title: Structural and Functional Brain Connectivity of People with Obesity and Prediction of Body Mass Index Using Connectivity.
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 group-wise differences than did rs-fMRI-driven connectivity. We predicted BMI values using multi-modal 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.
authors.affiliation: Department of Electronic, Electrical and Computer Engineering, Sungkyunkwan University, Suwon, Korea.
authors.forename: Bo-yong
authors.initials: BY
authors.lastname: Park
authors.affiliation: Department of Biomedical Engineering, Yonsei University, Wonju, Korea.
authors.forename: Jongbum
authors.initials: J
authors.lastname: Seo
authors.affiliation: School of Electronic Electrical Engineering, Sungkyunkwan University, Suwon, Korea.
authors.forename: Juneho
authors.initials: J
authors.lastname: Yi
authors.affiliation: School of Electronic Electrical Engineering, Sungkyunkwan University, Suwon, Korea.|Center for Neuroscience Imaging Research (CNIR), Institute for Basic Science, Suwon, Korea.
authors.forename: Hyunjin
authors.initials: H
authors.lastname: Park
mesh_terms: D000328:Adult; D015992:Body Mass Index; D001921:Brain / Q000473:pathology*; D001931:Brain Mapping / Q000379:methods*; D016022:Case-Control Studies; D056324:Diffusion Tensor Imaging / Q000379:methods; D005260:Female; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods; D008297:Male; D009434:Neural Pathways / Q000473:pathology*; D059906:Neuroimaging / Q000379:methods*; D009765:Obesity / Q000503:physiopathology*; D012189:Retrospective Studies