Motor Unit Decomposition

During the submaximal ramp contractions, multi-channel surface EMG signals were recorded from the vastus lateralis (VL) muscle of the right leg using a semi-disposable adhesive grid of 64 electrodes (13 rows and 5 columns with one missing electrode at the corner) with a 1-mm diameter and 8-mm inter-electrode distance (ELSCH064NM2, OT Bioelettronica, Torino, Italy). Prior to attaching the electrodes, the skin was shaved and cleaned by alcohol. Conductive gels were inserted into the cavities of the electrode grid to assure proper electrode-skin contact. Monopolar surface EMG signals were recorded by a multi-channel surface EMG amplifier (quattrocento, OT Bioelettronica, Torino, Italy) and amplified by a factor of 150, sampled at 2,048 Hz, and converted to digital form by a 16-bit analog-to-digital converter together with the force transducer signal. The center of the electrode grid was located at the mid-point of the longitudinal axis of the VL muscle, i.e., the line between the head of the greater trochanter and inferior lateral edge of the patella, and the columns were aligned along the VL longitudinal axis. A reference electrode was placed at the right iliac crest. These electrode locations were the same as in our previous studies (9, 21, 22).

From the recorded multi-channel surface EMG signals, individual motor unit firing patterns were decomposed by the Convolution Kernel Compensation (CKC) technique (23–26). We used the decomposition procedure that was previously extensively validated for signals from various skeletal muscles (21, 26, 27) and used the pulse-to-noise ratio (PNR), introduced by Holobar et al. (28), as an indicator of the motor unit identification accuracy (28). Only motor units with PNR > 30 dB, corresponding to an accuracy of motor unit firing identification > 90%, were used for further analysis; all other motor units were discarded (28). After decomposition, we performed visual inspection to identify discharge times for individual motor units based on PNR, and the discharge times were used for calculation of instantaneous motor unit firing rates for individual motor units. We excluded the discharges with inter-discharge intervals <33.3 or >250 ms, since firing rates calculated from this range of inter-discharge intervals are unusually high (>30 Hz) or low (<4 Hz) for the VL muscle (21, 26).

Median firing rates of individual motor units were calculated from instantaneous firing rates during each 5% of MVC for Ramp30 and 10% of MVC for Ramp70. For example, the median firing rate at 15% of MVC for Ramp30 was calculated from the instantaneous firing rates in the interval from 12.5 to 17.5% of MVC, and the median firing rate at 50% of MVC for Ramp70 was calculated from instantaneous firing rates in the 45–55% MVC interval. Median firing rates with >30% coefficient of variation were excluded for further analysis (29). We divided the detected motor units into three groups by the recruitment force: motor units recruited at <10% (MU10), 10–20% (MU20), and 20–30% of MVC (MU30) for Ramp30, and 0-20% (MU20), 20–40% (MU40), and 40–60% of MVC (MU60) for Ramp70. Averaged values were calculated for each motor unit group with varying recruitment thresholds in different intervention periods. It is well-known that firing patterns are inconsistent among motor unit groups with different recruitment thresholds (30, 31). Our previous studies demonstrated recruitment threshold-dependent changes in motor unit firing patterns following resistance training (9, 22). Also, since the relationship between motor unit firing rate and exerted force is not linear (21, 32), firing rate should be analyzed at various force levels in order to assume the detailed neural adaptations. Therefore, the present study performed analyses of individual motor unit groups with different recruitment thresholds at various force levels. These procedures for calculating motor unit firing rates were employed in our previous studies (9, 21, 22).