Abstract
Accurate prediction of Alzheimer’s disease (AD) is important for the early diagnosis and treatment of this condition. Mild cognitive impairment (MCI) is an early stage of AD. Therefore, patients with MCI who are at high risk of fully developing AD should be identified to accurately predict AD. However, the relationship between brain images and AD is difficult to construct because of the complex characteristics of neuroimaging data. To address this problem, we present a longitudinal measurement of MCI brain images and a hierarchical classification method for AD prediction. Longitudinal images obtained from individuals with MCI were investigated to acquire important information on the longitudinal changes, which can be used to classify MCI subjects as either MCI conversion (MCIc) or MCI non-conversion (MCInc) individuals. Moreover, a hierarchical framework was introduced to the classifier to manage high feature dimensionality issues and incorporate spatial information for improving the prediction accuracy. The proposed method was evaluated using 131 patients with MCI (70 MCIc and 61 MCInc) based on MRI scans taken at different time points. Results showed that the proposed method achieved 79.4% accuracy for the classification of MCIc versus MCInc, thereby demonstrating very promising performance for AD prediction.
Highlights
Accurate prediction of Alzheimer’s disease (AD) is important for the early diagnosis and treatment of this condition
The performance of the proposed AD prediction method was tested on 131 patients with Mild cognitive impairment (MCI) with MRI scans taken at different time points
Given that only normal controls (NC) and MCI data were available in practice for AD prediction, the longitudinal data at time points that were at least 6 months ahead of the conversion were used to train hierarchical classifiers
Summary
Accurate prediction of Alzheimer’s disease (AD) is important for the early diagnosis and treatment of this condition. The proposed method was evaluated using 131 patients with MCI (70 MCIc and 61 MCInc) based on MRI scans taken at different time points. Basing on the type of features extracted from MRI, the MCInc/MCIc classification methods can be divided into three categories: the voxel-based approach[6,7,8,9], the vertex-based approach[1,10,11], and the region of interest (ROI)-based approach[12,13,14,15]. The main limitations of the voxel-based approach are the high dimensionality of feature vectors and the lack of spatial information[16]. The local spatial contiguity of the selected discriminative features (voxels) should be carefully considered during feature selection or classification[2,5,16]
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