Determining the Functional Brain Networks that Underlie Children’s Overeating and Adiposity Gain

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Project Summary Childhood obesity is a global pandemic associated with negative physical and psychosocial health outcomes4, and behavioral interventions to prevent childhood obesity produce small and variable effects5. Increased eating in the absence of hunger (EAH) has been identified as an obesogenic eating phenotype in children6, but the mechanisms that contribute to increased EAH prior to the development of obesity are unclear. A better understanding of the mechanisms that engender increased EAH and weight gain in children is critical to the development of more effective obesity prevention programs. Overeating is posited to result from an imbalance in brain regions involved in food cue reactivity and reward processing (i.e., a reactive system) with those involved in inhibition and cognitive control (i.e., a regulatory system)7–12. However, the patterns of functional connectivity between these neural systems which increase overeating and risk for obesity are unclear. Building on my sponsor’s R01 study, which is designed to examine neural and cognitive predictors of adiposity gain in children 7-8 years old who vary by familial risk for obesity, this proposal aims to identify the functional connectivity patterns (i.e., neural network properties) between reactive and regulatory brain systems that underlie EAH and adiposity gain. It is hypothesized that weaker connectivity between the reactive and regulatory system, and stronger connectivity within the reactive system, will be related to greater EAH and adiposity gain in children. To test these hypotheses, neuroimaging (fMRI) data collected during exposure to food cues will be used alongside food intake data from a laboratory assessment of EAH, during which children are offered a variety of palatable snack foods after eating a standard meal to fullness. Anthropomorphic assessments of adiposity will be collected at baseline and 1-year follow-up using dual x-ray absorptiometry (DXA). Innovative network analyses and advanced statistical methods will be used to identify and characterize child-specific neural networks from a priori “reactive” and “regulatory” brain regions of interest. Innovations offered by this proposal are (1) the use of sophisticated quantitative techniques to examine children’s neural networks during exposure to food cues and (2) the integration of network analyses with objectively-assessed hedonic eating and longitudinal measures of adiposity, which together will provide novel insight into the neural factors that promote overeating and risk for weight gain during the vulnerable pre-adolescent period. In addition, the inter-disciplinary mentorship team assembled in this proposal will provide rigorous training in experimental design for ingestive behavior research, neural network analyses, and scientific communication that will help advance my career as an independent researcher. The proposed study will enhance our understanding of the neural mechanisms supporting overeating and adiposity gain, which will inform the development of interventions to mitigate excess energy intake and the development of obesity.