  Volumetric neuroimaging examinations like structural Magnetic Resonance
Imaging (sMRI) are routinely applied to support the clinical diagnosis of
dementia like Alzheimer's Disease (AD). Neuroradiologists examine 3D sMRI to
detect and monitor abnormalities in brain morphology due to AD, like global
and/or local brain atrophy and shape alteration of characteristic structures.
There is a strong research interest in developing diagnostic systems based on
Deep Learning (DL) models to analyse sMRI for AD. However, anatomical
information extracted from an sMRI examination needs to be interpreted together
with patient's age to distinguish AD patterns from the regular alteration due
to a normal ageing process. In this context, part-prototype neural networks
integrate the computational advantages of DL in an interpretable-by-design
architecture and showed promising results in medical imaging applications. We
present PIMPNet, the first interpretable multimodal model for 3D images and
demographics applied to the binary classification of AD from 3D sMRI and
patient's age. Despite age prototypes do not improve predictive performance
compared to the single modality model, this lays the foundation for future work
in the direction of the model's design and multimodal prototype training
process
