PhD Proposal by Sean O'Connell

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Sean O'Connell
BME PhD Proposal Presentation

Date: 2022-11-04
Time: 1:00 PM-3:00 PM ET
Location / Meeting Link: HSRB E182 / https://emory.zoom.us/j/98567559878

Committee Members:
Chethan Pandarinath, PhD (Advisor); Samuel Sober, PhD (Advisor); Gordon Berman, PhD; Young-Hui Chang, PhD; Chris Rodgers, PhD; Matthew Tresch, PhD

Title: Analyzing Flexible Motor Unit Coordination Patterns Underlying Dynamic Behaviors with Novel Experimental and Computational Tools

Every movement an animal makes is dependent on sophisticated coordination across thousands of motor neurons controlling hundreds of thousands of muscle fibers. Each motor neuron can innervate 1’s to 100’s of muscle fibers in a single muscle, acting as a synchronized group called a motor unit (MU). From over 60 years of research, we have established models of individual MU physiology and function, but still lack comprehensive knowledge of large-scale MU population coordination across a range of dynamic behaviors. One of the earliest known coordination patterns is the size principle, an observation that smaller MUs, innervating fewer muscle fibers, tend to be recruited first, followed by progressively larger ones. However, many studies have also shown counter examples revealing a shift in the MU coordination towards earlier recruitment of larger MUs when behaviors are dynamic, with high rates of force output (dF/dt) or high ranges of motion, such as in multifunctional muscles. While these shifts in coordination are well documented, it is currently unknown exactly how this change occurs. Is there a discrete shift in MU population coordination at a threshold rate of force output, or are patterns of coordination shifted continuously? By leveraging novel, flexible electromyography arrays developed in the Sober Lab, we will be able to record from large numbers of MUs during multiple dynamic behaviors in rats. However, being able to collect this data also requires establishing tasks that can be parametrically adjusted to elicit a range of coordination patterns. This proposed project is focused on 1) establishing two tasks that allow parametric force condition adjustment, and 2) developing and applying novel experimental and computational methods to investigate the changes in MU coordination across task conditions. So far, I have implemented a treadmill locomotion task, including motion tracking across a range of inclines. In addition, I made significant contributions toward establishing a skilled forelimb knob rotation task, which can flexibly apply arbitrary knob torque profiles. Using novel computational methods I have developed, which compare similarity of MU population activity patterns, I will be able to determine whether shifts in MU population coordination are continuous or if they occur discretely during natural behaviors. In addition, I will investigate whether the MU coordination patterns across the two tasks are significantly different within single muscles, which would likely be due to differences in the upstream circuitry driving each behavior. For Aims 1 and 2, I hypothesize MU coordination adjustment will be continuous, rather than discrete, across a range of treadmill inclines (Aim 1) and knob torques (Aim 2) due to the corresponding smooth changes in behavior. For Aim 3, I hypothesize separate patterns of coordination will drive the behavior in each task. Critically, regardless of the experimental results of these aims, the scientific outcomes of this study will provide valuable insights into how the nervous system flexibly adjusts MU coordination across a range of dynamic behaviors, impacting basic neuroscience and development of restorative therapies in the future.


  • Workflow Status: Published
  • Created By: Tatianna Richardson
  • Created: 10/27/2022
  • Modified By: Tatianna Richardson
  • Modified: 10/27/2022


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