Spatiotemporal clustering of fMRI time series in the spectral domain

We propose a new method for the analysis of functional magnetic resonance images (fMRI). The decision that a voxel v0 is activated is based not solely on the value of the fMRI signal at v0, but rather on the comparison of all time series s(v)(t) in a small neighborhood Nv0 around v0. Our approach explicitly takes into account the intrinsic spatiotemporal correlations that exist in the data. We focus on experimental designs with periodic stimuli, and therefore we can capture most of the features of the BOLD signal with a low dimensional subspace in the frequency domain. The presence of activated time series can be detected by partitioning the time series in this low dimensional space. Experiments with simulated data, and experimental fMRI data, demonstrate that our approach can outperform standard methods of analysis, such as the t-test.