Prediction of Alzheimer's disease and mild cognitive impairment using cortical morphological patterns

Abstract

This article describes a novel approach to extract cortical morphological abnormality patterns from structural magnetic resonance imaging (MRI) data to improve the prediction accuracy of Alzheimer's disease (AD) and its prodromal stage, i.e., mild cognitive impairment (MCI). Conventional approaches extract cortical morphological information, such as regional mean cortical thickness and regional cortical volumes, independently at different regions of interest (ROIs) without considering the relationship between these regions. Our approach involves constructing a similarity map where every element in the map represents the correlation of regional mean cortical thickness between a pair of ROIs. We will demonstrate in this article that this correlative morphological information gives significant improvement in classification performance when compared with ROI-based morphological information. Classification performance is further improved by integrating the correlative information with ROI-based information via multi-kernel support vector machines. This integrated framework achieves an accuracy of 92.35% for AD classification with an area of 0.9744 under the receiver operating characteristic (ROC) curve, and an accuracy of 83.75% for MCI classification with an area of 0.9233. In differentiating MCI subjects who converted to AD within 36 months from non-converters, an accuracy of 75.05% with an area of 0.8426 under ROC curve was achieved, indicating excellent diagnostic power and generalizability. The current work provides an alternative approach to extraction of high-order cortical information from structural MRI data for prediction of neurodegenerative diseases such as AD.

Keywords: Alzheimer's disease (AD); cortical thickness; magnetic resonance imaging (MRI); mild cognitive impairment (MCI); multi-kernel support vector machine (SVM).

Figure 1

Conversion from MCI to AD up to 36 months in pMCI subgroup. [Color figure can be viewed in the online issue, which is available at http://wileyonlinelibrary.com.]

Figure 2

Schematic overview of the proposed AD/MCI classification framework.

Figure 3

Desikan–Killiany Cortical Atlas used for brain space parcellation. The medial and lateral views of the atlas are obtained from http://web.mit.edu/mwaskom/pyroi/freesurfer_ref.html. [Color figure can be viewed in the online issue, which is available at http://wileyonlinelibrary.com.]

Figure 4

Population average similarity maps for the NC, MCI, and AD groups. NC and MCI maps are similar, but they are both different from AD map. [Color figure can be viewed in the online issue, which is available at http://wileyonlinelibrary.com.]

Figure 5

Population average similarity maps for the pMCI and sMCI subgroups, which look similar to each other. [Color figure can be viewed in the online issue, which is available at http://wileyonlinelibrary.com.]