A Classification Mechanism of Brain Functional fMRI in Resting State

Hui Zhou; Zhen Cheng Chen; Jian Ming Zhu; Dong Cui Wang; Biao Xu

doi:10.4028/www.scientific.net/AMM.687-691.1087

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 687-691A Classification Mechanism of Brain Functional...

A Classification Mechanism of Brain Functional fMRI in Resting State

Abstract:

To investigate the brain default mode network (DMN) of healthy young people, a novel hierarchical clustering method was proposed to detect similarities of low-frequency fluctuations between any two out of 160 regions of interest (ROI) all over the brain. Feature of these ROIs were firstextractedand analyzed the feature using hierarchical clustering approach.Combining with the strongest connected network node identified by network centric criterion, the default mode network which presented the strongest connectivity in resting state was then determined. The results demonstrated that cingulate had the highest value of average degree, making it the most suspectof where the centrality indices of DMN lay.The comparative results between nodes included by DMN returned by our method and these given by Dosenbach’s research showed quite high coincidence rates,indicating the proposed method of combining complex network theory and hierarchical clustering analysis feasible method to parse brain regions.

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Periodical:

Applied Mechanics and Materials (Volumes 687-691)

Pages:

1087-1090

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.687-691.1087

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Online since:

November 2014

Authors:

Hui Zhou, Zhen Cheng Chen*, Jian Ming Zhu, Dong Cui Wang, Biao Xu

Keywords:

Complex Network, Default Mode Network, Feature Extraction, Hierarchical Clustering

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References

[1] D.A. Handwerker,V. Roopchansingh, J. Gonzalez-Castillo, P.A. Bandettini. Periodic changes in fMRI connectivity. NeuroImage 2012; 63(3): 1712 – 1719.

DOI: 10.1016/j.neuroimage.2012.06.078

Google Scholar

[2] B. Biswal, F.Z. Yetkin V.M. Haughton, J.S. Hyde. Functional connectivity in the motor cortex of resting human brain using echo-planar MRI. Magn Reson Med 1995; 34(4): 537- 541.

DOI: 10.1002/mrm.1910340409

Google Scholar

[3] Y.Y. Ma, G. Zhen J.M. Zhou. Review of brain function integration method based on fMRI data. Biophysical Journal 2011; 27(1): 18-27.

Google Scholar

[4] N.U.F. Dosenbach, D.A. Fair, F.M. Miezin. Distinct brain networks for adaptive and stable task controlinHumans. Proceedingsof the National Academy of Sciences 2007; 104(26): 11073-11081.

DOI: 10.1073/pnas.0704320104

Google Scholar

[5] P. Filzmoser, R. Baumgartner E.A. Moser. Hierarchical clustering method for analyzing functional MR images. Magn Reson Imaging 1999; 17(8): 17–26.

DOI: 10.1016/s0730-725x(99)00014-4

Google Scholar

[6] C. Goutte,P. Toft,E. Rostrup F.A. Nielsen, L.K. Hanson. Onclustering fMRI time series. Neuroimage 1999; 9(11): 298 –310.

DOI: 10.1006/nimg.1998.0391

Google Scholar

[7] X.H. Chen, J.J. Jiao, M. Ke, Y. Wu. The Brain Network Modeling and Characters of Functional Connectivity in Resting State. Journal of LanZhou University of Technology 2010; 36(5): 88-92.

Google Scholar