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Reliability evaluation on weighted graph metrics of fNIRS brain networks
Mengjing Wang1; Zhen Yuan2; Haijing Niu1
2019-05
Source PublicationQUANTITATIVE IMAGING IN MEDICINE AND SURGERY
ISSN2223-4292
Volume9Issue:5Pages:832-841
Abstract

Background: Resting-state fNIRS (R-fNIRS) imaging data has proven to be a valuable technique to quantitatively characterize functional architectures of human brain network. However, whether the brain network metrics derived using weighted brain network model is test-retest (TRT) reliable remains largely unknown.

Methods: Here, we firstly constructed weighted brain networks on a group of 18 participants, and then applied graph-theory approach to quantify topological parameters of each weighted network. The intraclass correlation coefficient (ICC) was further applied to quantify the TRT reliability of network metrics.

Results: We found that the reliability of the weighted network metrics is threshold-sensitive, and most of these network metrics showed fair to excellent reliability. Specifically, the global network metrics, e.g., clustering coefficient, path length, local efficiency and global efficiency were of excellent level reliability (ICC >0.75) on both HbO and HbR signals. The nodal network metrics, e.g., degree and efficiency, generally also showed excellent level reliability on both HbO and HbR signals, and the reliability of these two metrics was better than that of nodal betweenness.

Conclusions: Overall, these findings demonstrated that most weighted network metrics derived from fNIRS are TRT reliable and can be used for brain network research.

KeywordFunctional Connectivity Module Weighted Network Graph Theory Small-world
DOI10.21037/qims.2019.05.08
Indexed BySCIE
Language英语
WOS Research AreaRadiology, Nuclear Medicine & Medical Imaging
WOS SubjectRadiology, Nuclear Medicine & Medical Imaging
WOS IDWOS:000470002000010
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Cited Times [WOS]:0   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionFaculty of Health Sciences
INSTITUTE OF COLLABORATIVE INNOVATION
Corresponding AuthorHaijing Niu
Affiliation1.State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
2.Faculty of Health Sciences, University of Macau, Macao 999078, China
Recommended Citation
GB/T 7714
Mengjing Wang,Zhen Yuan,Haijing Niu. Reliability evaluation on weighted graph metrics of fNIRS brain networks[J]. QUANTITATIVE IMAGING IN MEDICINE AND SURGERY,2019,9(5):832-841.
APA Mengjing Wang,Zhen Yuan,&Haijing Niu.(2019).Reliability evaluation on weighted graph metrics of fNIRS brain networks.QUANTITATIVE IMAGING IN MEDICINE AND SURGERY,9(5),832-841.
MLA Mengjing Wang,et al."Reliability evaluation on weighted graph metrics of fNIRS brain networks".QUANTITATIVE IMAGING IN MEDICINE AND SURGERY 9.5(2019):832-841.
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