Revealing Patterns of Symptomatology in Parkinson's Disease: A Latent Space Analysis with 3D Convolutional Autoencoders

TL;DR

This paper introduces a novel deep learning method using 3D convolutional variational autoencoders to analyze brain imaging data, revealing patterns linked to Parkinson's disease symptoms and aiding early diagnosis.

Contribution

The study presents a new application of 3D CVAEs for quantifying neurodegeneration and symptomatology in Parkinson's disease through brain imaging analysis.

Findings

Effective linkage of symptomatology to low-dimensional representations

R2>0.25 in predicting UPDRS scores from imaging data

Potential for early diagnosis and understanding neurodegeneration

Abstract

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.