Abstract
Objective. Chronic muscle weakness impacts the majority of individuals after a stroke. The origins of this hemiparesis is multifaceted, and an altered spinal control of the motor unit (MU) pool can lead to muscle weakness. However, the relative contribution of different MU recruitment and discharge organization is not well understood. In this study, we sought to examine these different effects by utilizing a MU simulation with variations set to mimic the changes of MU control in stroke. Approach. Using a well-established model of the MU pool, this study quantified the changes in force output caused by changes in MU recruitment range and recruitment order, as well as MU firing rate organization at the population level. We additionally expanded the original model to include a fatigue component, which variably decreased the output force with increasing length of contraction. Differences in the force output at both the peak and fatigued time points across different excitation levels were quantified and compared across different sets of MU parameters. Main results. Across the different simulation parameters, we found that the main driving factor of the reduced force output was due to the compressed range of MU recruitment. Recruitment compression caused a decrease in total force across all excitation levels. Additionally, a compression of the range of MU firing rates also demonstrated a decrease in the force output mainly at the higher excitation levels. Lastly, changes to the recruitment order of MUs appeared to minimally impact the force output. Significance. We found that altered control of MUs alone, as simulated in this study, can lead to a substantial reduction in muscle force generation in stroke survivors. These findings may provide valuable insight for both clinicians and researchers in prescribing and developing different types of therapies for the rehabilitation and restoration of lost strength after stroke.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Similar Papers
More From: Journal of Neural Engineering
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.