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Abstract #1656

Global Signal Regression acts as a Temporal Downweighting Process in Resting-State fMRI

Alican Nalci1 and Thomas T. Liu1

1UCSD Center for Functional MRI, La Jolla, CA, United States

Global signal regression (GSR) is a controversial preprocessing method in resting-state fMRI. It has been claimed that the process can introduce artifactual anti-correlations in resting-state connectivity maps. However, a consensus regarding its use has been lacking, due in part to the difficulty in understanding its effects. We show that GSR can be well approximated by a temporal downweighting of the voxel time series, where the weighting factor is a function of the global signal magnitude and is uniform across space. This helps address the concerns about GSR and provides a novel framework for understanding its effects on resting-state data.

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