We describe and evaluate a new statistical generative model of functional magnetic resonance imaging (fMRI) data. The model, topographic latent source analysis (TLSA), assumes that fMRI images are generated by a covariate-dependent superposition of latent sources. These sources are defined in terms of basis functions over space. The number of parameters in the model does not depend on the number of voxels, enabling a parsimonious description of activity patterns that avoids many of the pitfalls of traditional voxel-based approaches. We develop a multi-subject extension where latent sources at the subject-level are perturbations of a group-level template. We evaluate TLSA according to prediction, reconstruction and reproducibility. We show that it compares favorably to a Naive Bayes model while using fewer parameters. We also describe a hypothesis testing framework that can be used to identify significant latent sources.
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