Lin Lan, Fu Zhenrong, Jin Cong, Tian Miao, Wu Shuicai
Biomedical Research Center, College of Life Science and Bioengineering, Beijing University of Technology, Beijing, 100124, China.
Medical Engineering Department, Beijing Friendship Hospital, Capital Medical University, Beijing, 100050, China.
Exp Brain Res. 2018 Oct;236(10):2677-2689. doi: 10.1007/s00221-018-5326-z. Epub 2018 Jul 6.
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.
小世界架构在大脑解剖连接性研究中受到了广泛关注。然而，如何在扩散网络中充分量化小世界特性仍是一个问题。我们解决了小世界测度的局限性，并定义了新的度量指标：小世界效率（SWE）和小世界角度（SWA），两者均基于高全局效率和局部效率之间的权衡。为了证实新指标的有效性，我们研究了基于Watts-Strogatz模型的网络以及来自75名健康老年受试者（年龄在50 - 70岁之间）的扩散张量成像（DTI）数据的SWE和SWA行为。我们发现SWE可以将受试者分为不同年龄组，并且与韦氏成人智力量表第四版（WAIS-IV）测试中的个体表现相关。此外，为了评估所提出的测度对网络的敏感性，应用了两种网络攻击策略。我们的结果表明，在DTI数据分析中，新指标优于其前身。
