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Global Signal Regression Strengthens Association between Resting-State Functional Connectivity and Behavior

View ORCID ProfileJingwei Li, View ORCID ProfileRu Kong, Raphael Liegeois, View ORCID ProfileCsaba Orban, Yanrui Tan, Nanbo Sun, View ORCID ProfileAvram J. Holmes, View ORCID ProfileMert R. Sabuncu, Tian Ge, View ORCID ProfileB.T. Thomas Yeo
doi: https://doi.org/10.1101/548644
Jingwei Li
1Department of Electrical and Computer Engineering, ASTAR-NUS Clinical Imaging Research Centre, Singapore Institute for Neurotechnology and Memory Networks Program, National University of Singapore, Singapore
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Ru Kong
1Department of Electrical and Computer Engineering, ASTAR-NUS Clinical Imaging Research Centre, Singapore Institute for Neurotechnology and Memory Networks Program, National University of Singapore, Singapore
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Raphael Liegeois
1Department of Electrical and Computer Engineering, ASTAR-NUS Clinical Imaging Research Centre, Singapore Institute for Neurotechnology and Memory Networks Program, National University of Singapore, Singapore
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Csaba Orban
1Department of Electrical and Computer Engineering, ASTAR-NUS Clinical Imaging Research Centre, Singapore Institute for Neurotechnology and Memory Networks Program, National University of Singapore, Singapore
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Yanrui Tan
1Department of Electrical and Computer Engineering, ASTAR-NUS Clinical Imaging Research Centre, Singapore Institute for Neurotechnology and Memory Networks Program, National University of Singapore, Singapore
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Nanbo Sun
1Department of Electrical and Computer Engineering, ASTAR-NUS Clinical Imaging Research Centre, Singapore Institute for Neurotechnology and Memory Networks Program, National University of Singapore, Singapore
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Avram J. Holmes
2Yale University, New Haven, CT, USA
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Mert R. Sabuncu
3School of Electrical and Computer Engineering, Cornell University, USA
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Tian Ge
4Psychiatric & Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
5Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
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B.T. Thomas Yeo
1Department of Electrical and Computer Engineering, ASTAR-NUS Clinical Imaging Research Centre, Singapore Institute for Neurotechnology and Memory Networks Program, National University of Singapore, Singapore
5Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
6Centre for Cognitive Neuroscience, Duke-NUS Medical School, Singapore
7NUS Graduate School for Integrative Sciences and Engineering, National University of Singapore, Singapore
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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.

Highlights

  1. Global signal regression improves RSFC-behavior associations

  2. Global signal regression improves RSFC-based behavioral prediction accuracies

  3. Improvements replicated across two large-scale datasets and methods

  4. Task-performance measures enjoyed greater improvements than self-reported ones

  5. GSR beneficial even after ICA-FIX

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted February 13, 2019.
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Global Signal Regression Strengthens Association between Resting-State Functional Connectivity and Behavior
Jingwei Li, Ru Kong, Raphael Liegeois, Csaba Orban, Yanrui Tan, Nanbo Sun, Avram J. Holmes, Mert R. Sabuncu, Tian Ge, B.T. Thomas Yeo
bioRxiv 548644; doi: https://doi.org/10.1101/548644
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Global Signal Regression Strengthens Association between Resting-State Functional Connectivity and Behavior
Jingwei Li, Ru Kong, Raphael Liegeois, Csaba Orban, Yanrui Tan, Nanbo Sun, Avram J. Holmes, Mert R. Sabuncu, Tian Ge, B.T. Thomas Yeo
bioRxiv 548644; doi: https://doi.org/10.1101/548644

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