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Graph-based network analysis of resting-state fMRI: test-retest reliability of binarized and weighted networks

  • Jie Xiang
  • Jiayue Xue
  • Hao Guo
  • Dandan LiEmail author
  • Xiaohong Cui
  • Yan Niu
  • Ting Yan
  • Rui Cao
  • Yao Ma
  • Yanli Yang
  • Bin WangEmail author
ORIGINAL RESEARCH
  • 3 Downloads

Abstract

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.

Keywords

Resting-state fMRI Graph-based measures Binarized and weighted edges Test-retest reliability 

Notes

Acknowledgements

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.

Funding

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.

Ethics approval

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.

Supplementary material

11682_2019_42_MOESM1_ESM.pdf (1.5 mb)
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© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.College of Information and ComputerTaiyuan University of TechnologyTaiyuanChina
  2. 2.Translational Medicine Research CenterShanxi Medical UniversityTaiyuanChina

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