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• Technical Article •
Study on spectral clustering of amygdala using resting state functional connectivity
TIAN Hong-jun  Gao Quan-sheng  GAO Jun-feng  Dou Shun-yang 

DOI:10.12015/issn.1674-8034.2016.10.010.


[Abstract] Objective: According to the similarity of the functional connectivity pattern of the whole brain, the feasibility of using spectral clustering to segment the brain regions of the amygdala was studied.Materials and Methods: By resting state functional imaging according to the functional connectivity pattern of each voxel in a specific brain region, spectral clustering was performed to segment the amygdala region.Results: The segmentation results of subregion on the right side of the central medial nucleus (CM) were similar to that of the map, and the results of subregion on the right superficial nuclei (SF) were relatively less similar to the spectra. Cluster analysis results of the stability analysis results showed that the signal-to-noise ratio was more than 70 db, each subregion maintained 100% similarity to the results without adding noise, the pearson correlation coefficient could be kept about 0.7 in the low signal-to-noise ratio of 10 db.Conclusion: The clustering results and JULICH maps were highly consistent, which indicated the feasibility of this method. In addition, satisfactory results were also obtained in terms of stability and anti-interference. Compared with the traditional manual segmentation method, the clustering segmentation method proposed in this paper had the advantages of simplicity, high efficiency, and high repeatability.
[Keywords] Amygdala；Magnetic resonance imaging；Functional connectivity；Spectral clustering

TIAN Hong-jun Shenzhen Basda Medical Apparatus Co., Ltd., Shenzhen 518172, China

Gao Quan-sheng* Laboratory of the Animal Center, Academy of Military Medical Sciences, Beijing 100850, China

GAO Jun-feng Key Laboratory of Cognitive Science of State Ethnic Affairs Commission, College of Biomedical Engineering, South-Central University for Nationalities, Wuhan 430074, China; School of Life Science and Technology, University of Electronic Science and Technology, Chengdu 610054, China

Dou Shun-yang Institute of Biomedical Engineering, Xi’an Jiaotong University, Xi’an 710049, China

*Correspondence to: Gao QS, E-mail: 13691251828@163.com

Conflicts of interest   None.

ACKNOWLEDGMENTS  The work was supported by the National Nature Science Foundation of China No. 81271659, 81171385 the China Postdoctoral Science Foundation No. 2014M552346
Received  2016-08-20
Accepted  2016-09-25
DOI: 10.12015/issn.1674-8034.2016.10.010
DOI:10.12015/issn.1674-8034.2016.10.010.

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