Interpretation of Event-related Fmri Using Cluster Analysis

Event-related fMRI can access more complex experimental paradigms than traditional block designs. A new problem, however, becomes the data analysis, i.e., the generation of appropriate statistical models. For parametric paradigms where a set of parameters describing task and stimulus can be varied, the number of diierent conditions becomes large. Even if the combinatorics of contrasts between conditions is reduced by assumptions, the number of contrasts that potentially may contribute to the functional interpretation becomes huge. We propose a new explorative method of data interpretation for paradigms with many diierent conditions. The method is based on a cluster analysis (dynamical clustering 2]) that extracts a set of represenatative time courses from the original data. We select interesting clusters using cluster size and a compactness criterion. On this highly reduced data set it is possible to study systematically the temporal patterns of activation and derive a functional interpretation of the data.