Abstract
Currently, human subjects performed a cognitive reappraisal of emotion task while being scanned with functional magnetic resonance imaging (fMRI). Both sparse spectral clustering and independent component analysis (ICA) were applied to characterize the interactions between brain areas involved in cognitive reappraisal of emotion. The results revealed that the sparse spectral clustering method can get a higher sensitivity of polymerization compared with ICA. Furthermore, Voxel-based aggregation index (VBAI) has been presented to confirm that sparse spectral clustering is more excellent in identifying correlation patterns with weaker connectivity, such as temporal network. Thus, the study concluded that sparse spectral clustering provides a more practical and accurate way for researching brain functional connectivity in the process of emotional stimuli.
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Acknowledgments
This work has been partially supported by National Natural Science Foundation of China (61201096, 51307010), University Natural Science Research Program of Jiangsu Province (13KJB510002), the Science and Technology Program of Changzhou City (CE20145055) and Qing Lan Project of Jiangsu Province.
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Zou, L., Xu, Y., Jiang, Z., Jiao, Z., Pan, C., Zhou, R. (2016). Functional Connectivity Analysis of Cognitive Reappraisal Using Sparse Spectral Clustering Method. In: Wang, R., Pan, X. (eds) Advances in Cognitive Neurodynamics (V). Advances in Cognitive Neurodynamics. Springer, Singapore. https://doi.org/10.1007/978-981-10-0207-6_40
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DOI: https://doi.org/10.1007/978-981-10-0207-6_40
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