Abstract
Introduction:
In resting-state functional magnetic resonance imaging (rs-fMRI) studies, global signal regression (GSR) is a controversial preprocessing strategy. It effectively eliminates global noise driven by motion and respiration but also can introduce artifacts and remove functionally relevant metabolic information. Most preclinical rs-fMRI studies are performed in anesthetized animals, and anesthesia will alter both metabolic and neuronal activity.
Methods:
In this study, we explored the effect of GSR on rs-fMRI data collected under anesthetized and awake state in mice (
Results:
We found that global signal amplitude was similar between the awake and anesthetized states. However, GSR had a different impact on connectivity networks and brain modularity changes between states. We demonstrated that GSR had a more prominent impact on the anesthetized state, with a greater decrease in functional connectivity and increased brain modularity. We classified mice using the change in amplitude of brain modularity coefficient (Δ
Discussion:
This study suggests that, for rs-fMRI studies which compare different physiological states, researchers should use GSR processing with caution.
Impact statement
Global signal regression (GSR) is a commonly used denoising technique, however, it can also introduce specific artifacts. In functional connectivity (FC) studies of functional magnetic resonance imaging, it is crucial to examine the impact of GSR on animals under various anesthesia conditions. Our results showed that GSR had distinct effects on FC and modularity of awake versus anesthetized mice. Therefore, caution should be taken when using GSR to remove noise, especially when comparing different neurometabolic states.
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References
Supplementary Material
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