Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
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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. Two algorithms for causal discovery were applied to fMRI data, the greedy equivalence search (GES) algorithm and the independent multiple-sample greedy equivalence search (iMAGES). GES applies to individual datasets, and iMAGES to multiple datasets. We consider the stability of the GES results across subjects and experimental repetitions with the same subject. We find that some iMAGES connections agree with previous knowledge of the functional roles of the brain regions. The strengths and limitations of the research work and opportunities for future work are also discussed.