title: [A brain functional connectivity analysis based on independent component analysis].
abstract: The resting state cortical functional connectivity is an important method in current brain researches. In this paper, we propose an approach for analyzing and manipulating the resting state functional magnetic resonance imaging (fMRI) data using spatial independent component analysis (sICA) method, and applying the low-frequency oscillations theory to the choice of component of interest (COI) from the component obtained by sICA method. Firstly, we remove all the inactive voxels and independent voxels via Z value. Then, by making a spectrum analysis, we choose the COI with concentrations of energy between 0.01 and 0.1 Hz. And after that, we obtain the functional connectivity networks using hierarchical clustering.
authors.affiliation: School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China.
authors.forename: Ling
authors.initials: L
authors.lastname: Zeng
authors.forename: Qin
authors.initials: Q
authors.lastname: Yang
authors.forename: Bin
authors.initials: B
authors.lastname: Lin
authors.forename: Huafu
authors.initials: H
authors.lastname: Chen
mesh_terms: D001921:Brain / Q000502:physiology*; D016000:Cluster Analysis; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008959:Models, Neurological; D025341:Principal Component Analysis