Global signal regression strengthens association between resting-state functional connectivity and behavior

Li, Jingwei; Kong, Ru; Liégeois, Raphaël; Orban, Csaba; Tan, Yanrui; Sun, Nanbo; Holmes, Avram J.; Sabuncu, Mert R.; Ge, Tian; Yeo, B.T. Thomas

doi:10.1016/j.neuroimage.2019.04.016

Cited by 380Functional Brain Connectivity StudiesAdvanced MRI Techniques and ApplicationsAdvanced Neuroimaging Techniques and Applications

Global signal regression strengthens association between resting-state functional connectivity and behavior

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Li, Jingwei, Kong, Ru, Liégeois, Raphaël, Orban, Csaba, Tan, Yanrui, Sun, Nanbo, Holmes, Avram J., Sabuncu, Mert R. +2 more·2019·NeuroImage

DOI 10.1016/j.neuroimage.2019.04.016PMC PMC6585462

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Abstract

Global signal regression (GSR) is one of the most debated preprocessing strategies for resting-state functional MRI. GSR effectively removes global artifacts driven by motion and respiration, but also discards globally distributed neural information and introduces negative correlations between certain brain regions. The vast majority of previous studies have focused on the effectiveness of GSR in removing imaging artifacts, as well as its potential biases. Given the growing interest in functional connectivity fingerprinting, here we considered the utilitarian question of whether GSR strengthens or weakens associations between resting-state functional connectivity (RSFC) and multiple behavioral measures across cognition, personality and emotion. By applying the variance component model to the Brain Genomics Superstruct Project (GSP), we found that behavioral variance explained by whole-brain RSFC increased by an average of 47% across 23 behavioral measures after GSR. In the Human Connectome Project (HCP), we found that behavioral variance explained by whole-brain RSFC increased by an average of 40% across 58 behavioral measures, when GSR was applied after ICA-FIX de-noising. To ensure generalizability, we repeated our analyses using kernel regression. GSR improved behavioral prediction accuracies by an average of 64% and 12% in the GSP and HCP datasets respectively. Importantly, the results were consistent across methods. A behavioral measure with greater RSFC-explained variance (using the variance component model) also exhibited greater prediction accuracy (using kernel regression). A behavioral measure with greater improvement in behavioral variance explained after GSR (using the variance component model) also enjoyed greater improvement in prediction accuracy after GSR (using kernel regression). Furthermore, GSR appeared to benefit task performance measures more than self-reported measures. Since GSR was more effective at removing motion-related and respiratory-related artifacts, GSR-related increases in variance explained and prediction accuracies were unlikely the result of motion-related or respiratory-related artifacts. However, it is worth emphasizing that the current study focused on whole-brain RSFC, so it remains unclear whether GSR improves RSFC-behavioral associations for specific connections or networks. Overall, our results suggest that at least in the case for young healthy adults, GSR strengthens the associations between RSFC and most (although not all) behavioral measures. Code for the variance component model and ridge regression can be found here: https://github.com/ThomasYeoLab/CBIG/tree/master/stable_projects/preprocessing/Li2019_GSR.

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NBS used two-sample t-tests (P<0.001) controlling for age, gender, and scanner, with permutation tests (N=10,000) and family-wise error correction (PFWE<0.05).
“We utilized NBS to localize edge-level group differences in both positive and negative connectivity using two sample t tests(P<0.001), controlling for age, gender, and scanner(58).”
From Functional disruption of cerebello-thalamo-cortical networks in obsessive-compulsive disorder (2020) · cites this paper as [58]

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Global Signal Removal (GSR) as graph spatial filteringPreprint
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Authors: Li, J., Kong, R., Liégeois, R., Orban, C., Tan, Y., Sun, N., Holmes, A. J., Sabuncu, M. R., Ge, T., & Yeo, B. T. T.
Affiliations: National University of Singapore (SG)
Yale University (US)
Cornell University (US)
Massachusetts General Hospital (US)
Corresponding author: Yeo, B.T. Thomas
ORCID: Li, Jingwei
Kong, Ru
Liégeois, Raphaël
Sun, Nanbo
Holmes, Avram J.
Sabuncu, Mert R.
Ge, Tian
Yeo, B.T. Thomas
Venue: NeuroImage
Publisher: Elsevier BV
Published: 2019-08
Volume: 196
Pages: 126-141
ISSN: 1053-8119 (ISSN-L)
DOI: 10.1016/j.neuroimage.2019.04.016
PMC: PMC6585462
OpenAlex: W2953136735
Cited by: 380
References: 108
Citations by year: 2026: 44 · 2025: 59 · 2024: 55 · 2023: 48 · 2022: 54 · 2021: 52 · 2020: 47 · 2019: 19 · 2018: 1
FWCI: 24.82
Type: journal article
Language: en
Open access: bronze
doi.org
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
www.elsevier.com/legal/tdmrep-license (tdm)
www.elsevier.com/open-access/userlicense/1.0/ (am, 256 d)
Locations: NeuroImage (acceptedVersion)
bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
PubMed (publishedVersion)
PubMed Central (acceptedVersion)
Funders: National Medical Research Council (CBRG/0088/2015)
National University of Singapore (R185000271720, DPRT/944/09/14)
NIH Blueprint for Neuroscience Research
Harvard University
Massachusetts General Hospital
National Research Foundation Singapore
Ministry of Education - Singapore (MOE2014-T2-2-016)
McDonnell Center for Systems Neuroscience
National Institutes of Health (K99AG054573)
Topics: Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
Keywords: global signal regression, resting-state functional connectivity, behavior prediction, variance component models, kernel regression, whole-brain connectivity, Human Connectome Project
MeSH terms: Adolescent, Adult, Brain, Brain Mapping, Cognition, Emotions, Female, Humans, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Male, Neural Pathways, Personality, Signal Processing, Computer-Assisted, Artifacts, Young Adult

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Global signal regression strengthens association between resting-state functional connectivity and behavior

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