title: Functional connectivity networks with and without global signal correction.
abstract: In functional connectivity analyses in BOLD (blood oxygenation level dependent) fMRI data, there is an ongoing debate on whether to correct global signals in fMRI time series data. Although the discussion has been ongoing in the fMRI community since the early days of fMRI data analyses, this subject has gained renewed attention in recent years due to the surging popularity of functional connectivity analyses, in particular graph theory-based network analyses. However, the impact of correcting (or not correcting) for global signals has not been systematically characterized in the context of network analyses. Thus, in this work, I examined the effect of global signal correction on an fMRI network analysis. In particular, voxel-based resting-state fMRI networks were constructed with and without global signal correction. The resulting functional connectivity networks were compared. Without global signal correction, the distributions of the correlation coefficients were positively biased. I also found that, without global signal correction, nodes along the interhemisphic fissure were highly connected whereas some nodes and subgraphs around white-matter tracts became disconnected from the rest of the network. These results from this study show differences between the networks with or without global signal correction.
authors.affiliation: Department of Biostatistical Sciences, Wake Forest School of Medicine Winston-Salem, NC, USA ; Department of Radiology, Wake Forest School of Medicine Winston-Salem, NC, USA.
authors.forename: Satoru
authors.initials: S
authors.lastname: Hayasaka
keywords: brain network analysis; brain networks; fMRI analysis; functional connectivity; graph theory; network modules; resting-state fMRI