Full metadata
Title
Maximin designs for event-related fMRI with uncertain error correlation
Description
One of the premier technologies for studying human brain functions is the event-related functional magnetic resonance imaging (fMRI). The main design issue for such experiments is to find the optimal sequence for mental stimuli. This optimal design sequence allows for collecting informative data to make precise statistical inferences about the inner workings of the brain. Unfortunately, this is not an easy task, especially when the error correlation of the response is unknown at the design stage. In the literature, the maximin approach was proposed to tackle this problem. However, this is an expensive and time-consuming method, especially when the correlated noise follows high-order autoregressive models. The main focus of this dissertation is to develop an efficient approach to reduce the amount of the computational resources needed to obtain A-optimal designs for event-related fMRI experiments. One proposed idea is to combine the Kriging approximation method, which is widely used in spatial statistics and computer experiments with a knowledge-based genetic algorithm. Through case studies, a demonstration is made to show that the new search method achieves similar design efficiencies as those attained by the traditional method, but the new method gives a significant reduction in computing time. Another useful strategy is also proposed to find such designs by considering only the boundary points of the parameter space of the correlation parameters. The usefulness of this strategy is also demonstrated via case studies. The first part of this dissertation focuses on finding optimal event-related designs for fMRI with simple trials when each stimulus consists of only one component (e.g., a picture). The study is then extended to the case of compound trials when stimuli of multiple components (e.g., a cue followed by a picture) are considered.
Date Created
2019
Contributors
- Alrumayh, Amani (Author)
- Kao, Ming-Hung (Thesis advisor)
- Stufken, John (Committee member)
- Reiser, Mark R. (Committee member)
- Pan, Rong (Committee member)
- Cheng, Dan (Committee member)
- Arizona State University (Publisher)
Topical Subject
Resource Type
Extent
xii, 89 pages : color illustrations
Language
eng
Copyright Statement
In Copyright
Primary Member of
Peer-reviewed
No
Open Access
No
Handle
https://hdl.handle.net/2286/R.I.55595
Statement of Responsibility
by Amani Alrumayh
Description Source
Viewed on November 18, 2020
Level of coding
full
Note
thesis
Partial requirement for: Ph.D., Arizona State University, 2019
bibliography
Includes bibliographical references (pages 83-87)
Field of study: Statistics
System Created
- 2020-01-14 09:17:22
System Modified
- 2021-08-26 09:47:01
- 3 years 2 months ago
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