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Objective. All motor commands flow through motoneurons, which entrain control of their innervated muscle fibers, forming a motor unit (MU). Owing to the high fidelity of action potentials within MUs, their discharge profiles detail the organization of ionotropic excitatory/inhibitory as well as metabotropic neuromodulatory commands to motoneurons. Neuromodulatory inputs (e.g. norepinephrine, serotonin) enhance motoneuron excitability and facilitate persistent inward currents (PICs). PICs introduce quantifiable properties in MU discharge profiles by augmenting depolarizing currents upon activation (i.e. PIC amplification) and facilitating discharge at lower levels of excitatory input than required for recruitment (i.e. PIC prolongation). Approach . Here, we introduce a novel geometric approach to estimate neuromodulatory and inhibitory contributions to MU discharge by exploiting discharge non-linearities introduced by PIC amplification during time-varying linear tasks. In specific, we quantify the deviation from linear discharge ('brace height') and the rate of change in discharge (i.e. acceleration slope, attenuation slope, angle). We further characterize these metrics on a simulated motoneuron pool with known excitatory, inhibitory, and neuromodulatory inputs and on human MUs (number of MUs; Tibialis Anterior: 1448, Medial Gastrocnemius: 2100, Soleus: 1062, First Dorsal Interosseus: 2296). Main results . In the simulated motor pool, we found brace height and attenuation slope to consistently indicate changes in neuromodulation and the pattern of inhibition (excitation-inhibition coupling), respectively, whereas the paired MU analysis (Δ F ) was dependent on both neuromodulation and inhibition pattern. Furthermore, we provide estimates of these metrics in human MUs and show comparable variability in Δ F and brace height measures for MUs matched across multiple trials. Significance . Spanning both datasets, we found brace height quantification to provide an intuitive method for achieving graded estimates of neuromodulatory and inhibitory drive to individual MUs. This complements common techniques and provides an avenue for decoupling changes in the level of neuromodulatory and pattern of inhibitory motor commands., (Creative Commons Attribution license.)

Objective . Successive improvements in high density surface electromyography and decomposition techniques have facilitated an increasing yield in decomposed motor unit (MU) spike times. Though these advancements enhance the generalizability of findings and promote the application of MU discharge characteristics to inform the neural control of motor output, limitations remain. Specifically, (1) common approaches for generating smooth estimates of MU discharge rates introduce artifacts in quantification, which may bias findings, and (2) discharge characteristics of large MU populations are often difficult to visualize. Approach . In the present study, we propose support vector regression (SVR) as an improved approach for generating smooth continuous estimates of discharge rate and compare the fit characteristics of SVR to traditionally used methods, including Hanning window filtering and polynomial regression. Furthermore, we introduce ensembles as a method to visualize the discharge characteristics of large MU populations. We define ensembles as the average discharge profile of a subpopulation of MUs, composed of a time normalized ensemble average of all units within this subpopulation. Analysis was conducted with MUs decomposed from the tibialis anterior ( N = 2128), medial gastrocnemius ( N = 2673), and soleus ( N = 1190) during isometric plantarflexion and dorsiflexion contractions. Main result . Compared to traditional approaches, we found SVR to alleviate commonly observed inaccuracies and produce significantly less absolute fit error in the initial phase of MU discharge and throughout the entire duration of discharge. Additionally, we found the visualization of MU populations as ensembles to intuitively represent population discharge characteristics with appropriate accuracy for visualization. Significance . The results and methods outlined here provide an improved method for generating estimates of MU discharge rate with SVR and present a unique approach to visualizing MU populations with ensembles. In combination, the use of SVR and generation of ensembles represent an efficient method for rendering population discharge characteristics., (© 2022 IOP Publishing Ltd.)