Resilient Emotion Regulation Development in a South African Birth Cohort

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Project Summary Adolescents in low-/middle-income countries (LMICs) lose substantial developmental potential from exposure to early adversities, with an estimated 250 million children at risk of not reaching their developmental potential. Such catastrophic loss of human potential occurs in the context of the 10/90 divide – 10% of our current scientific knowledge is produced in or by LMICs that comprise 90% of the world’s population. This skewed evidence base has led to a limited understanding of patterns of risk and resilience in brain development for the vast majority of the world’s adolescents. Emotion regulation (ER), the internal and external process that modifies the experience or expression of an emotion, is a key mediator between early adversity and outcomes, and yet, the lack of data on ER in youth from most of the world is of concern given evidence that context shapes the socialization of emotion. The proposed research addresses this significant mental health problem by combining a) sophisticated data analytic techniques and b) community-guided/-participatory research applied to longitudinal multimodal brain imaging, high-dimensional behavioral assessments, and comprehensive defining of environmental exposures (both adverse and protective) from a well-established and deeply-characterized birth cohort of early adolescents in Cape Town, South Africa. The goal of the present work is to create an explanatory model for the impact of protective factors on neurodevelopmental trajectories underlying ER resilience. The premise is that early adversities are heterogeneous, powerful events that significantly increase the risk of poor ER, but also that resilience factors experienced during early adolescence can mitigate/ameliorate these risks. Therefore, prediction of ER outcomes requires cutting-edge, sophisticated data analytic methods. We hypothesize that data- driven approaches will 1) more precisely define ER behavioral profiles for adolescents living in LMICs who are exposed to heterogeneous adversities, and 2) provide a robust explanatory model for links between resilience factors and ER neurobehavioral trajectories. Aim 1 subtypes 525 12-13 year-old adolescents in an established LMIC birth cohort based on early environmental exposures and then tests for neural correlates of ER behavioral resilience (based on MRI measures of functional connectivity, task-based activity, and morphometry) accounting for early adversity subtype. Aim 2 identifies changes in neurobiology that underlie improvements in adolescent ER and develops an explanatory model to predict 2-year (Time2-Time1) longitudinal ER resilience trajectory subtypes based on concurrent environmental exposures (protective and adverse), accounting for early adversity subtype. The inclusion of participant-sex and pubertal status will identify potential divergence in pathways across early adolescence. We use a prospective approach together with machine learning methods with the goal of improving precision and inclusivity in recognizing and characterizing resilience to socio-economic, structural, health, and interpersonal adversities.