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 Dpt. Signal Theory, Networking and Communications. University of Granada (Spain) 1. INTRODUCTION 2. METHODOLOGY 2.2 3D CONVOLUTIONAL VAE 3. RESULTS 96 128 3 3 3 3 3 3 3 3 512 σ μ d 32 64 128 256 48 64 24 32 12 16 6 8 96 128 48 64 24 32 12 16 6 8 3 3 3 3 3 3 3 3 SAMPLING 256 128 64 32 ENCODER DECODER Nd(μd,σd) 2.1 DATASET 2.3. EVALUATION 5. CONCLUSIONS This work proposes a novel approach to detect and quantify subtle changes in DaT concentration and distribution in the brain using 3D convolutional variational AEs (CVAEs). The composite variables of the latent space are then modelled using Decission Trees and XGBoost regression, and the spaces are interpreted through visualization and SHAP. Data used for this work was obtained from the Parkinson's Progression Markers Initiative. Speciﬁc cohort from the PPMI database that follows individuals diagnosed with PD and Healthy Control subjects (HC) during 5 years, recording symptomatology via the MDS-UPDRS scale. All the Ioﬂupane I-123 SPECT scans have been intensity normalized to non-speciﬁc areas and a sigmoid function was applied to compress the high-intensity values. - Use the μ parameters of the latent distribution Nd(μd,σd) as features. - K-Means features (KMF) are generated to capture important data characteristics. - Regression using Decision Trees (DT) and XGBoost. - Performance metrics such as Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and coefﬁcient of determination R2 are computed using 10-fold cross- validation. - SHapley Additive exPlanations (SHAP) is applied to the outputs of each system. Parkinson's disease (PD) affects more than 6.2 million people worldwide. It is characterized by a loss of dopamine-producing neurons in the brain, causing tremors, rigidity, and cognitive decline among other symptoms. Ioﬂupane I-123 binds to the presynaptic dopamine transporters (DaTs), allowing to visualize and quantify DaT concentration at the striata, For top performing 20-D XGB model of UPDRS (total), SHAP reveals: - top-3 features that contribute to the output of the algorithm are latent variables 2, 10 and 0. - Approx. linear dependency between importance to the algorithm output and values of the latent variables. - Composition of variables 2 and 10 accounts for relevant characteristics of Ioﬂupane SPECT imaging: the general intensity of the striata, the separation between them and the uptake ratio between its anterior and posterior parts. - Anterior-posterior uptake ratio more related to progression in the ﬁrst symptoms. Average intensity for the automatic diagnosis of PD. which is key to characterize PD in SPECT imaging. Early detection and monitoring of PD progression could be tackled by modelling a self- supervised, low-dimensional representation of a FPCIT dataset, which enables us to longitudinally compare images and identify patterns of change that are indicative of neurodegeneration. - The latent features of trained CVAEs are related to different aspects of the MDS-UPDRS scale with R2>0.20. - Best performance for UPDRS (total) with latent variables 2 and 10. - Anterior/Posterior ratio are more related to progression of the ﬁrst symptoms. Average striatal intensity for the diagnosis of PD. This work paves the way for exploratory analysis of links between neuroimaging patterns and neurological disorders in an hypothesis-free environment. MANIFOLD EXAMINATION *fjesusmartinez@ugr.es. Signal Processing and Biomedical Applications (SiPBA). Signal Processing and Biomedical Applications http://sipba.ugr.es SiPBA - R2>0 for almost all target UPDRS categories and d- dimensional latent spaces. - Low latent dimensionality (3, 8) is generally for individual perceived symptomatology (UPDRS 1, 2 and 4). - UPDRS - total is better accou