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New Insights into Signed Path Coefficient Granger Causality Analysis
Zhang, Jian1,2; Li, Chong1; Jiang, Tanzi2
2016-10-27
发表期刊FRONTIERS IN NEUROINFORMATICS
卷号10
文章类型Article
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.
关键词Signed Path Coefficient Granger Causality Fmri Model Order Vector Autoregression
WOS标题词Science & Technology ; Life Sciences & Biomedicine
DOI10.3389/fninf.2016.00047
关键词[WOS]PARTIAL DIRECTED COHERENCE ; MULTIVARIATE TIME-SERIES ; EFFECTIVE CONNECTIVITY ; FMRI DATA ; INFORMATION-FLOW ; FUNCTIONAL CONNECTIVITY ; BRAIN STRUCTURES ; BOLD SIGNALS ; NETWORK ; CORTEX
收录类别SCI
语种英语
项目资助者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)
WOS研究方向Mathematical & Computational Biology ; Neurosciences & Neurology
WOS类目Mathematical & Computational Biology ; Neurosciences
WOS记录号WOS:000386259600001
引用统计
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/13332
专题脑网络组研究中心
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
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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).
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