fMRI Data Analysis with Dynamic Causal Modeling and Bayesian Networks

Brain, an amazing organ of human body comprises electrical signal that can be helpful for interaction between various brain regions. Functional Magnetic resonance Imaging (fMRI) is a specialized type of Magnetic Resonance Imaging scan. Though nature of fMRI data posses various challenges in the analysis. But, even after all challenges too, it is used as an effective method to diagnose various disease and the relationships between various brain regions. In this paper, we have proposed a model that will result a better fMRI data analysis. The effective interactions among the brain regions can be explored using dynamic causal modeling (DCM) that will help us to understand the functionality of brain up to some extent. Bayesian networks can be used for causal discovery purpose in support with markov blanket which can be evaluated with the help of evaluation metrics.