Abstract
Mild Cognitive Impairment (MCI) is a clinically intermediate stage in the course of Alzheimer’s disease (AD). MCI does not always lead to dementia. Some MCI patients may stay in the MCI status for the rest of their life, while others will develop AD eventually. Therefore, classification methods that help to distinguish MCI from earlier or later stages of the disease are important to understand the progression of AD. In this paper, we propose a novel computational framework - named Augmented Graph Embedding, or AGE - to tackle this challenge. In this new AGE framework, the random walk approach is first applied to brain structural networks derived from diffusion-weighted MRI to extract nodal feature vectors. A technique adapted from natural language processing is used to analyze these nodal feature vectors, and a multimodal augmentation procedure is adopted to improve classification accuracy. We validated this new AGE framework on data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Results show advantages of the proposed framework, compared to a range of existing methods.
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Tang, H. et al. (2019). Classifying Stages of Mild Cognitive Impairment via Augmented Graph Embedding. In: Zhu, D., et al. Multimodal Brain Image Analysis and Mathematical Foundations of Computational Anatomy. MBIA MFCA 2019 2019. Lecture Notes in Computer Science(), vol 11846. Springer, Cham. https://doi.org/10.1007/978-3-030-33226-6_4
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DOI: https://doi.org/10.1007/978-3-030-33226-6_4
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