Interparticipant correlations: A model free FMRI analysis technique

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Abstract
FMRI analysis techniques can be broadly divided into model based and data driven techniques. The most widely used approach assumes an explicit temporal hemodynamic model based upon the experimental paradigm. Such an approach has proven very useful and powerful even though it is limited by the accuracy of the prespecified model. An alternative approach is to use data driven techniques like independent component analysis or fuzzy cluster analysis. These approaches have proven useful for exploratory analysis in a multivariate sense; however, they can present additional difficulties in the interpretation of the results. An alternative to these approaches is to take advantage of similarities in the patterns of the hemodynamics between participants [i.e., interparticipant correlation (IPC)]. This FMRI analysis technique enjoys the parsimony of the general linear model (GLM) but does not assume a specific FMRI time course. The technique consists of calculating voxel-wise correlations between participants resulting in IPC maps, which indicate the activated regions the participants have in common. We applied the IPC approach to data collected from healthy controls in an auditory oddball task. As expected, high inter-participant correlations were detected in auditory cortical regions in the temporal lobes where highest correlations were evident. In addition, areas that appear to be involved in the task were detected using IPC's but not the GLM regression. This technique, designed to have increased sensitivity to inter-subject correlations that are not necessarily task-related, may potentially be useful as a compliment to model-based approaches.