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Imaging three-dimensional innervation zone distribution in muscles from M-wave recordings.

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OBJECTIVE To localize neuromuscular junctions in skeletal muscles in vivo which is of great importance in understanding, diagnosing and managing of neuromuscular disorders. APPROACH A three-dimensional global innervation zone imaging… Click to show full abstract

OBJECTIVE To localize neuromuscular junctions in skeletal muscles in vivo which is of great importance in understanding, diagnosing and managing of neuromuscular disorders. APPROACH A three-dimensional global innervation zone imaging technique was developed to characterize the global distribution of innervation zones, as an indication of the location and features of neuromuscular junctions, using electrically evoked high-density surface electromyogram recordings. MAIN RESULTS The performance of the technique was evaluated in the biceps brachii of six intact human subjects. The geometric centers of the distributions of the reconstructed innervation zones were determined with a mean distance of 9.4  ±  1.4 cm from the reference plane, situated at the medial epicondyle of the humerus. A mean depth was calculated as 1.5  ±  0.3 cm from the geometric centers to the closed points over the skin. The results are consistent with those reported in previous histology studies. It was also found that the volumes and distributions of the reconstructed innervation zones changed as the stimulation intensities increased until the supramaximal muscle response was achieved. SIGNIFICANCE Results have demonstrated the high performance of the proposed imaging technique in noninvasively imaging global distributions of the innervation zones in the three-dimensional muscle space in vivo, and the feasibility of its clinical applications, such as guiding botulinum toxin injections in spasticity management, or in early diagnosis of neurodegenerative progression of amyotrophic lateral sclerosis.

Keywords: three dimensional; innervation zones; innervation; innervation zone; distribution

Journal Title: Journal of neural engineering
Year Published: 2017

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