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Thesis
Discovering Causal Relationships from FMRI Data
University of West Florida Libraries
Master of Science (MS), University of West Florida
2009
Abstract
Functional Magnetic Resonance Imaging (fMRI) data have been used for identifying brain regions that activate when a subject is presented a stimulus or performs a task. Beyond identifying which regions of the brain are active during a task, it is also of interest to discover causal relationships among activity in those regions, that is, which regions of the brain influence which other regions of the brain during a task. In this thesis, two algorithms for causal discovery were evaluated, the Greedy Equivalence Search (GES) algorithm and the independent Multiple-sample Greedy Equivalence Search (iMAGES) algorithm on fMRI data. GES is a local search algorithm, and iMAGES is a generalization of GES to handle multiple data sets. The results of the algorithms were analyzed for stability and evaluated for validity. The evidence shows that iMAGES outperforms GES when evaluated for stability. iMAGES also produces more valid results when evaluated using previous knowledge from the functional roles of the brain regions. The strengths and limitations of the research work and opportunities for future work are also discussed.
Details
- Title
- Discovering Causal Relationships from FMRI Data
- Resource Type
- Thesis
- Publisher
- University of West Florida Libraries
- Format
- pdf
- Number of pages
- 90
- Copyright
- © 2020 Lydia Faith Gyulavics Reyes-Wright
- Identifiers
- 99380090721006600
- Academic Unit
- Computer Science
- Language
- English
- Awarding Institution
- University of West Florida; Master of Science (MS)
- Theses and Dissertations
- Master of Science (MS), University of West Florida