Brain functional networks extraction based on fMRI artifact removal: Single subject and group approaches

Abstract

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 (IRPG). 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.

Applications of hypergraph-based methods in classifying and subtyping psychiatric disorders: a survey

“…Many researchers have used GIG-ICA to extract network features in their work and have achieved high disease classification accuracy [66][67][68][69][70][71][72][73][74][75][76][77][78][79]. In addition, the method performs well in removing artifacts [80,81]. Du et al have also proposed a framework called NeuroMark [82], which provides a common node definition for big data analysis and has been widely applied to explore the association between symptom severity and functional connectivity in patients with schizophrenia [83], to investigate sex-specific differences in brain functional network connectivity by using ICNs [72,84], and to evaluate the association between dynamic functional network connectivity and the risk of Alzheimer's disease (AD) [85][86][87].…”