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| Graph Theoretical Analysis of Functional Brain Networks: Test-Retest Evaluation on Short- and Long-Term Resting-State Functional MRI Data | |
Wang, Jin-Hui1; Zuo, Xi-Nian2,3 ; Gohel, Suril4; Milham, Michael P.3; Biswal, Bharat B.4; He, Yong1; Wang, JH (reprint author), Beijing Normal Univ, State Key Lab Cognit Neurosci & Learning, Beijing 100875, Peoples R China.
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| 2011-07-19 | |
| Source Publication | PLOS ONE
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| ISSN | 1932-6203 |
| Subtype | Article |
| Volume | 6Issue:7Pages:e21976 |
| Contribution Rank | 2 |
| Abstract | 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. |
| Subject Area | Cognitive Neuroscience |
| URL | 查看原文 |
| Indexed By | SCI |
| Language | 英语 |
| Funding Organization | Natural Science Foundation of China [81030028, 30870667] ; Beijing Natural Science Foundation [7102090] ; Scientific Research Foundation for the Returned Overseas Chinese Scholars (State Education Ministry) |
| Project Intro. | 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 ID | WOS:000292929500024 |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://ir.psych.ac.cn/handle/311026/12834 |
| Collection | 中国科学院行为科学重点实验室 |
| Corresponding Author | Wang, JH (reprint author), Beijing Normal Univ, State Key Lab Cognit Neurosci & Learning, Beijing 100875, Peoples R China. |
| Affiliation | 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 |
| Recommended Citation 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. |
| Files in This Item: | ||||||
| File Name/Size | DocType | Version | Access | License | ||
| WOS_000292929500024.（4920KB） | 期刊论文 | 出版稿 | 限制开放 | CC BY-NC-SA | Application Full Text | |
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