title: 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 Guided ICA (GIG-ICA), performs ICA on group data, then removes the group-level artifact components, and finally performs subject-specific ICAs using the group-level non-artifact components as spatial references. We used simulations to evaluate the two approaches with respect to the effects of data quality, data quantity, variable number of sources among subjects, and spatially unique artifacts. Resting-state test-retest datasets were also employed to investigate the reliability of functional networks. Results from simulations demonstrate GIG-ICA has greater performance compared with IRPG, even in the case when single-subject artifacts removal is perfect and when individual subjects have spatially unique artifacts. Experiments using test-retest data suggest that GIG-ICA provides more reliable functional networks. Based on high estimation accuracy, ease of implementation, and high reliability of functional networks, we find GIG-ICA to be a promising approach.
authors: The Mind Research Network, Albuquerque, New Mexico.|School of Information and Communication Engineering, North University of China, Taiyuan, China.
authors: Yuhui
authors: Y
authors: Du
authors: The Mind Research Network, Albuquerque, New Mexico.|Department of Biological and Medical Psychology, K.G. Jebsen Center for Research on Neuropsychiatric Disorders, University of Bergen, Bergen, Norway.
authors: Elena A
authors: EA
authors: Allen
authors: The Mind Research Network, Albuquerque, New Mexico.|Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, New Mexico.
authors: Hao
authors: H
authors: He
authors: The Mind Research Network, Albuquerque, New Mexico.
authors: Jing
authors: J
authors: Sui
authors: The Mind Research Network, Albuquerque, New Mexico.
authors: Lei
authors: L
authors: Wu
authors: The Mind Research Network, Albuquerque, New Mexico.|Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, New Mexico.
authors: Vince D
authors: VD
authors: Calhoun
mesh_terms: D016477:Artifacts; D001921:Brain / Q000502:physiology*; D003198:Computer Simulation; D066264:Datasets as Topic; D005260:Female; D006801:Humans; D016015:Logistic Models; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D010363:Pattern Recognition, Automated; D012146:Rest; D012815:Signal Processing, Computer-Assisted; D055815:Young Adult
keywords: artifacts removal; brain functional networks; group ICA; group information guided ICA; independent component analysis (ICA)