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Motor unit number estimation by MScanFit in myotonic dystrophies.

Authors :
Schneider C
Svačina MKR
Kohle F
Sprenger-Svačina A
Fink GR
Lehmann HC
Source :
Journal of the neurological sciences [J Neurol Sci] 2023 Aug 15; Vol. 451, pp. 120728. Date of Electronic Publication: 2023 Jul 11.
Publication Year :
2023

Abstract

Background: MScanFit is a new motor unit number estimation (MUNE) technique applied in motor neuron diseases and polyneuropathies to assess clinical progression and underlying disease pathology. So far, its value in myopathies, especially myotonic dystrophies (MD), has not been investigated.<br />Methods: Motor unit loss and characteristics of patients with genetically confirmed MD type 1 (n = 7) and type 2 (n = 5) were investigated using MScanFit of the abductor pollicis brevis muscle and compared to age-matched healthy controls. MUNE measures were correlated with muscle impairment determined by the MRC sum score and handgrip strength.<br />Results: MScanFit detected motor unit loss in patients with MD (p = 0.017). There was no significant difference in motor unit loss between MD type 1 and type 2 (p = 0.64). CMAP-discontinuities which, when added up, exceed 50% of the CMAP amplitude were reduced in MD patients (p = 0.0284), but motor unit amplitudes were not significantly different (p = 0.0597). The motor unit loss strongly correlated with the MRC sum score (p = 0.014, Rho = 0.678).<br />Conclusions: Our study shows the feasibility of MScanFit in MD and its potential to serve as a surrogate marker for overall muscle impairment. Motor unit analysis indicates that neurogenic alterations in both MD subtypes might be present.<br />Competing Interests: Declaration of Competing Interest This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.<br /> (Copyright © 2023 Elsevier B.V. All rights reserved.)

Details

Language :
English
ISSN :
1878-5883
Volume :
451
Database :
MEDLINE
Journal :
Journal of the neurological sciences
Publication Type :
Academic Journal
Accession number :
37478794
Full Text :
https://doi.org/10.1016/j.jns.2023.120728