Reproducibility of graph metrics in fMRI networks

• Main Authors: Qawi K Telesford, Ashley R Morgan, Satoru eHayasaka, Sean L Simpson, William Barret, Robert A Kraft, Jennifer L Mozolic, Paul J Laurienti
• Format: Article
• Language: English
• Published: Frontiers Media S.A. 2010-12-01
• Series: Frontiers in Neuroinformatics
• Subjects: network; functional MRI; graph theory; resting-state; Bland-Altman plot; intraclass correlation coefficient
• Online Access: http://journal.frontiersin.org/Journal/10.3389/fninf.2010.00117/full

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 (fMRI) 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.