Dennis Emily L, Jahanshad Neda, Toga Arthur W, McMahon Katie L, de Zubicaray Greig I, Martin Nicholas G, Wright Margaret J, Thompson Paul M
Imaging Genetics Center, Laboratory of Neuro Imaging, UCLA, CA, USA.
Med Image Comput Comput Assist Interv. 2012;15(Pt 3):305-12. doi: 10.1007/978-3-642-33454-2_38.
The human connectome has recently become a popular research topic in neuroscience, and many new algorithms have been applied to analyze brain networks. In particular, network topology measures from graph theory have been adapted to analyze network efficiency and 'small-world' properties. While there has been a surge in the number of papers examining connectivity through graph theory, questions remain about its test-retest reliability (TRT). In particular, the reproducibility of structural connectivity measures has not been assessed. We examined the TRT of global connectivity measures generated from graph theory analyses of 17 young adults who underwent two high-angular resolution diffusion (HARDI) scans approximately 3 months apart. Of the measures assessed, modularity had the highest TRT, and it was stable across a range of sparsities (a thresholding parameter used to define which network edges are retained). These reliability measures underline the need to develop network descriptors that are robust to acquisition parameters.
人类连接组最近已成为神经科学中一个热门的研究课题，并且许多新算法已被应用于分析脑网络。特别是，来自图论的网络拓扑测量方法已被用于分析网络效率和“小世界”特性。虽然通过图论研究连通性的论文数量激增，但关于其重测信度（TRT）仍存在问题。特别是，结构连通性测量的可重复性尚未得到评估。我们研究了17名年轻成年人的图论分析所生成的全局连通性测量的TRT，这些成年人在大约3个月的间隔内接受了两次高角分辨率扩散（HARDI）扫描。在所评估的测量方法中，模块化具有最高的TRT，并且在一系列稀疏度（用于定义保留哪些网络边的阈值参数）范围内是稳定的。这些可靠性测量结果强调了开发对采集参数具有鲁棒性的网络描述符的必要性。
