Methods for Analysis of High-Resolution Muscle Recordings During Dynamic Movement and Generation of Realistic Motor Unit Datasets for Ground Truth
Author(s)
O'Connell, Sean
Advisor(s)
Editor(s)
Collections
Supplementary to:
Permanent Link
Abstract
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 control dozens to hundreds of muscle fibers, which forms a functional unit of the motor system, called a motor unit. From over 60 years of research, we have learned a lot about individual motor unit physiology and function, but we still have a limited understanding of large-scale motor unit population coordination. By leveraging novel, flexible Myomatrix arrays (Chung et al., 2023), we were able to record from many motor units during multiple dynamic behaviors in rats. This project focused on producing new methods for spike sorting, that is, robustly identifying the activation times and identities of many simultaneously recorded motor units across a range of force conditions during dynamic behavior. The methods we developed were shown to provide a substantial performance boost in the accuracy of identifying when each motor unit became active during behavior. In addition to this, we developed new methods for generating simulated motor unit datasets that closely replicate the properties of the real recorded data we collected. These new datasets enabled the development of our novel methods to match candidate motor units with ground truth units to be able to robustly compute the accuracy of motor unit spike sorting. Overall, this work provides improved methods for spike sorting motor unit datasets, unlocking the potential for discovery of new diagnostic techniques for motor coordination disabilities and for addressing gaps in our scientific understanding of motor unit coordination.
Sponsor
Date
2025-11-14
Extent
Resource Type
Text
Resource Subtype
Dissertation (PhD)