Functional Neural Plasticity and Associated Changes in Positive Affect After Compassion Training

The development of social emotions such as compassion is crucial for successful social interactions as well as for the maintenance of mental and physical health, especially when confronted with distressing life events. Yet, the neural mechanisms supporting the training of these emotions are poorly understood. To study affective plasticity in healthy adults, we measured functional neural and subjective responses to witnessing the distress of others in a newly developed task (Socio-affective Video Task). Participants' initial empathic responses to the task were accompanied by negative affect and activations in the anterior insula and anterior medial cingulate cortex—a core neural network underlying empathy for pain. Whereas participants reacted with negative affect before training, compassion training increased positive affective experiences, even in response to witnessing others in distress. On the neural level, we observed that, compared with a memory control group, compassion training elicited activity in a neural network including the medial orbitofrontal cortex, putamen, pallidum, and ventral tegmental area—brain regions previously associated with positive affect and affiliation. Taken together, these findings suggest that the deliberate cultivation of compassion offers a new coping strategy that fosters positive affect even when confronted with the distress of other

Structural Covariance Networks of the Dorsal Anterior Insula Predict Females' Individual Differences in Empathic Responding

Previous functional imaging studies have shown key roles of the dorsal anterior insula (dAI) and anterior midcingulate cortex (aMCC) in empathy for the suffering of others. The current study mapped structural covariance networks of these regions and assessed the relationship between networks and individual differences in empathic responding in 94 females. Individual differences in empathy were assessed through average state measures in response to a video task showing others' suffering, and through questionnaire-based trait measures of empathic concern. Overall, covariance patterns indicated that dAI and aMCC are principal hubs within prefrontal, temporolimbic, and midline structural covariance networks. Importantly, participants with high empathy state ratings showed increased covariance of dAI, but not aMCC, to prefrontal and limbic brain regions. This relationship was specific for empathy and could not be explained by individual differences in negative affect ratings. Regarding questionnaire-based empathic trait measures, we observed a similar, albeit weaker modulation of dAI covariance, confirming the robustness of our findings. Our analysis, thus, provides novel evidence for a specific contribution of frontolimbic structural covariance networks to individual differences in social emotions beyond negative affec

Virtual faces as a tool to study emotion recognition deficits in schizophrenia

Studies investigating emotion recognition in patients with schizophrenia predominantly presented photographs of facial expressions. Better control and higher flexibility of emotion displays could be afforded by virtual reality (VR). VR allows the manipulation of facial expression and can simulate social interactions in a controlled and yet more naturalistic environment. However, to our knowledge, there is no study that systematically investigated whether patients with schizophrenia show the same emotion recognition deficits when emotions are expressed by virtual as compared to natural faces. Twenty schizophrenia patients and 20 controls rated pictures of natural and virtual faces with respect to the basic emotion expressed (happiness, sadness, anger, fear, disgust, and neutrality). Consistent with our hypothesis, the results revealed that emotion recognition impairments also emerged for emotions expressed by virtual characters. As virtual in contrast to natural expressions only contain major emotional features, schizophrenia patients already seem to be impaired in the recognition of basic emotional features. This finding has practical implication as it supports the use of virtual emotional expressions for psychiatric research: the ease of changing facial features, animating avatar faces, and creating therapeutic simulations makes validated artificial expressions perfectly suited to study and treat emotion recognition deficits in schizophrenia

Recognition Profile of Emotions in Natural and Virtual Faces

BACKGROUND: Computer-generated virtual faces become increasingly realistic including the simulation of emotional expressions. These faces can be used as well-controlled, realistic and dynamic stimuli in emotion research. However, the validity of virtual facial expressions in comparison to natural emotion displays still needs to be shown for the different emotions and different age groups. METHODOLOGY/PRINCIPAL FINDINGS: Thirty-two healthy volunteers between the age of 20 and 60 rated pictures of natural human faces and faces of virtual characters (avatars) with respect to the expressed emotions: happiness, sadness, anger, fear, disgust, and neutral. Results indicate that virtual emotions were recognized comparable to natural ones. Recognition differences in virtual and natural faces depended on specific emotions: whereas disgust was difficult to convey with the current avatar technology, virtual sadness and fear achieved better recognition results than natural faces. Furthermore, emotion recognition rates decreased for virtual but not natural faces in participants over the age of 40. This specific age effect suggests that media exposure has an influence on emotion recognition. CONCLUSIONS/SIGNIFICANCE: Virtual and natural facial displays of emotion may be equally effective. Improved technology (e.g. better modelling of the naso-labial area) may lead to even better results as compared to trained actors. Due to the ease with which virtual human faces can be animated and manipulated, validated artificial emotional expressions will be of major relevance in future research and therapeutic applications

Short-Term Compassion Training Increases Prosocial Behavior in a Newly Developed Prosocial Game

Compassion has been suggested to be a strong motivator for prosocial behavior. While research has demonstrated that compassion training has positive effects on mood and health, we do not know whether it also leads to increases in prosocial behavior. We addressed this question in two experiments. In Experiment 1, we introduce a new prosocial game, the Zurich Prosocial Game (ZPG), which allows for repeated, ecologically valid assessment of prosocial behavior and is sensitive to the influence of reciprocity, helping cost, and distress cues on helping behavior. Experiment 2 shows that helping behavior in the ZPG increased in participants who had received short-term compassion training, but not in participants who had received short-term memory training. Interindividual differences in practice duration were specifically related to changes in the amount of helping under no-reciprocity conditions. Our results provide first evidence for the positive impact of short-term compassion training on prosocial behavior towards strangers in a training-unrelated task

Towards a human self-regulation system: Common and distinct neural signatures of emotional and behavioural control

Self-regulation refers to controlling our emotions and actions in the pursuit of higher-order goals. Although research suggests commonalities in the cognitive control of emotion and action, evidence for a shared neural substrate is scant and largely circumstantial. Here we report on two large-scale meta-analyses of human neuroimaging studies on emotion or action control, yielding two fronto-parieto-insular networks. The networks' overlap, however, was restricted to four brain regions: posteromedial prefrontal cortex, bilateral anterior insula, and right temporo-parietal junction. Conversely, meta-analytic contrasts revealed major between-network differences, which were independently corroborated by clustering domain-specific regions based on their intrinsic functional connectivity, as well as by functionally characterizing network sub-clusters using the BrainMap database for quantitative forward and reverse inference. Collectively, our analyses identified a core system for implementing self-control across emotion and action, beyond which, however, either regulation facet appears to rely on broadly similar yet distinct subnetworks. These insights into the neurocircuitry subserving affective and executive facets of self-control suggest both processing commonalities and differences between the two aspects of human self-regulation