Using rDCM Method in the Mixed Model in order to Inference Effective Connectivity in Emotions

Purpose: Recently, data from functional magnetic resonance imaging in the field of neuroscience have been strongly considered for the modeling of cognitive activities. Therefore, the use of a suitable method is important for evaluating functional magnetic resonance imaging data. Regression dynamic causal modeling is introduced as a new version of dynamic causal modeling in order to extract and derive effective connectivity in functional magnetic resonance imaging data. We used this method to investigate the distinction between effective connectivity and the pair of emotional states. Materials and Methods: In this article, the effective connectivity between regions and activity of brain regions of interest during the application of a particular type of stimulation, which simulates the emotions created during human life, is examined in the form of an audio-movie. To do this, we applied the regression dynamic causal modeling method to a network consisting of 18 regions of interest that named the mixed model. Results: In the mixed model, the distinction between happiness-anger, happiness-fear, and happiness-love was more intense. Finally, significant effective connectivities were observed in the auditory regions and regions related to emotion processing. Conclusion: Ultimately, we could represent the distinction between emotions by applying the regression dynamic causal modeling to the mixed model.