Structural and functional brain connectivity of people with obesity and prediction of body mass index using connectivity

Bo Yong Park, Jongbum Seo, Juneho Yi, Hyunjin Park

Research output: Contribution to journalArticle

13 Citations (Scopus)

Abstract

Obesity is a medical condition affecting billions of people. Various neuroimaging methods including magnetic resonance imaging (MRI) have been used to obtain information about obesity. We adopted a multi-modal approach combining diffusion tensor imaging (DTI) and resting state functional MRI (rs-fMRI) to incorporate complementary information and thus better investigate the brains of non-healthy weight subjects. The objective of this study was to explore multi-modal neuroimaging and use it to predict a practical clinical score, body mass index (BMI). Connectivity analysis was applied to DTI and rs-fMRI. Significant regions and associated imaging features were identified based on group-wise differences between healthy weight and non-healthy weight subjects. Six DTI-driven connections and 10 rs-fMRI-driven connectivities were identified. DTI-driven connections better reflected groupwise differences than did rs-fMRI-driven connectivity. We predicted BMI values using multimodal imaging features in a partial least-square regression framework (percent error 15.0%). Our study identified brain regions and imaging features that can adequately explain BMI. We identified potentially good imaging biomarker candidates for obesity-related diseases.

Original languageEnglish
Article numbere0141376
JournalPloS one
Volume10
Issue number11
DOIs
Publication statusPublished - 2015 Nov 4

All Science Journal Classification (ASJC) codes

  • Biochemistry, Genetics and Molecular Biology(all)
  • Agricultural and Biological Sciences(all)
  • General

Fingerprint Dive into the research topics of 'Structural and functional brain connectivity of people with obesity and prediction of body mass index using connectivity'. Together they form a unique fingerprint.

  • Cite this