  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.
