Temporal characteristics of intrinsic brain activity based on functional magnetic resonance imaging
Abstract/Contents
- Abstract
- Since its inception in 1992, functional Magnetic Resonance Imaging (fMRI) has become an indispensable tool for studying cognition in both healthy and dysfunctional brains. FMRI monitors changes in the oxygenation of brain tissue resulting from altered metabolism consequent to a task-based evoked neural response, or from spontaneous fluctuations in neural activity in the absence of conscious mentation (the "resting state"). Efforts undertaken in this dissertation focus on how temporal information collected by fMRI can be harnessed and carefully processed to exploit two new directions of resting state functional connectivity: brain network dynamics and high-frequency functional connectivity. The first part of the dissertation presents advancements on a single-time-frame-based methodology that builds upon co-activation pattern analysis, to quantify and synthesize dynamic functional information. With this proposed approach, we reveal elaborate changes exerted by a sustained working memory task compared to resting state, as well as distinct anti-correlation patterns of dorsal and ventral branches of the brain default-mode network. The second part of this dissertation presents first and preliminary explorations into the mechanisms of recently reported high-frequency (> 0.1 Hz) spontaneous neural activity. We show that fractional contributions of non-blood-oxygen-level-dependent components increase as frequency goes up; and that part of existing observations on high-frequency phenomena may stem from improper preprocessing.
Description
| Type of resource | text |
|---|---|
| Form | electronic; electronic resource; remote |
| Extent | 1 online resource. |
| Publication date | 2017 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Associated with | Chen, Jingyuan |
|---|---|
| Associated with | Stanford University, Department of Electrical Engineering. |
| Primary advisor | Glover, Gary H |
| Thesis advisor | Glover, Gary H |
| Thesis advisor | Greicius, Michael D |
| Thesis advisor | Pauly, John (John M.) |
| Thesis advisor | Poldrack, Russell A |
| Advisor | Greicius, Michael D |
| Advisor | Pauly, John (John M.) |
| Advisor | Poldrack, Russell A |
Subjects
| Genre | Theses |
|---|
Bibliographic information
| Statement of responsibility | Jingyuan Chen. |
|---|---|
| Note | Submitted to the Department of Electrical Engineering. |
| Thesis | Thesis (Ph.D.)--Stanford University, 2017. |
| Location | electronic resource |
Access conditions
- Copyright
- © 2017 by Jingyuan Chen
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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