Literature DB >> 11467053

Motor unit number estimation, ALS and clinical trials.

C L Gooch1, Y Harati.   

Abstract

Motor unit number estimation (MUNE) techniques have evolved substantially over the past decade and have been applied, with increasing frequency, to the study of amyotrophic lateral sclerosis (ALS). As major clinical therapeutic trials in motor neuron disease continue to appear, the need for a clear quantitative method of following motor unit physiology in the living subject grows ever more pressing. In this article, we review the major modern techniques of MUNE and the data supporting their reproducibility and utility in patients with ALS, with particular attention to their role in evaluating the efficacy of new therapeutic interventions.

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Year:  2000        PMID: 11467053     DOI: 10.1080/14660820050515368

Source DB:  PubMed          Journal:  Amyotroph Lateral Scler Other Motor Neuron Disord        ISSN: 1466-0822


  5 in total

1.  An Examination of the Motor Unit Number Index (MUNIX) in muscles paralyzed by spinal cord injury.

Authors:  Xiaoyan Li; Faezeh Jahanmiri-Nezhad; William Zev Rymer; Ping Zhou
Journal:  IEEE Trans Inf Technol Biomed       Date:  2012-04-04

2.  Transcranial magnetic stimulation in ALS: utility of central motor conduction tests.

Authors:  A G Floyd; Q P Yu; P Piboolnurak; M X Tang; Y Fang; W A Smith; J Yim; L P Rowland; H Mitsumoto; S L Pullman
Journal:  Neurology       Date:  2009-02-10       Impact factor: 9.910

Review 3.  An overview of motor unit number index reproducibility in amyotrophic lateral sclerosis.

Authors:  Davood Fathi; Shahriar Nafissi; Shahram Attarian; Christoph Neuwirth; Farzad Fatehi
Journal:  Iran J Neurol       Date:  2019-07-06

Review 4.  Strategies for Peripheral Nerve Repair.

Authors:  Matthew Wilcox; Holly Gregory; Rebecca Powell; Tom J Quick; James B Phillips
Journal:  Curr Tissue Microenviron Rep       Date:  2020-04-21

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

Authors:  Chengjun Huang; Maoqi Chen; Yingchun Zhang; Sheng Li; Ping Zhou
Journal:  Neural Plast       Date:  2021-06-22       Impact factor: 3.599

  5 in total

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