Modeling of Muscle Motor Unit Innervation Process Correlation and Common Drive

Concurrently active motor units (MUs) of a given muscle can exhibit a certain degree of synchronous firings, and a certain degree of common variation in their firing rates. The former property is referred to as motor unit synchrony in the literature, which is termed motor unit innervation process (MUIP) correlation in this study. The latter is referred to as motor unit common drive and can be quantified by the common drive coefficient, which is the correlation coefficient between the smoothed firing rates of the two MUs. Both properties have important roles and implications in the generation and resulting characteristics of the myoelectric signal and for the development of signal processing algorithms in myoelectric signal (MES) applications. In order to study these implications and characteristics, in this paper estimation procedures are developed to quantify the degree of MUIP correlation and common drive as functions of physiological parameters. Also, the interaction between MUIP correlation and motor unit common drive is studied in a physiologically realistic simulation model. Neurons modeled by Hodgkin-Huxley systems form the framework of the simulation model in which excitation and synaptic characteristics can be modified. MUIP correlation and common drive degree and interaction are studied through a number of simulations. To support the simulation results, experimental in vivo motor unit trains were collected at low levels of contraction from 11 subjects, and decomposed into the constituent unit trains giving 50 concurrently active motor unit pairs. The simulation demonstrated that the innervation process correlation coefficient is controlled primarily by the postsynaptic conductance, gsyn, and was less than 0.05 mS/cm2 for realistic values of gsyn. The common drive was found to be controlled by the exciting neuron input with no statistically significant interaction between it and the MUIP correlation. The experimental data gave results in close agreement with those of the simulation

[1]  G. P. Moore,et al.  Neuronal spike trains and stochastic point processes. II. Simultaneous spike trains. , 1967, Biophysical journal.

[2]  B Hudgins,et al.  Myoelectric signal processing for control of powered limb prostheses. , 2006, Journal of electromyography and kinesiology : official journal of the International Society of Electrophysiological Kinesiology.

[3]  Armando Malanda-Trigueros,et al.  Automated decomposition of intramuscular electromyographic signals , 2006, IEEE Transactions on Biomedical Engineering.

[4]  C J De Luca,et al.  Synchronization of motor-unit firings in several human muscles. , 1993, Journal of neurophysiology.

[5]  Neville Hogan,et al.  Myoelectric Signal Processing: Optimal Estimation Applied to Electromyography - Part II: Experimental Demonstration of Optimal Myoprocessor Performance , 1980, IEEE Transactions on Biomedical Engineering.

[6]  C. Morris,et al.  Voltage oscillations in the barnacle giant muscle fiber. , 1981, Biophysical journal.

[7]  P.A. Parker,et al.  Signal processing for the multistate myoelectric channel , 1977, Proceedings of the IEEE.

[8]  R. Enoka,et al.  Motor-unit synchronization alters spike-triggered average force in simulated contractions. , 2002, Journal of neurophysiology.

[9]  R N Scott.  Myoelectric control of prostheses. , 1966, Archives of physical medicine and rehabilitation.

[10]  Zeynep Erim,et al.  Common drive of motor units in regulation of muscle force , 1994, Trends in Neurosciences.

[11]  L. Adams,et al.  Synchronization of motor unit firing during different respiratory and postural tasks in human sternocleidomastoid muscle. , 1989, The Journal of physiology.

[12]  A Schmied,et al.  Human spinal lateralization assessed from motoneurone synchronization: dependence on handedness and motor unit type. , 1994, The Journal of physiology.

[13]  Michael A. Nordstrom,et al.  Influence of handedness on motor unit discharge properties and force tremor , 2004, Experimental Brain Research.

[14]  Wulfram Gerstner,et al.  Spiking Neuron Models , 2002 .

[15]  Michael A. Nordstrom,et al.  Relationship between motor unit short-term synchronization and common drive in human first dorsal interosseous muscle , 1997, Brain Research.

[16]  José A. González-Cueto,et al.  Deconvolution estimation of nerve conduction velocity distribution , 2002, IEEE Transactions on Biomedical Engineering.

[17]  José A. González-Cueto,et al.  The contribution of motor unit pairs to the correlation functions computed from surface myoelectric signals , 2005, IEEE Transactions on Biomedical Engineering.

[18]  R. Scott,et al.  A Nonstationary Model for the Electromyogram , 1977, IEEE Transactions on Biomedical Engineering.

[19]  Shi Zhou,et al.  Motor unit synchronisation is enhanced during slow lengthening contractions of a hand muscle , 2002, The Journal of physiology.

[20]  T. Sears,et al.  Short‐term synchronization of intercostal motoneurone activity. , 1976, The Journal of physiology.

[21]  A J Fuglevand,et al.  Estimating the strength of common input to human motoneurons from the cross‐correlogram. , 1992, The Journal of physiology.

[22]  Ning Jiang,et al.  A Novel Simulation Model for the Motor Unit Innervation Process , 2005, 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference.

[23]  C. D. De Luca,et al.  Control scheme governing concurrently active human motor units during voluntary contractions , 1982, The Journal of physiology.

[24]  José A. González-Cueto,et al.  Deconvolution estimation of motor unit conduction velocity distribution , 2002, IEEE Transactions on Biomedical Engineering.

[25]  R. Enoka,et al.  Motor-unit synchronization increases EMG amplitude and decreases force steadiness of simulated contractions. , 2000, Journal of neurophysiology.

[26]  S. Farmer,et al.  Central nervous pathways underlying synchronization of human motor unit firing studied during voluntary contractions. , 1991, The Journal of physiology.

[27]  A. Hodgkin,et al.  A quantitative description of membrane current and its application to conduction and excitation in nerve , 1952, The Journal of physiology.

[28]  T. Kuiken,et al.  A simulation study to examine the use of cross-correlation as an estimate of surface EMG cross talk. , 2003, Journal of applied physiology.

[29]  J A Stephens,et al.  Variation in the degree of synchronization exhibited by motor units lying in different finger muscles in man. , 1991, The Journal of physiology.

[30]  J. Bendat,et al.  Random Data: Analysis and Measurement Procedures , 1987 .

[31]  J. Semmler,et al.  Motor unit discharge and force tremor in skill- and strength-trained individuals , 1998, Experimental Brain Research.

[32]  P. Parker,et al.  Motor unit power spectrum and firing rate , 2006, Medical and Biological Engineering and Computing.

[33]  D M Halliday.  Generation and characterization of correlated spike trains. , 1998, Computers in biology and medicine.

[34]  Michael A Nordstrom,et al.  A comparison of cross-correlation and surface EMG techniques used to quantify motor unit synchronization in humans , 1999, Journal of Neuroscience Methods.