Spatiotemporal Exploratory Analysis of FMRI Data

Radu Mutihac¹

Spatiotemporal characteristics of brain activity are frequently unknown and variable, which preclude their evaluation by confirmatory methods only. Revealing unanticipated or missed patterns of activation, exploratory data analysis (EDA) allows to improve or even to change the original hypotheses. Artifactual behavior that EDA may easily discover could raise questions on data appropriateness, if additional preprocessing steps are required, or if the preprocessing employed has introduced spurious effects. Spatial independent component analysis (sICA) and temporal fuzzy cluster analysis (tFCA) were comparatively investigated as typifying EDA of neuroimaging data.