Open AccessConstruction of functional brain connectivity from fMRI data with driving and modulatory inputs: an extended conditional Granger causality approach
Author(s) -
Evangelos Almpanis,
Constantinos Siettos
Publication year - 2020
Publication title -
aims neuroscience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.257
H-Index - 12
eISSN - 2373-7972
pISSN - 2373-8006
DOI - 10.3934/neuroscience.2020005
Subject(s) - granger causality , computer science , causality (physics) , artificial intelligence , benchmark (surveying) , machine learning , causal model , pattern recognition (psychology) , mathematics , statistics , physics , geodesy , quantum mechanics , geography
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.