Response inhibition in adolescents is moderated by brain connectivity and social network structure

The social environment an individual is embedded in influences their ability and motivation to engage self-control processes, but little is known about the neural mechanisms underlying this effect. Many individuals successfully regulate their behavior even when they do not show strong activation in canonical self-control brain regions. Thus, individuals may rely on other resources to compensate, including daily experiences navigating and managing complex social relationships that likely bolster self-control processes. Here, we employed a network neuroscience approach to investigate the role of social context and social brain systems in facilitating self-control in adolescents. We measured brain activation using functional magnetic resonance imaging (fMRI) as 62 adolescents completed a Go/No-Go response inhibition task. We found that self-referential brain systems compensate for weaker activation in executive function brain systems, especially for adolescents with more friends and more communities in their social networks. Collectively, our results indicate a critical role for self-referential brain systems during the developmental trajectory of self-control throughout adolescence.

Variability in the analysis of a single neuroimaging dataset by many teams

Data analysis workflows in many scientific domains have become increasingly complex and flexible. Here we assess the effect of this flexibility on the results of functional magnetic resonance imaging by asking 70 independent teams to analyse the same dataset, testing the same 9 ex-ante hypotheses1. The flexibility of analytical approaches is exemplified by the fact that no two teams chose identical workflows to analyse the data. This flexibility resulted in sizeable variation in the results of hypothesis tests, even for teams whose statistical maps were highly correlated at intermediate stages of the analysis pipeline. Variation in reported results was related to several aspects of analysis methodology. Notably, a meta-analytical approach that aggregated information across teams yielded a significant consensus in activated regions. Furthermore, prediction markets of researchers in the field revealed an overestimation of the likelihood of significant findings, even by researchers with direct knowledge of the dataset2-5. Our findings show that analytical flexibility can have substantial effects on scientific conclusions, and identify factors that may be related to variability in the analysis of functional magnetic resonance imaging. The results emphasize the importance of validating and sharing complex analysis workflows, and demonstrate the need for performing and reporting multiple analyses of the same data. Potential approaches that could be used to mitigate issues related to analytical variability are discussed.

Functional brain network architecture supporting the learning of social networks in humans

Most humans have the good fortune to live their lives embedded in richly structured social groups. Yet, it remains unclear how humans acquire knowledge about these social structures to successfully navigate social relationships. Here we address this knowledge gap with an interdisciplinary neuroimaging study drawing on recent advances in network science and statistical learning. Specifically, we collected BOLD MRI data while participants learned the community structure of both social and non-social networks, in order to examine whether the learning of these two types of networks was differentially associated with functional brain network topology. We found that participants learned the community structure of the networks, as evidenced by a slower reaction time when a trial moved between communities than when a trial moved within a community. Learning the community structure of social networks was also characterized by significantly greater functional connectivity of the hippocampus and temporoparietal junction when transitioning between communities than when transitioning within a community. Furthermore, temporoparietal regions of the default mode were more strongly connected to hippocampus, somatomotor, and visual regions for social networks than for non-social networks. Collectively, our results identify neurophysiological underpinnings of social versus non-social network learning, extending our knowledge about the impact of social context on learning processes. More broadly, this work offers an empirical approach to study the learning of social network structures, which could be fruitfully extended to other participant populations, various graph architectures, and a diversity of social contexts in future studies.

Cognitive chimera states in human brain networks

The human brain is a complex dynamical system, and how cognition emerges from spatiotemporal patterns of regional brain activity remains an open question. As different regions dynamically interact to perform cognitive tasks, variable patterns of partial synchrony can be observed, forming chimera states. We propose that the spatial patterning of these states plays a fundamental role in the cognitive organization of the brain and present a cognitively informed, chimera-based framework to explore how large-scale brain architecture affects brain dynamics and function. Using personalized brain network models, we systematically study how regional brain stimulation produces different patterns of synchronization across predefined cognitive systems. We analyze these emergent patterns within our framework to understand the impact of subject-specific and region-specific structural variability on brain dynamics. Our results suggest a classification of cognitive systems into four groups with differing levels of subject and regional variability that reflect their different functional roles.

