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Vettel, J. M.

Publications and source records attributed to Vettel, J. M..

2 recordsLinked to original sources

Response Inhibition in Adolescents is Moderated by Brain Connectivity and Social Network Structure

Self-control is vital for a wide range of outcomes across our lifespan, yet the developmental trajectory of its core components during adolescence remains elusive. Many adolescents can successfully regulate their behavior even when they do not show strong activation in brain regions typically recruited during self-control in adults. Thus, adolescents may rely on other neural and cognitive resources to compensate, including daily experiences navigating and managing complex social relationships that likely bolster self-control processes. Here, we tested whether activity and connectivity in brain systems associated with social cognition (i.e., self-processing and mentalizing) facilitated successful self-control. We measured brain activity using fMRI as 62 adolescents completed a Go/No-Go response inhibition task. Recruitment of social brain systems, especially the self-processing system, was associated with better response inhibition in adolescents. Interestingly, the reliance on the self-processing system was stronger in adolescents with weaker activation in the canonical response inhibition system, suggesting a compensatory role for social brain systems during adolescent development. Furthermore, we examined the importance of social context by computing the size, number of communities, and modularity of our participants real-life social network. We found that adolescents with more friends and more communities in their social networks demonstrated a stronger relationship between response inhibition and recruitment of social brain systems. Collectively, our results identify the importance of social context and its moderating role on the relationship between brain activity and behavior. Furthermore, our results indicate a critical role for social brain systems during the developmental trajectory of self-control throughout adolescence.\n\nSignificance StatementWe employed a network neuroscience approach to investigate the role of social context and social brain systems in facilitating self-control in adolescents. We found that recruitment of social brain systems was associated with better response inhibition in adolescents, especially for adolescents with weaker activation in the response inhibition system. Moreover, adolescents with more friends and communities in their social networks showed stronger relationships between response inhibition and recruitment of social brain systems. Our results advance understanding of how brain systems facilitate self-control in adolescents, and how these brain responses are associated with features of an adolescents real-life social network. Bringing together findings related to brain networks and social networks provides key insights into how biology and environment mutually influence development.

neuroscience

Applications of community detection techniques to brain graphs: Algorithmic considerations and implications for neural function

The human brain can be represented as a graph in which neural units such as cells or small volumes of tissue are heterogeneously connected to one another through structural or functional links. Brain graphs are parsimonious representations of neural systems that have begun to offer fundamental insights into healthy human cognition, as well as its alteration in disease. A critical open question in network neuroscience lies in how neural units cluster into densely interconnected groups that can provide the coordinated activity that is characteristic of perception, action, and adaptive behaviors. Tools that have proven particularly useful for addressing this question are community detection approaches, which can be used to identify communities or modules in brain graphs: groups of neural units that are densely interconnected with other units in their own group but sparsely interconnected with units in other groups. In this paper, we describe a common community detection algorithm known as modularity maximization, and we detail its applications to brain graphs constructed from neuroimaging data. We pay particular attention to important algorithmic considerations, especially in recent extensions of these techniques to graphs that evolve in time. After recounting a few fundamental insights that these techniques have provided into brain function, we highlight potential avenues of methodological advancements for future studies seeking to better characterize the patterns of coordinated activity in the brain that accompany human behavior. This tutorial provides a naive reader with an introduction to theoretical considerations pertinent to the generation of brain graphs, an understanding of modularity maximization for community detection, a resource of statistical measures that can be used to characterize community structure, and an appreciation of the utility of these approaches in uncovering behaviorally-relevant network dynamics in neuroimaging data.

neuroscience