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Talking about emotion and sharing emotional experiences is a key component of human interaction. Specifically, individuals often consider the reactions of other people when evaluating the meaning and impact of an emotional stimulus. It has not yet been investigated, however, how emotional arousal ratings and physiological responses elicited by affective stimuli are influenced by the rating of an interaction partner. In the present study, pairs of participants were asked to rate and communicate the degree of their emotional arousal while viewing affective pictures. Strikingly, participants adjusted their arousal ratings to match up with their interaction partner. In anticipation of the affective picture, the interaction partner’s arousal ratings correlated positively with activity in anterior insula and prefrontal cortex. During picture presentation, social influence was reflected in the ventral striatum, that is, activity in the ventral striatum correlated negatively with the interaction partner’s ratings. Results of the study show that emotional alignment through the influence of another person’s communicated experience has to be considered as a complex phenomenon integrating different components including emotion anticipation and conformity.
Pattern recognition applied to whole-brain neuroimaging data, such as functional Magnetic Resonance Imaging (fMRI), has proved successful at discriminating psychiatric patients from healthy participants. However, predictive patterns obtained from whole-brain voxel-based features are difficult to interpret in terms of the underlying neurobiology. Many psychiatric disorders, such as depression and schizophrenia, are thought to be brain connectivity disorders. Therefore, pattern recognition based on network models might provide deeper insights and potentially more powerful predictions than whole-brain voxel-based approaches. Here, we build a novel sparse network-based discriminative modeling framework, based on Gaussian graphical models and L1-norm regularized linear Support Vector Machines (SVM). In addition, the proposed framework is optimized in terms of both predictive power and reproducibility/stability of the patterns. Our approach aims to provide better pattern interpretation than voxel-based whole-brain approaches by yielding stable brain connectivity patterns that underlie discriminative changes in brain function between the groups. We illustrate our technique by classifying patients with major depressive disorder (MDD) and healthy participants, in two (event- and block-related) fMRI datasets acquired while participants performed a gender discrimination and emotional task, respectively, during the visualization of emotional valent faces.