Neural precursors of future liking and affective reciprocity.

Why do certain group members end up liking each other more than others? How does affective reciprocity arise in human groups? The prediction of interpersonal sentiment has been a long-standing pursuit in the social sciences. We combined fMRI and longitudinal social network data to test whether newly acquainted group members' reward-related neural responses to images of one another's faces predict their future interpersonal sentiment, even many months later. Specifically, we analyze associations between relationship-specific valuation activity and relationship-specific future liking. We found that one's own future (T2) liking of a particular group member is predicted jointly by actor's initial (T1) neural valuation of partner and by that partner's initial (T1) neural valuation of actor. These actor and partner effects exhibited equivalent predictive strength and were robust when statistically controlling for each other, both individuals' initial liking, and other potential drivers of liking. Behavioral findings indicated that liking was initially unreciprocated at T1 yet became strongly reciprocated by T2. The emergence of affective reciprocity was partly explained by the reciprocal pathways linking dyad members' T1 neural data both to their own and to each other's T2 liking outcomes. These findings elucidate interpersonal brain mechanisms that define how we ultimately end up liking particular interaction partners, how group members' initially idiosyncratic sentiments become reciprocated, and more broadly, how dyads evolve. This study advances a flexible framework for researching the neural foundations of interpersonal sentiments and social relations that-conceptually, methodologically, and statistically-emphasizes group members' neural interdependence.

Neural mechanisms tracking popularity in real-world social networks.

Differences in popularity are a key aspect of status in virtually all human groups and shape social interactions within them. Little is known, however, about how we track and neurally represent others' popularity. We addressed this question in two real-world social networks using sociometric methods to quantify popularity. Each group member (perceiver) viewed faces of every other group member (target) while whole-brain functional MRI data were collected. Independent functional localizer tasks were used to identify brain systems supporting affective valuation (ventromedial prefrontal cortex, ventral striatum, amygdala) and social cognition (dorsomedial prefrontal cortex, precuneus, temporoparietal junction), respectively. During the face-viewing task, activity in both types of neural systems tracked targets' sociometric popularity, even when controlling for potential confounds. The target popularity-social cognition system relationship was mediated by valuation system activity, suggesting that observing popular individuals elicits value signals that facilitate understanding their mental states. The target popularity-valuation system relationship was strongest for popular perceivers, suggesting enhanced sensitivity to differences among other group members' popularity. Popular group members also demonstrated greater interpersonal sensitivity by more accurately predicting how their own personalities were perceived by other individuals in the social network. These data offer insights into the mechanisms by which status guides social behavior.

Getting Over It: Long-Lasting Effects of Emotion Regulation on Amygdala Response.

Little is known about whether emotion regulation can have lasting effects on the ability of a stimulus to continue eliciting affective responses in the future. We addressed this issue in this study. Participants cognitively reappraised negative images once or four times, and then 1 week later, they passively viewed old and new images, so that we could identify lasting effects of prior reappraisal. As in prior work, active reappraisal increased prefrontal responses but decreased amygdala responses and self-reported emotion. At 1 week, amygdala responses remained attenuated for images that had been repeatedly reappraised compared with images that had been reappraised once, new control images, and control images that had been seen as many times as reappraised images but had never been reappraised. Prefrontal activation was not selectively elevated for repeatedly reappraised images and was not related to long-term attenuation of amygdala responses. These results suggest that reappraisal can exert long-lasting "dose-dependent" effects on amygdala response that may cause lasting changes in the neural representation of an unpleasant event's emotional value.

Social demographic change and autism.

Parental age at child's birth--which has increased for U.S. children in the 1992-2000 birth cohorts--is strongly associated with an increased risk of autism. By turning a social demographic lens on the historical patterning of concordance among twin pairs, we identify a central mechanism for this association: de novo mutations, which are deletions, insertions, and duplications of DNA in the germ cells that are not present in the parents' DNA. Along the way, we show that a demographic eye on the rising prevalence of autism leads to three major discoveries. First, the estimated heritability of autism has been dramatically overstated. Second, heritability estimates can change over remarkably short periods of time because of increases in germ cell mutations. Third, social demographic change can yield genetic changes that, at the population level, combine to contribute to the increased prevalence of autism.

The spatial structure of autism in California, 1993-2001.

This article identifies significant high-risk clusters of autism based on residence at birth in California for children born from 1993 to 2001. These clusters are geographically stable. Children born in a primary cluster are at four times greater risk for autism than children living in other parts of the state. This is comparable to the difference between males and females and twice the risk estimated for maternal age over 40. In every year roughly 3% of the new caseload of autism in California arises from the primary cluster we identify-a small zone 20 km by 50 km. We identify a set of secondary clusters that support the existence of the primary clusters. The identification of robust spatial clusters indicates that autism does not arise from a global treatment and indicates that important drivers of increased autism prevalence are located at the local level.