A Hierarchical Bayesian Mixture Model Approach for Analysis of Resting-State Functional Brain Connectivity: An Alternative to Thresholding.

ABSTRACT: This article proposes a Bayesian hierarchical mixture model to analyze functional brain connectivity where mixture components represent "positively connected" and "non-connected" brain regions. Such an approach provides a data-informed separation of reliable and spurious connections in contrast to arbitrary thresholding of a connectivity matrix. The hierarchical structure of the model allows simultaneous inferences for the entire population as well as for each individual subject. A new connectivity measure, the posterior probability of a given pair of brain regions of a specific subject to be connected given the observed correlation of regions' activity, can be computed from the model fit. The posterior probability reflects the connectivity of a pair of regions relative to the overall conn

SUBMITTER: Gorbach T 

PROVIDER: S-EPMC7310299 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature