Review of methods for functional brain connectivity detection using fMRI.
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.
Li
Kaiming
K
Department of Automation, Northwestern Polytechnical University, Xi'an, China.
Guo
Lei
L
Nie
Jingxin
J
Li
Gang
G
Liu
Tianming
T
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