Feature-space clustering for fMRI meta-analysis

Clustering functional magnetic resonance imaging (fMRI) time series has eme rged in recent years as a possible alternative to parametric modeling appro aches. Most of the work so far has been concerned with clustering raw time series. In this contribution we investigate the applicability of a clusteri ng method applied to features extracted from the data. This approach is ext remely versatile and encompasses previously published results [Goutte et al ., 1999] as special cases. A typical application is in data reduction: as t he increase in temporal resolution of fMRI experiments routinely yields fMR I sequences containing several hundreds of images, it is sometimes necessar y to invoke feature extraction to reduce the dimensionality of the data spa ce. A second interesting application is in the meta-analysis of fMRI experi ment, where features are obtained from a possibly large number of single-vo xel analyses. Ln particular this allows the checking of the differences and agreements between different methods of analysis. Both approaches are illu strated on a fMRI data set involving visual stimulation, and we show that t he feature space clustering approach yields nontrivial results and, in part icular, shows interesting differences between individual voxel analysis per formed with traditional methods. (C) 2001 Wiley-Liss, Inc.