Neuroanatomical profile of BMI implicates impulsive delay discounting and general cognitive ability.
Journal
Obesity (Silver Spring, Md.)
ISSN: 1930-739X
Titre abrégé: Obesity (Silver Spring)
Pays: United States
ID NLM: 101264860
Informations de publication
Date de publication:
11 2023
11 2023
Historique:
revised:
21
06
2023
received:
03
03
2023
accepted:
30
06
2023
medline:
23
10
2023
pubmed:
19
10
2023
entrez:
19
10
2023
Statut:
ppublish
Résumé
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. 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. Using five-fold cross-validation, the multiregion neuroanatomical profile substantively predicted BMI (R 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.
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
2799-2808Subventions
Organisme : National Institutes of Health/National Institute on Drug Abuse
ID : P50DA051361
Informations de copyright
© 2023 The Obesity Society.
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