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Global Signal Removal (GSR) as graph spatial filtering
Fahimeh Arab1, Benjamin Snow Sipes1, Srikantan S Nagarajan1
1Department of Radiology and Biomedical Imaging, University of California San Francisco.
Biorxiv : the Preprint Server for Biology
|April 17, 2026
Summary
Global Signal Removal (GSR) in fMRI preprocessing is reframed as graph spatial filtering. New variants are introduced, offering a clearer understanding of their impact on brain connectivity and task-state separability.
Area of Science:
- Neuroimaging
- Computational Neuroscience
- Graph Theory
Background:
- Global Signal Removal (GSR) is a common but controversial step in fMRI preprocessing.
- Concerns exist regarding GSR's potential to introduce spurious anticorrelations and remove neural signals.
Purpose of the Study:
- To geometrically characterize Global Signal Removal (GSR) as graph spatial filtering.
- To introduce and analyze novel GSR variants within a unified spatial filter framework.
- To differentiate the projection properties of common and novel GSR methods.
Main Methods:
- Formalized GSR as graph spatial filtering.
- Characterized Regression-GSR as a rank-1 deflation of the covariance matrix.
- Developed and analyzed Naive-GSR, PCA-GSR, and SC-GSR variants.
Main Results:
- Regression-GSR approximates first eigenmode removal.
- Naive, PCA, and SC-GSR are orthogonal projections, while Regression-GSR is an oblique projection.
- All GSR variants induce covariance matrix singularity but affect task-state separability differently.
Conclusions:
- GSR can be understood as a family of graph spatial filters.
- This reframing enhances interpretability of GSR's effects on brain connectivity.
- Systematic differences in network connectivity effects exist across GSR variants.
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