Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 이종민 | - |
dc.date.accessioned | 2017-03-03T06:01:54Z | - |
dc.date.available | 2017-03-03T06:01:54Z | - |
dc.date.issued | 2015-06 | - |
dc.identifier.citation | PLOS ONE, v. 10, NO 6, Page. 1-19 | en_US |
dc.identifier.issn | 1932-6203 | - |
dc.identifier.uri | http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0129250 | - |
dc.identifier.uri | http://hdl.handle.net/20.500.11754/25825 | - |
dc.description.abstract | Structural MR image (MRI) and F-18-Fluorodeoxyglucose-positron emission tomography (FDG-PET) have been widely employed in diagnosis of both Alzheimer's disease (AD) and mild cognitive impairment (MCI) pathology, which has led to the development of methods to distinguish AD and MCI from normal controls (NC). Synaptic dysfunction leads to a reduction in the rate of metabolism of glucose in the brain and is thought to represent AD progression. FDG-PET has the unique ability to estimate glucose metabolism, providing information on the distribution of hypometabolism. In addition, patients with AD exhibit significant neuronal loss in cerebral regions, and previous AD research has shown that structural MRI can be used to sensitively measure cortical atrophy. In this paper, we introduced a new method to discriminate AD from NC based on complementary information obtained by FDG and MRI. For accurate classification, surface-based features were employed and 12 predefined regions were selected from previous studies based on both MRI and FDG-PET. Partial least square linear discriminant analysis was employed for making diagnoses. We obtained 93.6% classification accuracy, 90.1% sensitivity, and 96.5% specificity in discriminating AD from NC. The classification scheme had an accuracy of 76.5% and sensitivity and specificity of 46.5% and 89.6%, respectively, for discriminating MCI from AD. Our method exhibited a superior classification performance compared with single modal approaches and yielded parallel accuracy to previous multimodal classification studies using MRI and FDG-PET. | en_US |
dc.description.sponsorship | This work was supported by the National Research Foundation of Korea (NRF) grant, funded by the Korea government (MEST) (2011-0028333). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. | en_US |
dc.language.iso | en | en_US |
dc.publisher | PUBLIC LIBRARY SCIENCE | en_US |
dc.subject | MILD COGNITIVE IMPAIRMENT | en_US |
dc.subject | POSITRON-EMISSION-TOMOGRAPHY | en_US |
dc.subject | MONKEY RETROSPLENIAL CORTEX | en_US |
dc.subject | PARTIAL VOLUME CORRECTION | en_US |
dc.subject | AUTOMATED 3-D EXTRACTION | en_US |
dc.subject | VOXEL-BASED MORPHOMETRY | en_US |
dc.subject | SURFACE-BASED ANALYSIS | en_US |
dc.subject | FDG-PET | en_US |
dc.subject | FEATURE-SELECTION | en_US |
dc.subject | NEUROFIBRILLARY TANGLES | en_US |
dc.title | Multimodal Discrimination of Alzheimer's Disease Based on Regional Cortical Atrophy and Hypometabolism | en_US |
dc.type | Article | en_US |
dc.relation.no | 6 | - |
dc.relation.volume | 10 | - |
dc.identifier.doi | 10.1371/journal.pone.0129250 | - |
dc.relation.page | 1-19 | - |
dc.relation.journal | PLOS ONE | - |
dc.contributor.googleauthor | Yun, Hyuk Jin | - |
dc.contributor.googleauthor | Kwak, Kichang | - |
dc.contributor.googleauthor | Lee, Jong-Min | - |
dc.relation.code | 2015008685 | - |
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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