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

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• 作者：Xiaoying Tang^a ; ^b ; ¹ ; ^{tangxiaoy@mail.sysu.edu.cn" class="auth_mail" title="E-mail the corresponding author} ; ^{txiaoyin@andrew.cmu.edu" class="auth_mail" title="E-mail the corresponding author} ; Yuanyuan Qin^c ; ¹ ; Jiong Wu^a ; Min Zhang^d ; Wenzhen Zhu^c ; Michael I. Miller^e ; ^f ; ^g
• 关键词：AD ; Alzheimer's Disease ; HC ; Healthy Control ; DTI ; Diffusion Tensor Imaging ; CA ; Cornu Ammonis ; FA ; Fractional Anisotropy ; NINCDS&ndash ; ADRDA ; National Institute of Neurological and Communicative Disorders and Stroke and the Alzheimer's Disease and Related Disorders Association ; MTL ; Medial Temporal Lobe ; MRI ; Magnetic Resonance Imaging ; MCI ; Mild Cognitive Impairment ; WM ; White Matter ; MD ; Mean Diffusivity ; LDDMM ; Large Deformation Diffeomorphic Metric Mapping ; PCA ; Principal Component Analysis
• 刊名：Magnetic Resonance Imaging
• 出版年：2016
• 出版时间：October 2016
• 年：2016
• 卷：34
• 期：8
• 页码：1087-1099
• 全文大小：1348 K

文摘

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%).