title: Review of methods for functional brain connectivity detection using fMRI.
abstract: Since the mid of 1990s, functional connectivity study using fMRI (fcMRI) has drawn increasing attention of neuroscientists and computer scientists, since it opens a new window to explore functional network of human brain with relatively high resolution. A variety of methods for fcMRI study have been proposed. This paper intends to provide a technical review on computational methodologies developed for fcMRI analysis. From our perspective, these computational methods are classified into two general categories: model-driven methods and data-driven methods. Data-driven methods are a large family, and thus are further sub-classified into decomposition-based methods and clustering analysis methods. For each type of methods, principles, main contributors, and their advantages and drawbacks are discussed. Finally, potential applications of fcMRI are overviewed.
authors.affiliation: Department of Automation, Northwestern Polytechnical University, Xi'an, China.
authors.forename: Kaiming
authors.initials: K
authors.lastname: Li
authors.forename: Lei
authors.initials: L
authors.lastname: Guo
authors.forename: Jingxin
authors.initials: J
authors.lastname: Nie
authors.forename: Gang
authors.initials: G
authors.lastname: Li
authors.forename: Tianming
authors.initials: T
authors.lastname: Liu
mesh_terms: D001185:Artificial Intelligence; D001921:Brain / Q000502:physiology* / Q000503:physiopathology*; D016000:Cluster Analysis; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008959:Models, Neurological; D009415:Nerve Net / Q000502:physiology / Q000503:physiopathology; D025341:Principal Component Analysis