Changes in motor unit discharge patterns following strength training.

Abstract

Previous work has found that only a few weeks of strength training is necessary to elicit increases in muscle strength. Furthermore, it has been found that the increase in muscle strength seen after a few training sessions is primarily the result of neural adaptations, with only minor changes in the contractile element. However, the neural changes that underlie this increase in muscle force are still poorly understood. Such knowledge would provide crucial insights regarding neural involvement in strength training. In a recent issue of the Journal of Physiology, Del Vecchio et al. (2019) report on their study that aimed to provide critical information regarding these adaptations at the motor unit level. Several groups have previously attempted to investigate these changes; however, the results were based on pooling data across subjects because of the limited number of motor units identified from each subject. More importantly, the identified motor units were not tracked at the same relative force levels, making it impossible to identify the specific adaptions taken place. Recent advancements in motor unit decomposition have afforded the ability to decompose the large number of motor units from a single muscle and also track these motor units across multiple sessions. (Martinez-Valdes et al. 2017). Employing the newly developed methods, Del Vecchio et al. (2019) were able to reliably track the same motor units over the course of training, which allowed a comparison of motor unit properties at the level of individual subjects. Del Vecchio et al. (2019) hypothesized that the increase in muscle force would be accompanied by adaptations in the discharge characteristics of the motor units. In their study, Del Vecchio et al. (2019) identified 2560 motor units from the tibialis anterior of 28 young and healthy male subjects during trapezoidal dorsiflexion contractions. Subjects were split into an intervention and a control group, which were matched for anthropometric characteristics, habitual levels of physical activity and peak force with the dorsiflexor muscle. Subjects in the intervention group underwent 4 weeks of unilateral isometric strength training of the ankle dorsiflexors, whereas subjects in the control group simply maintained previous levels of physical activity. Electromyogram signals were collected from the tibialis anterior using high-density surface array electrodes (HDsEMG) and individual motor units were identified by a convolutive blind source separation method. In total, 30% of the identified motor units could be tracked across sessions. The reliability of the tracking technique was based on average two-dimensional correlation value for the action-potential waveforms. Discharge times for motor units were used as triggers for extracting the action-potential waveforms. Del Vecchio et al. (2019) reported: (i) an increase in maximum voluntary force in the intervention group; (ii) a decrease in motor unit recruitment threshold (both in terms of absolute force levels and as a percentage of maximum voluntary force); (iii) a decrease in absolute motor unit derecruitment threshold, although no change was found in the relative motor unit derecruitment threshold; (iv) an increased discharge rate during the plateau phase of the trapezoidal dorsiflexion ramps, yet no change in motor unit discharge rate at recruitment or derecruitment; and (v) an unchanged rate of change of discharge rate with respect to the change of force. These main findings suggest that the increase in muscle force seen after 4 weeks of strength training is primarily the result of an increase in excitatory inputs to the motor neuron. Del Vecchio et al. (2019) employed HDsEMG combined with a blind source separation signal decomposition algorithm to identify a robust number of motor units and, more impressively, they were able to track them over multiple training sessions. By taking advantage of the latest technology, they were able to record and track activities from thousands of motor units, enabling an investigation of synaptic control and intrinsic properties of motor neurons in more depth. They proposed that the changes in motor unit excitability after 4 weeks of strength training are attributable to changes in synaptic inputs from the motor cortex or to adaptations in intrinsic properties of motor neurons. Del Vecchio et al. (2019) suggested that, because there is no difference in the relationship between discharge rate and force, the intrinsic properties of motor neurons were not changed after 4 weeks of training. Instead, they suggested the possibility of the modulation of lower motor neurons by the cortex as the source of adaptation. Although not directly addressed in their study, the data that they collected may be analysed to investigate the role of monoaminergic drive from the brainstem in the increased muscle force. The increased muscle excitation could be elicited through an increase in monoamine-regulated persistent inward currents (PICs). Motor unit recruitment hysteresis is often used to estimate the level of PICs and, correspondingly, the level of monoaminergic input to motor units (Johnson et al. 2017). The results show that, although the motor unit recruitment thresholds significantly decreased, the motor unit de-recruitment thresholds were not decreased with respect to muscle force. These results suggest that there is reduced motor unit hysteresis, with respect to torque, and also may suggest that an increase neuromodulatory inputs from the brainstem did not contribute to the modulation of the spinal motor neuron excitability; however, a more formal investigation of this excitability with established methods (e.g. delta-F) would be necessary to understand any possible changes during strength training (Gorassini et al. 2002). Johnson et al. (2017) also showed that increased corticospinal input tends to compress motor unit recruitment range, which may result in lowered recruitment thresholds. Assessments of PIC levels could provide support for the idea that the motor cortex is responsible for any changes observed in motor neuron excitability. This is further supported by the relationship between the change in force and the change in motor unit discharge rate not being affected by strength training. The results reported by Del Vecchio et al. (2019) suggest that the changes in spinal motor neuron excitability occurring after 4 weeks of strength training are more attributable to changes in synaptic inputs from the cortex, rather than adaptations in intrinsic properties of motor neurons. An electroencephalography study exploring changes in the motor cortex activity after strength training in aging population showed that, after 5 weeks of training, high mental effort training significantly increased the motor activity-related cortical potential. Although statistical significance was lacking (P = 0.061), conventional strength training also increased motor activity-related cortical potential (Jiang et al. 2016). These results further solidify the role of the motor cortex with respect to changes in excitatory inputs to the spinal motor neurons. Corticomuscular coherence is a technique that is used to assess the coherence between electroencephalography and electromyography. Future studies that aim to relate the changes in motor unit discharge patterns reported by Del Vecchio et al. (2019) with cortical measures such as corticomuscular coherence may provide stronger evidence to support increased excitatory inputs to the spinal motor neurons from the cortex being the major source of adaptation in increased muscle force. Furthermore, the methods outlined in the study by Del Vecchio et al. (2019) should be applied to more diverse population groups (e.g. women and older adults) to generalize these results further. No competing interests declared. All authors have approved the final version of the manuscript and agree to be accountable for all aspects of the work. All persons designated as authors qualify for authorship, and all those who qualify for authorship are listed. This work is supported by NIH grants R01NS098509 (EHK) and T32HD07418 (ASH).