Effective connectivity inference in the whole-brain network by using rDCM method for investigating the distinction between emotional states in fMRI data

In recent years, the regression dynamic causal modelling (rDCM) method was introduced as a new version of dynamic causal modelling (DCM) to derive effective connectivity in whole-brain networks for functional magnetic resonance imaging (fMRI) data. In this research, we used data obtained while applying the stimulation of audio movie comprised different emotional states. We applied this method to two networks consisting of ten auditory and forty-four regions, respectively. This method was used to study effective connections between emotional states and represent the distinction between emotions. Finally, significant effective connections were found in emotional processing and auditory regions, and between visual and memory-related regions. We also observed the distinctive connections between the pair of emotions in both models. The greatest number of significant distinctions in the coupling between regions was represented in happiness-anger and happiness-fear for the whole-brain model and happiness-sadness, sadness-love, and anger-love for the auditory model.