Literature DB >> 22514208

A simulation-based analysis of motor unit number index (MUNIX) technique using motoneuron pool and surface electromyogram models.

Xiaoyan Li1, William Zev Rymer, Ping Zhou.   

Abstract

Motor unit number index (MUNIX) measurement has recently achieved increasing attention as a tool to evaluate the progression of motoneuron diseases. In our current study, the sensitivity of the MUNIX technique to changes in motoneuron and muscle properties was explored by a simulation approach utilizing variations on published motoneuron pool and surface electromyogram (EMG) models. Our simulation results indicate that, when keeping motoneuron pool and muscle parameters unchanged and varying the input motor unit numbers to the model, then MUNIX estimates can appropriately characterize changes in motor unit numbers. Such MUNIX estimates are not sensitive to different motor unit recruitment and rate coding strategies used in the model. Furthermore, alterations in motor unit control properties do not have a significant effect on the MUNIX estimates. Neither adjustment of the motor unit recruitment range nor reduction of the motor unit firing rates jeopardizes the MUNIX estimates. The MUNIX estimates closely correlate with the maximum M-wave amplitude. However, if we reduce the amplitude of each motor unit action potential rather than simply reduce motor unit number, then MUNIX estimates substantially underestimate the motor unit numbers in the muscle. These findings suggest that the current MUNIX definition is most suitable for motoneuron diseases that demonstrate secondary evidence of muscle fiber reinnervation. In this regard, when MUNIX is applied, it is of much importance to examine a parallel measurement of motor unit size index (MUSIX), defined as the ratio of the maximum M-wave amplitude to the MUNIX. However, there are potential limitations in the application of the MUNIX methods in atrophied muscle, where it is unclear whether the atrophy is accompanied by loss of motor units or loss of muscle fiber size.

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Year:  2012        PMID: 22514208      PMCID: PMC3556460          DOI: 10.1109/TNSRE.2012.2194311

Source DB:  PubMed          Journal:  IEEE Trans Neural Syst Rehabil Eng        ISSN: 1534-4320            Impact factor:   3.802


  49 in total

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  10 in total

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Authors:  Ryan D Kaya; Masato Nakazawa; Richard L Hoffman; Brian C Clark
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2.  Motor unit number estimation based on high-density surface electromyography decomposition.

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3.  Application of the F-Response for Estimating Motor Unit Number and Amplitude Distribution in Hand Muscles of Stroke Survivors.

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Authors:  Xiaoyan Li; Jie Liu; Sheng Li; Ying-Chih Wang; Ping Zhou
Journal:  IEEE Trans Biomed Eng       Date:  2014-06-25       Impact factor: 4.538

5.  Modified motor unit number index: A simulation study of the first dorsal interosseous muscle.

Authors:  Xiaoyan Li; Sanjeev D Nandedkar; Ping Zhou
Journal:  Med Eng Phys       Date:  2015-11-28       Impact factor: 2.242

6.  Reliability of a modified motor unit number index (MUNIX) technique.

Authors:  Ryan D Kaya; Richard L Hoffman; Brian C Clark
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7.  Alterations in multidimensional motor unit number index of hand muscles after incomplete cervical spinal cord injury.

Authors:  Le Li; Xiaoyan Li; Jie Liu; Ping Zhou
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8.  Application Value of the Motor Unit Number Index in Patients With Kennedy Disease.

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Journal:  Front Neurol       Date:  2021-12-21       Impact factor: 4.003

9.  Motor Unit Number Estimation (MUNE) Free of Electrical Stimulation or M Wave Recording: Feasibility and Challenges.

Authors:  Maoqi Chen; James Bashford; Ping Zhou
Journal:  Front Aging Neurosci       Date:  2022-02-11       Impact factor: 5.702

10.  Model-Based Analysis of Muscle Strength and EMG-Force Relation with respect to Different Patterns of Motor Unit Loss.

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  10 in total

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