Research of Functional Connectivity Based on Independent Component Analysis and Temporal Correlation Analysis
- VernacularTitle:基于独立成分分析和时间相关分析的脑功能连通研究方法
- Author:
Xinmei XU
;
Huinan WANG
;
Wei HUANG
- Publication Type:Journal Article
- Keywords:
fMRI;
functional connectivity;
ICA;
temporal correlation
- From:
Chinese Journal of Medical Physics
2008;25(1):477-480,483
- CountryChina
- Language:Chinese
-
Abstract:
Objective: Combining spatial independent component analysis (sICA) with temporal correlation analysis to investigate the functional connectivity of human brain using resting state fMRI. Methods: First, activated area was localized by performing sICA on the data from block design run, then one of the activated brain areas was chosen as a region of interest (ROI)and low frequency correlations between ROI and other regions were calculated in resting state to detect the functional connectivity networks. To validate the method, neural connectivity to primary motor cortex was assessed using this method during a resting state. Results: Functional connectivity network of motor cortex was detected, including primary motor cortex (M1), supplementary motor area (SMA), primary sensory cortex (S1), dorsal premotor cortex (PMd) and posterior parietal somatosensory association area (PSAAp). The connectivity implied by the resting state correlation was far more similar to the connectivity established by non-imaging methods. Conclusion: Functional connectivity of human motor primary cortex was investigated by combining sICA with temporal correlation using resting fMRI data. It provided a simple and noninvasive method for the research of brain functional connectivity.
