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Graph Theoretical Analysis of Functional Brain Networks: Test-Retest Evaluation on Short- and Long-Term Resting-State Functional MRI Data
文献类型：期刊论文
| 作者 | Wang, Jin-Hui1; Zuo, Xi-Nian2,3 ; Gohel, Suril4; Milham, Michael P.3; Biswal, Bharat B.4; He, Yong1
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| 刊名 | PLOS ONE
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| 出版日期 | 2011-07-19 |
| 卷号 | 6期号:7页码:e21976 |
| ISSN号 | 1932-6203 |
| 产权排序 | 2 |
| 通讯作者 | Wang, JH (reprint author), Beijing Normal Univ, State Key Lab Cognit Neurosci & Learning, Beijing 100875, Peoples R China. |
| 英文摘要 | 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. |
| 学科主题 | Cognitive neuroscience |
| 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. |
| 原文出处 | http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3139595/pdf/pone.0021976.pdf |
| 语种 | 英语 |
| WOS记录号 | WOS:000292929500024 |
| 源URL | [http://ir.psych.ac.cn/handle/311026/12834]  |
| 专题 | 心理研究所_中国科学院行为科学重点实验室 |
| 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.,&He, Yong.(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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来源：心理研究所
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