Conversion Discriminative Analysis on Mild Cognitive Impairment Using Multiple Cortical Features from MR Images - Guo, Shengwen; Lai, Chunren; Wu, Congling; Cen, Guiyin; , The Alzheimer's Disease Neuroimaging Initiative; Weiner, Michael W.; Aisen, Paul; Weiner, Michael; Aisen, Paul; Petersen, Ronald; Jack, Clifford R.; Jagust, William; et al. (Frontiers in Aging Neuroscience, 2017)
Neuroimaging measures derived from magnetic resonance (MR) imaging provide important information in detecting changes related to the progression of mild cognitive impairment (MCI). Cortical features and changes play a vital role in revealing unique anatomical patterns of brain regions, and further differentiate MCI patients from normal states. Four cortical features, namely gray matter (GM) 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). In this study, 158 subjects (72 NC, 46 sMCI and 40 cMCI) were selected from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). A sparse-constrained regression model based on l2-1-norm was introduced to reduce feature dimensionality and retrieve essential features for discrimination of the three groups using a support vector machine (SVM). An optimized strategy of feature adding by the weight of feature in turn was adopted to the SVM classifier, so as to achieve the best performance in classification. The baseline cortical features combined with longitudinal measures in 2 years of follow-up data yielded prominent classification results, especially cortical thickness led to a classification with 98.84% accuracy, 97.5% sensitivity, 100% specificity for sMCI-cMCI comparison, 92.37% accuracy, 84.78% sensitivity, 97.22% specificity for cMCI-NC comparison, and 93.75% accuracy, 92.5% sensitivity and 94.44% specificity in sMCI-NC comparison. The best performances using the essential features were 5% 40% more than that using all of retained features. The cortical features were certified their feasibilities for the recognition of anatomical pattern and the proposed method has the potential to improve the clinical diagnosis of sub-types of MCI and risk prediction of its conversion.