摘要
Background: Directional connectivity measures, such as partial directed coherence (PDC), give us means to explore effective connectivity in the human brain. By utilizing independent component analysis (ICA), the original data-set reduction was performed for further PDC analysis. Purpose: To test this cascaded ICA-PDC approach in causality studies of human functional magnetic resonance imaging (fMRI) data. Material and Methods: Resting state group data was imaged from 55 subjects using a 1.5 T scanner (TR 1800 ms, 250 volumes). Temporal concatenation group ICA in a probabilistic ICA and further repeatability runs (n = 200) were overtaken. The reduced data-set included the time series presentation of the following nine ICA components: secondary somatosensory cortex, inferior temporal gyrus, intracalcarine cortex, primary auditory cortex, amygdala, putamen and the frontal medial cortex, posterior cingulate cortex and precuneus, comprising the default mode network components. Re-normalized PDC (rPDC) values were computed to determine directional connectivity at the group level at each frequency. Results: The integrative role was suggested for precuneus while the role of major divergence region may be proposed to primary auditory cortex and amygdala. Conclusion: This study demonstrates the potential of the cascaded ICA-PDC approach in directional connectivity studies of human fMRI.
- 出版日期2011-11