Graph-based network analysis of resting-state fMRI: test-retest reliability of binarized and weighted networks
- 3 Downloads
In the past decade, resting-state functional magnetic resonance imaging (rs-fMRI) and graph-based measures have been widely used to quantitatively characterize the architectures of brain functional networks in healthy individuals and in patients with abnormalities related to psychopathic and neurological disorders. To accurately evaluate the topological organization of brain functional networks, the definition of the nodes and edges for the construction of functional networks is critical. Furthermore, both types of brain functional networks (binarized networks and weighted networks) are widely used to analyze topological organization. However, how to best select the network type is still debated. Consequently, we investigated the test-retest reliability of brain functional networks with binarized and weighted edges using two independent datasets and four strategies for defining nodes. We revealed fair to good reliability for a majority of network topological attributes and overall higher reliabilities for weighted networks than for binarized networks. For regional nodal efficiency, weighted networks also showed higher reliability across nodes. Thus, our findings imply that weighted networks contain more information, leading to more stable results. In addition, we found that the reliability of brain functional networks was influenced by the node definition strategy and that more precise of nodal definition were associated with higher reliability. The time effect of reliability was restricted, as no differences between long-term and short-term reliability were observed. In conclusion, our results suggest that weighted networks have better reliability because they reflect more topological information, implying broader applications of weighted networks related to normal and disordered function of the human brain.
KeywordsResting-state fMRI Graph-based measures Binarized and weighted edges Test-retest reliability
This project is supported by the National Natural Science Foundation of China (61503272, 61873178 and 61876124), the Natural Science Foundation of Shanxi (201801D121135), the International Science and Technology Cooperation Project of Shanxi (201803D421047), and the Youth Science and Technology Research Fund (201701D221119). Also, we would like to thank PhD Lynne Hyman for the professional language editing services.
This project is supported by the National Natural Science Foundation of China (61503272, 61873178 and 61876124), the Natural Science Foundation of Shanxi (201801D121135), the International Science and Technology Cooperation Project of Shanxi (201803D421047), and the Youth Science and Technology Research Fund (201701D221119).
Compliance with ethical standards
Conflict of interest
Jie Xiang, Jiayue Xue, Hao Guo, Dandan Li, Xiaohong Cui, Yan Niu, Ting Yan, Rui Cao, Yao Ma, Yanli Yang and Bin Wang declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
The dataset of IPCAS_1 and NYU CSC are used in the study. The IPCAS_1 dataset was approved by the Institute of Psychology, Chinese Academy of Sciences, the NYU CSC dataset was approved by the New York University, Child Study Center.
- Achard, S., Salvador, R., Whitcher, B., Suckling, J., & Bullmore, E. (2006). A resilient, low-frequency, small-world human brain functional network with highly connected association cortical hubs. Journal of Neuroscience: The Official Journal of the Society for Neuroscience, 26(1), 63–72.CrossRefGoogle Scholar
- Bassett, D. S., & Bullmore, E. T. (2016). Small-world brain networks revisited. Neuroscientist A Review Journal Bringing Neurobiology Neurology & Psychiatry, 23(5).Google Scholar
