Data-driven modeling of neurodegeneration in Alzheimer's disease
Abstract/Contents
- Abstract
- Alzheimer's disease constitutes one of the biggest public health challenges of our time. About one out of nine Americans over the age of 65 is affected today, with numbers rising steeply as our demographics shift towards an older society. The hallmarks of Alzheimer's disease entail an abnormal aggregation of the proteins amyloid-beta and tau in the brain, widespread brain atrophy, and gradually advancing cognitive impairment. The development of disease-modifying treatments requires early diagnosis and a deep understanding of disease causes and progression. However, to date much of our understanding of the early disease mechanisms and their interactions is vague and based on qualitative observations. In this thesis, we introduce computational models describing the prion-like aggregation of protein, the accumulation of pathological tau protein in the brain, and the coupling between tau pathology and regional tissue atrophy in Alzheimer's disease. Validating these models with longitudinal imaging data of real subjects allows us to identify numerical ranges for disease parameters on a personalized basis, thereby shifting our understanding of pathological events and causal relationships to a quantitative level. We leverage hierarchical modeling and Bayesian inference to take advantage of group level similarities and quantify uncertainty in our simulations. With the calibrated models we are able to generate personalized predictions forecasting how tau pathology and atrophy may evolve in a certain patient over time. Our approach also enables us to discover supporting evidence that the presence of amyloid-beta is a driver for tau pathology and tau-induced atrophy.
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 | 2022; ©2022 |
| Publication date | 2022; 2022 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Schäefer, Amelie |
|---|---|
| Degree supervisor | Kuhl, Ellen, 1971- |
| Thesis advisor | Kuhl, Ellen, 1971- |
| Thesis advisor | Chaudhuri, Ovijit |
| Thesis advisor | Delp, Scott |
| Degree committee member | Chaudhuri, Ovijit |
| Degree committee member | Delp, Scott |
| Associated with | Stanford University, Department of Mechanical Engineering |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Amelie Schäefer. |
|---|---|
| Note | Submitted to the Department of Mechanical Engineering. |
| Thesis | Thesis Ph.D. Stanford University 2022. |
| Location | https://purl.stanford.edu/hh180gr8733 |
Access conditions
- Copyright
- © 2022 by Amelie Schaefer
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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