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Bayesian inference and testing of group differences in brain networks. (English) Zbl 06873717

Summary: Network data are increasingly collected along with other variables of interest. Our motivation is drawn from neurophysiology studies measuring brain connectivity networks for a sample of individuals along with their membership to a low or high creative reasoning group. It is of paramount importance to develop statistical methods for testing of global and local changes in the structural interconnections among brain regions across groups. We develop a general Bayesian procedure for inference and testing of group differences in the network structure, which relies on a nonparametric representation for the conditional probability mass function associated with a network-valued random variable. By leveraging a mixture of low-rank factorizations, we allow simple global and local hypothesis testing adjusting for multiplicity. An efficient Gibbs sampler is defined for posterior computation. We provide theoretical results on the flexibility of the model and assess testing performance in simulations. The approach is applied to provide novel insights on the relationships between human brain networks and creativity.

MSC:

62F15 Bayesian inference
90B15 Stochastic network models in operations research
62H30 Classification and discrimination; cluster analysis (statistical aspects)
62G05 Nonparametric estimation
62P10 Applications of statistics to biology and medical sciences; meta analysis
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