title: A method to determine the necessity for global signal regression in resting-state fMRI studies.
abstract: In resting-state functional MRI studies, the global signal (operationally defined as the global average of resting-state functional MRI time courses) is often considered a nuisance effect and commonly removed in preprocessing. This global signal regression method can introduce artifacts, such as false anticorrelated resting-state networks in functional connectivity analyses. Therefore, the efficacy of this technique as a correction tool remains questionable. In this article, we establish that the accuracy of the estimated global signal is determined by the level of global noise (i.e., non-neural noise that has a global effect on the resting-state functional MRI signal). When the global noise level is low, the global signal resembles the resting-state functional MRI time courses of the largest cluster, but not those of the global noise. Using real data, we demonstrate that the global signal is strongly correlated with the default mode network components and has biological significance. These results call into question whether or not global signal regression should be applied. We introduce a method to quantify global noise levels. We show that a criteria for global signal regression can be found based on the method. By using the criteria, one can determine whether to include or exclude the global signal regression in minimizing errors in functional connectivity measures.
authors.affiliation: Department of Biophysics, Medical College of Wisconsin, Milwaukee, Wisconsin 53226, USA.
authors.forename: Gang
authors.initials: G
authors.lastname: Chen
authors.forename: Guangyu
authors.initials: G
authors.lastname: Chen
authors.forename: Chunming
authors.initials: C
authors.lastname: Xie
authors.forename: B Douglas
authors.initials: BD
authors.lastname: Ward
authors.forename: Wenjun
authors.initials: W
authors.lastname: Li
authors.forename: Piero
authors.initials: P
authors.lastname: Antuono
authors.forename: Shi-Jiang
authors.initials: SJ
authors.lastname: Li
mesh_terms: D000200:Action Potentials / Q000502:physiology*; D000368:Aged; D000465:Algorithms; D001921:Brain / Q000502:physiology*; D001931:Brain Mapping / Q000379:methods*; D003198:Computer Simulation; D005260:Female; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D008959:Models, Neurological; D009415:Nerve Net / Q000502:physiology*; D012044:Regression Analysis; D015203:Reproducibility of Results; D012146:Rest / Q000502:physiology; D012680:Sensitivity and Specificity