Multiscale modeling of brain mechanics
Abstract/Contents
- Abstract
- During our entire lifetime, we continuously rely on the brain to facilitate functioning of almost every part of our body. A proper development of the brain and its safe protection are, therefore, of utmost importance. Mechanics plays an important role in both of these aspects. Although evidence for the presence and significance of mechanics in the brain dates back several decades, a thorough understanding of its precise role has not yet been established. Computational modeling provides a powerful tool to investigate the role of mechanics and to probe causal relationships between mechanics and brain development or brain injury that are extremely difficult to obtain with experiments alone. We use and develop computational tools to investigate the role of mechanics in the brain at and across three spatial scales: the subcellular level of individual proteins, the cellular scale of the nerve cell and its axon, and the tissue scale of whole brain. We design a novel finite element framework that allows to assign a molecular mechanism to individual elements that represents the characteristic dynamic behavior of a particular protein at the subcellular level. By embedding these mechanisms into our computational model of the axon, we investigate their effects on emergent properties at the cellular level, such as axon stiffness, viscosity, and damage. Our results demonstrate mechanisms by which dynein and myosin motors, microtubule dynamics, and microtubule disassembly affect axon outgrowth. Our axon model further predicts critical thresholds for axon damage that are consistent with values reported in the literature. We use these results to develop a constitutive model for axon damage that can be embedded in a continuum model at the tissue level. Finally, we propose a continuum multifield model at the tissue level that accurately predicts the development of brain volume and structure during weeks 10-29 of gestation. We couple cell migration to mechanics and volume growth. Our model predicts a wide variety of complex folding patterns ranging from single creases to sinusoidal folding and provides additional insights into the role of mechanics during the complex process of cortical folding.
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 | 2018; ©2018 |
| Publication date | 2018; 2018 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | De Rooij, Rijk |
|---|---|
| Degree supervisor | Kuhl, Ellen, 1971- |
| Thesis advisor | Kuhl, Ellen, 1971- |
| Thesis advisor | Cai, Wei, 1977- |
| Thesis advisor | Pinsky, P |
| Degree committee member | Cai, Wei, 1977- |
| Degree committee member | Pinsky, P |
| Associated with | Stanford University, Department of Mechanical Engineering. |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Rijk De Rooij. |
|---|---|
| Note | Submitted to the Department of Mechanical Engineering. |
| Thesis | Thesis Ph.D. Stanford University 2018. |
| Location | electronic resource |
Access conditions
- Copyright
- © 2018 by Rijk De Rooij
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