Revealing Patterns of Symptomatology in Parkinson's Disease: A Latent
  Space Analysis with 3D Convolutional Autoencoders
E. Delgado de las Heras F. J. Martinez-Murcia I. A. Illán C. Jiménez-Mesa D. Castillo-Barnes J. Ramírez J. M. Górriz
This work proposes the use of 3D convolutional variational autoencoders
(CVAEs) to trace the changes and symptomatology produced by neurodegeneration
in Parkinson's disease (PD). In this work, we present a novel approach to
detect and quantify changes in dopamine transporter (DaT) concentration and its
spatial patterns using 3D CVAEs on Ioflupane (FPCIT) imaging. Our approach
leverages the power of deep learning to learn a low-dimensional representation
of the brain imaging data, which then is linked to different symptom categories
using regression algorithms. We demonstrate the effectiveness of our approach
on a dataset of PD patients and healthy controls, and show that general
symptomatology (UPDRS) is linked to a d-dimensional decomposition via the CVAE
with R2>0.25. Our work shows the potential of representation learning not only
in early diagnosis but in understanding neurodegeneration processes and
symptomatology.