Abstract
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.
Highlights
As the most common type of dementia [1], Alzheimer's disease (AD) is expected to affect 1 out of 85 people in the world by the year 2050
The proportions of ApoE4 and ApoE2 carried in Minimal atrophy AD (MAD) were the highest, where the proportions of ApoE2 were significantly different from other subtypes
We identified four AD subtypes based on mixture of experts (MOEs) algorithm
Summary
As the most common type of dementia [1], Alzheimer's disease (AD) is expected to affect 1 out of 85 people in the world by the year 2050. As the understanding of AD has advanced, there is an increasing evidence of heterogeneity in the etiology, pathological changes, and pathogenesis of AD [3,4,5]. Recent advances in neuropathological post-mortem studies greatly enhanced our understanding of the pathophysiology of AD subtypes. Structural MRI measurements of regional brain atrophy have been shown to correlate well with the distribution and extent of neurofibrillary tangle pathology [7], which makes MRI a potential alternative marker of regional tangle distribution. The correlation between subtypes defined by neuropathology and structural MRI has been confirmed in the literature [8].
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