Inferring functional connectivity in fmri using minimum partial correlation

	Inferring functional connectivity in fmri using minimum partial correlation
	Nie Lei 1; Yang Xian 1; Matthews Paul M 1; Xu Zhiwei 2; Guo Yike 1
	2017
发表期刊	International journal of automation and computing
ISSN	1476-8186
卷号	14 期号:4 页码:371
摘要	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 Pearson's 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 Berkson's 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.
关键词	Functional connectivity Functional magnetic resonance imaging (fmri) Network modelling Partial correlation Pc-algorithm Resting-state networks
语种	英语
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/35108
专题	中国科学院计算技术研究所期刊论文_中文
作者单位	1.伦敦帝国学院 2.中国科学院计算技术研究所
推荐引用方式 GB/T 7714	Nie Lei,Yang Xian,Matthews Paul M,et al. Inferring functional connectivity in fmri using minimum partial correlation[J]. International journal of automation and computing,2017,14(4):371.
APA	Nie Lei,Yang Xian,Matthews Paul M,Xu Zhiwei,&Guo Yike.(2017).Inferring functional connectivity in fmri using minimum partial correlation.International journal of automation and computing,14(4),371.
MLA	Nie Lei,et al."Inferring functional connectivity in fmri using minimum partial correlation".International journal of automation and computing 14.4(2017):371.