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Related Concept Videos

Obesity01:24

Obesity

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The Body Mass Index (BMI) is a numerical value derived from a person's weight and height, used to categorize individuals into weight ranges. It is calculated using the formula: weight in kilograms divided by height in meters squared. Obesity is a health condition characterized by excessive accumulation of adipose tissue that poses health risks, often diagnosed with a BMI ≥ 30. This excess fat storage occurs when surplus dietary calories are converted into triglycerides and stored in...
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Multivariate resting-state functional connectomes predict and characterize obesity phenotypes.

Junjie Wang1,2, Debo Dong1,2,3, Yong Liu1,2

  • 1Faculty of Psychology, Southwest University, Chongqing, China.

Cerebral Cortex (New York, N.Y. : 1991)
|April 10, 2023
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Summary

Machine learning models accurately predicted obesity phenotypes using brain connectivity data. Brain-obesity links vary by obesity measure, highlighting visual and reward circuit roles.

Keywords:
body fat percentagefunctional connectomemachine learningneuroimagingobesity phenotype

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Area of Science:

  • Neuroscience
  • Obesity Research
  • Machine Learning

Background:

  • Univariate obesity-brain associations are known, but multivariate links remain unclear.
  • Resting-state functional connectivity (RSFC) is a key area for investigation.

Purpose of the Study:

  • To develop and validate predictive models of obesity phenotypes using RSFC.
  • To explore multivariate obesity-brain associations.

Main Methods:

  • Utilized machine learning and RSFC on three large neuroimaging datasets (n=2,992).
  • Developed predictive models for four obesity phenotypes: body fat percentage, BMI, waist circumference, and waist-height ratio.
  • Validated models for generalizability across longitudinal and independent datasets.

Main Results:

  • RSFC effectively predicted obesity status across phenotypes with good generalizability.
  • Obesity-brain associations differed based on the specific obesity measure used.
  • Identified reproducible neuroimaging biomarkers, including connections within the visual cortex and between visual, parietal, and amygdala regions.

Conclusions:

  • Obesity-brain associations are complex and depend on the obesity phenotype.
  • Dysfunctions in visual information processing and reward circuitry are potential neurobiological underpinnings of obesity.
  • Future studies require multiple obesity phenotypes to identify reproducible brain associations.