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

Abstract

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%).

A review on neuroimaging-based classification studies and associated feature extraction methods for Alzheimer's disease and its prodromal stages

“…The former relies on prior biological knowledge about the discriminating ability of certain regions, generally obtained from existing literature, whereas the latter selects features based on general data characteristics, without prior knowledge. Among the automated methods, various ranking-based methods, such as t-tests (Tang et al, 2016; Wee et al, 2013) and earson’s correlation coefficient test (Davatzikos et al, 2008; Wee et al, 2011), wrapper-based methods, a combination of ranking and wrapper-based methods, such as mRMR (Wee et al, 2013), and embedded methods, such as elastic net regression, were used in the reviewed studies, and improved the classification performance. It is feasible that variations in feature-selection methods will lead to differences in AD classification performance.…”

“…In earlier studies, the regional volumetric measures, calculated from structural MRI, and FA, calculated from WM tracts, have been combined for SVM-based MCI and AD classification (Cui et al, 2012; Li et al, 2014a). Among recent studies, Tang et al used volumetric, shape, and diffusion features of the hippocampus and amygdala for AD classification (Tang et al, 2016). They used PCA and tudent’s t-test for reducing the feature set, and LDA and SVM for classification.…”

Atrophy of the Posterior Subiculum Is Associated with Memory Impairment, Tau- and Aβ Pathology in Non-demented Individuals

“…Another way to investigate how atrophy is distributed along the anterior-posterior axis of HC is to perform shape analysis of the whole HC. While this approach often shows involvement of the hippocampal body in AD (Gerardin et al, 2009 ; Lindberg et al, 2012 ; Tang et al, 2016 ), the method is limited in the sense that it does not produce volumetric data on specific subfields.…”

Changes of white matter integrity and structural network connectivity in nondemented cerebral small‐vessel disease

“…As an alternative, atlas‐based analysis (ABA) using a multi‐DTI atlas and a fully automated segmentation pipeline was recently proposed to segment the whole brain into multiple parcels with high accuracy . This technique has been applied to various clinical studies; for example, in patients with Alzheimer's disease . The DTI‐based whole‐brain parcellation delivered by this pipeline also provides a great opportunity for structural connectivity analysis, where each parcel serves as a network node.…”

“…6 This technique has been applied to various clinical studies; for example, in patients with Alzheimer's disease. 7,8 The DTI-based whole-brain parcellation delivered by this pipeline also provides a great opportunity for structural connectivity analysis, where each parcel serves as a network node.…”