The long-term goal of this research is to understand the architecture and behavior of motor units in different human muscles. The motor unit is the basic functional unit of the neuromuscular system. The way in which muscle fibers are organized into motor units and the way in which motor units are coordinated are important determinants of a muscle's ability to produce force and movement. The only method currently available for studying individual motor units in intact humans involves analyzing electromyographic (EMG) signals. Decomposition of multi-unit EMG signals allows identification of individual motor-unit discharge patterns and action-potential waveforms. Further analysis of the action-potential waveforms provides information about motor-unit architecture, including the locations of motor endplates and muscle/tendon junctions. EMG decomposition in general, and motor-unit architecture analysis in particular, are not utilized as widely as they could be, in large part because of the lack of available software and because of concerns about the validity of decomposition. We propose to establish a firm scientific foundation for the validity of EMG decomposition by developing a rigorous and objective method for quantifying the accuracy of decomposition results. This will be based on cross-checking results obtained from multi- or single-unit electrodes located at nearby sites in the same muscle. We further propose to enhance and disseminate a powerful, versatile, and accurate EMG decomposition program that we have developed over the past 20 years. All results will be made available via an internet site that will serve as a forum for the exchange of software, test signals, and information related to EMG decomposition. The proposed work will benefit the scientific community by making a powerful research tool more trustworthy and more widely available. This tool will be useful for studying the basic structure and function of human muscles, and it will be relevant to several clinical applications, including tendon-transfer and reconstructive surgery, rehabilitation in stroke and cerebral palsy, and clinical electromyography. ? ?

Agency
National Institute of Health (NIH)
Institute
National Institute of Neurological Disorders and Stroke (NINDS)
Type
Research Project (R01)
Project #
1R01NS051507-01A1
Application #
7033115
Study Section
Musculoskeletal Rehabilitation Sciences Study Section (MRS)
Program Officer
Pancrazio, Joseph J
Project Start
2006-01-15
Project End
2009-12-31
Budget Start
2006-01-15
Budget End
2006-12-31
Support Year
1
Fiscal Year
2006
Total Cost
$308,250
Indirect Cost
Name
Palo Alto Institute for Research & Edu, Inc.
Department
Type
DUNS #
624218814
City
Palo Alto
State
CA
Country
United States
Zip Code
94304
Marateb, H R; McGill, K C; Holobar, A et al. (2011) Accuracy assessment of CKC high-density surface EMG decomposition in biceps femoris muscle. J Neural Eng 8:066002
McGill, Kevin C; Marateb, Hamid R (2011) Rigorous a posteriori assessment of accuracy in EMG decomposition. IEEE Trans Neural Syst Rehabil Eng 19:54-63
McGill, Kevin C; Lateva, Zoia C (2011) History dependence of human muscle-fiber conduction velocity during voluntary isometric contractions. J Appl Physiol 111:630-41
Lateva, Zoia C; McGill, Kevin C; Johanson, M Elise (2010) The innervation and organization of motor units in a series-fibered human muscle: the brachioradialis. J Appl Physiol 108:1530-41
Marateb, Hamid Reza; McGill, Kevin C (2009) Resolving superimposed MUAPs using particle swarm optimization. IEEE Trans Biomed Eng 56:916-9
Lateva, Zoia C; McGill, Kevin C (2007) Electrophysiological evidence of doubly innervated branched muscle fibers in the human brachioradialis muscle. Clin Neurophysiol 118:2612-9
Koch, Volker M; McGill, Kevin C; Loeliger, Hans-Andrea (2006) Resolution of superpositions in EMG signals using belief propagation: results for the known constituent problem. Conf Proc IEEE Eng Med Biol Soc 1:1260-3