Abstract
We propose a numerical-based approach extending the conditional MVAR Granger causality (MVGC) analysis for the construction of directed connectivity networks in the presence of both exogenous/stimuli and modulatory inputs. The performance of the proposed scheme is validated using both synthetic stochastic data considering also the influence of haemodynamics latencies and a benchmark fMRI dataset related to the role of attention in the perception of visual motion. The particular fMRI dataset has been used in many studies to evaluate alternative model hypotheses using the Dynamic Causal Modelling (DCM) approach. Based on the use of the Bayes factor, we show that the obtained GC connectivity network compares well to a reference model that has been selected through DCM analysis among other candidate models. Thus, our findings suggest that the proposed scheme can be successfully used as a stand-alone or complementary to DCM approach to find directed causal connectivity patterns in task-related fMRI studies.
Highlights
Unfolding the complexity of the human brain function, as this is related to brain networking during cognitive tasks and/or at resting state, is one of the most significant and challenging research pursuits of our time
We propose a numerical-based approach extending the conditional multivariate AR (MVAR) Granger causality (MVGC) analysis for the construction of directed connectivity networks in the presence of both exogenous/stimuli and modulatory inputs
GC is considered as a generic data-driven approach that is mostly used for the reconstruction of the emergent functional connectivity without dealing with the modelling and influence of exogenous and/or modulatory inputs on the network structure
Summary
Unfolding the complexity of the human brain function, as this is related to brain networking during cognitive tasks and/or at resting state, is one of the most significant and challenging research pursuits of our time. On the the other hand, Dynamic Causal Modeling (DCM) is the most known representative method for reconstructing the underlying task-dependent effective connectivity from fMRI data and it is mainly model-driven [30, 54, 55]. It requires a prior knowledge (or guess) of the specific brain areas that are involved in the function of a specific cognitive task.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
