Literature DB >> 17530407

Model based sensitivity analysis of EMG-force relation with respect to motor unit properties: applications to muscle paresis in stroke.

Ping Zhou1, Nina L Suresh, William Z Rymer.   

Abstract

The sensitivity of the electromyogram (EMG)-force relation to changes in motoneuron and muscle properties was explored using a simulation approach, and by applying existing motoneuron pool, muscle force, and surface EMG models. The simulation results indicate that several factors contribute potently to known changes in the EMG-force relation in paretic stroke muscles. First, compression of the motor unit recruitment range with respect to the injected current tends to generate greater EMG amplitude at a given force, and to produce a highly nonlinear EMG-force relation. The overall mean slope of the EMG-force relation tends to be flatter, primarily because of this non-linear behavior. Second, with reductions of the mean motor unit firing rates, the slope of the EMG-force relation also tends to increase especially as the mean firing rates dropped substantially below the motor unit fusion frequency. Finally, similar effects were observed with a reduction in the number of motor units, and with variation in motor unit contractile properties, which also altered the EMG-force relation. These findings provide new insight toward our understanding of experimental EMG-force relations in both normal and pathological states, such as the abnormal EMG-force relations of paresis muscles in stroke.

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Year:  2007        PMID: 17530407     DOI: 10.1007/s10439-007-9329-3

Source DB:  PubMed          Journal:  Ann Biomed Eng        ISSN: 0090-6964            Impact factor:   3.934


  21 in total

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Authors:  Jason J Kutch; Arthur D Kuo; William Z Rymer
Journal:  J Neurophysiol       Date:  2010-04-14       Impact factor: 2.714

2.  Modifying motor unit territory placement in the Fuglevand model.

Authors:  Jason W Robertson; Jamie A Johnston
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3.  Neural control of muscle force: indications from a simulation model.

Authors:  Paola Contessa; Carlo J De Luca
Journal:  J Neurophysiol       Date:  2012-12-12       Impact factor: 2.714

4.  A computational model of use-dependent motor recovery following a stroke: optimizing corticospinal activations via reinforcement learning can explain residual capacity and other strength recovery dynamics.

Authors:  David J Reinkensmeyer; Emmanuel Guigon; Marc A Maier
Journal:  Neural Netw       Date:  2012-02-13

5.  Computational Models for Neuromuscular Function.

Authors:  Francisco J Valero-Cuevas; Heiko Hoffmann; Manish U Kurse; Jason J Kutch; Evangelos A Theodorou
Journal:  IEEE Rev Biomed Eng       Date:  2009

6.  Examination of Poststroke Alteration in Motor Unit Firing Behavior Using High-Density Surface EMG Decomposition.

Authors:  Xiaoyan Li; Ales Holobar; Marco Gazzoni; Roberto Merletti; William Zev Rymer; Ping Zhou
Journal:  IEEE Trans Biomed Eng       Date:  2014-11-07       Impact factor: 4.538

7.  EMG-force relation in the first dorsal interosseous muscle of patients with amyotrophic lateral sclerosis.

Authors:  Faezeh Jahanmiri-Nezhad; Xiaogang Hu; Nina L Suresh; William Z Rymer; Ping Zhou
Journal:  NeuroRehabilitation       Date:  2014-01-01       Impact factor: 2.138

8.  Examination of hand muscle activation and motor unit indices derived from surface EMG in chronic stroke.

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

9.  Spasticity, weakness, force variability, and sustained spontaneous motor unit discharges of resting spastic-paretic biceps brachii muscles in chronic stroke.

Authors:  Shuo-Hsiu Chang; Gerard E Francisco; Ping Zhou; W Zev Rymer; Sheng Li
Journal:  Muscle Nerve       Date:  2013-04-21       Impact factor: 3.217

10.  Endpoint force fluctuations reveal flexible rather than synergistic patterns of muscle cooperation.

Authors:  Jason J Kutch; Arthur D Kuo; Anthony M Bloch; William Z Rymer
Journal:  J Neurophysiol       Date:  2008-09-17       Impact factor: 2.714

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