Functional integration in the brain refers to distributed interactions among functionally segregated regions. Investigation of effective connectivity in brain networks, i.e, the directed causal influence that one brain region exerts over another region, is being increasingly recognized as an important tool for understanding brain function in neuroimaging studies. Methods for identifying intrinsic relationships among elements in a network are increasingly in demand.
Over the last few decades several techniques such as Bayesian networks, Granger causality, and dynamic causal models have been developed to identify causal relations in dynamic systems. At the same time, established techniques such as structural equation modeling (SEM) are being modified and extended in order to reveal underlying interactions in imaging data. In the R package FIAR, which stands for Functional Integration Analysis in R, we have implemented many of the latest techniques for analyzing brain networks based on functional magnetic resonance imaging (fMRI) data. The package can be used to analyze experimental data, but also to simulate data under certain models.
