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Updated: Dec 20, 2025

Studying Food Reward and Motivation in Humans
Published on: March 19, 2014
Neural-based valuation of functional foods among lean and obese individuals
Oren Contreras-Rodriguez1, Fernanda Mata2, Juan Verdejo-Román3
1Department of Psychiatry, Bellvitge Biomedical Research Institute-IDIBELL, CIBERSAM-17, Barcelona, Spain; Turner Institute for Brain and Mental Health, Monash University, Melbourne, Australia.
Abstract:
Functional foods may contribute to establish healthy eating habits and reduce obesity and related comorbidities. Differences in the brain mechanisms underpinning the valuation of functional foods in individuals with excess weight may inform the development of attractive functional foods. We aimed to compare brain function during the Willingness to Pay task for functional vs standard foods between individuals with healthy weight (HW), overweight (OW), and obesity (OB). We hypothesized that, in participants with OB, willingness to pay for functional foods would evoke greater activation/connectivity in brain regions previously associated with subjective value. Thirty-six HW, 19 OW, and 20 OB adults performed a functional magnetic resonance imaging-Willingness to Pay task that requires them to decide how much they would pay for presented standard and functional food images tasted in a previous buffet. Whole-brain analyses compared task-related activation and connectivity between participants with OB, OW, and HW. Individuals with OB, relative to HW, showed more similar willingness to pay for functional and standard food. At the brain level, they also showed hyperactivation in the ventral posterior cingulate cortex and the right angular gyrus, as well as an increased functional connectivity between the ventral posterior cingulate cortex and the intraparietal cortices to the valuation of the functional vs the standard foods. Increased willingness to pay for functional foods in people with excessive weight may be driven by recruitment of brain regions that direct attention to internal goals.
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