title: Brain functional networks extraction based on fMRI artifact removal: Single subject and group approaches.
abstract: Independent component analysis (ICA) has been widely applied to identify brain functional networks from multiple-subject fMRI. However, the best approach to handle artifacts is not yet clear. In this work, we study and compare two ICA approaches for artifact removal using simulations and real fMRI data. The first approach, recommended by the human connectome project, performs ICA on individual data to remove artifacts, and then applies group ICA on the cleaned data from all subjects. We refer to this approach as Individual ICA artifact Removal Plus Group ICA (TRPG). A second approach, Group Information Guided ICA (GIG-ICA), performs ICA on group data, and then removes the artifact group independent components (ICs), followed by individual subject ICA using the remaining group ICs as spatial references. Experiments demonstrate that GIG-ICA is more accurate in estimation of sources and time courses, more robust to data quality and quantity, and more reliable for identifying networks than IRPG.
authors.forename: Yuhui
authors.initials: Y
authors.lastname: Du
authors.forename: Elena A
authors.initials: EA
authors.lastname: Allen
authors.forename: Hao
authors.initials: H
authors.lastname: He
authors.forename: Jing
authors.initials: J
authors.lastname: Sui
authors.forename: Vince D
authors.initials: VD
authors.lastname: Calhoun
mesh_terms: D000465:Algorithms; D016477:Artifacts; D001921:Brain / Q000000981:diagnostic imaging*; D063132:Connectome; D000068598:Data Accuracy; D064368:Healthy Volunteers; D006801:Humans; D008279:Magnetic Resonance Imaging; D011859:Radiography; D012044:Regression Analysis; D059629:Signal-To-Noise Ratio