New Insights into Signed Path Coefficient Granger Causality Analysis

doi:10.3389/fninf.2016.00047

	New Insights into Signed Path Coefficient Granger Causality Analysis
	Zhang, Jian 1,2; Li, Chong 1; Jiang, Tanzi 2
刊名	FRONTIERS IN NEUROINFORMATICS
	2016-10-27
卷号	10
关键词	Signed Path Coefficient Granger Causality Fmri Model Order Vector Autoregression
DOI	10.3389/fninf.2016.00047
文献子类	Article
英文摘要	Granger causality analysis, as a time series analysis technique derived from econometrics, has been applied in an ever-increasing number of publications in the field of neuroscience, including fMRI, EEG/MEG, and fNIRS. The present study mainly focuses on the validity of "signed path coefficient Granger causality," a Granger-causality-derived analysis method that has been adopted by many fMRI researches in the last few years. This method generally estimates the causality effect among the time series by an order-1 autoregression, and defines a positive or negative coefficient as an "excitatory" or "inhibitory" influence. In the current work we conducted a series of computations from resting-state fMRI data and simulation experiments to illustrate the signed path coefficient method was flawed and untenable, due to the fact that the autoregressive coefficients were not always consistent with the real causal relationships and this would inevitablely lead to erroneous conclusions. Overall our findings suggested that the applicability of this kind of causality analysis was rather limited, hence researchers should be more cautious in applying the signed path coefficient Granger causality to fMRI data to avoid misinterpretation.
WOS关键词	PARTIAL DIRECTED COHERENCE ; MULTIVARIATE TIME-SERIES ; EFFECTIVE CONNECTIVITY ; FMRI DATA ; INFORMATION-FLOW ; FUNCTIONAL CONNECTIVITY ; BRAIN STRUCTURES ; BOLD SIGNALS ; NETWORK ; CORTEX
WOS研究方向	Mathematical & Computational Biology ; Neurosciences & Neurology
语种	英语
WOS记录号	WOS:000386259600001
资助机构	National Key Basic Research and Development Program (973)(2011CB707801) ; Strategic Priority Research Program of the Chinese Academy of Sciences(XDB02030300) ; Natural Science Foundation of China(91132301) ; National Natural Science Foundation of China(11571308)
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/13332]
专题	自动化研究所_脑网络组研究中心
作者单位	1.Zhejiang Univ, Sch Math Sci, Hangzhou, Zhejiang, Peoples R China 2.Chinese Acad Sci, Inst Automat, Brainnetome Ctr, Beijing, Peoples R China
推荐引用方式 GB/T 7714	Zhang, Jian,Li, Chong,Jiang, Tanzi. New Insights into Signed Path Coefficient Granger Causality Analysis[J]. FRONTIERS IN NEUROINFORMATICS,2016,10.
APA	Zhang, Jian,Li, Chong,&Jiang, Tanzi.(2016).New Insights into Signed Path Coefficient Granger Causality Analysis.FRONTIERS IN NEUROINFORMATICS,10.
MLA	Zhang, Jian,et al."New Insights into Signed Path Coefficient Granger Causality Analysis".FRONTIERS IN NEUROINFORMATICS 10(2016).