A brain functional connectivity analysis based on independent component analysis.
- Author:
Ling ZENG
1
;
Qin YANG
;
Bin LIN
;
Huafu CHEN
Author Information
1. School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China.
- Publication Type:Journal Article
- MeSH:
Brain;
physiology;
Cluster Analysis;
Humans;
Image Processing, Computer-Assisted;
Magnetic Resonance Imaging;
methods;
Models, Neurological;
Principal Component Analysis
- From:
Journal of Biomedical Engineering
2009;26(2):408-412
- CountryChina
- Language:Chinese
-
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.
