Full metadata record
DC Field | Value | Language |
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dc.contributor.author | 이종민 | - |
dc.date.accessioned | 2017-10-23T02:16:48Z | - |
dc.date.available | 2017-10-23T02:16:48Z | - |
dc.date.issued | 2015-12 | - |
dc.identifier.citation | Alzheimer's and Dementia, 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/20.500.11754/30184 | - |
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 | Data collection and sharing for this project was funded by the Alzheimer''s Disease Neuroimaging Initiative (ADNI) (National Institutes of Health grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: Alzheimer''s Association; Alzheimer''s Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen Idec; Bristol-Myers Squibb Company; Eisai; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd. and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICOLtd.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Medpace; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Synarc; and Takeda Pharmaceutical Company. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer''s Disease Cooperative Study at the University of California, San Diego. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California. 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 | - |
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.contributor.googleauthor | Alzheimer's Disease Neuroimaging Initiative | - |
dc.relation.code | 2015018207 | - |
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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