positioned for optimal M-response recorded over the thenar muscle with stimulation of the median nerve. This muscle was chosen because the shape of the CMAP is usually easy to optimize and is mainly generated from one muscle. Hypothenar recordings are often more complex.

With this recording position, a single fibre EMG

Discussion

A computer simulation model for the study of motor nerve conduction has been developed. This allows the quantitative study of the relationship between some anatomic and physiologic nerve characteristics and the results obtained in conventional electrophysiological testing of motor nerves.

The model itself was constructed on the basis of information in literature regarding axon diameters, number of alpha motor axons and conduction velocity. The contribution from each motor unit to the CMAP was

Acknowledgements

The study was supported by the Swedish Medical Research Council (Grant 135 ES). We thank Dr Michael Nicolle, London, Ontario, Canada for his contribution in discussions regarding the manuscript and for language revision.

References (21)

There are more references available in the full text version of this article.

Cited by (10)

  • Standards for quantification of EMG and neurography

    2019, Clinical Neurophysiology
    Citation Excerpt :

    Due to the cancellation of the positive and negative phases of the individual MUPs, especially with abnormal slowing of the conduction velocities and increased temporal dispersion among individual MUPs, there is decay in the amplitude and area without conduction block. The influence of mean motor conduction velocity on amplitude decay was investigated in a simulation study (Stålberg and Karlsson, 2001): the normal nerve the conduction velocity was set to 61 m/s. This resulted in amplitude change and duration change of −6% and 19% respectively.

  • Influence of timing variability between motor unit potentials on M-wave characteristics

    2016, Journal of Electromyography and Kinesiology
    Citation Excerpt :

    Different motor unit properties influence the timing variability between MUPs, including differences in motor unit conduction velocities (CVs) (Dimitrova and Dimitrov, 2002), the spread of the distribution of these CVs (Keenan et al., 2006), and the spread of motor unit activation times (Rothwell et al., 1987; Magistris et al., 1998). This variability in the arrival times of motor unit potentials (MUPs) at the recording electrode can influence the size of the evoked monopolar M waves (Lee et al., 1975; Rhee et al., 1990; Stålberg and Karlsson, 2001; Farina et al., 2004). Therefore, it might be difficult to judge whether the increase in M-wave size after a brief contraction is due primarily to real physiological changes induced by the contraction, or due to changes in the timing between MUPs.

  • Sensitivity of conventional motor nerve conduction examination in detecting patchy demyelination: A simulated model

    2007, Clinical Neurophysiology
    Citation Excerpt :

    A simulation model was used to perform a detailed study and evaluation of the distribution of conduction velocity in demyelinated motor nerves. In earlier studies, the simulator had been employed to assess the changes of F-CV with respect to a uniform slowing which affected all the axons of a nerve, as well as the effect of a conduction block of either large or small axons on CMAP (Stålberg and Karlsson, 2001). Here we simulated various degrees of slowing in different groups of axons, therefore our model is mainly applicable to immune-mediated demyelinating polyneuropathies.

  • A new method for the estimation of motor nerve conduction block

    2007, Clinical Neurophysiology
    Citation Excerpt :

    For these reasons, the validation of a method should be based on simulations, which can assess the accuracy of the CB estimation and the conditions of validity of a method. The effect of both CB and temporal dispersion on the detected CMAP was addressed in many simulation studies (Rhee et al., 1990; Tani et al., 1997; Stalberg and Karlsson, 2001; Reutskiy et al., 2003). CB estimated considering the area and the amplitude was compared to the simulated CB.

View all citing articles on Scopus
View full text