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
Resting-state functional connectivity (RSFC) is a powerful tool for measuring the synchronization of fMRI signals between brain regions, while participants are lying at rest without any “extrinsic” task (Biswal et al, 1995; Fox and Raichle 2007; Buckner et al, 2013)
FMRI is contaminated by various noise sources, which have been shown to be problematic for resting-state functional MRI studies (Power et al, 2012; Satterthwaite et al, 2012; Van Dijk et al, 2012; Yan et al, 2013)
Using the variance component model, we showed that global signal regression (GSR) strengthened the association between behavior and RSFC in young healthy adults from two large-scale datasets
Summary
Resting-state functional connectivity (RSFC) is a powerful tool for measuring the synchronization of fMRI signals between brain regions, while participants are lying at rest without any “extrinsic” task (Biswal et al, 1995; Fox and Raichle 2007; Buckner et al, 2013). Studies have shown that GSR reduces the correlation magnitude between quality control metrics and RSFC (Satterthwaite et al, 2013; Power et al, 2014; Burgess et al, 2016; Ciric et al, 2016; Parkes et al, 2018), as well as removes prominent increases and/or decreases in signal intensities lasting many TRs (Power et al, 2014; Byrge et al, 2017; Glasser et al, 2018). GSR reduces the correlation magnitude between quality control metrics and RSFC, this reduction is distance dependent, which might potentially introduce biases in certain analyses (Power et al, 2014; Parkes et al, 2018)
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