Revealing Patterns of Symptomatology in Parkinson's Disease: A Latent Space Analysis with 3D Convolutional Autoencoders
This addresses the problem of understanding neurodegeneration processes and symptomatology in Parkinson's disease patients, representing an incremental advancement in applying deep learning to medical imaging.
This work tackled the problem of detecting and quantifying changes in dopamine transporter concentration and spatial patterns in Parkinson's disease using 3D convolutional variational autoencoders on brain imaging data, achieving an R2>0.25 in linking the learned representation to general symptomatology.
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.