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. 2021 Feb 23;11(2):278.
doi: 10.3390/brainsci11020278.

Multiple Subtypes of Alzheimer's Disease Base on Brain Atrophy Pattern

Baiwen Zhang  1   2 , Lan Lin  1   2 , Shuicai Wu  1   2 , Zakarea H M A Al-Masqari  1   2
Affiliations

Multiple Subtypes of Alzheimer's Disease Base on Brain Atrophy Pattern

Baiwen Zhang et al. Brain Sci. .

Abstract

Alzheimer's disease (AD) is a disease of a heterogeneous nature, which can be disentangled by exploring the characteristics of each AD subtype in the brain structure, neuropathology, and cognition. In this study, a total of 192 AD and 228 cognitively normal (CN) subjects were obtained from the Alzheimer's disease Neuroimaging Initiative database. Based on the cortical thickness patterns, the mixture of experts method (MOE) was applied to the implicit model spectrum of transforms lined with each AD subtype, then their neuropsychological and neuropathological characteristics were analyzed. Furthermore, the piecewise linear classifiers composed of each AD subtype and CN were resolved, and each subtype was comprehensively explained. The following four distinct AD subtypes were discovered: bilateral parietal, frontal, and temporal atrophy AD subtype (occipital sparing AD subtype (OSAD), 29.2%), left temporal dominant atrophy AD subtype (LTAD, 22.4%), minimal atrophy AD subtype (MAD, 16.1%), and diffuse atrophy AD subtype (DAD, 32.3%). These four subtypes display their own characteristics in atrophy pattern, cognition, and neuropathology. Compared with the previous studies, our study found that some AD subjects showed obvious asymmetrical atrophy in left lateral temporal-parietal cortex, OSAD presented the worst cerebrospinal fluid levels, and MAD had the highest proportions of APOE ε4 and APOE ε2. The subtype characteristics were further revealed from the aspect of the model, making it easier for clinicians to understand. The results offer an effective support for individual diagnosis and prognosis.

Keywords: Alzheimer’s disease; atrophy subtypes; cortical thickness; mixture of experts; neuropathology; neuropsychology; structural magnetic resonance imaging.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
The algorithm procedure of the mixture of experts (MOE) method.
Figure 2
Figure 2
These subtypes were identified from MOE compared with controls. Effect size maps are thresholded at false discovery rate (FDR) adjusted p-value of 0.0005.
Figure 3
Figure 3
The items of Alzheimer’s Disease Neuroimaging Initiative (ADNI)-composite scores. a: significant differences (p < 0.05) between OSAD and MAD; b: significant differences (p < 0.05) between OSAD and DAD; c: significant differences (p < 0.05) between LTAD and DAD; d: significant differences (p < 0.05) between MAD and DAD.
Figure 4
Figure 4
The number of subjects in longitudinal analysis. 12M, at 12-month follow-up; 24M, at 24-month follow-up; OSAD, occipital sparing Alzheimer’s Disease (AD) subtype; LTAD, left temporal dominant atrophy AD subtype; MAD, minimal atrophy AD subtype; DAD, diffuse atrophy AD subtype.
Figure 5
Figure 5
Rate of changes in regions of interest. ROIs, regions of interest.
Figure 6
Figure 6
Longitudinal cognitive changes in global cognitive scales and FAQ. 0, baseline.
Figure 7
Figure 7
Longitudinal cognitive changes in ADNI-composite scores.
Figure 8
Figure 8
Subtype changes with the progression of disease. The number on each line represents the number of subjects who are transformed from the current subtype into another subtype or remained itself.
Figure 9
Figure 9
The cross-validation for the support vector machine (SVM) of MOE.
See this image and copyright information in PMC

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