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. 2017 Jan 12:7:39880.
doi: 10.1038/srep39880.

Longitudinal measurement and hierarchical classification framework for the prediction of Alzheimer's disease

Meiyan Huang  1 , Wei Yang  1 , Qianjin Feng  1 , Wufan Chen  1 ; Alzheimer’s Disease Neuroimaging Initiative
Collaborators, Affiliations

Longitudinal measurement and hierarchical classification framework for the prediction of Alzheimer's disease

Meiyan Huang et al. Sci Rep. .

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.

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Figures

Figure 1
Figure 1. An example using longitudinal data to predict AD conversion.
Figure 2
Figure 2
(a) Flowchart and (b) illustration of the proposed LMHC method.
Figure 3
Figure 3
ROC curves for the classification of MCIc and MCInc obtained with (a) baseline visit data and longitudinal data and (b) a single classifier and hierarchical classification.
Figure 4
Figure 4
An example using longitudinal data to predict (a) AD conversion and clinical scores; (b) a rough time of AD conversion.
See this image and copyright information in PMC

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