Is Granger Causality a Viable Technique for Analyzing fMRI Data?

Abstract

Multivariate neural data provide the basis for assessing interactions in brain networks. Among myriad connectivity measures, Granger causality (GC) has proven to be statistically intuitive, easy to implement, and generate meaningful results. Although its application to functional MRI (fMRI) data is increasing, several factors have been identified that appear to hinder its neural interpretability: (a) latency differences in hemodynamic response function (HRF) across different brain regions, (b) low-sampling rates, and (c) noise. Recognizing that in basic and clinical neuroscience, it is often the change of a dependent variable (e.g., GC) between experimental conditions and between normal and pathology that is of interest, we address the question of whether there exist systematic relationships between GC at the fMRI level and that at the neural level. Simulated neural signals were convolved with a canonical HRF, down-sampled, and noise-added to generate simulated fMRI data. As the coupling parameters in the model were varied, fMRI GC and neural GC were calculated, and their relationship examined. Three main results were found: (1) GC following HRF convolution is a monotonically increasing function of neural GC; (2) this monotonicity can be reliably detected as a positive correlation when realistic fMRI temporal resolution and noise level were used; and (3) although the detectability of monotonicity declined due to the presence of HRF latency differences, substantial recovery of detectability occurred after correcting for latency differences. These results suggest that Granger causality is a viable technique for analyzing fMRI data when the questions are appropriately formulated.

Resting-state networks link invasive and noninvasive brain stimulation across diverse psychiatric and neurological diseases

“…First, although both rs-fcMRI and brain stimulation are polysynaptic, they do not necessarily reflect the same polysynaptic phenomena, and discrepancies exist (93)(94)(95). More advanced rs-fcMRI processing techniques designed to predict the influence of one region on another may prove better for identifying stimulation-based brain networks (96)(97)(98). Second, although there is a strong relationship between rs-fcMRI and anatomical white matter connectivity, Diseases in which a specific site of noninvasive brain stimulation has been reported to be ineffective.…”

Sex differences in effective fronto-limbic connectivity during negative emotion processing

“…It is therefore possible that the sex-differences in brain connectivity observed here may have been different had we examined various phases of the menstrual cycle. Finally, even though the Granger causality analysis is a well-established method to examine effective connectivity between brain regions using neuroimaging data (Bressler and Seth, 2011;Roebroeck et al, 2005;Wen et al, 2013), the interpretation of the results has its limitation. One cannot claim true causation, but rather the extent to which past fluctuations (information) from one regionare associated with present fluctuations (information) in another region.…”

“…Granger causality modelling (GCM) was originally developed in the field of economics (Granger, 1980) to account for temporal dependencies in time series and has been tested and successfully used in estimating functional lagged connectivity between real or simulated neural time series (Bressler and Seth, 2011;Roebroeck et al, 2005;Wen et al, 2013). Using a particular approach in regression analysis (see below), the Granger causality method allows one to statistically test the hypothesis that immediate past values from one time series are associated with current values from another time series, controlling for the influence from its own past values.…”

Functional magnetic resonance imaging reveals abnormal brain connectivity in EGR3 gene transfected rat model of schizophrenia

“…Multivariate neural data provide the basis for assessing interactions in brain networks. Among myriad connectivity measures, Granger causality (GC) has proven to be statistically intuitive, easy to implement, and a viable technique for analyzing fMRI Data [19]. Limited study is available on fMRI study of schizophrenia using Granger causality model.…”