School of Biomedical Engineering and Sciences, Virginia Tech-Wake Forest University Winston-Salem, NC, USA.
Front Neuroinform. 2010 Dec 8;4:117. doi: 10.3389/fninf.2010.00117. eCollection 2010.
The reliability of graph metrics calculated in network analysis is essential to the interpretation of complex network organization. These graph metrics are used to deduce the small-world properties in networks. In this study, we investigated the test-retest reliability of graph metrics from functional magnetic resonance imaging data collected for two runs in 45 healthy older adults. Graph metrics were calculated on data for both runs and compared using intraclass correlation coefficient (ICC) statistics and Bland-Altman (BA) plots. ICC scores describe the level of absolute agreement between two measurements and provide a measure of reproducibility. For mean graph metrics, ICC scores were high for clustering coefficient (ICC = 0.86), global efficiency (ICC = 0.83), path length (ICC = 0.79), and local efficiency (ICC = 0.75); the ICC score for degree was found to be low (ICC = 0.29). ICC scores were also used to generate reproducibility maps in brain space to test voxel-wise reproducibility for unsmoothed and smoothed data. Reproducibility was uniform across the brain for global efficiency and path length, but was only high in network hubs for clustering coefficient, local efficiency, and degree. BA plots were used to test the measurement repeatability of all graph metrics. All graph metrics fell within the limits for repeatability. Together, these results suggest that with exception of degree, mean graph metrics are reproducible and suitable for clinical studies. Further exploration is warranted to better understand reproducibility across the brain on a voxel-wise basis.
在网络分析中计算的图度量的可靠性对于解释复杂的网络组织至关重要。这些图度量用于推断网络中的小世界特性。在这项研究中，我们调查了 45 名健康老年人两次运行的功能磁共振成像数据的图度量的测试-重测可靠性。在两次运行的数据上计算了图度量，并使用组内相关系数（ICC）统计量和 Bland-Altman（BA）图进行比较。ICC 得分描述了两次测量之间的绝对一致性水平，并提供了可重复性的度量。对于平均图度量，聚类系数（ICC=0.86）、全局效率（ICC=0.83）、路径长度（ICC=0.79）和局部效率（ICC=0.75）的 ICC 得分较高；度的 ICC 得分较低（ICC=0.29）。ICC 得分还用于在大脑空间中生成再现性图，以测试未平滑和平滑数据的体素水平再现性。全局效率和路径长度在大脑中具有均匀的再现性，但聚类系数、局部效率和度仅在网络枢纽中具有高再现性。BA 图用于测试所有图度量的测量重复性。所有图度量都在可重复性的范围内。这些结果表明，除了度之外，平均图度量是可重复的，适合临床研究。需要进一步探索，以更好地了解基于体素的大脑中各个部位的再现性。
