Artifact removal in the context of group ICA: A comparison of single-subject and group approaches.

ABSTRACT: Independent component analysis (ICA) has been widely applied to identify intrinsic brain networks from fMRI data. Group ICA computes group-level components from all data and subsequently estimates individual-level components to recapture intersubject variability. However, the best approach to handle artifacts, which may vary widely among subjects, is not yet clear. In this work, we study and compare two ICA approaches for artifacts removal. One approach, recommended in recent work by the Human Connectome Project, first performs ICA on individual subject data to remove artifacts, and then applies a group ICA on the cleaned data from all subjects. We refer to this approach as Individual ICA based artifacts Removal Plus Group ICA (IRPG). A second proposed approach, called Group Information Gu

SUBMITTER: Du Y 

PROVIDER: S-EPMC5784424 | biostudies-literature | 2016 Mar

REPOSITORIES: biostudies-literature