Bayesian Structure Learning for Functional Neuroimaging

Predictive modeling of functional neuroimag-ing data has become an important tool for analyzing cognitive structures in the brain. Brain images are high-dimensional and ex-hibit large correlations, and imaging experi-ments provide a limited number of samples. Therefore, capturing the inherent statistical properties of the imaging data is critical for robust inference. Previous methods tackle this problem by exploiting either spatial spar-sity or smoothness, which does not fully ex-ploit the structure in the data. Here we de-velop a flexible, hierarchical model designed to simultaneously capture spatial block spar-sity and smoothness in neuroimaging data. We exploit a function domain representation for the high-dimensional small-sample data and develop efficient inference, parameter es-timation, and prediction procedures. Em-pirical results with simulated and real neu-roimaging data suggest that simultaneously capturing the block sparsity and smoothness properties can significantly improve struc-ture recovery and predictive modeling per-formance.