Neural Topographic Factor Analysis for fMRI Data

Functional magnetic resonance imaging experiments produce gigabytes of high-dimensional spatio-temporal data for a small number of sampled participants and stimuli. Analyses of this data commonly average over all trials, ignoring variation among participants and stimuli. As a means of reasoning about this variation, we propose Neural Topographic Factor Analysis (NTFA), a deep generative model that parameterizes factors as functions of embeddings for participants and stimuli. We evaluate NTFA on a synthetically generated dataset, results from an in-house pilot study, and two publicly available datasets. We demonstrate that NTFA produces more accurate reconstructions with fewer parameters than related methods. Moreover, NTFA constitutes a first step towards reasoning about individual variation; learned embeddings uncover latent categories of stimuli, and partially separate latent groups of participants.