ArticleObesity (Silver Spring, Md.)2023
Neuroanatomical profile of BMI implicates impulsive delay discounting and general cognitive ability.
Article in Obesity (Silver Spring, Md.), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed, 6 citations in OpenAlex.
- Identification of critical brain regions for young adults with obesity and their relationships with impulsivity using machine learning based on neuroanatomical features.International journal of obesity (2005) · 2026Article
- Machine learning-derived multimodal Neurobiological profiles of behavioral activation traits in adolescents.European child & adolescent psychiatry · 2025Article
- Neuroanatomical prediction of individual anxiety problems level using machine learning models: A population-based cohort study of young adults.Neurobiology of stress · 2025Article
- Investigating causality and shared genetic architecture between body mass index and cognitive function: a genome-wide cross-trait analysis and bi-directional Mendelian randomization study.Frontiers in aging neuroscience · 2024Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
objectiveObesity 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.
methodsMachine 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.
resultsUsing five-fold cross-validation, the multiregion neuroanatomical profile substantively predicted BMI (R
conclusionsTaken 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.
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