Asymmetry of motor unit number estimate and its rate of decline in patients with amyotrophic lateral sclerosis

J Clin Neurophysiol. 2011 Oct;28(5):528-32. doi: 10.1097/WNP.0b013e318231c9e0.

Abstract

This study was performed to investigate the asymmetry of motor unit number estimate (MUNE) and its longitudinal course in patients with amyotrophic lateral sclerosis. A modified statistical MUNE was performed at the hypothenar muscles bilaterally in a total of 135 patients, and 18 of these patients underwent a follow-up study. The degree of asymmetry varied considerably among those patients whose average MUNE of both sides was moderately reduced, whereas it tended to be low in those whose average MUNE was either severely reduced or close to normal. The rate of motor unit loss was also asymmetric, and two distinct patterns were identified. In patients whose MUNE was greater than 30 in both sides (n = 7), the rate of motor unit loss tended to be greater in the initially more affected side compared with the contralateral one, yielding the so-called lead phenomenon. In contrast, the other patients (n = 11) tended to show the opposite pattern of "catch-up," that is, MUNE declined faster in the initially less affected side compared with the contralateral one. This study shows that not only the MUNE but also the rate of motor unit loss are frequently asymmetric in amyotrophic lateral sclerosis patients.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Action Potentials
  • Adult
  • Aged
  • Amyotrophic Lateral Sclerosis / diagnosis*
  • Amyotrophic Lateral Sclerosis / pathology
  • Amyotrophic Lateral Sclerosis / physiopathology
  • Disease Progression
  • Electric Stimulation
  • Electromyography*
  • Female
  • Functional Laterality
  • Humans
  • Male
  • Middle Aged
  • Models, Statistical
  • Motor Neurons / pathology*
  • Muscle, Skeletal / innervation*
  • Nerve Degeneration / pathology*
  • Nerve Degeneration / physiopathology
  • Republic of Korea
  • Severity of Illness Index
  • Signal Processing, Computer-Assisted
  • Time Factors
  • Young Adult