title: Motor unit estimates obtained using the new "MUESA" method.
abstract: Motor unit number estimates were obtained from the extensor digitorum brevis and thenar muscles using a new method called MUESA. MUESA is distinguished from other estimation methods in the manner in which it deals with probabilistic motor unit activation, which is more commonly referred to as "alternation." Because of "alternation," incremental increases in the observed muscle potentials often cannot be interpreted in terms of the successive activation of single motor units. In the MUESA method, the nerve is subjected to a number of constant-intensity stimulus trains, and the resultant muscle response sequences are decomposed into their constituent motor unit action potentials. In general, if a stimulus train results in the probabilistic activation of n motor units, we can expect to see up to 2n different potentials, with each potential representing a unique combination of active and/or inactive motor units. If all 2n potentials are indeed observed, the decomposition of the observed potential sequence into its constituent motor unit action potentials is very straightforward. For the majority of the cases in which the number of observed potentials is not an integer power of 2, we have developed a novel decomposition method based on the analysis of the relative firing rates of the motor units.
authors.affiliation: Department of Anatomy, University of British Columbia, Vancouver, Canada.
authors.forename: M
authors.initials: M
authors.lastname: Slawnych
authors.forename: C
authors.initials: C
authors.lastname: Laszlo
authors.forename: C
authors.initials: C
authors.lastname: Herschler
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D016000:Cluster Analysis; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D008959:Models, Neurological; D009046:Motor Neurons / Q000166:cytology* / Q000473:pathology; D009132:Muscles / Q000294:innervation*; D009462:Neurology / Q000379:methods*; D011051:Poliomyelitis / Q000473:pathology; D012016:Reference Values; D015203:Reproducibility of Results; D013269:Stochastic Processes