Identification of a brain fingerprint for overweight and obesity

Michael C Farruggia; Maria J van Kooten; Emily E Perszyk; Mary V Burke; Dustin Scheinost; R Todd Constable; Dana M Small

doi:10.1016/j.physbeh.2020.112940

Identification of a brain fingerprint for overweight and obesity

Physiol Behav. 2020 Aug 1:222:112940. doi: 10.1016/j.physbeh.2020.112940. Epub 2020 May 14.

Authors

Michael C Farruggia¹, Maria J van Kooten², Emily E Perszyk³, Mary V Burke⁴, Dustin Scheinost⁵, R Todd Constable⁶, Dana M Small⁷

Affiliations

¹ Interdepartmental Neuroscience Program, Yale University, 333 Cedar Street, New Haven, CT, U.S.; Department of Psychiatry, Yale University School of Medicine, 300 George Street, New Haven, CT 06511, USA. Electronic address: michael.farruggia@yale.edu.
² Department of Psychiatry, Yale University School of Medicine, 300 George Street, New Haven, CT 06511, USA; University of Groningen, Faculty of Medical Sciences, Antonius Deusinglaan 1, 9713 AV Groningen, The Netherlands. Electronic address: m.j.van.kooten@umcg.nl.
³ Interdepartmental Neuroscience Program, Yale University, 333 Cedar Street, New Haven, CT, U.S.; Department of Psychiatry, Yale University School of Medicine, 300 George Street, New Haven, CT 06511, USA. Electronic address: emily.perszyk@yale.edu.
⁴ Interdepartmental Neuroscience Program, Yale University, 333 Cedar Street, New Haven, CT, U.S.; Department of Psychiatry, Yale University School of Medicine, 300 George Street, New Haven, CT 06511, USA. Electronic address: mary.burke@yale.edu.
⁵ Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, CT, United States; Department of Statistics and Data Science, Yale University, New Haven, CT, United States; Child Study Center, Yale School of Medicine, New Haven, CT, United States. Electronic address: dustin.scheinost@yale.edu.
⁶ Interdepartmental Neuroscience Program, Yale University, 333 Cedar Street, New Haven, CT, U.S.; Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, CT, United States; Department of Neurosurgery, Yale University School of Medicine, New Haven, CT, United States. Electronic address: todd.constable@yale.edu.
⁷ Interdepartmental Neuroscience Program, Yale University, 333 Cedar Street, New Haven, CT, U.S.; Department of Psychiatry, Yale University School of Medicine, 300 George Street, New Haven, CT 06511, USA; Department of Psychology, Yale University, New Haven, CT, United States; fMEG Center, University of Tübingen, Tübingen, Germany. Electronic address: dana.small@yale.edu.

Abstract

The brain plays a central role in the pathophysiology of overweight and obesity. Connectome-based Predictive Modeling (CPM) is a newly developed, data-driven approach that exploits whole-brain functional connectivity to predict a behavior or trait that varies across individuals. We used CPM to determine whether brain "fingerprints" evoked during milkshake consumption could be isolated for common measures of adiposity in 67 adults with overweight and obesity. We found that CPM captures more variance in waist circumference than either percent body fat or BMI, the most frequently used measures to assess brain correlates of obesity. In a post-hoc analysis, we were also able to derive a largely separable functional connectivity network predicting fasting blood insulin. These findings suggest that, in individuals with overweight and obesity, brain network patterns may be more tightly coupled to waist circumference than BMI or percent body fat and that adiposity and glucose tolerance are associated with distinct maps, pointing to dissociable central pathophysiological phenotypes for obesity and diabetes.

Keywords: BMI; Functional connectivity; Insulin; Obesity; Prediction; Waist circumference.

Publication types

Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't

MeSH terms

Adiposity
Adult
Body Mass Index
Brain / diagnostic imaging
Humans
Obesity*
Overweight*
Risk Factors
Waist Circumference

Abstract

Publication types

MeSH terms

Grants and funding