Decoding firings of a large population of human motor units from high‐density surface electromyogram in response to transcranial magnetic stimulation

Abstract

We describe a novel application of methodology for high‐density surface electromyography (HDsEMG) decomposition to identify motor unit (MU) firings in response to transcranial magnetic stimulation (TMS). The method is based on the MU filter estimation from HDsEMG decomposition with convolution kernel compensation during voluntary isometric contractions and its application to contractions elicited by TMS. First, we simulated synthetic HDsEMG signals during voluntary contractions followed by simulated motor evoked potentials (MEPs) recruiting an increasing proportion of the motor pool. The estimation of MU filters from voluntary contractions and their application to elicited contractions resulted in high (>90%) precision and sensitivity of MU firings during MEPs. Subsequently, we conducted three experiments in humans. From HDsEMG recordings in first dorsal interosseous and tibialis anterior muscles, we demonstrated an increase in the number of identified MUs during MEPs evoked with increasing stimulation intensity, low variability in the MU firing latency and a proportion of MEP energy accounted for by decomposition similar to voluntary contractions. A negative relationship between the MU recruitment threshold and the number of identified MU firings was exhibited during the MEP recruitment curve, suggesting orderly MU recruitment. During isometric dorsiflexion we also showed a negative association between voluntary MU firing rate and the number of firings of the identified MUs during MEPs, suggesting a decrease in the probability of MU firing during MEPs with increased background MU firing rate. We demonstrate accurate identification of a large population of MU firings in a broad recruitment range in response to TMS via non‐invasive HDsEMG recordings. Key points Transcranial magnetic stimulation (TMS) of the scalp produces multiple descending volleys, exciting motor pools in a diffuse manner. The characteristics of a motor pool response to TMS have been previously investigated with intramuscular electromyography (EMG), but this is limited in its capacity to detect many motor units (MUs) that constitute a motor evoked potential (MEP) in response to TMS. By simulating synthetic signals with known MU firing patterns, and recording high‐density EMG signals from two human muscles, we show the feasibility of identifying firings of many MUs that comprise a MEP. We demonstrate the identification of firings of a large population of MUs in the broad recruitment range, up to maximal MEP amplitude, with fewer required stimuli compared to intramuscular EMG recordings. The methodology demonstrates an emerging possibility to study responses to TMS on a level of individual MUs in a non‐invasive manner.

Motor unit discharge rate modulation during isometric contractions to failure is intensity‐ and modality‐dependent

“…It is also worth considering methodological differences between studies, with many prior studies utilising intramuscular EMG and template matching algorithms that carry a higher risk of erroneous MU identification during prolonged contraction where MU action potential waveforms are likely to change due to modulation in conduction velocity (Farina et al., 2009). In this study, we utilised an approach of estimating and applying MU filters in successive, short windows, which has been shown to be a robust approach when attempting to accommodate small changes in MU waveforms (Francic & Holobar, 2021; Kramberger & Holobar, 2021; Škarabot, Ammann et al., 2023). From this perspective, our results broadly agree with a study that utilised multichannel surface EMG decomposition, and a time‐domain MU tracking approach (Martinez‐Valdes, Negro, Falla et al., 2020).…”

“…HDsEMG signals were decomposed using the extensively validated Convolution Kernel Compensation algorithm (Holobar & Zazula, 2007). This algorithm is based on the blind source separation principles whereby the EMG mixing model is inverted, and MU filters are estimated, yielding the estimation of an MU spike train (Holobar & Farina, 2021; Škarabot, Ammann et al., 2023).…”

“…The calculation of MU filters and their application to a different portion of the signal from the same contraction or another contraction allows identification of discharges from the same MUs, even in the case of filter application to higher contraction intensities (Francic & Holobar, 2021), or different levels of MU synchronisation (Škarabot, Ammann et al., 2023). These factors are important for tasks in the present study where recruitment of additional MUs is likely (and thus similar to higher contraction levels, leads to the presence of MUs with larger potentials in the signal), as are increases in MU discharge rate, which is mathematically linked to higher MU synchronisation (De La Rocha et al., 2007).…”

Human motor unit discharge patterns reveal differences in neuromodulatory and inhibitory drive to motoneurons across contraction levels

“…To identify unique MUs, we used the separation vectors (MU filters), to identify MU spike trains that were common across contraction levels. The reader is referred to previously published work on the mathematical basis and validation of this approach (Francic & Holobar, 2021; Škarabot et al ., 2023a). Briefly, decomposition results of individual contractions performed at different intensities were concatenated.…”

Motor unit discharge rate modulation during isometric contractions to failure is intensity and task dependent

“…HDsEMG signals were decomposed using the extensively validated Convolution Kernel Compensation algorithm (Holobar & Zazula, 2007). This algorithm is based on the blind source separation principles whereby the EMG mixing model is inverted, and MU filters are estimated, yielding the estimation of a MU spike train (Holobar & Farina, 2021; Škarabot et al ., 2023a).…”