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Abstract #2467

Test-retest reliability of resting-state brain small-world network properties across different data processing and modeling strategies

Qianying Wu1, Ya Chai2, Hui Lei2, Fan Yang2, Jieqiong Wang2, Xue Zhong2, John Detre2, and Hengyi Rao2

1University of Science and Technology of China, Hefei, China, 2University of Pennsylvania, Philadelphia, PA, United States

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.

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