This article reports on the development of a revised version of the Obsessive-Compulsive Inventory (OCI; E. B. Foa, M. J. Kozak, P. Salkovskis, M. E. Coles, & N. Amir, 1998), a psychometrically sound, theoretically driven, self-report measure. The revised OCI (OCI-R) improves on the parent version in 3 ways: It eliminates the redundant frequency scale, simplifies the scoring of the subscales, and reduces overlap across subscales. The reliability and validity of the OCI-R were examined in 215 patients with obsessive-compulsive disorder (OCD), 243 patients with other anxiety disorders, and 677 nonanxious individuals. The OCI-R, which contains 18 items and 6 subscales, has retained excellent psychometric properties. The OCI-R and its subscales differentiated well between individuals with and without OCD. Receiver operating characteristic (ROC) analyses demonstrated the usefulness of the OCI-R as a diagnostic tool for screening patients with OCD, utilizing empirically derived cutscores.

Although empathy is crucial for successful social interactions, excessive sharing of others' negative emotions may be maladaptive and constitute a source of burnout. To investigate functional neural plasticity underlying the augmentation of empathy and to test the counteracting potential of compassion, one group of participants was first trained in empathic resonance and subsequently in compassion. In response to videos depicting human suffering, empathy training, but not memory training (control group), increased negative affect and brain activations in anterior insula and anterior midcingulate cortex-brain regions previously associated with empathy for pain. In contrast, subsequent compassion training could reverse the increase in negative effect and, in contrast, augment self-reports of positive affect. In addition, compassion training increased activations in a non-overlapping brain network spanning ventral striatum, pregenual anterior cingulate cortex and medial orbitofrontal cortex. We conclude that training compassion may reflect a new coping strategy to overcome empathic distress and strengthen resilience.

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 others.

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.

after the empathy induction and in selfish types [motive induction × individual type, F(1,30) = 4.9, P = 0.034] (Fig. 4C). Motives are purely mental constructs that are not directly observable. Here we show, however, that distinct motives have a distinct neurophys-iological representation in the brain. Although the empathy and the reciprocity motive increase the frequency of altruistic acts by the same amount relative to the baseline condition, they are associated with different patterns of brain connectivity that enabled us to predict the different motives with relatively high accuracy. We predicted each subject's induced motive with a classifier whose parameters were not influenced by that subject's brain data (nor by that subject's behavioral data). Instead, the parameters of the classifier were solely informed by other subjects' brain data. This means that the motive-specific brain connectivity patterns are generalizable across subjects. The distinct and across-subject-generalizable neural representation of the different motives thus provides evidence for a distinct neurophysiological existence of motives. The findings also provide mechanistic insights into the neural underpinnings of important altruistic motives and how motive inductions change the underlying neural network. In particular, predominantly selfish individuals were characterized by a low or even negative connectivity from ACC→AI in the baseline condition, whereas predominantly prosocial individuals displayed a positive connectivity between these regions. However, when we induce the empathy motive, the selfish, but not the prosocial, types become more altruistic and show a substantial increase in ACC→AI con-nectivity. Thus, after the empathy induction, selfish individuals resemble "homegrown" unconditional altruists in terms of both brain connectivity and altruistic behavior. This contrasts with the effect of inducing the reciprocity motive, which renders the prosocial, but not the selfish, types more altruistic and increases only the prosocial types' AI→VS connectivity. We obtain these mechanistic insights because the inputs into the support vector machine are not merely brain activations but small brain models of how relevant brain regions interact with each other (i.e., functional neural architec-tures). Thus, by correctly predicting the induced motives, we simultaneously determine those mechanistic models of brain interaction that best predict the motives. And it is these models that deliver the mechanistic insights into brain function and how changes in brain function relate to behavioral changes due to motive inductions. Our study, therefore, also demonstrates how "mere prediction" and "insights into the mechanisms" that underlie psychological concepts (such as motives) can be simultaneously achieved if functional neural architectures are the inputs for the prediction.. 29. If we use both the number of altruistic decisions in the baseline condition and the increase in the frequency of altruistic decisions in the motive-induction conditions...

Despite the importance of valuing another person's welfare for prosocial behavior, currently we have only a limited understanding of how these values are represented in the brain and, more importantly, how they give rise to individual variability in prosociality. In the present study, participants underwent functional magnetic resonance imaging while performing a prosocial learning task in which they could choose to benefit themselves and/or another person. Choice behavior indicated that participants valued the welfare of another person, although less so than they valued their own welfare. Neural data revealed a spatial gradient in activity within the medial prefrontal cortex (MPFC), such that ventral parts predominantly represented self-regarding values and dorsal parts predominantly represented other-regarding values. Importantly, compared with selfish individuals, prosocial individuals showed a more gradual transition from selfregarding to other-regarding value signals in the MPFC and stronger MPFC-striatum coupling when they made choices for another person rather than for themselves. The present study provides evidence of neural markers reflecting individual differences in human prosociality.R anging from small acts of kindness in daily life to self-sacrificing altruism under life-threatening situations, we often observe large individual differences in how humans value another person's welfare. This differential valuation process seems to be the key to understanding various human prosocial behaviors, which are fundamental to the sustainability of human society (1). The underlying neural mechanisms and their relationship to individual differences in prosociality remain unclear, however.Perhaps the most powerful way of assessing how an outcome is valued is to use an instrumental learning paradigm that examines whether the occurrence of a response increases when it is followed by that outcome (2). The mechanisms underlying this type of learning have been described more formally with a computational model, known as the advantage learning model (3-5), which has been used successfully to reveal the neuroanatomical substrates of subjective valuation (3,4,6). Previous research has further refined the neurobiological model of reinforcement learning by emphasizing the specific roles played by the medial frontal cortex and the striatum; the medial frontal cortex computes the value of the chosen action, whereas the striatum processes reward prediction errors during reinforcement learning (4, 6-10).Unlike our current understanding of the valuation process for self-regarding choices (3, 6-12), it is much less clear whether learning also can be driven by other-regarding values, and whether this other-regarding valuation relies on the same mechanisms of reinforcement learning as those used for self. Moreover, despite the rapidly accumulating research on reward processing in social domains (13-19), the question remains of how neural representation of self-regarding vs. other-regarding values is related to individual differen...

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.

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