Structural and functional brain connectivity of people with obesity and prediction of body mass index using connectivity
DC Field | Value | Language |
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dc.contributor.author | Bo-yong Park | - |
dc.contributor.author | Jongbum Seo | - |
dc.contributor.author | Juneho Yi | - |
dc.contributor.author | Hyunjin Park | - |
dc.date.accessioned | 2016-01-25T00:11:58Z | - |
dc.date.available | 2016-01-25T00:11:58Z | - |
dc.date.created | 2016-01-11 | - |
dc.date.issued | 2015-11 | - |
dc.identifier.issn | 1932-6203 | - |
dc.identifier.uri | https://pr.ibs.re.kr/handle/8788114/2259 | - |
dc.description.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. | - |
dc.description.uri | 1 | - |
dc.language | 영어 | - |
dc.publisher | PUBLIC LIBRARY SCIENCE | - |
dc.title | Structural and functional brain connectivity of people with obesity and prediction of body mass index using connectivity | - |
dc.type | Article | - |
dc.type.rims | ART | - |
dc.identifier.wosid | 000364298400061 | - |
dc.identifier.scopusid | 2-s2.0-84951023158 | - |
dc.identifier.rimsid | 21959 | - |
dc.date.tcdate | 2018-10-01 | - |
dc.contributor.affiliatedAuthor | Hyunjin Park | - |
dc.identifier.doi | 10.1371/journal.pone.0141376 | - |
dc.identifier.bibliographicCitation | PLOS ONE, v.10, no.11, pp.1 - 14 | - |
dc.citation.title | PLOS ONE | - |
dc.citation.volume | 10 | - |
dc.citation.number | 11 | - |
dc.citation.startPage | 1 | - |
dc.citation.endPage | 14 | - |
dc.date.scptcdate | 2018-10-01 | - |
dc.description.wostc | 9 | - |
dc.description.scptc | 8 | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.subject.keywordPlus | MILD COGNITIVE IMPAIRMENT | - |
dc.subject.keywordPlus | HUMAN CONNECTOME PROJECT | - |
dc.subject.keywordPlus | HUMAN CEREBRAL-CORTEX | - |
dc.subject.keywordPlus | ALZHEIMERS-DISEASE | - |
dc.subject.keywordPlus | CORTICAL NETWORKS | - |
dc.subject.keywordPlus | FOOD-INTAKE | - |
dc.subject.keywordPlus | REWARD | - |
dc.subject.keywordPlus | TRACTOGRAPHY | - |
dc.subject.keywordPlus | MRI | - |
dc.subject.keywordPlus | ADULTS | - |