Motor unit synchronous firing as revealed by determinism of surface myoelectric signal
Introduction
Human single motor unit recordings have been extensively used to study the functional coupling (synchronization) of pairs of motor units (Adam et al., 1978, Datta et al., 1991, Kim et al., 2001, Mattei et al., 2003, Schmied et al., 1993, Schmied et al., 1994, Sears and Stagg, 1976, Turker and Powers, 2001). However, this technique has a number of drawbacks: (1) it is invasive and causes discomfort, (2) recordings from pairs of voluntary activated motor units can be reliably obtained only for weak or moderate levels of tonic muscle contraction, and (3) motor unit discrimination is time-consuming. In addition, correlating the firing of pairs of voluntary activated motor units in the time-domain (cross-correlation analysis) (Mattei et al., 2003, Schmied et al., 1993) yields findings which may not reflect the properties of the entire motoneurone population.
Although the surface electromyogram (EMG) is a global measure of motor unit activation in humans, there are limits to the information that can be extracted from this signal using conventional techniques (Disselhorst-Klug et al., 2000, Farina et al., 2002, Farina et al., 2004b, Semmler and Nordstrom, 1999, Yue et al., 1995). It has been proposed that motor strategies can be examined with variables extracted from the surface EMG by non-linear recurrence methods (Farina et al., 2004b, Filligoi and Felici, 1999, Nieminen and Takala, 1996, Webber et al., 1995). Recurrence quantification analysis (RQA) (Eckmann et al., 1987) is a technique for the detection of state changes in drifting dynamic systems that does not necessitate any a priori constraint on data size, stationarity, and statistical distribution (Fattorini et al., 2005, Filligoi and Felici, 1999). Subtle changes in surface EMG can be detected by different indexes extracted from RQA such as the percentage of determinism (%DET), which reflects the amount of rule-obeying structure in the signal dynamic (Farina et al., 2002, Farina et al., 2004b, Webber et al., 1995). The %DET may be a sensitive variable to detect motor unit synchronization, because it reveals embedded determinisms in an apparently stochastic signal (Eckmann et al., 1987).
The purpose of the present study was to investigate whether RQA is effective in detecting motor unit synchronization from the surface EMG. This methodology was compared with findings obtained in parallel from single muscle fibre recordings and cross-correlation analysis.
Section snippets
Material and subjects
The data for the present study was retrieved from original recordings obtained in our lab and used in a previous publication (Mattei et al., 2003). In that work, an increased synchronization of pairs of voluntarily activated motor units recorded from extensor carpi radialis (ECR) muscle was demonstrated following the acute injection of l-acetylcarnitine (l-Ac), a cholinergic substance shown to potentiate spinal recurrent inhibition (Mazzocchio and Rossi, 1997), but not after saline injection.
Results
Fig. 2 shows the group data relative to the amount of synchronization between pairs of voluntary activated ECR motor units before, during and after l-Ac (A) and saline (B) injection. There was a significant difference (t = 8.71, P < 0.0001) in the synchronization peak area, as expressed by SIP, between the pre-injection period (0.053 ± 0.002) and the l-Ac injection period (0.071 ± 0.001), whereas no significant difference in the synchronization peak area was found between the pre-injection period (0.053
Discussion
This study demonstrates that the application of RQA to the surface EMG signal (Farina et al., 2004b, Filligoi and Felici, 1999, Nieminen and Takala, 1996, Webber et al., 1995) is as effective as the cross-correlation analysis of single motor unit activity (Adam et al., 1978, Datta et al., 1991, Kim et al., 2001, Mattei et al., 2003, Schmied et al., 1993, Schmied et al., 1994, Sears and Stagg, 1976, Turker and Powers, 2001) in extracting information on the synchronous behaviour of motoneurones
Acknowledgements
This research was supported by grants from the University of Siena (Piano di Ateneo per la Ricerca: PAR), Italian Ministry for Scientific Research and Association Française contre les Myopathies (AFM), la Fondation pour la Recherche Médicale (FRM), the Délégation Générale à L’Armement (DGA).
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