Characterizing the Network Architecture of Emotion Regulation Neurodevelopment

Abstract

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 in conjunction with machine learning to: (i) parse the neural underpinnings of chronological age and emotion regulation skill acquisition, and (ii) build a working taxonomy of brain network activity supporting emotion regulation in youth. We were able to predict age and emotion regulation ability using metrics from six wholebrain networks during an emotion regulation task. Further, by leveraging analytic techniques traditionally used in evolutionary biology (e.g., cophenetic correlations), we were able to demonstrate that brain networks evince reliable taxonomic organization to meet emotion regulation demands in youth. This work shows that meaningful information about emotion regulation development is encoded in whole-brain network activity, and opens up a realm of possibilities for network neuroscience applications in the study of emotion regulation development.evolutionary biology (agglomerative clustering, cophenetic correlation coefficient) to build a working taxonomy of brain networks as they pertain to the development of emotion regulation.

Spatial and temporal cortical variability track with age and affective experience during emotion regulation in youth.

“…During the fMRI scan, participants completed a computerized version of a cognitive reappraisal task adapted from prior developmental studies of emotion regulation (McLaughlin et al, 2015; Silvers et al, 2016). Though youth have many emotion regulation strategies at their disposal (Braunstein et al, 2017; Guassi Moreira & Silvers, 2018), a cognitive reappraisal task was chosen for a number of reasons. It is one of the most widely studied emotion regulation strategies in adult, pediatric, and clinical samples and thus had a robust literature to reference.…”

“…Emotion regulation , frequently defined as modulation of affective processes in accordance with explicit or implicit goals (Etkin, Büchel, & Gross, 2015), requires years of development before maturity is reached (Guassi Moreira & Silvers, 2018). Over the last 3 decades, an explosion of theoretical and empirical advances has helped to establish various frameworks for studying emotion regulation (Camras, 2011; Cole, Martin, & Dennis, 2004; Gross & Barrett, 2011).…”

Enlarged Ingroup Effect: How a Shared Culture Shapes In-Group Perception

“…All participants were young adults (students or recently graduated). Age differences were reported in studies involving in-group and out-group (Guassi Moreira et al, 2016) and social cognition areas (Colten et al, 2013). Therefore, the effect of enlarged cultural ingroup is necessary to be investigated across generations to generalize the findings wholly.…”

“…The FG is implicated in top-down and bottom-up perception of visual input. Bottom-up perception includes higher FG activation to racial ingroup faces than out-group faces (Guassi Moreira et al, 2016;Molenberghs, 2013;Shkurko, 2012;Van Bavel et al, 2008). The top-down modulation of FG in social perception involves indexing socially significant perceptual input according to preexisting schemas of the social environment (Shkurko, 2012).…”

“…When out-and in-group faces of typical ethnicities (Chinese, Indian) were viewed in the ethnic context, contrasted to cultural contexts, higher activation of the FG was obtained, which is one of the core brain regions for face recognition (Tovée, 1995;Zhen et al, 2013). FG is commonly associated with in-group face processing (Guassi Moreira et al, 2016;Molenberghs, 2013;Shkurko, 2012;Van Bavel et al, 2008). However, there are studies that have reported, similarly, FG activations during out-group face processing (Ronquillo, 2010;Shkurko, 2010;Iidaka, 2014).…”