Graph-based network analysis of resting-state fMRI: test-retest reliability of binarized and weighted networks
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ORIGINAL RESEARCH
Graph-based network analysis of resting-state fMRI: test-retest reliability of binarized and weighted networks Jie Xiang 1 & Jiayue Xue 1 & Hao Guo 1 & Dandan Li 1 & Xiaohong Cui 1 & Yan Niu 1 & Ting Yan 2 & Rui Cao 1 & Yao Ma 1 & Yanli Yang 1 & Bin Wang 1
# Springer Science+Business Media, LLC, part of Springer Nature 2019
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
Introduction The human brain is considered an extremely complex system characterized as a functional connectome that links related functional areas via a structural connectome consisting of Electronic supplementary material The online version of this article (https://doi.org/10.1007/s11682-019-00042-6) contains supplementary material, which is available to authorized users. * Dandan Li [email protected] * Bin Wang [email protected] 1
College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
2
Translational Medicine Research Center, Shanxi Medical University, Taiyuan, China
neural elements and neural connections (Biswal et al. 2009; Finn et al. 2015; Sporns et al. 2005). Resting-state functional magnetic resonance im
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