Full metadata record
DC Field | Value | Language |
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dc.contributor.author | 이종민 | - |
dc.date.accessioned | 2017-12-11T07:05:52Z | - |
dc.date.available | 2017-12-11T07:05:52Z | - |
dc.date.issued | 2016-02 | - |
dc.identifier.citation | Alzheimer's and Dementia: Diagnosis, Assessment and Disease Monitoring, v. 2, Page. 58-67 | en_US |
dc.identifier.issn | 2352-8729 | - |
dc.identifier.uri | http://www.sciencedirect.com/science/article/pii/S2352872915000937?via%3Dihub | - |
dc.identifier.uri | http://hdl.handle.net/20.500.11754/34061 | - |
dc.description.abstract | Introduction: Recent studies have shown that pathologically defined subtypes of Alzheimer's disease (AD) represent distinctive atrophy patterns and clinical characteristics. We investigated whether a cortical thickness-based clustering method can reflect such findings. Methods: A total of 77 AD subjects from the Alzheimer's Disease Neuroimaging Initiative 2 data set who underwent 3-T magnetic resonance imaging, [18F]-fluorodeoxyglucose-positron emission tomography (PET), [18F]-Florbetapir PET, and cerebrospinal fluid (CSF) tests were enrolled. After clustering based on cortical thickness, diverse imaging and biofluid biomarkers were compared between these groups. Results: Three cortical thinning patterns were noted: medial temporal (MT; 19.5%), diffuse (55.8%), and parietal dominant (P; 24.7%) atrophy subtypes. The P subtype was the youngest and represented more glucose hypometabolism in the parietal and occipital cortices and marked amyloid-beta accumulation in most brain regions. The MT subtype revealed more glucose hypometabolism in the left hippocampus and bilateral frontal cortices and less performance in memory tests. CSF test results did not differ between the groups. Discussion: Cortical thickness patterns can reflect pathophysiological and clinical changes in AD. © 2016 The Authors. | en_US |
dc.description.sponsorship | This work was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT and Future Planning (2013R1A1A1012925), grants of the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea (grant number: HI14C3319, A120798), a grant from Ministry of Trade, Industry and Energy (MOTIE; grant number: 10060305), a grant from the Korea Institute of Science and Technology Institutional Open Research Program (2E24242-13-110), and grants (2015-590; 2014-0783) from the Asan Institute for Life Sciences (J.H.R.). | en_US |
dc.language.iso | en | en_US |
dc.publisher | Elsevier | en_US |
dc.subject | Alzheimer's disease | en_US |
dc.subject | Alzheimer's Disease Neuroimaging Initiative | en_US |
dc.subject | Cortical thickness | en_US |
dc.subject | Magnetic resonance imaging | en_US |
dc.subject | Positron emission tomography | en_US |
dc.title | Prediction of Alzheimer's disease pathophysiology based on cortical thickness patterns | en_US |
dc.type | Article | en_US |
dc.relation.volume | 2 | - |
dc.identifier.doi | 10.1016/j.dadm.2015.11.008 | - |
dc.relation.page | 58-67 | - |
dc.relation.journal | Alzheimer's and Dementia: Diagnosis, Assessment and Disease Monitoring | - |
dc.contributor.googleauthor | Hwang, Jihye | - |
dc.contributor.googleauthor | Kim, Chan Mi | - |
dc.contributor.googleauthor | Jeon, Seun | - |
dc.contributor.googleauthor | Lee, Jong Min | - |
dc.contributor.googleauthor | Hong, Yun Jeong | - |
dc.contributor.googleauthor | Roh, Jee Hoon | - |
dc.contributor.googleauthor | Lee, Jae-Hong | - |
dc.contributor.googleauthor | Koh, Jae-Young | - |
dc.contributor.googleauthor | Na, Duk L. | - |
dc.relation.code | 2016020612 | - |
dc.sector.campus | S | - |
dc.sector.daehak | COLLEGE OF ENGINEERING[S] | - |
dc.sector.department | DIVISION OF ELECTRICAL AND BIOMEDICAL ENGINEERING | - |
dc.identifier.pid | ljm | - |
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