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A method to determine the necessity for global signal regression in resting‐state fMRI studies
Author(s) -
Chen Gang,
Chen Guangyu,
Xie Chunming,
Ward B. Douglas,
Li Wenjun,
Antuono Piero,
Li ShiJiang
Publication year - 2012
Publication title -
magnetic resonance in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.696
H-Index - 225
eISSN - 1522-2594
pISSN - 0740-3194
DOI - 10.1002/mrm.24201
Subject(s) - resting state fmri , signal (programming language) , communication noise , regression , noise (video) , default mode network , computer science , preprocessor , functional connectivity , functional magnetic resonance imaging , artificial intelligence , pattern recognition (psychology) , statistics , mathematics , psychology , neuroscience , linguistics , philosophy , image (mathematics) , programming language
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. Magn Reson Med, 2012. © 2012 Wiley Periodicals, Inc.