Capturing brain dynamics with topological data analysis
Abstract/Contents
- Abstract
- As brain imaging technologies measure increasingly higher spatial resolutions and faster time scales, complementary advances in analyzing whole-brain activation time-series data are necessary. Topological models offer a powerful framework for this analysis by describing the dynamical organization of the brain as a graph and can effectively capture the underlying shape of the space explored by the brain, for example, during ongoing cognition. A recently established approach using the Mapper algorithm from topological data analysis (TDA) now enables the construction of these graphs from whole-brain functional imaging data. The work described in this thesis advances that approach in three ways. First, we provide new open-source tools for visualizing and extracting insights from shape graph representations of neuroscientific data learned by Mapper. Second, we introduce a new Mapper algorithm inspired by the high dimensionality of brain imaging data designed to reduce both information loss and computational cost. Third, we extend the Mapper-based approach to naturalistic fMRI data analysis, quantifying more ecologically valid transitions in the unstructured data and leveraging annotations provided by the paradigm (e.g., tasks, stimuli-derived features). By simultaneously addressing usability, scalability, and ecological validity, this dissertation takes us three steps closer to translational applications of our Mapper-based approach, and ultimately, to realizing the promise of precision neuroimaging. Along the way, we introduce a new fMRI dataset collected using a naturalistic self-viewing paradigm and describe a novel link between brain dynamics and behavior.
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 | Geniesse, Caleb W |
|---|---|
| Degree supervisor | Saggar, Manish |
| Thesis advisor | Saggar, Manish |
| Thesis advisor | Carlsson, G. (Gunnar), 1952- |
| Thesis advisor | Hosseini, Hadi |
| Degree committee member | Carlsson, G. (Gunnar), 1952- |
| Degree committee member | Hosseini, Hadi |
| Associated with | Stanford University, School of Humanities and Sciences |
| Associated with | Stanford University, Biophysics Program |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Caleb William Geniesse. |
|---|---|
| Note | Submitted to the Biophysics Program. |
| Thesis | Thesis Ph.D. Stanford University 2023. |
| Location | https://purl.stanford.edu/gh427hg8925 |
Access conditions
- Copyright
- © 2023 by Caleb W Geniesse
- License
- This work is licensed under a Creative Commons Attribution Non Commercial Share Alike 3.0 Unported license (CC BY-NC-SA).
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