Decoding six basic emotions from brain functional connectivity patterns.

Although distinctive neural and physiological states are suggested to underlie the six basic emotions, basic emotions are often indistinguishable from functional magnetic resonance imaging (fMRI) voxelwise activation (VA) patterns. Here, we hypothesize that functional connectivity (FC) patterns across brain regions may contain emotion-representation information beyond VA patterns. We collected whole-brain fMRI data while human participants viewed pictures of faces expressing one of the six basic emotions (i.e., anger, disgust, fear, happiness, sadness, and surprise) or showing neutral expressions. We obtained FC patterns for each emotion across brain regions over the whole brain and applied multivariate pattern decoding to decode emotions in the FC pattern representation space. Our results showed that the whole-brain FC patterns successfully classified not only the six basic emotions from neutral expressions but also each basic emotion from other emotions. An emotion-representation network for each basic emotion that spanned beyond the classical brain regions for emotion processing was identified. Finally, we demonstrated that within the same brain regions, FC-based decoding consistently performed better than VA-based decoding. Taken together, our findings revealed that FC patterns contained emotional information and advocated for paying further attention to the contribution of FCs to emotion processing.