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Conference proceeding
Using Causal Modeling for Determining Connectivity among Brain Regions
Proceedings of the International Conference on Artificial Intelligence ICAI 2010, pp.653-659
International Conference on Artificial Intelligence (ICAI) (Las Vegas, NV, USA, 07/12/2010–07/15/2010)
2010
Abstract
Identifying brain regions that activate when a subject is presented a stimulus or performs a task can be done by analyzing Functional Magnetic Resonance Imaging (f/IRl) data. Causal modeling methods can be used to discover causal relationships among activity in brain regions, or which regions of the brain influence which other regions during a task. To determine effective connectivity among brain regions, two causal discovery algorithms, the Greedy Equivalence Search (GES) algorithm and the independent Multiple-sample Greedy Equivalence Search (iMAGES), were applied to fMRI data. GES was applied to individual fMRI data sets, and iMAGES to multiple data sets. The algorithms were evaluated across multiple experimental repetitions and subjects. The results indicate that some iMAGES connections agree with previous knowledge of the functional roles of the brain regions. The strengths and limitations of the research work are discussed, as well as paths for future work.
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Details
- Title
- Using Causal Modeling for Determining Connectivity among Brain Regions
- Publication Details
- Proceedings of the International Conference on Artificial Intelligence ICAI 2010, pp.653-659
- Resource Type
- Conference proceeding
- Conference
- International Conference on Artificial Intelligence (ICAI) (Las Vegas, NV, USA, 07/12/2010–07/15/2010)
- Publisher
- CSREA Press
- Identifiers
- 99380460967306600
- Academic Unit
- Office of Teaching, Learning, and Technology; College of Arts, Social Sciences, and Humanities; Center for Cybersecurity and AI
- Language
- English