How computational technique and spike train properties affect coherence detection.

Spike train coherence is used to characterize common inputs that drive motor unit synchronization. However, data segmentation, overlap, and taper can affect coherence magnitude, thereby influencing the incidence at which significant coherence is detected. Also, the effect of spike train firing rate and common input variability on the detection of significant coherence is unknown. 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), trial durations (30, 60, 90 and 120 s), 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%). Tapered segments overlapped by at least 50% maximized coherence, regardless of taper type. Coherence for 30-s trials revealed significant coherence for less than half of the motor unit pairs, demonstrating the advantages of longer trails. The 2048-sample segments produced similar coherence values with twice the frequency resolution. Increasing the common input variability from 0.15 to 0.50 reduced coherence incidence by approximately 60%, indicating that synchronized physiological motor unit pairs may fail to show significant coherence if the common input frequency is sufficiently unstable.