Bayesian Joint Modeling of Multiple Brain Functional Networks.

ABSTRACT: Investigating the similarity and changes in brain networks under different mental conditions has become increasingly important in neuroscience research. A standard separate estimation strategy fails to pool information across networks and hence has reduced estimation accuracy and power to detect between-network differences. Motivated by a fMRI Stroop task experiment that involves multiple related tasks, we develop an integrative Bayesian approach for jointly modeling multiple brain networks that provides a systematic inferential framework for network comparisons. The proposed approach explicitly models shared and differential patterns via flexible Dirichlet process-based priors on edge probabilities. Conditional on edges, the connection strengths are modeled via Bayesian spike and slab pri

SUBMITTER: Lukemire J 

PROVIDER: S-EPMC8274571 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature