High-Density surface EMG for the investigation of myotonic discharges in subjects with myotonic dystrophy

Myotonic dystrophy is a rare and slowly progressive neuromuscular disease caused by genetic defects. To date, two forms of myotonic dystrophy have been identified: myotonic dystrophy type 1 (DM1 or Steinert’s disease) and myotonic dystrophy type 2 (DM2 or PROMM). Myotonic dystrophy is characterized by progressive myopathy, muscle weakness, multiorgan failure involvement, and myotonia. Myotonia can be defined as excessive and prolonged muscle excitability and muscle contraction at rest, right after muscle percussion or at the end of the excitatory stimulus. It is possible to identify two forms of myotonia: clinical myotonia that consists in incomplete relaxation of muscle following either voluntary contraction or direct percussion, and electric myotonia that is an abnormal muscle fibre membrane electrical activity observed by needle EMG examination. Nowadays, needle electromyography is the gold standard to confirm the presence of myotonic discharges. Nevertheless, due to its invasiveness and limited detection volume, needle EMG present a number of drawbacks. The present study proposes to objectively characterize the phenomenon of myotonia in a population of patients affected by myotonic dystrophy in a non-invasive manner by means of the so-called high-density surface EMG (HDsEMG). This technique allows studying in a non-invasive way the peripheral and central properties of the neuromuscular system. The main aims are: a) to assess the involvement of specific muscles or muscle compartments of the forearm during myotonic discharges, b) to investigate motor units’ characteristics and recruitment in pathological and healthy patients . Six patients with myotonic dystrophy and 10 healthy controls underwent experimental examination. HDsEMG and force signals were acquired by means of a 96-electrode grid overlying the forearm flexor muscles and a handgrip dynamometer. According to the clinical tests used to elicit myotonic contraction, we studied EMG activity during and after hand closing. The subject was requested to perform three sets of contractions with 10-minute interval of rest between them. Each set consisted of 6 maximal voluntary isometric contractions, each lasting 3 seconds, with a 10-second rest period in between. Signals were processed to assess muscle recruitment and motor unit behaviour. Firstly, an algorithm for the detection of the activation instants was applied. Secondly, the single motor unit templates and discharges were extracted through an automated signal decomposition algorithm. Some indexes were extracted from the force signals, such as the relaxation time (indicating the time required for decline in force from 90% to 5% of the peak force after each contraction) and rise time (the time taken for the rising edge of the force profile to go from 5% to 90% of the maximum). According to the main hypothesis, patients showed longer relaxation times and rise times, as well as lower levels of muscle strength. The assessment of myotonic discharges through HDsEMG allowed the identification of the main muscles and muscle compartments involved in the prolonged muscle contraction and the pattern of motor units’ recruitment.