Graph Theoretical Analysis of Functional Brain Networks: Test-Retest Evaluation on Short- and Long-Term Resting-State Functional MRI Data

	Graph Theoretical Analysis of Functional Brain Networks: Test-Retest Evaluation on Short- and Long-Term Resting-State Functional MRI Data
	Wang, Jin-Hui 1; Zuo, Xi-Nian2,3 ; Gohel, Suril 4; Milham, Michael P.3; Biswal, Bharat B.4; He, Yong 1; Wang, JH (reprint author), Beijing Normal Univ, State Key Lab Cognit Neurosci & Learning, Beijing 100875, Peoples R China.
心理所单位排序	2
摘要	Graph-based computational network analysis has proven a powerful tool to quantitatively characterize functional architectures of the brain. However, the test-retest (TRT) reliability of graph metrics of functional networks has not been systematically examined. Here, we investigated TRT reliability of topological metrics of functional brain networks derived from resting-state functional magnetic resonance imaging data. Specifically, we evaluated both short-term (<1 hour apart) and long-term (>5 months apart) TRT reliability for 12 global and 6 local nodal network metrics. We found that reliability of global network metrics was overall low, threshold-sensitive and dependent on several factors of scanning time interval (TI, long-term>short-term), network membership (NM, networks excluding negative correlations>networks including negative correlations) and network type (NT, binarized networks>weighted networks). The dependence was modulated by another factor of node definition (ND) strategy. The local nodal reliability exhibited large variability across nodal metrics and a spatially heterogeneous distribution. Nodal degree was the most reliable metric and varied the least across the factors above. Hub regions in association and limbic/paralimbic cortices showed moderate TRT reliability. Importantly, nodal reliability was robust to above-mentioned four factors. Simulation analysis revealed that global network metrics were extremely sensitive (but varying degrees) to noise in functional connectivity and weighted networks generated numerically more reliable results in compared with binarized networks. For nodal network metrics, they showed high resistance to noise in functional connectivity and no NT related differences were found in the resistance. These findings provide important implications on how to choose reliable analytical schemes and network metrics of interest.
学科领域	Cognitive Neuroscience
	2011-07-19
语种	英语
发表期刊	PLOS ONE
ISSN	1932-6203
卷号	6 期号:7 页码:e21976
期刊论文类型	Article
URL	查看原文
收录类别	SCI
项目简介	This work was supported by the Natural Science Foundation of China (Grant Nos. 81030028 and 30870667), Beijing Natural Science Foundation (Grant No. 7102090) and the Scientific Research Foundation for the Returned Overseas Chinese Scholars (State Education Ministry). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
WOS记录号	WOS:000292929500024
资助机构	Natural Science Foundation of China [81030028, 30870667] ; Beijing Natural Science Foundation [7102090] ; Scientific Research Foundation for the Returned Overseas Chinese Scholars (State Education Ministry)
引用统计	被引频次：303[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.psych.ac.cn/handle/311026/12834
专题	中国科学院行为科学重点实验室
通讯作者	Wang, JH (reprint author), Beijing Normal Univ, State Key Lab Cognit Neurosci & Learning, Beijing 100875, Peoples R China.
作者单位	1.Beijing Normal Univ, State Key Lab Cognit Neurosci & Learning, Beijing 100875, Peoples R China 2.Chinese Acad Sci, Inst Psychol, Key Lab Behav Sci, Lab Funct Connectome & Dev, Beijing 100101, Peoples R China 3.NYU, Langone Med Ctr, Phyllis Green & Randolph Cowen Inst Pediat Neuros, New York, NY USA 4.Univ Med & Dent New Jersey, Dept Radiol, Newark, NJ 07103 USA
推荐引用方式 GB/T 7714	Wang, Jin-Hui,Zuo, Xi-Nian,Gohel, Suril,et al. Graph Theoretical Analysis of Functional Brain Networks: Test-Retest Evaluation on Short- and Long-Term Resting-State Functional MRI Data[J]. PLOS ONE,2011,6(7):e21976.
APA	Wang, Jin-Hui.,Zuo, Xi-Nian.,Gohel, Suril.,Milham, Michael P..,Biswal, Bharat B..,...&Wang, JH .(2011).Graph Theoretical Analysis of Functional Brain Networks: Test-Retest Evaluation on Short- and Long-Term Resting-State Functional MRI Data.PLOS ONE,6(7),e21976.
MLA	Wang, Jin-Hui,et al."Graph Theoretical Analysis of Functional Brain Networks: Test-Retest Evaluation on Short- and Long-Term Resting-State Functional MRI Data".PLOS ONE 6.7(2011):e21976.

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