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뇌과학이미징연구단
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Predicting cerebrovascular age and its clinical relevance: Modeling using 3D morphological features of brain vessels

DC Field Value Language
dc.contributor.authorCho, Hwan-ho-
dc.contributor.authorKim, Jonghoon-
dc.contributor.authorNa, Inye-
dc.contributor.authorSong, Ha-Na-
dc.contributor.authorChoi, Jong-Un-
dc.contributor.authorBaek, In-Young-
dc.contributor.authorLee, Ji-Eun-
dc.contributor.authorChung, Jong-Won-
dc.contributor.authorKim, Chi-Kyung-
dc.contributor.authorOh, Kyungmi-
dc.contributor.authorBang, Oh-Young-
dc.contributor.authorKim, Gyeong-Moon-
dc.contributor.authorSeo, Woo-Keun-
dc.contributor.authorHyunjin Park-
dc.date.accessioned2024-06-19T05:50:17Z-
dc.date.available2024-06-19T05:50:17Z-
dc.date.created2024-06-10-
dc.date.issued2024-
dc.identifier.urihttps://pr.ibs.re.kr/handle/8788114/15289-
dc.description.abstractAging manifests as many phenotypes, among which age-related changes in brain vessels are important, but underexplored. Thus, in the present study, we constructed a model to predict age using cerebrovascular morphological features, further assessing their clinical relevance using a novel pipeline. Age prediction models were first developed using data from a normal cohort (n = 1181), after which their relevance was tested in two stroke cohorts (n = 564 and n = 455). Our novel pipeline adapted an existing framework to compute generic vessel features for brain vessels, resulting in 126 morphological features. We further built various machine learning models to predict age using only clinical factors, only brain vessel features, and a combination of both. We further assessed deviation from healthy aging using the age gap and explored its clinical relevance by correlating the predicted age and age gap with various risk factors. The models constructed using only brain vessel features and those combining clinical factors with vessel features were better predictors of age than the clinical factor-only model (r = 0.37, 0.48, and 0.26, respectively). Predicted age was associated with many known clinical factors, and the associations were stronger for the age gap in the normal cohort. The age gap was also associated with important factors in the pooled cohort atherosclerotic cardiovascular disease risk score and white matter hyperintensity measurements. Cerebrovascular age, computed using the morphological features of brain vessels, could serve as a potential individualized marker for the early detection of various cerebrovascular diseases. © 2024 The Authors-
dc.language영어-
dc.publisherCell Press-
dc.titlePredicting cerebrovascular age and its clinical relevance: Modeling using 3D morphological features of brain vessels-
dc.typeArticle-
dc.type.rimsART-
dc.identifier.wosid001251296300001-
dc.identifier.scopusid2-s2.0-85195102533-
dc.identifier.rimsid83225-
dc.contributor.affiliatedAuthorHyunjin Park-
dc.identifier.doi10.1016/j.heliyon.2024.e32375-
dc.identifier.bibliographicCitationHeliyon, v.10, no.11-
dc.relation.isPartOfHeliyon-
dc.citation.titleHeliyon-
dc.citation.volume10-
dc.citation.number11-
dc.description.journalClass1-
dc.description.journalClass1-
dc.description.isOpenAccessY-
dc.description.journalRegisteredClassscie-
dc.description.journalRegisteredClassscopus-
dc.subject.keywordAuthorCardiovascular disease-
dc.subject.keywordAuthorCerebrovascular morphology-
dc.subject.keywordAuthorMachine learning-
dc.subject.keywordAuthorPersonalized marker-
dc.subject.keywordAuthorRisk factors-
dc.subject.keywordAuthorAge prediction-
Appears in Collections:
Center for Neuroscience Imaging Research (뇌과학 이미징 연구단) > 1. Journal Papers (저널논문)
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