Electrophysiological Techniques for Motor Unit Number Estimation


Cite item

Full Text

Open Access Open Access
Restricted Access Access granted
Restricted Access Subscription Access

Abstract

Various neurological diseases involving motor neurons damage lead to a decrease in the number of functioning motor units (MUs). Accurate estimation of the number of intact MUs plays significant role in evaluating motor neuron death. Quantitative analysis of MUs by methods of routine electromyography is usually not possible. Therefore, electrophysiological techniques for MU number estimation (so-called motor unit number estimation, MUNE), have been rapidly developing over the past decades. The first article on MUNE was published in 1971. Promising, accurate, and less time-consuming modifications have been developed since, and new methods for counting MU have been proposed. In recent years, one can see increasing interest in MUNE explained by the ongoing research on new possibilities of motor neuron disease treatment, and evaluation of their effectiveness, dynamic control of the disease. Today, MUNE is considered to be a potential biomarker in many clinical trials involving patients with motor neuron disease. This review provides in sights on available MUNE techniques, describes their comparative characteristics, advantages and disadvantages of each method and their application perspectives.

About the authors

A. F. Murtazina

Federal Medical and Biophysical Center n.a. A.I. Burnazyan of the Federal Medical and Biological Agency
of the Russian Federation

Author for correspondence.
Email: aysylumurtazina@gmail.com
Russian Federation, Moscow, 123098

A. I. Belyakova-Bodina

Research Center of Neurology

Email: aysylumurtazina@gmail.com
Russian Federation, Moscow, 125367

A. G. Brutyan

Research Center of Neurology

Email: aysylumurtazina@gmail.com
Russian Federation, Moscow, 125367


Copyright (c) 2018 Pleiades Publishing, Inc.

This website uses cookies

You consent to our cookies if you continue to use our website.

About Cookies