Motor Unit Action Potential Clustering—Theoretical Consideration for Muscle Activation during a Motor Task

Abstract

During dynamic or sustained isometric contractions, bursts of muscle activity appear in the electromyography (EMG) signal. Theoretically, these bursts of activity likely occur because motor units are constrained to fire temporally close to one another and thus the impulses are “clustered” with short delays to elicit bursts of muscle activity. The purpose of this study was to investigate whether a sequence comprised of “clustered” motor unit action potentials (MUAP) can explain spectral and amplitude changes of the EMG during a simulated motor task. This question would be difficult to answer experimentally and thus, required a model to study this type of muscle activation pattern. To this end, we modeled two EMG signals, whereby a single MUAP was either convolved with a randomly distributed impulse train (EMG-rand) or a “clustered” sequence of impulses (EMG-clust). The clustering occurred in windows lasting 5–100 ms. A final mixed signal of EMG-clust and EMG-rand, with ratios (1:1–1:10), was also modeled. A ratio of 1:1 would indicate that 50% of MUAP were randomly distributed, while 50% of “clustered” MUAP occurred in a given time window (5–100 ms). The results of the model showed that clustering MUAP caused a downshift in the mean power frequency (i.e., ~30 Hz) with the largest shift occurring with a cluster window of 10 ms. The mean frequency shift was largest when the ratio of EMG-clust to EMG-rand was high. Further, the clustering of MUAP also caused a substantial increase in the amplitude of the EMG signal. This model potentially explains an activation pattern that changes the EMG spectra during a motor task and thus, a potential activation pattern of muscles observed experimentally. Changes in EMG measurements during fatiguing conditions are typically attributed to slowing of conduction velocity but could, per this model, also result from changes of the clustering of MUAP. From a clinical standpoint, this type of muscle activation pattern might help describe the pathological movement issues in people with Parkinson’s disease or essential tremor. Based on our model, researchers moving forward should consider how MUAP clustering influences EMG spectral and amplitude measurements and how these changes influence movements.

Intermuscular Coherence Between Surface EMG Signals Is Higher for Monopolar Compared to Bipolar Electrode Configurations

“…The monopolar recordings demonstrate a pronounced peak in the power spectrum between frequencies of 30–50 Hz. This 40 Hz peak in the EMG power spectrum of dynamic tasks has been observed previously and has been connected to rhythmic bursts of clustered motor unit activity, where multiple motor units are firing within a short time window of 10 ms ( Yao et al, 2000 ; Maurer et al, 2013 ; Asmussen et al, 2018 ). If the two electrodes of a bipolar amplifier are recording motor unit action potentials from different motor units that are virtually firing at the same time, the common mode rejection would likely remove a significant amount of this information and explain why the 40 Hz peak is absent or much reduced in amplitude in the power spectra obtained from bipolar recordings.…”

“…The discrepancy between the monopolar and bipolar recording systems could originate from additional synchronized inputs that the vasti muscles received from the central nervous system during the unstable squat as suggested by the corresponding increase in VL-VM coherence during this exercise. Such synchronized motor unit activity would increase the overall EMG intensity ( Yao et al, 2000 ; Asmussen et al, 2018 ) but may not be detected by the bipolar EMG recording system due to the elimination or reduction of common input signals as explained above.…”

Development of a multichannel current-EMG system for coherence modulation with visual biofeedback

“…Thus, it seems that for the biofeedback condition, the muscles have an increased common input. This could be explained by the clustering effect shown in recent work that suggests that with a shorter activation period during a dynamic contraction, MUs activate in clusters that share a common input [ 13 ]. Our experiments show that, to increase the intermuscular coherence during the biofeedback condition, subjects performed a shorter contraction with higher EMG activity.…”

“…Synchronization of MUs generate spectral changes by grouping, and thus clustering the MUAPs, a process that affects the low frequencies of the EMG spectrum. [ 13 ]. Muscles are also controlled by varying the contributions from the motor cortex that can be seen in the coherence between electroencephalography (EEG) and EMG signals at the frequencies of the beta (12 − 30 Hz) to gamma bands (30 − 80 Hz) [ 14 ].…”

Muscle Activation Profile During Perturbed Walking is Modulated According to Body State

“…properties, that affect the frequency spectrum, are the firing behavior of the motor units (i.e., synchronization and clustering), and the shape of the motor unit action potential (1,10,44). In recent years, there has been growing interest in muscle activation frequency while walking (5,23,38,47).…”