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Volume 13
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10.3390/brainsci13050825
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Article

Test-Retest Reliability of Resting Brain Small-World Network Properties across Different Data Processing and Modeling Strategies

by
Qianying Wu
1,2,3,†,
Hui Lei
2,4,†,
Tianxin Mao
1,2,
Yao Deng
1,2,
Xiaocui Zhang
2,5,6,7,
Yali Jiang
2,5,
Xue Zhong
2,5,
John A. Detre
2,
Jianghong Liu
8,* and
Hengyi Rao
1,2,*
1
Key Laboratory of Brain-Machine Intelligence for Information Behavior (Ministry of Education and Shanghai), School of Business and Management, Shanghai International Studies University, Shanghai 201613, China
2
Department of Neurology, University of Pennsylvania, Philadelphia, PA 19104, USA
3
School of Life Sciences, University of Science and Technology of China, Hefei 230026, China
4
College of Education, Hunan Agricultural University, Changsha 410127, China
5
Medical Psychological Center, The Second Xiangya Hospital, Central South University, Changsha 410017, China
6
Medical Psychological Institute, Central South University, Changsha 410017, China
7
National Clinical Research Center for Mental Disorders, Changsha 410011, China
8
Department of Family and Community Health, School of Nursing, University of Pennsylvania, Philadelphia, PA 19104, USA
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Brain Sci. 2023, 13(5), 825; https://doi.org/10.3390/brainsci13050825
Submission received: 4 March 2023 / Revised: 2 May 2023 / Accepted: 12 May 2023 / Published: 19 May 2023
(This article belongs to the Special Issue Effects of Sleep Deprivation on Cognition, Emotion, and Behavior)
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Abstract

Resting-state functional magnetic resonance imaging (fMRI) with graph theoretical modeling has been increasingly applied for assessing whole brain network topological organization, yet its reproducibility remains controversial. In this study, we acquired three repeated resting-state fMRI scans from 16 healthy controls during a strictly controlled in-laboratory study and examined the test-retest reliability of seven global and three nodal brain network metrics using different data processing and modeling strategies. Among the global network metrics, the characteristic path length exhibited the highest reliability, whereas the network small-worldness performed the poorest. Nodal efficiency was the most reliable nodal metric, whereas betweenness centrality showed the lowest reliability. Weighted global network metrics provided better reliability than binary metrics, and reliability from the AAL90 atlas outweighed those from the Power264 parcellation. Although global signal regression had no consistent effects on the reliability of global network metrics, it slightly impaired the reliability of nodal metrics. These findings provide important implications for the future utility of graph theoretical modeling in brain network analyses.
Keywords: resting-state fMRI; test-retest reliability; graph theoretical modeling; small-world network (SWN); intra-class correlation coefficient (ICC) resting-state fMRI; test-retest reliability; graph theoretical modeling; small-world network (SWN); intra-class correlation coefficient (ICC)

Share and Cite

MDPI and ACS Style

Wu, Q.; Lei, H.; Mao, T.; Deng, Y.; Zhang, X.; Jiang, Y.; Zhong, X.; Detre, J.A.; Liu, J.; Rao, H. Test-Retest Reliability of Resting Brain Small-World Network Properties across Different Data Processing and Modeling Strategies. Brain Sci. 2023, 13, 825. https://doi.org/10.3390/brainsci13050825

AMA Style

Wu Q, Lei H, Mao T, Deng Y, Zhang X, Jiang Y, Zhong X, Detre JA, Liu J, Rao H. Test-Retest Reliability of Resting Brain Small-World Network Properties across Different Data Processing and Modeling Strategies. Brain Sciences. 2023; 13(5):825. https://doi.org/10.3390/brainsci13050825

Chicago/Turabian Style

Wu, Qianying, Hui Lei, Tianxin Mao, Yao Deng, Xiaocui Zhang, Yali Jiang, Xue Zhong, John A. Detre, Jianghong Liu, and Hengyi Rao. 2023. "Test-Retest Reliability of Resting Brain Small-World Network Properties across Different Data Processing and Modeling Strategies" Brain Sciences 13, no. 5: 825. https://doi.org/10.3390/brainsci13050825

APA Style

Wu, Q., Lei, H., Mao, T., Deng, Y., Zhang, X., Jiang, Y., Zhong, X., Detre, J. A., Liu, J., & Rao, H. (2023). Test-Retest Reliability of Resting Brain Small-World Network Properties across Different Data Processing and Modeling Strategies. Brain Sciences, 13(5), 825. https://doi.org/10.3390/brainsci13050825

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