Evaluating the amygdala network induced by neurofeedback training for emotion regulation using hierarchical clustering

Abstract

BackgroundPrevious studies have shown that regulating the target region by real-time fMRI-based neurofeedback training can influence the activation of other regions and the functional connectivity between them. However, it is not clear whether the training effect of neurofeedback, especially in emotion regulation, is manifested in local network specialization or global network integration. In the current study, we chose the left amygdala (LA) as the target region to regulate positive emotion through real-time fMRI training. Average-linkage hierarchical clustering was employed to cluster the fMRI data recorded during the training to construct whole-brain networks and the LA network to which the LA belongs. ResultsThe activation in the LA and those in some other regions were significantly up-regulated during the training. The clustering analysis at group level showed that the feedback training did not affect the number of networks in the whole brain but altered the distribution and functional connectivity in the LA network. ConclusionThese findings suggested that the feedback training effects in emotion regulation pattern reflected by the activity of the target brain network and the connections within the network were robustly embodied in local network specialization instead of in global network integration.