Computational modeling of axon mechanics : viscoelasticity, growth, and damage
Abstract/Contents
- Abstract
- Axons play a vital role in the function of the nervous system by carrying electrical signals between neurons and other cells. Their long and thin structure, however, makes them especially vulnerable to damage. In fact, axons are a frequent lesion site in traumatic brain injury, and diffuse axonal injury has been linked to coma, cognitive impairment, and the development of neurodegenerative disease. While loading applied at high rates and magnitudes can damage the axon, the axon exhibits different responses to mechanical loading at other loading rates and magnitudes. At physiological levels of loading, the axon deforms as a viscoelastic solid, and at intermediate levels of loading, mechanical forces can even induce growth and promote regeneration. In fact, the gradual application of tension is being explored as an avenue to promote axon regeneration in peripheral nerve repair. The success of approaches like this is dependent on knowledge of the mechanical responses of the axon - viscoelasticity, growth, and damage - as well as the thresholds separating these responses. Here, computational modeling can aid the generalization from in vitro measurements to engineering applications. In this thesis, I introduce computational models describing the complex mechanical response of the axon. First, I present a mathematical framework modeling axon viscoelasticity, growth, and damage and discuss its application in informing nerve repair strategies. Then, focusing in on damage, I introduce an image-based axon model used to pinpoint factors influencing susceptibility to injury. Finally, I discuss the application of mechanics-informed neural networks to model viscoelastic response. These computational models assemble myriad experimental measurements into frameworks that provide a comprehensive representation of axon mechanical response.
Description
| Type of resource | text |
|---|---|
| Form | electronic resource; remote; computer; online resource |
| Extent | 1 online resource. |
| Place | California |
| Place | [Stanford, California] |
| Publisher | [Stanford University] |
| Copyright date | 2023; ©2023 |
| Publication date | 2023; 2023 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Wang, Lucy Monyue |
|---|---|
| Degree supervisor | Kuhl, Ellen |
| Thesis advisor | Kuhl, Ellen |
| Thesis advisor | Chaudhuri, Ovijit |
| Thesis advisor | Goodman, Miriam |
| Degree committee member | Chaudhuri, Ovijit |
| Degree committee member | Goodman, Miriam |
| Associated with | Stanford University, School of Engineering |
| Associated with | Stanford University, Department of Mechanical Engineering |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Lucy Monyue Wang. |
|---|---|
| Note | Submitted to the Department of Mechanical Engineering. |
| Thesis | Thesis Ph.D. Stanford University 2023. |
| Location | https://purl.stanford.edu/kb752kr8956 |
Access conditions
- Copyright
- © 2023 by Lucy Monyue Wang
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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