Extracting Transition Rules from a 3-Dimensional Cellular Automaton Representing fMRI Data of a Visual Stimulus Experiment
2014 Second International Symposium on Computing and Networking (CANDAR) (2014)
Dec. 10, 2014 to Dec. 12, 2014
Functional magnetic resonance imaging (fMRI) is a technique to measure brain activity dynamics as the time series of spatial measurement units (voxels). Each voxel aggregates the neural activity in its spatial region, which changes over time depending on an external stimulus and the activities of other voxels. This paper uses fMRI data from a visual stimulus experiment to extract transition rules in a cellular automaton-like formulation. We identify states in the brain through k-means clustering and determine basic transition rules from the sequences of states. The relevant voxels in each rule are selected based on their statistical characteristics, resulting in a compact formulation of rewriting rules.
Time series analysis, Visualization, Correlation, Vectors, Time measurement, Data mining, Brain
K. Leibnitz, T. Shimokawa and F. Peper, "Extracting Transition Rules from a 3-Dimensional Cellular Automaton Representing fMRI Data of a Visual Stimulus Experiment," 2014 Second International Symposium on Computing and Networking (CANDAR), Shizuoka, Japan, 2014, pp. 457-462.