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Small-world indices via network efficiency for brain networks from diffusion MRI

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收录情况： ◇ SCIE

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单位： [1]Biomedical Research Center, College of Life Science and Bioengineering, Beijing University of Technology, Beijing 100124, China [2]Medical Engineering Department, Beijing Friendship Hospital, Capital Medical University, Beijing 100050, China

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关键词： Small world Connectome DTI Brain network

摘要：

The small-world architecture has gained considerable attention in anatomical brain connectivity studies. However, how to adequately quantify small-worldness in diffusion networks has remained a problem. We addressed the limits of small-world measures and defined new metric indices: the small-world efficiency (SWE) and the small-world angle (SWA), both based on the tradeoff between high global and local efficiency. To confirm the validity of the new indices, we examined the behavior of SWE and SWA of networks based on the Watts-Strogatz model as well as the diffusion tensor imaging (DTI) data from 75 healthy old subjects (aged 50-70). We found that SWE could classify the subjects into different age groups, and was correlated with individual performance on the WAIS-IV test. Moreover, to evaluate the sensitivity of the proposed measures to network, two network attack strategies were applied. Our results indicate that the new indices outperform their predecessors in the analysis of DTI data.

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出版当年[2017]版：

大类 | 4 区医学

小类 | 4 区神经科学

最新[2025]版：

大类 | 4 区医学

小类 | 4 区神经科学

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出版当年[2016]版：

Q3 NEUROSCIENCES

最新[2023]版：

Q4 NEUROSCIENCES

影响因子： 最新[2023版] 1.7 最新五年平均[2021-2025] 1.8 出版当年[2016版] 1.917 出版当年五年平均[2012-2016] 2.183 出版前一年[2015版] 2.057 出版后一年[2017版] 1.806

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第一作者单位： [1]Biomedical Research Center, College of Life Science and Bioengineering, Beijing University of Technology, Beijing 100124, China

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