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.

How to access this content:

For one year after publication, abstracts and videos are only open to registrants of this annual meeting. Registrants should use their existing login information. Non-registrant access can be purchased via the ISMRM E-Library.

After one year, current ISMRM & ISMRT members get free access to both the abstracts and videos. Non-members and non-registrants must purchase access via the ISMRM E-Library.

After two years, the meeting proceedings (abstracts) are opened to the public and require no login information. Videos remain behind password for access by members, registrants and E-Library customers.

Click here for more information on becoming a member.