Analysis of event-related fMRI data using best clustering bases

We explore a new paradigm for the analysis of event-related functional magnetic resonance images (fMRI) of brain activity. We regard the fMRI data as a very large set of time series x(i)(t), indexed by the position i of a voxel inside the brain. The decision that a voxel i0 is activated is based not solely on the value of the fMRI signal at i0, but rather on the comparison of all time series x(i)(t) in a small neighborhood Wi0 around i0. We construct basis functions on which the projection of the fMRI data reveals the organization of the time-series x(i)(t) into "activated", and "non-activated" clusters. These "clustering basis functions" are selected from large libraries of wavelet packets according to their ability to separate the fMRI time-series into the activated cluster and a non activated cluster. This principle exploits the intrinsic spatial correlation that is present in the data.