- Biswal, B. B., Mennes, M., Zuo, X. N., Gohel, S., Kelly, C., Smith, S. M., Beckmann, C. F., Adelstein, J. S., Buckner, R. L., & Colcombe, S. (2009). Toward discovery science of human brain function. Proceedings of the National Academy of Sciences of the United States of America, 71(10), 4734–4739.CrossRefGoogle Scholar
- Braun, U., Plichta, M. M., Esslinger, C., Sauer, C., Haddad, L., Grimm, O., Mier, D., Mohnke, S., Heinz, A., Erk, S., Walter, H., Seiferth, N., Kirsch, P., & Meyer-Lindenberg, A. (2012). Test–retest reliability of resting-state connectivity network characteristics using fMRI and graph theoretical measures. NeuroImage, 59(2), 1404–1412.CrossRefGoogle Scholar
- Cao, H., Plichta, M. M., Schäfer, A., Haddad, L., Grimm, O., Schneider, M., Esslinger, C., Kirsch, P., Meyer Lindenberg, A., & Tost, H. (2014). Test-retest reliability of fMRI-based graph theoretical properties during working memory, emotion processing, and resting state. Neuroimage, 84(1), 888–900.CrossRefGoogle Scholar
- Cocito, C., Vanlinden, F., & Branlant, C. (2012). Exposure to subliminal arousing stimuli induces robust activation in the amygdala, hippocampus, anterior cingulate, insular cortex and primary visual cortex: A systematic meta-analysis of fMRI studies. Neuroimage, 59(3), 2962–2973.CrossRefGoogle Scholar
- Doria, V., Beckmann, C. F., Arichi, T., Merchant, N., Groppo, M., Turkheimer, F. E., Counsell, S. J., Murgasova, M., Aljabar, P., & Nunes, R. G. (2010). Emergence of resting state networks in the preterm human brain. Proceedings of the National Academy of Sciences of the United States of America, 107(46), 20015–20020.CrossRefGoogle Scholar
- He, Y., Chen, Z., & Evans, A. (2008). Structural insights into aberrant topological patterns of large-scale cortical networks in Alzheimer's disease. Journal of Neuroscience, 4(4), T284–T285.Google Scholar
- Horn, A., Ostwald, D., Reisert, M., & Blankenburg, F. (2014). The structural-functional connectome and the default mode network of the human brain. Neuroimage, 102142–102151.Google Scholar
- Klimm, F., Bassett, D.S., Carlson, J.M., Mucha, P.J., (2014). Resolving Structural Variability in Network Models and the Brain. PLoS Computational Biology, 10,3(2014-3-27) 10(3), e1003491.Google Scholar
- Mazoyer, B., Zago, L., Mellet, E., Bricogne, S., Etard, O., Houdé, O., Crivello, F., Joliot, M., Petit, L., & Tzourio-Mazoyer, N. (2001). Cortical networks for working memory and executive functions sustain the conscious resting state in man. Brain Research Bulletin, 54(3), 287–298.CrossRefGoogle Scholar
- Newman, M. E. (2006). Modularity and community structure in networks. APS March Meeting, pp., 8577–8582.Google Scholar
- Sanabriadiaz, G., Meliegarcía, L., Iturriamedina, Y., Alemángómez, Y., Hernándezgonzález, G., Valdésurrutia, L., Galán, L., & Valdéssosa, P. (2010). Surface area and cortical thickness descriptors reveal different attributes of the structural human brain networks. Neuroimage, 50(4), 1497–1510.CrossRefGoogle Scholar
- Tzouriomazoyer, N., Landeau, B., Papathanassiou, D., Crivello, F., Etard, O., Delcroix, N., Mazoyer, B., & Joliot, M. (2002). Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. Neuroimage, 15(1), 273–289.CrossRefGoogle Scholar
- Wang, J., Zuo, X., & He, Y. (2010a). Graph-based network analysis of resting-state functional MRI. Frontiers in Systems Neuroscience, 4(16), 16.Google Scholar
- Watts, D.J., Strogatz, S.H., (1998). Collective dynamics of 'small-world' networks, Collective dynamics of ‘small-world’ networks.Google Scholar
- Weber, M. J., Detre, J. A., Thompsonschill, S. L., & Avants, B. B. (2013). Reproducibility of functional network metrics and network structure: A comparison of task-related BOLD, resting ASL with BOLD contrast, and resting cerebral blood flow. Cognitive, Affective, & Behavioral Neuroscience, 13(3), 627–640.CrossRefGoogle Scholar
- Winer, B. J. (1962). Statistical principles in experimental design. International Student Edition, 29, 7304–7309.Google Scholar