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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
PubMed
Summary
This summary is machine-generated.

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.

Keywords:
fMRI preprocessingfunctional connectivityglobal signal regressiongraph filteringresting-state networksspatial projection

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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.