Many classical machine learning techniques have
been used to explore Alzheimer’s Disease, evolving from image
decomposition techniques such as Principal Component Analysis
towards higher-complexity, non-linear decomposition algorithms.
With the arrival of the deep learning paradigm, it has become
possible to extract high-level abstract features directly from
MRI images that internally describe the distribution of data
in low-dimensional manifolds. In this work, we try a new
exploratory data analysis of Alzheimer’s Disease (AD) based
on deep convolutional autoencoders. We aim at finding links
between cognitive symptoms and the underlying neurodegenera-
tion process by fusing the information of neuropsychological test
outcomes, diagnoses and other clinical data with the imaging
features extracted solely via a data-driven decomposition of
MRI. The distribution of the extracted features in different
combinations is then analysed and visualized using regression
and classification analysis, and the influence of each coordinate
of the autoencoder manifold over the brain is estimated. The
imaging-derived markers could then predict clinical variables
with correlations above 0.6 in the case of neuropsychological
evaluation variables such as the MMSE or the ADAS11 scores,
achieving a classification accuracy over 80% for the diagnosis of
AD.
