Bayesian structure learning for functional neuroimaging

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Predictive modeling of functional neuroimaging data has become an important tool for analyzing cognitive structures in the brain. Brain images are high-dimensional and exhibit large correlations, and imaging experiments 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 sparsity or smoothness, which does not fully exploit the structure in the data. Here we develop a flexible, hierarchical model designed to simultaneously capture spatial block sparsity and smoothness in neuroimaging data. We exploit a function domain representation for the high-dimensional small-sample data and develop efficient inference, parameter estimation, and prediction procedures. Empirical results with simulated and real neuroimaging data suggest that simultaneously capturing the block sparsity and smoothness properties can significantly improve structure recovery and predictive modeling performance.

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