  fMRI semantic category understanding using linguistic encoding models attempt
to learn a forward mapping that relates stimuli to the corresponding brain
activation. Classical encoding models use linear multi-variate methods to
predict the brain activation (all voxels) given the stimulus. However, these
methods essentially assume multiple regions as one large uniform region or
several independent regions, ignoring connections among them. In this paper, we
present a mixture of experts-based model where a group of experts captures
brain activity patterns related to particular regions of interest (ROI) and
also show the discrimination across different experts. The model is trained
word stimuli encoded as 25-dimensional feature vectors as input and the
corresponding brain responses as output. Given a new word (25-dimensional
feature vector), it predicts the entire brain activation as the linear
combination of multiple experts brain activations. We argue that each expert
learns a certain region of brain activations corresponding to its category of
words, which solves the problem of identifying the regions with a simple
encoding model. We showcase that proposed mixture of experts-based model indeed
learns region-based experts to predict the brain activations with high spatial
accuracy.
