Abstract
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.
Highlights
Granger causality analysis is an important time series analysis technique that originally derived from econometrics
First by AICc criteria we computed the optimal model orders and order-1 autoregressive coefficients of respectively 2, 4, 8, 16, and 32 regions of interest (ROI), each group for 90 combinations (for example, anatomical labeling (AAL) k to AAL k + 31, for k = 1 to 90) and the value were averaged for every person, on filtered and unfiltered data, the result was shown in Figure 1 (TR = 0.645 s)
The optimal model order that was determined by AICc turned to be lower when more ROI time series were considered in the autoregression, since the penalty term in the criteria formula the signed path coefficient Granger causality will be contradictory to the real causal relationship between the time series
Summary
Granger causality analysis is an important time series analysis technique that originally derived from econometrics. In recent years it has been widely employed across the field of neuroscience, especially for constructing effective networks among brain regions in fMRI causal modeling studies (Friston, 2009, 2011; Bressler and Seth, 2011; Valdes-Sosa et al, 2011; Friston et al, 2012; Stephan and Roebroeck, 2012). The concept is based on the idea that the “cause” will precede and help to improve the prediction of the “effect.” As a broadly applied time series analysis method for assessing directional influence in neuroscience data, Granger causality has been proven to be useful and informative, while on the other hand it has drawn a lot of debates about its applicability and interpretation in fMRI studies. Various factors could impact the detectability of the neuronal interactions by Granger causality analysis in fMRI, and a great deal of detailed
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