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影响因子：0.659

DOI码：10.1166/jmihi.2016.1881

发表刊物：Journal of Medical Imaging and Health Informatics

刊物所在地：UNITED STATES

关键字：BRAIN NETWORK MEASURE; DYNAMIC ANALYSIS; GLIOMA; RS-FMRI; SMALL WORLD CHARACTER; WINDOW WIDTH

摘要：Traditional Resting-State functional Magnetic Resonance imaging (RS-fMRI) analysis takes the whole time sequence as an input to measure brain functional network, which inevitably neglects dynamic modification of brain functional connections. In order to observe the instantaneous change, a sliding window resampling method was proposed to divide entire signal sequence into several sub-sequences before extract network from the signal. However, verification of the method was not completed and a reasonable way to determine window width has not been presented. To confirm the legitimacy, we took brain small-world character as criteria, which is widely agreed to be a critical organizational character of brain functional network and determined a reasonable window width range. The entire signal sequence was first resampled by sliding windows with different widths and brain networks were extracted from individual sub-sequences. An exponential truncated power-law function was then applied to fit the node degree distribution of these networks to evaluate the small-world character as well as the legitimacy of the corresponding window width. Further application of the method showed major discrepancies on glioma patient brain network in different brain regions and dynamic evolution on regional Hub network, compared to those of normal subjects. These discoveries, which physiologically conform to the impact of glioma to normal brain, extensively proved the legitimacy of the resampling method with the window width we determined. This method retains small-world character, discloses instantaneous modification and enables dynamic measure of brain network.

合写作者：Xiaojie Zhang, Weibei Dou, Min Lu, Shaowu Li,Weibei Dou, Mingyu Zhang, Hongyan Chen,Mingyu Zhang, Honhyan Chen

第一作者：Ziyi Wang,Xiaojie Zhang

论文类型：期刊论文

通讯作者：Weibei Dou

卷号：6

期号：7

页面范围：1735-1740

ISSN号：2156-7018

是否译文：否

发表时间：2016-11-01