Multivariate resting-state functional connectomes predict and characterize obesity phenotypes.

ABSTRACT: The univariate obesity-brain associations have been extensively explored, while little is known about the multivariate associations between obesity and resting-state functional connectivity. We therefore utilized machine learning and resting-state functional connectivity to develop and validate predictive models of 4 obesity phenotypes (i.e. body fat percentage, body mass index, waist circumference, and waist-height ratio) in 3 large neuroimaging datasets (n = 2,992). Preliminary evidence suggested that the resting-state functional connectomes effectively predicted obesity/weight status defined by each obesity phenotype with good generalizability to longitudinal and independent datasets. However, the differences between resting-state functional connectivity patterns characterizing differen

SUBMITTER: Wang J 

PROVIDER: S-EPMC10505423 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature