Neuroanatomical profile of BMI implicates impulsive delay discounting and general cognitive ability

Abstract

Objective: Obesity is a disorder of excessive adiposity, typically assessed via the anthropometric density measure of BMI. Numerous studies have implicated BMI with differences in brain structure, but with highly inconsistent findings. Methods: Machine learning elastic net regression models with cross-validation were conducted to characterize a neuroanatomical morphometry profile associated with BMI in 1100 participants (22% BMI > 30, n = 242) from the Human Connectome Project Young Adult project. Results: Using five-fold cross-validation, the multiregion neuroanatomical profile substantively predicted BMI (R2 = 10.05%), and this was robust in a held-out test set (R2 = 8.87%). In terms of specific regions, the neuroanatomical profile was enriched for nodes in the default mode, executive control, and salience networks. The relationship between the morphometry profile and BMI itself was partially mediated by impulsive delay discounting and general cognitive ability. Conclusions: Taken together, these findings reveal a robust machine learning-derived neuroanatomical profile of BMI, one that comprises nodes in motivational brain networks and suggests the functional links to obesity are via self-regulatory capacity and cognitive function.