Bayesian framework for robust seed-based correlation analysis.

ABSTRACT:

Purpose

One popular method of assessing brain functional connectivity (FC) is through seed-based correlation (SCA) analysis. One drawback of this method is when the seed location is varied slightly, the FC can vary dramatically. We propose a method superior to SCA, robust to variations in seed location, which confers a probabilistic interpretation.

Methods

We introduce a probabilistic method which generates a cloud of highly connected voxels to determine a stable set of voxels connected to the seed location (SC-SCA). This cloud can generate a correlation map or a probabilistic map. The method is applied to the default mode network (DMN) based on a posterior cingulate cortex (PCC) seed, and the auditory network (AN) as validation on a smaller network. A Bayesian interpretatio

SUBMITTER: Bell CS 

PROVIDER: S-EPMC9559859 | biostudies-literature | 2019 Jul

REPOSITORIES: biostudies-literature