Neural Mechanisms of Emotion Regulation Moderate the Predictive Value of Affective and Value-Related Brain Responses to Persuasive Messages

Emotionally evocative messages can be an effective way to change behavior, but the neural pathways that translate messages into effects on individuals and populations are not fully understood. We used a human functional neuroimaging approach to ask how affect-, value-, and regulation-related brain systems interact to predict effects of graphic anti-smoking messages for individual smokers (both males and females) and within a population-level messaging campaign. Results indicated that increased activity in the amygdala, a region involved in affective reactivity, predicted both personal quit intentions and population-level information-seeking and this was mediated by activity in ventromedial prefrontal cortex (vmPFC), a region involved in computing an integrative value signal. Further, the predictive value of these regions was moderated by expression of a meta-analytically defined brain pattern indexing emotion regulation. That is, amygdala and vmPFC activity strongly tracked with population behavior only when participants showed low recruitment of this brain pattern, which consists of regions involved in goal-driven regulation of affective responses. Overall, these findings suggest that affective and value-related brain responses can predict the success of persuasive messages and that neural mechanisms of emotion regulation can shape these responses, moderating the extent to which they track with population-level message impact.SIGNIFICANCE STATEMENT People and organizations often appeal to our emotions to persuade us, but how these appeals engage the brain to drive behavior is not fully understood. We present an fMRI-based model that integrates affect-, control-, and value-related brain responses to predict the impact of graphic anti-smoking stimuli within a small group of smokers and a larger-scale public messaging campaign. This model indicated that amygdala activity predicted the impact of the anti-smoking messages, but that this relationship was mediated by ventromedial prefrontal cortex and moderated by expression of a distributed brain pattern associated with regulating emotion. These results suggest that neural mechanisms of emotion regulation can shape the extent to which affect and value-related brain responses track with population behavioral effects.

Time-evolving dynamics in brain networks forecast responses to health messaging

Neuroimaging measures have been used to forecast complex behaviors, including how individuals change decisions about their health in response to persuasive communications, but have rarely incorporated metrics of brain network dynamics. How do functional dynamics within and between brain networks relate to the processes of persuasion and behavior change? To address this question, we scanned 45 adult smokers by using functional magnetic resonance imaging while they viewed anti-smoking images. Participants reported their smoking behavior and intentions to quit smoking before the scan and 1 month later. We focused on regions within four atlas-defined networks and examined whether they formed consistent network communities during this task (measured as allegiance). Smokers who showed reduced allegiance among regions within the default mode and fronto-parietal networks also demonstrated larger increases in their intentions to quit smoking 1 month later. We further examined dynamics of the ventromedial prefrontal cortex (vmPFC), as activation in this region has been frequently related to behavior change. The degree to which vmPFC changed its community assignment over time (measured as flexibility) was positively associated with smoking reduction. These data highlight the value in considering brain network dynamics for understanding message effectiveness and social processes more broadly.

Individual differences in learning social and nonsocial network structures

How do people acquire knowledge about which individuals belong to different cliques or communities? And to what extent does this learning process differ from the process of learning higher-order information about complex associations between nonsocial bits of information? Here, the authors use a paradigm in which the order of stimulus presentation forms temporal associations between the stimuli, collectively constituting a complex network. They examined individual differences in the ability to learn community structure of networks composed of social versus nonsocial stimuli. Although participants were able to learn community structure of both social and nonsocial networks, their performance in social network learning was uncorrelated with their performance in nonsocial network learning. In addition, social traits, including social orientation and perspective-taking, uniquely predicted the learning of social community structure but not the learning of nonsocial community structure. Taken together, the results suggest that the process of learning higher-order community structure in social networks is partially distinct from the process of learning higher-order community structure in nonsocial networks. The study design provides a promising approach to identify neurophysiological drivers of social network versus nonsocial network learning, extending knowledge about the impact of individual differences on these learning processes. (PsycINFO Database Record (c) 2019 APA, all rights reserved).