Over the course of the semester, our trainees are reviewing webinars in their given fields and preparing abstracts to help colleagues outside their discipline make an informed choice about watching them. As our program bridges diverse disciplines, these abstracts are beneficial for our own group in helping one another gain key knowledge in each other’s fields. We are happy to share these here for anyone else who may find them helpful.
Part of Georgia Tech’s 2020-2021 Neuro Seminar Series
Lena Ting, Professor of Biomedical Engineering, Emory University
November 2, 2020
In this talk Dr. Ting gives a very nice overview of the physiology and functions of muscle spindles, and connects the induced firing rate to many parameters such as length, velocity, area, force, inertia, dynamic, and static inputs. This illustration really emphasizes the complexity of muscle spindle firing mechanisms and resulting modulation of local tissue. Throughout her talk she builds upon the current model of muscle spindle firing through extremely controlled sets of experiments, and eventually establishes a new frame work to contextualize the multi-functionality of muscle spindles and involvement in conditions such as sensory neuropathy, aging-related soft tissue changes, muscular dystrophy, and hyper-resistance.
In the cross-disciplinary field of neuroscience studying neuromuscular junctions and tissue, the current aim for scientists and engineers is to take a readout of muscle spindle signals to establish a predictive model in the contexts of neuromuscular disorders/injuries. Although a very useful approach, the current understanding of muscle spindles is limited and requires more exploration to develop such a robust model. Dr. Ting does a very nice job of describing the necessary workflow to address this research aim. As such, she navigates through four sequential questions: (1) what can we describe about muscle spindle firing, (2) what key mechanisms underlie these relationships, (3) how to build simplest predictive model, and (4) with this model can we understand what muscle spindles encode?
Through this journey, Dr. Ting reveals that rather than than viewing muscle spindles as encoders, we should actually consider what they compute. This conclusion arises from the discovery that central effects on muscle spindle sensory information include feature detection and selection and have major dependencies on interactions between body and environment. This leaves the audience (assumed to be researchers) to extend these findings to now link a mechanistic and theoretical understanding of muscle spindle function in the aim to treat and restore patients with impaired movement.