Identification of extensive samples of motor units in two human muscles.

A. We used a blind source separation (BSS) algorithm to decompose the overlapping activity of motor units (MU) into trains of discharge times during a force-matching trapezoidal task. B. We reconstructed synthetic EMG signals by summing the trains of action potentials from all the identified motor units and interpreting the remaining EMG signal as the part of the signal not explained by the decomposition. C. We calculated the ratio between the powers of synthetic and original EMG signals to estimate the proportion of the signal variance explained by the decomposition. D. We estimated the uniqueness of each identified motor unit within the pool by calculating the root-mean-square error (RMSE) between the distributions of action potentials of the same motor unit across contractions (two panels on the left, reference value in E), and between motor units (left vs. right panels, distribution of RMSE between motor units in yellow in E). F. We considered each motor unit to be unique within the pool when the RMSE between its distributions of action potentials across contractions (reference value) was less than the 5th percentile of the distribution of RMSE with the rest of the motor units across contraction levels. Motor units considered as outliers in F (red data points) were removed from the analysis due to potential errors in tracking between contractions. Each data point is a motor unit, the box represents 25th-75th percentiles of the distribution of data, and the black line is the median. The horizontal thick line denotes a statistical difference between reference values and 5th percentiles for each muscle.

Non-linear rate coding of motor units.

A. We analysed the relation between motor unit firing rate (pulses per second, pps) and force with the assumption that a linear increase in force is proportional to a linear increase in net synaptic input. For this, we concatenated the values of instantaneous firing rate for each motor unit (grey data points) recorded over all the contractions where that motor unit was identified, as shown here for one motor unit (coloured data points for contractions at 20, 40, and 60%MVC). The force-firing rate relations were then fitted with three different functions: linear (green), rising exponential (dark red), and natural logarithm (yellow), to characterise the input-output function of each motoneuron. B. The motor units were grouped according to their best fit. We display the distribution of motor unit recruitment thresholds (RT) for each of these groups. The inset panels depict the percentage of motor units (MU) in each group. C. We further analysed the rate coding of motor units by reporting the acceleration of firing rate. For this, we fitted the force-firing rate relation with the natural logarithm (f(force) =a*ln(force)+b; yellow trace) and computed its first derivative (f(force)=a/force; dark red trace). The right panels depict the relations between the initial acceleration of motor unit firing rates and their recruitment threshold (RT) for all participants, with a total of 328 and 393 motor units for TA and VL, respectively. Each data point is a motor unit. The horizontal thick line denotes a statistical difference between motor units grouped according to their recruitment thresholds (low = Blue; medium = Grey; high = Pink). The green line depicts the non-linear fits of these relations for the Tibialis Anterior and the Vastus Lateralis. Similar fits were observed for all the participants (inset panels).

Motor unit firing rates across contraction levels.

We calculated the average firing rate (pulses per second, pps) during the force plateaus for each tracked motor unit for all participants from TA (n = 998 motor units; A) and VL (n = 1016 motor units; B). Each data point is a motor unit, and each line connects the firing rates of this motor unit across contractions. The colour scale identifies the three groups of motor units based on recruitment threshold: low (blue), medium (grey), and high (pink). The central panels depict the change in firing rates between contractions separated by 10% to 70% of the maximal voluntary contraction level (MVC). The right panels show the relation between the rate of increase in firing rate between successive levels of force (e.g., between 10% and 20%MVC) and the recruitment threshold of the motor unit. We fitted these relations for each participant with a linear function (coloured lines). The horizontal thick line denotes a statistical difference between motor units grouped according to their recruitment thresholds.

Hysteresis between recruitment and derecruitment thresholds.

The left panels depict the relations between the recruitment and derecruitment thresholds of each motor unit from TA (A) and VL (B) across all participants. Each grey data point is a motor unit. These relations were fitted for each participant (coloured lines) using either non-linear or linear regressions. The values below the dashed red line (recruitment threshold = derecruitment threshold) show a positive hysteresis between recruitment and derecruitment thresholds, the values above a negative hysteresis. The right panels show the difference between the recruitment and derecruitment thresholds, with negative values showing a positive hysteresis with recruitment threshold greater than derecruitment threshold and the positive values indicating the converse (a negative hysteresis). Each data point is a motor unit. The asterisk denotes a statistical difference between the hysteresis values for motor units grouped according to recruitment threshold (low = Blue; medium = Grey; high = Pink) and the absence of a hysteresis (dashed horizontal line).