Test-retest reliability of graph metrics in functional brain networks: a resting-state fNIRS study.

Recent research has demonstrated the feasibility of combining functional near-infrared spectroscopy (fNIRS) and graph theory approaches to explore the topological attributes of human brain networks. However, the test-retest (TRT) reliability of the application of graph metrics to these networks rema...

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Autores principales:	Haijing Niu, Zhen Li, Xuhong Liao, Jinhui Wang, Tengda Zhao, Ni Shu, Xiaohu Zhao, Yong He
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2013
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/fe2d468ea4dc4c5b9aa6b3b816c9913a
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spelling	oai:doaj.org-article:fe2d468ea4dc4c5b9aa6b3b816c9913a2021-11-18T08:56:24ZTest-retest reliability of graph metrics in functional brain networks: a resting-state fNIRS study.1932-620310.1371/journal.pone.0072425https://doaj.org/article/fe2d468ea4dc4c5b9aa6b3b816c9913a2013-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/24039763/?tool=EBIhttps://doaj.org/toc/1932-6203Recent research has demonstrated the feasibility of combining functional near-infrared spectroscopy (fNIRS) and graph theory approaches to explore the topological attributes of human brain networks. However, the test-retest (TRT) reliability of the application of graph metrics to these networks remains to be elucidated. Here, we used resting-state fNIRS and a graph-theoretical approach to systematically address TRT reliability as it applies to various features of human brain networks, including functional connectivity, global network metrics and regional nodal centrality metrics. Eighteen subjects participated in two resting-state fNIRS scan sessions held ∼20 min apart. Functional brain networks were constructed for each subject by computing temporal correlations on three types of hemoglobin concentration information (HbO, HbR, and HbT). This was followed by a graph-theoretical analysis, and then an intraclass correlation coefficient (ICC) was further applied to quantify the TRT reliability of each network metric. We observed that a large proportion of resting-state functional connections (∼90%) exhibited good reliability (0.6< ICC <0.74). For global and nodal measures, reliability was generally threshold-sensitive and varied among both network metrics and hemoglobin concentration signals. Specifically, the majority of global metrics exhibited fair to excellent reliability, with notably higher ICC values for the clustering coefficient (HbO: 0.76; HbR: 0.78; HbT: 0.53) and global efficiency (HbO: 0.76; HbR: 0.70; HbT: 0.78). Similarly, both nodal degree and efficiency measures also showed fair to excellent reliability across nodes (degree: 0.52∼0.84; efficiency: 0.50∼0.84); reliability was concordant across HbO, HbR and HbT and was significantly higher than that of nodal betweenness (0.28∼0.68). Together, our results suggest that most graph-theoretical network metrics derived from fNIRS are TRT reliable and can be used effectively for brain network research. This study also provides important guidance on the choice of network metrics of interest for future applied research in developmental and clinical neuroscience.Haijing NiuZhen LiXuhong LiaoJinhui WangTengda ZhaoNi ShuXiaohu ZhaoYong HePublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 8, Iss 9, p e72425 (2013)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Haijing Niu Zhen Li Xuhong Liao Jinhui Wang Tengda Zhao Ni Shu Xiaohu Zhao Yong He Test-retest reliability of graph metrics in functional brain networks: a resting-state fNIRS study.
description	Recent research has demonstrated the feasibility of combining functional near-infrared spectroscopy (fNIRS) and graph theory approaches to explore the topological attributes of human brain networks. However, the test-retest (TRT) reliability of the application of graph metrics to these networks remains to be elucidated. Here, we used resting-state fNIRS and a graph-theoretical approach to systematically address TRT reliability as it applies to various features of human brain networks, including functional connectivity, global network metrics and regional nodal centrality metrics. Eighteen subjects participated in two resting-state fNIRS scan sessions held ∼20 min apart. Functional brain networks were constructed for each subject by computing temporal correlations on three types of hemoglobin concentration information (HbO, HbR, and HbT). This was followed by a graph-theoretical analysis, and then an intraclass correlation coefficient (ICC) was further applied to quantify the TRT reliability of each network metric. We observed that a large proportion of resting-state functional connections (∼90%) exhibited good reliability (0.6< ICC <0.74). For global and nodal measures, reliability was generally threshold-sensitive and varied among both network metrics and hemoglobin concentration signals. Specifically, the majority of global metrics exhibited fair to excellent reliability, with notably higher ICC values for the clustering coefficient (HbO: 0.76; HbR: 0.78; HbT: 0.53) and global efficiency (HbO: 0.76; HbR: 0.70; HbT: 0.78). Similarly, both nodal degree and efficiency measures also showed fair to excellent reliability across nodes (degree: 0.52∼0.84; efficiency: 0.50∼0.84); reliability was concordant across HbO, HbR and HbT and was significantly higher than that of nodal betweenness (0.28∼0.68). Together, our results suggest that most graph-theoretical network metrics derived from fNIRS are TRT reliable and can be used effectively for brain network research. This study also provides important guidance on the choice of network metrics of interest for future applied research in developmental and clinical neuroscience.
format	article
author	Haijing Niu Zhen Li Xuhong Liao Jinhui Wang Tengda Zhao Ni Shu Xiaohu Zhao Yong He
author_facet	Haijing Niu Zhen Li Xuhong Liao Jinhui Wang Tengda Zhao Ni Shu Xiaohu Zhao Yong He
author_sort	Haijing Niu
title	Test-retest reliability of graph metrics in functional brain networks: a resting-state fNIRS study.
title_short	Test-retest reliability of graph metrics in functional brain networks: a resting-state fNIRS study.
title_full	Test-retest reliability of graph metrics in functional brain networks: a resting-state fNIRS study.
title_fullStr	Test-retest reliability of graph metrics in functional brain networks: a resting-state fNIRS study.
title_full_unstemmed	Test-retest reliability of graph metrics in functional brain networks: a resting-state fNIRS study.
title_sort	test-retest reliability of graph metrics in functional brain networks: a resting-state fnirs study.
publisher	Public Library of Science (PLoS)
publishDate	2013
url	https://doaj.org/article/fe2d468ea4dc4c5b9aa6b3b816c9913a
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Test-retest reliability of graph metrics in functional brain networks: a resting-state fNIRS study.

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