Frontiers in Neuroscience (Feb 2015)
Evaluating the Reliability of Different Preprocessing Steps to Estimate Graph Theoretical Measures in Resting State fMRI data
Abstract
With resting-state functional MRI (rs-fMRI) there are a variety of post-processing methods that quantify the human brain connectome. However, there is a choice of which preprocessing steps will be used prior to calculating the functional connectivity of the brain,. In this paper, we have tested seven different preprocessing schemes and assessed the reliability between and reproducibility within the various strategies by means of graph theoretical measures. Different schemes were tested on a publicly available dataset with rs-fMRI of healthy controls. The brain was parcellated into 190 nodes and four graph theoretical (GT) measures were calculated; global efficiency (GEFF), characteristic path length (CPL), average clustering coefficient (ACC), and average local efficiency (ALE). Our findings indicate that results can significantly differ based on which preprocessing steps are selected. We also found dependence between motion and GT measurements in most preprocessing strategies. We conclude that with the use of censoring based on outliers within the functional time-series, results indicate an increase in reliability of GT measurements with a reduction in dependency with head motion.
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