Conversion Discriminative Analysis on Mild Cognitive Impairment Using Multiple Cortical Features from MR Images

Shengwen Guo, Chunren Lai, Congling Wu, Guiyin Cen, Alzheimer's Disease Neuroimaging Initiative

Abstract

Neuroimaging measurements derived from magnetic resonance imaging provide important information required for detecting changes related to the progression of mild cognitive impairment (MCI). Cortical features and changes play a crucial role in revealing unique anatomical patterns of brain regions, and further differentiate MCI patients from normal states. Four cortical features, namely, gray matter volume, cortical thickness, surface area, and mean curvature, were explored for discriminative analysis among three groups including the stable MCI (sMCI), the converted MCI (cMCI), and the normal control (NC) groups. In this study, 158 subjects (72 NC, 46 sMCI, and 40 cMCI) were selected from the Alzheimer's Disease Neuroimaging Initiative. A sparse-constrained regression model based on the l2-1-norm was introduced to reduce the feature dimensionality and retrieve essential features for the discrimination of the three groups by using a support vector machine (SVM). An optimized strategy of feature addition based on the weight of each feature was adopted for the SVM classifier in order to achieve the best classification performance. The baseline cortical features combined with the longitudinal measurements for 2 years of follow-up data yielded prominent classification results. In particular, the cortical thickness produced a classification with 98.84% accuracy, 97.5% sensitivity, and 100% specificity for the sMCI-cMCI comparison; 92.37% accuracy, 84.78% sensitivity, and 97.22% specificity for the cMCI-NC comparison; and 93.75% accuracy, 92.5% sensitivity, and 94.44% specificity for the sMCI-NC comparison. The best performances obtained by the SVM classifier using the essential features were 5-40% more than those using all of the retained features. The feasibility of the cortical features for the recognition of anatomical patterns was certified; thus, the proposed method has the potential to improve the clinical diagnosis of sub-types of MCI and predict the risk of its conversion to Alzheimer's disease.

Keywords: classification; conversion; cortical feature; feature reduction; mild cognitive impairment; sparse-constrained regression.

Figures

Figure 1
Figure 1
Changes in classification performance with the increase in combined features (A) GMV, (B) Cortical thickness, (C) Surface area, and (D) Mean curvature. The vertical red dot lines denoted the best performances.
Figure 2
Figure 2
The normalized weights of combined cortical surface features with the highest (A) GM volume, (B) Cortical thickness, (C) Surface area, and (D) Mean curvature.
Figure 3
Figure 3
Selected count of GM volume and cortical thickness in 68 brain regions. Selected count means the count of combined features were selected for classification with the high performance, left, right denotes left and right hemisphere. (A) GM volume. (B) Cortical thickness.
Figure 4
Figure 4
Selected count of surface and mean curvature in 68 brain regions. (A) Surface area. (B) Mean curvature.

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