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Spatially guided functional correlation tensor: A new method to associate body mass index and white matter neuroimaging

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dc.contributor.authorKyoungseob Byeon-
dc.contributor.authorBo-yong Park-
dc.contributor.authorHyunjin Park-
dc.date.available2019-05-02T08:07:34Z-
dc.date.created2019-03-19-
dc.date.issued2019-04-
dc.identifier.issn0010-4825-
dc.identifier.urihttps://pr.ibs.re.kr/handle/8788114/5655-
dc.description.abstractObesity causes critical health problems including cardiovascular disease, diabetes, and stroke. Various neuroimaging methods including diffusion tensor imaging (DTI) are used to explore white matter (WM) alterations in obesity. The functional correlation tensor (FCT) is a method to simulate DTI in WM using resting-state functional magnetic resonance imaging (rs-fMRI). In this study, we enhanced the FCT with additional anatomical information from T1-weighted data in a regression framework. The goal was to 1) develop a spatially guided enhanced FCT (s-eFCT) and to 2) use it to identify imaging biomarkers for obesity. We computed fractional anisotropy (FA) and the mean diffusivity (MD) from the s-eFCT. The regional FA and MD values that can explain body mass index (BMI) well were chosen. The identified regional FA and MD values were used to predict BMI values. The correlation between real and predicted BMIs was 0.57. There was no significant correlation between real and predicted DTI using the MD. The BMI predicted using FA was used to classify participants into three obesity subgroups. The classification accuracy was 57.20%. In summary, we found potential imaging biomarkers of obesity based on the s-eFCT. © 2019 Elsevier Ltd-
dc.description.uri1-
dc.language영어-
dc.publisherPERGAMON-ELSEVIER SCIENCE LTD-
dc.subjectClassification-
dc.subjectFunctional correlation tensor-
dc.subjectImaging biomarker-
dc.subjectObesity-
dc.subjectPrediction-
dc.titleSpatially guided functional correlation tensor: A new method to associate body mass index and white matter neuroimaging-
dc.typeArticle-
dc.type.rimsART-
dc.identifier.wosid000465050400015-
dc.identifier.scopusid2-s2.0-85061924456-
dc.identifier.rimsid67547-
dc.contributor.affiliatedAuthorKyoungseob Byeon-
dc.contributor.affiliatedAuthorBo-yong Park-
dc.contributor.affiliatedAuthorHyunjin Park-
dc.identifier.doi10.1016/j.compbiomed.2019.02.010-
dc.identifier.bibliographicCitationCOMPUTERS IN BIOLOGY AND MEDICINE, v.107, pp.137 - 144-
dc.citation.titleCOMPUTERS IN BIOLOGY AND MEDICINE-
dc.citation.volume107-
dc.citation.startPage137-
dc.citation.endPage144-
dc.description.journalClass1-
dc.description.journalRegisteredClassscie-
dc.description.journalRegisteredClassscopus-
dc.subject.keywordPlusOBESITY-
dc.subject.keywordPlusCONNECTIVITY-
dc.subject.keywordPlusINTEGRITY-
dc.subject.keywordPlusDOPAMINE-
dc.subject.keywordPlusIMPACT-
dc.subject.keywordPlusBMI-
dc.subject.keywordPlusMCI-
dc.subject.keywordAuthorObesity-
dc.subject.keywordAuthorFunctional correlation tensor-
dc.subject.keywordAuthorImaging biomarker-
dc.subject.keywordAuthorClassification-
dc.subject.keywordAuthorPrediction-
Appears in Collections:
Center for Neuroscience Imaging Research (뇌과학 이미징 연구단) > 1. Journal Papers (저널논문)
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