Year: 2025
Type: article
Study design: Observational Study
Abstract: INTRODUCTION/AIMS: Surviving motor units in neurogenic diseases demonstrate collateral reinnervation. Scanning electromyography (EMG) reveals normal motor unit corridor length, but with "silent regions," suggesting that reinnervation does not result in increased motor unit size but may increase motor unit complexity. Motor unit magnetic resonance imaging (MUMRI) pairs MR imaging with electrical nerve stimulation to visualize individual motor units. This study aimed to assess the motor unit dimensions and complexity in patients with previous poliomyelitis compared to healthy controls. METHODS: Patients with a history of polio were recruited from the British Polio Fellowship, compared to a retrospective cohort of healthy controls. They underwent medical history and examination of lower limb power, fatigue assessment (fatigue severity score, FSS), and a 3 T MUMRI scan of the less-affected lower limb. The cross-sectional area, maximum, and minimum Feret diameter of the motor unit territories in tibialis anterior were calculated. Motor unit complexity was computed using the Hausdorff box-counting method. RESULTS: Of 12 polio survivors, n = 8 (6 female) were suitable for analysis and were compared to 19 controls. The mean motor unit maximum Feret diameter was 10.3 ± 3.1 mm compared to 8.4 ± 5.2 mm in controls (p = 0.34). The mean shape complexity was 0.59 ± 0.12 compared to 0.45 ± 0.2 in controls (p = 0.03). DISCUSSION: Polio survivors demonstrate motor units with normal dimensions but increased shape complexity, indicating nonuniform collateral reinnervation largely limited to existing territories. The size and shape of motor units could help in understanding the physiological processes behind reinnervation, both in polio and other neurogenic diseases such as amyotrophic lateral sclerosis.
Source: Muscle & Nerve
DOI: 10.1002/mus.70107
Language: English
Subfield: Genetics
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
Estimated APC paid: $4,610
OpenAlex ID: W4417311981
