Qianying Wu^{1}, Ya Chai^{2}, Hui Lei^{2}, Fan Yang^{2}, Jieqiong Wang^{2}, Xue Zhong^{2}, John Detre^{2}, and Hengyi Rao^{2}

Resting-state fMRI assessed with graph theoretical modeling provides a noninvasive approach for measuring brain network topological organization properties, yet their reproducibility remains uncertain. Here we examined the test-retest reliability of seven brain small-world network metrics from well-controlled resting-state scans of 16 healthy adults using different data processing and modeling strategies. Among the seven network metrics, Lambda exhibited highest reliability whereas Sigma performed the worst. Weighted network metrics provided better reliability than binary network metrics, while reliability from the AAL90 atlas outweighed those from the Power264 parcellation. Global signal regression had no consistent effect on reliability of these network metrics.

1. Bullmore, E. and O. Sporns, The economy of brain network organization. Nat Rev Neurosci, 2012. 13(5): p. 336-49.

2. Medaglia, J.D., Graph Theoretic Analysis of Resting State Functional MR Imaging. Neuroimaging Clin N Am, 2017. 27(4): p. 593-607.

3. Wang, J., et al., GRETNA: a graph theoretical network analysis toolbox for imaging connectomics. Front Hum Neurosci, 2015. 9: p. 386.

4. Murphy, K., et al., The impact of global signal regression on resting state correlations: are anti-correlated networks introduced? Neuroimage, 2009. 44(3): p. 893-905.

5. Braun, U., et al., Test-retest reliability of resting-state connectivity network characteristics using fMRI and graph theoretical measures. Neuroimage, 2012. 59(2): p. 1404-12.

6. Wang, J., et al., Parcellation-dependent small-world brain functional networks: a resting-state fMRI study. Hum Brain Mapp, 2009. 30(5): p. 1511-23.

7. Wang, J.H., et al., Graph theoretical analysis of functional brain networks: test-retest evaluation on short- and long-term resting-state functional MRI data. PLoS One, 2011. 6(7): p. e21976.

8. Rubinov, M. and O. Sporns, Complex network measures of brain connectivity: uses and interpretations. Neuroimage, 2010. 52(3): p. 1059-69.

**Fig.1 ICC values of seven metrics under six methods.** Lambda showed high ICCs using WG90 method. BG90: binary + global signal
regression + AAL90, BNG90: binary + no global signal regression + AAL90, WG90:
weighted + global signal regression + AAL90, WNG90: weighted + no global signal
regression +AAL90, WG264: weighted + global signal regression + POWER264,
WNG264: weighted + no global signal regression + POWER264

**Fig.2 Comparisons among three sets of
methods.** Weighted networks yielded significantly higher ICCs than Binary networks,
AAL90 yielded significantly higher ICCS than POWER264. *: p<0.05, **:
p<0.01, ***: p<0.001