Knowledge Commons of Institute of Automation，CAS
| Inferring Functional Connectivity in fMRI Using Minimum Partial Correlation | |
| Lei Nie1; Xian Yang1 Pau(l); M. Matthews2; Zhi-Wei Xu3; Yi-Ke Guo1,4 | |
| Source Publication | International Journal of Automation and Computing
 |
| ISSN | 1476-8186 |
| 2017 | |
| Volume | 14Issue:4Pages:371-385 |
| Subtype | IJAC-2016-08-210.pdf |
| Abstract | Functional connectivity has emerged as a promising approach to study the functional organisation of the brain and to define features for prediction of brain state. The most widely used method for inferring functional connectivity is Pearsons correlation, but it cannot differentiate direct and indirect effects. This disadvantage is often avoided by computing the partial correlation between two regions controlling all other regions, but this method suffers from Berksons paradox. Some advanced methods, such as regularised inverse covariance, have been applied. However, these methods usually depend on some parameters. Here we propose use of minimum partial correlation as a parameter-free measure for the skeleton of functional connectivity in functional magnetic resonance imaging (fMRI). The minimum partial correlation between two regions is the minimum of absolute values of partial correlations by controlling all possible subsets of other regions. Theoretically, there is a direct effect between two regions if and only if their minimum partial correlation is non-zero under faithfulness and Gaussian assumptions. The elastic PC-algorithm is designed to efficiently approximate minimum partial correlation within a computational time budget. The simulation study shows that the proposed method outperforms others in most cases and its application is illustrated using a resting-state fMRI dataset from the human connectome project. |
| Keyword | Functional connectivity functional magnetic resonance imaging (fMRI) network modelling partial correlation PCalgorithm resting-state networks. |
| DOI | 10.1007/s11633-017-1084-9 |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://ir.ia.ac.cn/handle/173211/42473 |
| Collection | 学术期刊_International Journal of Automation and Computing |
| Affiliation | 1.Department of Computing, Imperial College London, London SW7 2AZ, UK 2.Department of Medicine, Imperial College London, London SW7 2AZ, UK 3.Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China 4.School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China |
| Recommended Citation GB/T 7714 | Lei Nie,Xian Yang1 Pau,M. Matthews,et al. Inferring Functional Connectivity in fMRI Using Minimum Partial Correlation[J]. International Journal of Automation and Computing,2017,14(4):371-385. |
| APA | Lei Nie,Xian Yang1 Pau,M. Matthews,Zhi-Wei Xu,&Yi-Ke Guo.(2017).Inferring Functional Connectivity in fMRI Using Minimum Partial Correlation.International Journal of Automation and Computing,14(4),371-385. |
| MLA | Lei Nie,et al."Inferring Functional Connectivity in fMRI Using Minimum Partial Correlation".International Journal of Automation and Computing 14.4(2017):371-385. |
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| File Name/Size | DocType | Version | Access | License | ||
| IJAC-2016-08-210.pdf（2876KB） | 期刊论文 | 出版稿 | 开放获取 | CC BY-NC-SA | View Download | |
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