Jacob Stephens
BME PhD Proposal Presentation

Date: 2026-02-26
Time: 3:30
Location / Meeting Link: Callaway Manufacturing Research Building, Auditorium 101, https://gatech.zoom.us/j/99851533123?pwd=UFD1iSlz8zzVmntXBSLcZBNZP9sd3v.1

Committee Members:
Timothy Cope, Lena Ting, T. Richard Nichols, Gregory Sawicki, Gordon Berman, Garrett Stanley


Title: Multiscale modeling of proprioceptive sensory signaling of muscle mechanical state

Abstract:
Proprioceptive sensors in the muscle (i.e. the muscle spindle and tendon organ) provide mechanical feedback regarding the mechanical state of the muscle that is critical for the effective control of motion in both humans and animals. Motor disorders, such as stroke, cerebral palsy, Parkinson’s disease, dystonia, and more, are also associated with the dysregulation of these proprioceptive-motor circuits. As it is difficult to record from these sensory neurons in freely behaving animals, computational models are a promising tool in investigating proprioceptive sensory-motor integration. In this thesis, I will analyze the relationship between the mechanical state of the muscle tendon unit and the signals generated by muscle spindle and tendon organ sensory neurons. My previous work has investigated the impact of tendon compliance on sensory signaling and developed a theoretical model to propose how muscle spindle and tendon organ afferents signal muscle state as a population. In Aim 1, I will use this model to characterize different mechanical states of the muscle during a muscle work loop protocol in rats. My previous work on this model focused on passive stretches of the muscle, and the work loop protocol will build upon this with alternating stretch and muscle contraction. In Aim 2 I will use this same work loop protocol to further develop a biophysical muscle model to simultaneously predict the responses of different types of muscle spindle sensory neurons and tendon organ sensory neurons. Overall, this work will establish a framework to better understand the mechanical feedback produced by these sensors and how it is used for control and improve our ability to predictively model this feedback across a range of behaviors.