Emotion Representation in Whole-brain Functional Connectivity Patterns: An fMRI Study

The representation of emotion in human brain is an important question in the cognitive neuroscience. However, it was remained unclear to what extent the connections between different brain regions contribute to the emotion recognition. The present study mainly focused on the emotion decoding based on the functional connectivity patterns. We designed the experiment and collected the neural activities while participants viewed emotion stimuli using the functional magnetic resonance imaging (fMRI) technology. We constructed the whole-brain functional connectivity patterns for each emotion, and performed emotion classification using multivariate pattern analysis combined with machine learning algorithms. We found that emotions could be successfully decoded from the whole-brain functional connectivity patterns. These results provide new evidence that large-scale functional connectivity patterns contain rich emotion information and contribute to the emotion recognition. Our study extends exist fMRI studies on emotion perception and may further our understanding of how human beings achieve easy and quick recognition of emotions. Introduction Humans can effectively recognize others’ emotions and make the corresponding reactions. The mechanism under which human brains represented emotion information to achieve the easy and exact recognition of emotions is an important question and was intensively studied. Functional magnetic resonance imaging (fMRI) technology can accurately locate the increased neural activities in brain areas, providing a wealth of time and space information and does no harm to subjects, thus it was widely used in the cognitive studies. Previous fMRI studies had identified several brain regions which showed preferential activation for emotions. For instance, the superior temporal sulcus (STS) had been identified perceiving faces and bodies [1,2] and exhibited sensitivity to the movements, postures and emotions [3,4], the amygdala (AMG) was considered as a top-down control of the visual responses to fear [5] and the human motion cortex was also found sensitive to dynamic emotion information [5,6]. However, previous studies on emotion perception mainly focused on the activation in single brain regions by measuring the average response signals. As there are certain connections between different brain regions, it remains unclear to what extent these connections contribute to the decoding of emotion information. Machine learning methods take advantage of the multivariate nature of the fMRI data thus could be used to perform decoding analysis. In this study, we acquired the functional magnetic resonance imaging (fMRI) data while participants viewed emotion stimuli in our block design experiment. After preprocessing of the fMRI data, we explored the emotion classification based on the functional connectivity patterns using multivariate pattern analysis (MVPA) and machine learning. In order to examine the extensive connections in our brain, we constructed the whole-brain functional connectivity (FC) patterns rather than only estimated connections between several regions. Our classification scheme contained feature selection and then performed a cross-validation procedure to assess the decoding performance.