A method to determine the necessity for global signal regression in resting-state fMRI studies. Magn Reson Med 2012 Dec;68(6):1828-35
Date
02/16/2012Pubmed ID
22334332Pubmed Central ID
PMC3382008DOI
10.1002/mrm.24201Scopus ID
2-s2.0-84860325685 (requires institutional sign-in at Scopus site) 78 CitationsAbstract
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.
Author List
Chen G, Chen G, Xie C, Ward BD, Li W, Antuono P, Li SJAuthor
Piero G. Antuono MD Professor in the Neurology department at Medical College of WisconsinMESH terms used to index this publication - Major topics in bold
Action PotentialsAged
Algorithms
Brain
Brain Mapping
Computer Simulation
Female
Humans
Image Interpretation, Computer-Assisted
Magnetic Resonance Imaging
Male
Models, Neurological
Nerve Net
Regression Analysis
Reproducibility of Results
Rest
Sensitivity and Specificity
