Abstract
One of the most important achievements in understanding the brain isthat the emergence of complex behavior is guided by the activity ofbrain networks. To fully apply this theoretical approach fully, a methodis needed to extract both the location and time course of theactivities from the currently employed techniques. The spatialresolution of fMRI received great attention, and variousnon-conventional methods of analysis have previously been proposed forthe above-named purpose. Here, we briefly outline a new approach to dataanalysis, in order to extract both spatial and temporal activities fromfMRI recordings, as well as the pattern of causality between areas.This paper presents a completely data-driven analysis method thatapplies both independent components analysis (ICA) and the Grangercausality test (GCT), performed in two separate steps. First, ICA isused to extract the independent functional activities. Subsequently theGCT is applied to the independent component (IC) most correlated withthe stimuli, to indicate its causal relation with other ICs. Wetherefore propose this method as a promising data-driven tool for thedetection of cognitive causal relationships in neuroimaging data.