Characterizing the Network Architecture of Emotion Regulation Neurodevelopment

The ability to regulate emotions is key to goal attainment and wellbeing. Although much has been discovered about how the human brain develops to support the acquisition of emotion regulation, very little of this work has leveraged information encoded in whole-brain networks. Here we employed a network neuroscience framework to parse the neural underpinnings of emotion regulation skill acquisition while accounting for age in a sample of youth (N = 70, 34 female). Focusing on three key network metrics—network differentiation, modularity, and community structure differences between active regulation and a passive emotional baseline—we found that the control network, the default mode network, and limbic network were each related to emotion regulation ability while controlling for chronological age. Greater network differentiation in the control and limbic networks was related to better emotion regulation ability. With regards to network community structure, more communities and more crosstalk between modules (i.e., less modularity) in the control network were associated with better regulatory ability. By contrast, less crosstalk (i.e., more modularity) between modules in the default mode network was associated with better regulatory ability. Together, these findings highlight possible whole-brain connectome features that support the acquisition of emotion regulation in youth.