July 17, 2016
Gregory E. P. Pearcey1,3, David J. Bradbury-Squires2, Michael Monks1, Devin Philpott1, Kevin E. Power1,2, & Duane C. Button1,2
1 School of Human Kinetics and Recreation, St. John’s, NL
2 Faculty of Medicine, Memorial University, St. John’s, NL
3 Centre for Biomedical Research, University of Victoria, Victoria, BC
In general, the system that controls the movements of the human body can be thought of as a multi-level system that includes the central (i.e. brain and spinal cord) and peripheral (i.e. muscle) nervous systems. Examining the interactions between these systems may allow us to understand the contribution of each to neuromuscular fatigue that occurs as a result of exercise. By understanding how fatigue contributes to a decrease in exercise performance, this information may be able to contribute to the development of training protocols for rehabilitative and athletic purposes and enhance overall physical performance. One type of exercise that leads to significant neuromuscular fatigue is intermittent sprinting such as 10 maximal intensity cycling sprints for ~30s or less with ~3 minutes or less of rest between sprints. Currently we know that both peripheral and central nervous system fatigue occurs during repeated, maximal intensity leg-cycling sprints. Muscle fatigue develops within the first few sprints and increases even more during the remainder of the sprints whereas central fatigue occurs towards the end of the sprints. Central fatigue appears to occur at the point where a person begins to slow down (i.e. at the point of task failure) suggesting that the “task failure” portion of fatigue occurs within the central nervous system. While we know that central fatigue occurs during repeated sprints, we did not know whether the brain or spinal cord excitability was altered. Thus, our experiment attempted to differentiate the effects of maximal intensity arm-cycling sprints on the central (i.e. brain; cortical neurone and spinal cord; spinal motoneurone) and peripheral (i.e. muscle) nervous systems. The primary motor cortex, spinal cord and biceps brachii were all stimulated and the responses in the biceps brachii during maximal and submaximal contractions were measured. We first determined that arm-cycling sprints induced fatigue of the peripheral and central nervous system. Results showed that the following variables for each participant decreased from pre-sprint 1 to post-sprint 10: 1) sprinting power, 2) maximal elbow flexion force, 3) percent voluntary activation of the elbow flexors, and 4) elbow flexion contractile ability. Since it was confirmed that central nervous system fatigue occurred, several participants performed another series of sprints to determine changes in brain and spinal cord excitability of the biceps brachii. After the second series of sprints, we found that during submaximal elbow flexion there was a decrease in brain and increase in spinal cord excitability of the biceps brachii. The differential brain and spinal cord excitability indicate decreased output from the brain, which was offset by increased output from the spinal cord in order to maintain the muscle contraction. Decreased output from the brain may be accompanied by a compromised ability to produce maximal force and optimal arm-cycling sprint performance. Overall, the brain may be safeguarding the body from injury, but it remains to be determined if training can alter the compromised output from the brain as a result of fatigue.
This article is a summary of an article published in Applied Physiology, Nutrition & Metabolism. If you intend to cite any information in this article, please consult the original article and cite that source. This summary was written for the Canadian Society for Exercise Physiology and it has been reviewed by the CSEP Knowledge Translation Committee.
Original Article
Pearcey, G.E., Bradbury-Squires, D.J., Monks, M., Philpott, D., Power, K.E., and Button, D.C. 2014. Arm-cycling sprints induce neuromuscular fatigue of the elbow flexors and alter corticospinal excitability of the biceps brachii. Appl. Physiol. Nutr. Metab. 41:199-209. doi:10.1139/apnm-2015-0438