New Insights into Signed Path Coefficient Granger Causality Analysis.
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.
School of Mathematical Sciences, Zhejiang UniversityHangzhou, China; Brainnetome Center, Institute of Automation, Chinese Academy of SciencesBeijing, China.
Jian
J
Zhang
School of Mathematical Sciences, Zhejiang University Hangzhou, China.
Chong
C
Li
Brainnetome Center, Institute of Automation, Chinese Academy of Sciences Beijing, China.
Tianzi
T
Jiang
Granger causality; fMRI; model order; signed path coefficient; vector autoregression