Investigating Conversion from Mild Cognitive Impairment to Alzheimer's Disease using Latent Space Manipulation

• URL: http://arxiv.org/abs/2111.08794v2
• Date: Sun, 20 Aug 2023 19:40:45 GMT
• Title: Investigating Conversion from Mild Cognitive Impairment to Alzheimer's Disease using Latent Space Manipulation
• Authors: Deniz Sezin Ayvaz and Inci M. Baytas
• Abstract summary: We propose a deep learning framework to discover the variables which are identifiers of the conversion from MCI to Alzheimer's disease. In particular, the latent space of a variational auto-encoder network trained with the MCI and Alzheimer's patients is manipulated to obtain the significant attributes. By utilizing a generative decoder and the dimensions that lead to the Alzheimer's diagnosis, we generate synthetic dementia patients from MCI patients in the dataset.
• Score: 0.23931689873603598
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Alzheimer's disease is the most common cause of dementia that affects millions of lives worldwide. Investigating the underlying causes and risk factors of Alzheimer's disease is essential to prevent its progression. Mild Cognitive Impairment (MCI) is considered an intermediate stage before Alzheimer's disease. Early prediction of the conversion from the MCI to Alzheimer's is crucial to take necessary precautions for decelerating the progression and developing suitable treatments. In this study, we propose a deep learning framework to discover the variables which are identifiers of the conversion from MCI to Alzheimer's disease. In particular, the latent space of a variational auto-encoder network trained with the MCI and Alzheimer's patients is manipulated to obtain the significant attributes and decipher their behavior that leads to the conversion from MCI to Alzheimer's disease. By utilizing a generative decoder and the dimensions that lead to the Alzheimer's diagnosis, we generate synthetic dementia patients from MCI patients in the dataset. Experimental results show promising quantitative and qualitative results on one of the most extensive and commonly used Alzheimer's disease neuroimaging datasets in literature.

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