  3D image scans are an assessment tool for neurological damage in Parkinson's
disease (PD) patients. This diagnosis process can be automatized to help
medical staff through Decision Support Systems (DSSs), and Convolutional Neural
Networks (CNNs) are good candidates, because they are effective when applied to
spatial data. This paper proposes a 3D CNN ordinal model for assessing the
level or neurological damage in PD patients. Given that CNNs need large
datasets to achieve acceptable performance, a data augmentation method is
adapted to work with spatial data. We consider the Ordinal Graph-based
Oversampling via Shortest Paths (OGO-SP) method, which applies a gamma
probability distribution for inter-class data generation. A modification of
OGO-SP is proposed, the OGO-SP-$\beta$ algorithm, which applies the beta
distribution for generating synthetic samples in the inter-class region, a
better suited distribution when compared to gamma. The evaluation of the
different methods is based on a novel 3D image dataset provided by the Hospital
Universitario 'Reina Sof\'ia' (C\'ordoba, Spain). We show how the ordinal
methodology improves the performance with respect to the nominal one, and how
OGO-SP-$\beta$ yields better performance than OGO-SP.
