Xueqing Liu1, Zhihao Li2, Shiyang Chen2, and Xiaoping Hu2
1Department of Biomedical Engineering, Columbia University, New York, NY, United States, 2Coulter Department of Biomedical Engineering, Georgia Tech and Emory University, Atlanta, Georgia
We describe a novel global signal removal method, sparse parameter
global signal regression (SP-GSR), for fMRI data preprocessing. We
assume the global signal to be low-rank and the remaining signal can be
decomposed into orthogonal regressors
with spatially sparse parameters. We demonstrated by simulation that
SP-GSR can remove global signal and recovery true correlations without
introducing anti-correlations. Application of this method to experimental data led to a more prominent and focused
default mode network with isolated negative correlations.
