Coherence and short-term synchronization are insensitive to motor unit spike train nonstationarity.
Motor units discharge at variable rates and thereby produce spike trains that are inherently nonstationary. For signal analysis techniques that assume data stationarity, such as those for detecting and quantifying coherence and short-term synchronization (STS), nonstationarity presents a potential source of inaccuracy. If coherence and STS indices are sensitive to the level of nonstationarity, broad application of these techniques to nonstationary spike trains could diminish the validity of previous and future motor unit synchronization studies. To address this concern, spike trains taken from intramuscular EMG recorded in two hand muscles during a 2-min, submaximal isometric task were checked for significant nonstationarity using the Mann-Kendall test. Linear regressions were then performed for coherence and STS indices against the mean spike train nonstationarity strength for all physiological spike train pairs (n=92). Peak coherence, coherence index, k'-1, and CIS were used to quantify synchronization, whereas two indices (StIn and StatAv) were used to quantify nonstationarity. To extend the scope and power of this study, an integrate-to-fire model was used to create a much larger pool of simulated spike train pairs (n=2520) with predetermined synchronization and stationarity strengths and discharge properties that closely approximated those of the physiological pool. Regressions for both physiological and simulated pairs revealed that coherence, STS, and their indices were insensitive to stationarity. Therefore, motor unit spike train coherence and STS measurements should be robust across all synchronization and nonstationarity levels typical for steady submaximal tasks. Additionally, this finding should hold for all spike trains with similar levels of synchronization and nonstationarity, regardless of origin.
Terry
K
K
University of Texas at Austin, Department of Biomedical Engineering, Austin, TX, USA. kterry@kesslerfoundation.org
Griffin
L
L
D000200:Action Potentials / Q000502:physiology*; D000328:Adult; D000704:Analysis of Variance; D003198:Computer Simulation; D004558:Electric Stimulation / Q000379:methods; D004576:Electromyography / Q000379:methods; D019054:Evoked Potentials, Motor / Q000502:physiology*; D005260:Female; D005542:Forearm / Q000294:innervation; D006801:Humans; D016014:Linear Models; D008297:Male; D008875:Middle Aged; D008959:Models, Neurological; D009046:Motor Neurons / Q000502:physiology*; D018482:Muscle, Skeletal / Q000166:cytology / Q000502:physiology*; D015203:Reproducibility of Results; D055815:Young Adult