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 can be used to quantify the human brain connectome. However, there is also a choice of which preprocessing steps will be used prior to calculating the functional connectivity of the brain. In this manuscript, 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 preprocessing schemes were tested on a publicly available dataset, which includes rs-fMRI data 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 by using censoring based on outliers within the functional time-series as a processing, results indicate an increase in reliability of GT measurements with a reduction of the dependency of head motion.

The impact of diverse preprocessing pipelines on brain functional connectivity

“…We did not find convincing evidence for using slice-timing correction. In agreement with previous studies [18], [19], [20], [21] and following our results, we suggest to not use the filtering of global signal in the stage of data preprocessing. To further investigate the impact of preprocessing pipeline on functional connectivity, we will test the features for group discriminability, which is crucial when evaluating potential markers of a disease.…”

“…These studies, however, mostly target only a specific effect of preprocessing of the data. Follow-up studies such as [17], [18], [19], [20], [21] evaluated specific preprocessing steps and their impact of diverse features of functional connectivity.…”

“…Therefore, we examine different preprocessing pipelines covering usage of STC, RETROICOR [15] for cardiac and breathing filtering, high pass filtering, movement correction, WM, CSF and global signal (GS) filtering. Our study is inspired by the consensual pipeline for structural brain networks evaluating different parcellation atlases [22] and is a direct extension to [20], [18], [21].…”

Brain structure–function associations identified in large-scale neuroimaging data

“…As argued by several studies, preprocessing pipelines (e.g., how to correct for motion artifacts) influence the results of functional MRI studies (Strother et al 2004; Power et al 2014; Aurich et al 2015; Glatard et al 2015). Consequently, differences in preprocessing pipelines should be taken into account when comparing other findings to our results (i.e., using our results as a reference for common structural–functional associations).…”

Building a Science of Individual Differences from fMRI

“…Of course there is no such thing as “the” reliability of fMRI because different derived statistics are differentially affected by noise in the raw data and thus have different reliabilities [78] (for an overview of studies addressing the reliability of specific fMRI-derived measures, see Table S1). Moreover, although a general sense of the reliability of the most widely used fMRI measures can be derived from extant literature, details of the experimental procedures [79] and of the preprocessing applied to the raw data [80,81] matter. For these reasons, we believe that it is critical to provide an assessment of reliability in each and every study that looks at individual differences, under the specific pipeline used for the analysis.…”