Machine Learning-based Individual Assessment of Cortical Atrophy Pattern in Alzheimer's Disease Spectrum: Development of the Classifier and Longitudinal Evaluation
DC Field | Value | Language |
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dc.contributor.author | Lee, Jin San | - |
dc.contributor.author | Kim, Changsoo | - |
dc.contributor.author | Shin, Jeong-Hyeon | - |
dc.contributor.author | Cho, Hanna | - |
dc.contributor.author | Shin, Dae-seock | - |
dc.contributor.author | Kim, Nakyoung | - |
dc.contributor.author | Kim, Hee Jin | - |
dc.contributor.author | Kim, Yeshin | - |
dc.contributor.author | Lockhart, Samuel N. | - |
dc.contributor.author | Na, Duk L. | - |
dc.contributor.author | Seo, Sang Won | - |
dc.contributor.author | Seong, Joon-Kyung | - |
dc.date.accessioned | 2021-09-02T13:50:37Z | - |
dc.date.available | 2021-09-02T13:50:37Z | - |
dc.date.created | 2021-06-16 | - |
dc.date.issued | 2018-03-07 | - |
dc.identifier.issn | 2045-2322 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/76749 | - |
dc.description.abstract | To develop a new method for measuring Alzheimer's disease (AD)-specific similarity of cortical atrophy patterns at the individual-level, we employed an individual-level machine learning algorithm. A total of 869 cognitively normal (CN) individuals and 473 patients with probable AD dementia who underwent high-resolution 3T brain MRI were included. We propose a machine learning-based method for measuring the similarity of an individual subject's cortical atrophy pattern with that of a representative AD patient cohort. In addition, we validated this similarity measure in two longitudinal cohorts consisting of 79 patients with amnestic-mild cognitive impairment (aMCI) and 27 patients with probable AD dementia. Surface-based morphometry classifier for discriminating AD from CN showed sensitivity and specificity values of 87.1% and 93.3%, respectively. In the longitudinal validation study, aMCI-converts had higher atrophy similarity at both baseline (p < 0.001) and first year visits (p < 0.001) relative to non-converters. Similarly, AD patients with faster decline had higher atrophy similarity than slower decliners at baseline (p = 0.042), first year (p = 0.028), and third year visits (p = 0.027). The AD-specific atrophy similarity measure is a novel approach for the prediction of dementia risk and for the evaluation of AD trajectories on an individual subject level. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | NATURE PUBLISHING GROUP | - |
dc.subject | MILD COGNITIVE IMPAIRMENT | - |
dc.subject | CLINICAL-DIAGNOSIS | - |
dc.subject | DEMENTIA | - |
dc.subject | MRI | - |
dc.subject | CONVERSION | - |
dc.subject | DEFICITS | - |
dc.subject | DECLINE | - |
dc.subject | AD | - |
dc.title | Machine Learning-based Individual Assessment of Cortical Atrophy Pattern in Alzheimer's Disease Spectrum: Development of the Classifier and Longitudinal Evaluation | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Seong, Joon-Kyung | - |
dc.identifier.doi | 10.1038/s41598-018-22277-x | - |
dc.identifier.scopusid | 2-s2.0-85048269237 | - |
dc.identifier.wosid | 000426820700004 | - |
dc.identifier.bibliographicCitation | SCIENTIFIC REPORTS, v.8 | - |
dc.relation.isPartOf | SCIENTIFIC REPORTS | - |
dc.citation.title | SCIENTIFIC REPORTS | - |
dc.citation.volume | 8 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Science & Technology - Other Topics | - |
dc.relation.journalWebOfScienceCategory | Multidisciplinary Sciences | - |
dc.subject.keywordPlus | MILD COGNITIVE IMPAIRMENT | - |
dc.subject.keywordPlus | CLINICAL-DIAGNOSIS | - |
dc.subject.keywordPlus | DEMENTIA | - |
dc.subject.keywordPlus | MRI | - |
dc.subject.keywordPlus | CONVERSION | - |
dc.subject.keywordPlus | DEFICITS | - |
dc.subject.keywordPlus | DECLINE | - |
dc.subject.keywordPlus | AD | - |
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