Japanese Journal of Clinical Neurophysiology
Online ISSN : 2188-031X
Print ISSN : 1345-7101
ISSN-L : 1345-7101
Original Article
A new approach to motor unit number estimation using automated F-wave analyzer
Tatsuya AbeRyo OhtsukaAkiko HachisukaTetsuo Komori
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2024 Volume 52 Issue 1 Pages 1-10

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Abstract

Motor unit number estimation (MUNE) is a unique neurophysiological technique that estimates the number of motor units (MUs) and provides information on rates of motor unit decline and collateral reinnervation. This technique calculates the number of MU by dividing the averaged amplitude of several single motor unit potential (SMUP) from compound muscle action potential recorded with supramaximal stimulation. It is well known that can be a physiological biomarker and reflects their pathology in motor neuron disease. Meanwhile, F-wave is a backfiring motor response generated by electrical stimulation via spinal anterior horn cell, composed of several motor units in the motor neuron pool. The waveforms of F-wave become different because the firing probability of MU changes generally at every timing. Therefore, the repeater F-waves (RF), in which the same waveform is repeatedly recorded, are known to generate from the same MU in motor neuron disease, in which the number of motor units decreased in their pathology. MUNE is based on the automated analysis of F-wave (F-MUNE) that interprets RF as SMUP. We developed an automated F-MUNE analysis program and evaluated its utility. We compared its results between the patients with amyotrophic lateral sclerosis (ALS) and healthy controls (CNT). In addition, we determined the correlation between the results of F-MUNE and MUNE by multiple point stimulation (MPS-MUNE). F-MUNE in ALS was lower than that in CNT, and the results of MPS-MUNE were similar. In addition, there was a positive correlation between the results of the two MUNEs. F-MUNE give a quantitative measure of the number of MUs in ALS, hence this technique can refract the motor unit pathology in motor neuron disease.

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© 2024 Japanese Society of Clinical Neurophysiology
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