Subspace models for functional MRI data analysis

The models used for analyzing functional MRI (fMRI) data have profound impact on the detection of active brain areas. In this paper temporal and spatial linear subspace models for fMRI analysis are reviewed. General principles of how such subspaces should be constructed in order to obtain optimal detection performance are discussed and it is shown that customarily employed subspace models can be significantly improved upon.