Meeting Banner
Abstract #4304

Sparse Parameter Global Signal Correction for Resting State fMRI Analysis

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.

This abstract and the presentation materials are available to members only; a login is required.

Join Here