Hierarchical Mixture of Classification Experts Uncovers Interactions between Brain Regions

The human brain can be described as containing a number of functional regions. For a given task, these regions, as well as the connections between them, play a key role in information processing in the brain. However, most existing multi-voxel pattern analysis approaches either treat multiple functional regions as one large uniform region or several independent regions, ignoring the connections between regions. In this paper, we propose to model such connections in an Hidden Conditional Random Field (HCRF) framework, where the classifier of one region of interest (ROI) makes predictions based on not only its voxels but also the classifier predictions from ROIs that it connects to. Furthermore, we propose a structural learning method in the HCRF framework to automatically uncover the connections between ROIs. Experiments on fMRI data acquired while human subjects viewing images of natural scenes show that our model can improve the top-level (the classifier combining information from all ROIs) and ROI-level prediction accuracy, as well as uncover some meaningful connections between ROIs.