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Shape and diffusion tensor imaging based integrative analysis of the hippocampus and the amygdala in Alzheimer's disease

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单位: [1]Sun Yat Sen Univ, Joint Inst Engn, SYSU CMU, Guangzhou, Guangdong, Peoples R China [2]SYSU CMU, Shunde Int joint Res Inst, Shunde, Guangdong, Peoples R China [3]Huazhong Univ Sci & Technol,Tongji Med Coll,Tongji Hosp,Dept Radiol,Wuhan,Hubei,Peoples R China [4]Huazhong Univ Sci & Technol,Tongji Med Coll,Tongji Hosp,Dept Neurol,Wuhan,Hubei,Peoples R China [5]Johns Hopkins Univ, Ctr Imaging Sci, Baltimore, MD USA [6]Johns Hopkins Univ, Inst Computat Med, Baltimore, MD USA [7]Johns Hopkins Univ, Dept Biomed Engn, Baltimore, MD USA
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关键词: Hippocampus Amygdala Shape Diffusion tensor imaging Support vector machine Linear discriminant analysis

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We analyzed, in an integrative fashion, the morphometry and structural integrity of the bilateral hippocampi and amygdalas in Alzheimer's disease (AD) using T1-weighted images and diffusion tensor images (DTIs). We detected significant hippocampal and amygdalar volumetric atrophies in AD relative to healthy controls (HCs). Shape analysis revealed significant region-specific atrophies with the hippocampal atrophy mainly being concentrated on the CA1 and CA2 while the amygdalar atrophy was concentrated on the basolateral and basomedial. In all structures, the structural integrity displayed a significantly decreased mean fractional anisotropy (FA) value and an increased mean trace value in AD. In addition to the inter-group comparisons, we systematically evaluated the discriminative power of our three types of features (volume, shape, and DTI), both individually and in their possible combinations, when differentiating between AD and HCs. We found the volume features to be redundant when the more sophisticated shape features were available. A combination of the shape and DTI features of the right hippocampus, with classification automatically performed by support vector machine, yielded the strongest classification result (overall accuracy, 94.6%; sensitivity, 95.5%; specificity, 93.3%). (C) 2016 Elsevier Inc. All rights reserved.

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出版当年[2015]版:
大类 | 4 区 医学
小类 | 3 区 核医学
最新[2025]版:
大类 | 4 区 医学
小类 | 4 区 核医学
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出版当年[2014]版:
Q2 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
最新[2023]版:
Q2 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

影响因子: 最新[2023版] 最新五年平均 出版当年[2014版] 出版当年五年平均 出版前一年[2013版] 出版后一年[2015版]

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第一作者单位: [1]Sun Yat Sen Univ, Joint Inst Engn, SYSU CMU, Guangzhou, Guangdong, Peoples R China [2]SYSU CMU, Shunde Int joint Res Inst, Shunde, Guangdong, Peoples R China [*1]250 Roberts Engn Hall,5000 Forbes Ave, Pittsburgh, PA 15213 USA
通讯作者:
通讯机构: [1]Sun Yat Sen Univ, Joint Inst Engn, SYSU CMU, Guangzhou, Guangdong, Peoples R China [2]SYSU CMU, Shunde Int joint Res Inst, Shunde, Guangdong, Peoples R China [*1]250 Roberts Engn Hall,5000 Forbes Ave, Pittsburgh, PA 15213 USA
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