Abstract
Obesity is often associated with cardiovascular complications. Adolescent obesity is a risk factor for cardiovascular disease in adulthood; thus, intensive management is warranted in adolescence. The brain state contributes to the development of obesity in addition to metabolic conditions, and hence neuroimaging is an important tool for accurately assessing an individual’s risk of developing obesity. Here, we aimed to predict body mass index (BMI) progression in adolescents with neuroimaging features using machine learning approaches. From an open database, we adopted 76 resting-state functional magnetic resonance imaging (rs-fMRI) datasets from adolescents with longitudinal BMI scores. Functional connectivity analyses were performed on cortical surfaces and subcortical volumes. We identified baseline functional connectivity features in the prefrontal-, posterior cingulate-, sensorimotor-, and inferior parietal-cortices as significant determinants of BMI changes. A BMI prediction model based on the identified fMRI biomarkers exhibited a high accuracy (intra-class correlation = 0.98) in predicting BMI at the second visit (1~2 years later). The identified brain regions were significantly correlated with the eating disorder-, anxiety-, and depression-related scores. Based on these results, we concluded that these functional connectivity features in brain regions related to eating disorders and emotional processing could be important neuroimaging biomarkers for predicting BMI progression.
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Data availability
The imaging and phenotypic data are available from the Enhanced NKI-RS repository (http://fcon_1000.projects.nitrc.org/indi/enhanced/index.html). Interested researchers should contact the database administrator to request access to the data.
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This work was supported by the Institute for Basic Science (grant number IBS-R015-D1), the NRF (National Research Foundation of Korea, grant numbers NRF-2016H1A2A1907833, NRF-2016R1A2B4008545, NRF-2017R1A2B2009086, and NRF-2017R1A2B4007254), and the MIST (Ministry of Science and ICT), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2019-2018-0-01798) supervised by the IITP (Institute for Information & communications Technology Promotion).
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Park, By., Chung, CS., Lee, M.J. et al. Accurate neuroimaging biomarkers to predict body mass index in adolescents: a longitudinal study. Brain Imaging and Behavior 14, 1682–1695 (2020). https://doi.org/10.1007/s11682-019-00101-y
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DOI: https://doi.org/10.1007/s11682-019-00101-y