Evaluation of Model-Based Methods in Estimating Dynamic Functional Connectivity of Brain Regions

Today, neuroscientists are interested in discovering human brain functions through brain networks. In this regard, the evaluation of dynamic changes in functional connectivity of the brain regions by using functional magnetic resonance imaging data has attracted their attention. In this paper, we focus on two model-based approaches, called the exponential weighted moving average model and the dynamic conditional correlation model, to estimate the dynamic correlation between the two brain regions. Initially, the performance of these two models is evaluated using two new simulations. According to the results, in these simulation studies, the dynamic conditional correlation model has better performance than the exponential weighted moving average model. Therefore, a dynamic conditional correlation model is used to estimate the dynamic functional connectivity of two brain regions (the anterior cingulate cortex and the posterior cingulate cortex) for three Iranian addicted to methamphetamine in a resting state functional magnetic resonance imaging. The dynamic conditional correlation model has a good performance in assessing the dynamic functional connectivity of these addicted to methamphetamine. In addition, the dynamic functional connectivity varies between subjects.