Feature characterization in fMRI data: the Information Bottleneck approach.

Clustering is a well-known technique for the analysis of Functional Magnetic Resonance Imaging (fMRI) data, whose main advantage is certainly flexibility: given a metric on the dataset, it "summarizes" the main characteristics of the data. But intrinsic to this approach are also the problem of defining correctly the quantization accuracy, and the number of clusters necessary to describe the data. The Information Bottleneck (IB) approach to vector quantization, proposed by Bialek and Tishby, addresses these difficulties: (1) it deals with an explicit trade-off between quantization and data fidelity; (2) it does so during the clustering procedure and not post hoc; (3) it takes into account the full statistical distribution of the features within the feature space and not only their most likely value; last, it is principled through an information theoretic formulation, which is relevant in many situations. In this paper, we present how to benefit from this method to analyze fMRI data. Our application is the clustering of voxels according to the magnitude of their responses to several experimental conditions. The IB quantization provides a consistent representation of the data, allowing for an easy interpretation and comparison of datasets.