Statistical Motor Unit Number Estimation Assuming a Binomial Distribution

The statistical method of motor unit number estimation (MUNE) uses the natural stochastic variation in a muscle’s compound response to electrical stimulation to obtain an estimate of the number of recruitable motor units. The current method assumes that this variation follows a Poisson distribution. We present an alternative that instead assumes a binomial distribution. Results of computer simulations and of a pilot study on 19 healthy subjects showed that the binomial MUNE values are considerably higher than those of the Poisson method, and in better agreement with the results of other MUNE techniques. In addition, simulation results predict that the performance in patients with severe motor unit loss will be better for the binomial than Poisson method. The adapted method remains closer to physiology, because it can accommodate the increase in activation probability that results from rising stimulus intensity. It does not need recording windows as used with the Poisson method, and is therefore less user-dependent and more objective and quicker in its operation. For these reasons, we believe that the proposed modifications may lead to significant improvements in the statistical MUNE technique. Muscle Nerve 31: 182–191, 2005 STATISTICAL MOTOR UNIT NUMBER ESTIMATION ASSUMING A BINOMIAL DISTRIBUTION JOLEEN H. BLOK, MSc, GERHARD H. VISSER, MD, PhD, SÁNDOR de GRAAF, MACHIEL J. ZWARTS, MD, PhD, and DICK F. STEGEMAN, PhD Department of Clinical Neurophysiology, Erasmus Medical Center, P.O. Box 2040, 3000 CA Rotterdam, The Netherlands Institute for Fundamental and Clinical Movement Sciences, Amsterdam, The Netherlands Department of Clinical Neurophysiology, Institute of Neurology, University Medical Center Nijmegen, Nijmegen, The Netherlands Accepted 14 October 2004 Many neurogenic disorders are characterized by a reduction in the number of recruitable motor units (MUs) in affected muscles. A reliable count or estimate of this number is then of obvious importance, both for diagnostic purposes and for following disease progression or therapeutic effects. Comprehensive overviews of the available electrophysiological motor unit number estimation (MUNE) techniques, their underlying assumptions, and advantages and disadvantages have been provided elsewhere.4,11 All of these MUNE methods start by trying to find a mean MU potential (MUP) that is representative of the muscle as a whole. The MUNE is subsequently determined by dividing this representative MUP into the maximal compound muscle action potential (CMAP), which is generated by the muscle after supramaximal electrical stimulation of its motor nerve. The various methods differ primarily in their means of obtaining a representative sample of MUPs, as well as in how they deal with the probabilistic activation of motor units. When stimulus intensity is increased, starting from the recruitment threshold of an individual MU, the firing probability of this MU changes gradually from 0 (never activated) to 1 (always activated). When the recruitment ranges (range of stimulus intensities over which the firing probability increases from 0 to 1) of a number of MUs overlap, any combination of these units can be activated upon successive stimuli with equal strength, a phenomenon known as alternation. The statistical method of MUNE uses the stochastical properties of the resulting variation in the recorded CMAP amplitude to obtain an estimate of the mean electrical size of the MUs.2,8 The statistical method is implemented as proprietary software on one of the commercially available Abbreviations: CMAP, compound muscle action potential; EMG, electromyography; MU, motor unit; MUNE, motor unit number estimation/estimate; MUP, motor unit potential; SI, stimulus intensity