Separating motor and neural effects of somatosensory perturbations

Afferent feedback has been shown to modulate the excitability of the motor neuron pool and is known to play a central role in the regulation of muscle activity. The overall aim of this project is to enhance our understanding of the human somatosensory system and to determine the contribution of afferent feedback to motor control. Previous studies have demonstrated the complexity of sensory perturbations. Blood flow restriction provides an elegant model for analysing the effects of altered sensory feedback as it stimulates and inhibits different afferent species. A direct recording of neural information is hardly achievable in humans due to practical (nerves are just a few square millimetres in diameter) and ethical (they lay deep under the skin and are easy to damage) factors. Muscles, on the other hand, act as biological amplifiers of the descending signal. They allow for a convenient, non- (or minimally-) invasive, precise and reliable measurement of the efferent information, which goes under the name of electromyography (EMG). High-density EMG (HDEMG) allows to decompose the activity of a considerable amount of motor neurons and to infer the behaviour of the motor neuron pool, therefore representing the ideal methodology of investigation for this project.

We have modified the work packages of the preceding project (PN 2-3 A) as follows: WP 1 investigate the hypothesis that altered afferent feedback modifies muscle activity and leads to central compensatory mechanisms associated with fatigue. WP 2 generally investigate the behaviour of synergistic muscle pairs in the context of fatigue to better understand central compensatory strategies and combine these results with BFR in WP 3 to better understand the common drive and shared motor pool. WP 4 provide isolated metabolic effects by studying the contralateral side to generate a holistic picture of the coping strategy of the sensory nervous system with altered afferent feedback. The datasets generated with high quality baseline and perturbed neuromechanical data can be used for model development and validation.