An empirical Bayes normalization method for connectivity metrics in resting state fMRI.

ABSTRACT: Functional connectivity analysis using resting-state functional magnetic resonance imaging (rs-fMRI) has emerged as a powerful technique for investigating functional brain networks. The functional connectivity is often quantified by statistical metrics (e.g., Pearson correlation coefficient), which may be affected by many image acquisition and preprocessing steps such as the head motion correction and the global signal regression. The appropriate quantification of the connectivity metrics is essential for meaningful and reproducible scientific findings. We propose a novel empirical Bayes method to normalize the functional brain connectivity metrics on a posterior probability scale. Moreover, the normalization function maps the original connectivity metrics to values between zero and one, w

SUBMITTER: Chen S 

PROVIDER: S-EPMC4584951 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature