Modeling the interaction of Brain Regions based on Functional Magnetic Resonance Imaging time series

We propose a model that describes the interaction 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 modeling (DCM) 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. A further benefit is to estimate the effective connectivity between these regions. All designs, estimates, reviews are implemented using statistical parametric mapping (SPM), one of the free best software packages used for design models and analysis for inferring about FMRI functional magnetic resonance imaging time series.