Factor Topographic Latent Source Analysis: Factor Analysis for Brain Images

Traditional approaches to analyzing experimental functional magnetic resonance imaging (fMRI) data entail fitting per-voxel parameters to explain how the observed images reflect the thoughts and stimuli a participant experienced during the experiment. These methods implicitly assume that voxel responses are independent and that the unit of analysis should be the voxel. However, both of these assumptions are known to be untrue: it is well known that voxel activations exhibit strong spatial correlations, and common sense tells us that the true underlying brain activations are independent of the resolution at which the brain image happened to be taken. Here we propose a fundamentally different approach, whereby brain images are represented as weighted sums of spatial functions. Our technique yields compact representations of the brain images that leverage spatial correlations in the data and are independent of the image resolution.