摘要：Alzheimer’s disease (AD) is a heterogeneous disease. Exploring the characteristics of each AD subtype is the key to disentangling the heterogeneity. Minimal atrophy AD (MAD) is a common AD subtype that yields conflicting results. In order to evaluate this aspect across relatively large heterogeneous AD populations, a total of 192 AD and 228 cognitively normal (CN) subjects were processed by the automated segmentation scheme FreeSurfer, which generates regional cortical thickness measures. A machine learning driven approach, the mixture of expert models, which combines unsupervised modeling of mixtures of distributions with supervised learning of classifiers, was applied to approximates the non-linear boundary between AD and CN subjects with a piece-wise linear boundary. Multiple cortical thicknes patterns of AD were discovered, which includes: bilateral parietal/frontal atrophy AD, left temporal dominant atrophy AD, MAD, and diffuse atrophy AD. MAD had the highest proportions of ApoE4 and ApoE2. Further analysis revealed that ApoE genotype, disease stage and their interactions can partially explain the conflicting observations in MAD.
