A Cross-Modality Latent Representation for the Prediction of Clinical Symptomatology in Parkinson’s Disease

Parkinson’s disease (PD) is a neurodegenerative disorder that affects millions of people worldwide. The diagnosis of PD is based on clinical and neuroimaging data. This work proposes a novel approach that jointly models several Variational Autoencoder (VAE) architectures in order to maximize cross-modality prediction. We hypothesize that 123I-ioflupane SPECT could be related to motor symptomatology and other dopaminergic deficits. We propose a joint modelling of several VAE architectures for maximizing cross-modality prediction of the PD Clinical and Neuroimaging Data. The final model, with 5 common latents and 2 neuroimaging and data specific latents achieve R2 values up to 0.8 for scores related to PD, including well known PD symptomatology scales such as UPDRS (R2 = 0.545), at the same time that provides tools for interpreting the results and the common latent distribution for both clinical data and neuroimaging, paving the way for interpretable machine learning tools in neurodegeneration.