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New Insights into Signed Path Coefficient Granger Causality Analysis
Zhang, Jian1,2; Li, Chong1; Jiang, Tanzi2
Source PublicationFRONTIERS IN NEUROINFORMATICS
2016-10-27
Volume10
SubtypeArticle
AbstractGranger 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.
KeywordSigned Path Coefficient Granger Causality Fmri Model Order Vector Autoregression
WOS HeadingsScience & Technology ; Life Sciences & Biomedicine
DOI10.3389/fninf.2016.00047
WOS KeywordPARTIAL DIRECTED COHERENCE ; MULTIVARIATE TIME-SERIES ; EFFECTIVE CONNECTIVITY ; FMRI DATA ; INFORMATION-FLOW ; FUNCTIONAL CONNECTIVITY ; BRAIN STRUCTURES ; BOLD SIGNALS ; NETWORK ; CORTEX
Indexed BySCI
Language英语
Funding OrganizationNational 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 Research AreaMathematical & Computational Biology ; Neurosciences & Neurology
WOS SubjectMathematical & Computational Biology ; Neurosciences
WOS IDWOS:000386259600001
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Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/13332
Collection脑网络组研究中心
Affiliation1.Zhejiang Univ, Sch Math Sci, Hangzhou, Zhejiang, Peoples R China
2.Chinese Acad Sci, Inst Automat, Brainnetome Ctr, Beijing, Peoples R China
Recommended Citation
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).
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