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Abstract:
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We focus on the relationship between creativity and the human brain network for subjects in a brain connectome dataset. We identify brain regions and interconnections related to creativity. Brain networks are often expressed using adjacency matrices, with row and column indices representing regions of interest, and a cell entry signifying the number of fibers connecting the row and column ROIs. Current statistical practices for regression with the brain network as predictor and a response typically vectorize the network predictor matrices, thus failing to account for the structural information in the network. To answer the scientific questions discussed above, we develop a flexible Bayesian framework that avoids reshaping the network predictor matrix, draws inference on brain ROIs and interconnections significantly related to creativity, and enables accurate prediction of creativity from a brain network. The Bayesian framework allows characterization of uncertainty in the findings. Empirical results illustrate substantial inferential and predictive gains of the framework in comparison with the ordinary high dimensional Bayesian shrinkage priors and penalized optimization schemes.
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