Effect of synchronization of firings of different motor unit types on the force variability in a model of the rat medial gastrocnemius muscle

Postural control during the back squat at different load intensities in powerlifters and weightlifters

BACKGROUND

The movement of the barbell has been detected as success factor for the snatch and the clean and jerk events. As the barbell's movement has been shown to be related to the athlete's body movement, we hypothesized that the latter could be a success factor also for the back squat (BS) event. Hence, this study aimed to investigate postural control during the execution of the BS at different load intensities in powerlifters and weightlifters.

METHODS

Seventeen powerlifters and weightlifters were enrolled and the one-repetition maximum (1-RM) of the BS of each participant was measured. Afterwards, the assessment of postural control during the execution of the BS at different load intensities (i.e. 60%, 70%, 80%, 90%, 100%) of the 1-RM of each participant was carried out through a posturographic platform to measure the displacement of the centre of pressure (CoP). The following parameters were considered: sway path length (SPL), sway ellipse surface (SES), length/surface (LFS ratio), sway mean speed (SMS), CoP coordinates along X and Y planes.

RESULTS

We found a significant increase in SPL and LFS ratio, and a significant decrease in SMS as the load intensity increased. In detail, we detected a significant difference in: (a) SPL between the BS at 60% and 80%, 60% and 90%, 60% and 100%; between the BS at 70% and 90%, 70% and 100%; between the BS at 80% and 100%; and between the BS at 90% and 100%; (b) SMS between the BS at 60% and 80%, 60% and 90%; (c) LFS ratio between the BS at 60% and 90%, 60% and 100%.

CONCLUSIONS

These results suggest that powerlifters and weightlifters adopt different postural control strategies depending on the load intensity when performing the BS. Our findings showed that higher effort could affect postural control during the BS. Thus, postural control could be considered a success factor for the BS.

Skeletal muscle models composed of motor units: A review

The mathematical muscle models should include several aspects of muscle structure and physiology. First, muscle force is the sum of forces of multiple motor units (MUs), which have different contractile properties and play different roles in generating muscle force. Second, whole muscle activity is an effect of net excitatory inputs to a pool of motoneurons innervating the muscle, which have different excitability, influencing MU recruitment. In this review, we compare various methods for modeling MU twitch and tetanic forces and then discuss muscle models composed of different MU types and number. We first present four different analytical functions used for twitch modeling and show limitations related to the number of twitch describing parameters. We also show that a nonlinear summation of twitches should be considered in modeling tetanic contractions. We then compare different muscle models, most of which are variations of Fuglevand's model, adopting a common drive hypothesis and the size principle. We pay attention to integrating previously developed models into a consensus model based on physiological data from in vivo experiments on the rat medial gastrocnemius muscle and its respective motoneurons. Finally, we discuss the shortcomings of existing models and potential applications for studying MU synchronization, potentiation, and fatigue.

Influence of central and peripheral motor unit properties on isometric muscle force entropy: A computer simulation study

Approximate entropy of isometric force is a popular measure to characterize behavioral changes across muscle contraction conditions. The degree to which force entropy characterizes the randomness of the motor control strategy, however, is not known. In this study, we used a computational model to investigate the correlation between approximate entropy of the synaptic input to a motor neuron pool, the neural drive to muscle (cumulative spike train; CST), and the force. This comparison was made across several simulation conditions, that included different synaptic command signal bandwidths, motor neuron pool sizes, and muscle contractile properties. The results indicated that although force entropy to some degree reflects the entropy of the synaptic command to motor neurons, it is biased by changes in motor unit properties. As a consequence, there was a low correlation between approximate entropy of force and the motor neuron input signal across all simulation conditions (r² = 0.13). Therefore, force entropy should only be used to compare motor control strategies across conditions where motor neuron properties can be assumed to be maintained. Instead, we recommend that the entropy of the descending motor commands should be estimated from CSTs comprising spike trains of multiple motor units.