Brain causality investigation based on FMRI images time series using dynamic causal modelling augmented by Granger Causality

Abstract

We propose a model that describes the interactions of several Brain Regions based on Functional Magnetic Resonance Imaging (FMRI) time series to make inferences about functional integration and segregation within the human brain. The method is demonstrated using dynamic causal modelling (DCM) augmented by Granger Causality (GC) using real data to show how such models are able to characterize interregional dependence. We extend estimating and reviewing designed model to characterize the interactions between regions and showing the direction of the signal over regions. A further benefit is to estimate the effective connectivity between these regions. All designs, estimates, reviews are implemented using Statistical Parametric Mapping (SPM) and GCCA toolbox, one of the free best software packages and published toolbox used to design the models and analysis for inferring about FMRI functional magnetic resonance imaging time series.