Human motor unit synchrony and its relation to force steadiness

by 1961- Terry, Charles Kevin

Spike train coherence is an important metric used to characterize common inputs that drive motor unit synchrony. However, data segmentation, overlap, and taper can significantly affect coherence magnitude, thereby influencing the sensitivity of its detection. Also, increasing spike train variability can significantly reduce coherence for a fixed synchrony level. To address these issues, we used a pool of simulated synchronized spike trains with various firing rates (7-19 Hz), coefficients of variation (CV) (0.05-0.50), common input frequencies (10, 20, and 30 Hz, CV: 0.05-0.50) and trial durations (30, 60, 90 and 120 sec.) and synchronization strength to explore the effects of segment length (1024 and 2048 1-ms samples), tapering (Hann, Nuttall, and rectangular), and overlap (0, 37.5, 50, 62.5, and 75%) on coherence detection. The model incorporated a leaky integrator that modeled a branched common input as a periodic pulse train acting on two independent motor neurons. Tapered segments overlapped by at least 50% maximized coherence, regardless of taper type. Even at the highest synchronization level, coherence measurements for 30second trials failed to reveal significant coherence for even half of the motor unit pairs, even though a common input was present for all of them, demonstrating the need for the longest practical trial duration when measuring coherence. Also, 2048-sample segments produced similar coherence values with twice the frequency resolution. Finally, for a given synchrony level, increasing variabilities of firing rate and common input from 0.15- 15 0.50 significantly reduced coherence detection by approximately 5% and 60%, respectively.