Structural and Functional Brain Connectivity of People with Obesity and Prediction of Body Mass Index Using Connectivity |
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| Authors: | Bo-yong Park Jongbum Seo Juneho Yi Hyunjin Park |
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| Affiliation: | 1. Department of Electronic, Electrical and Computer Engineering, Sungkyunkwan University, Suwon, Korea.; 2. Department of Biomedical Engineering, Yonsei University, Wonju, Korea.; 3. School of Electronic Electrical Engineering, Sungkyunkwan University, Suwon, Korea.; 4. Center for Neuroscience Imaging Research (CNIR), Institute for Basic Science, Suwon, Korea.; Leibniz Institute for Neurobiology, GERMANY, |
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| 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. |
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