  Amyloid-$\beta$ (A$\beta$) plaques in conjunction with hyperphosphorylated
tau proteins in the form of neurofibrillary tangles are the two
neuropathological hallmarks of Alzheimer's disease (AD). In particular, the
accumulation of A$\beta$ plaques, as evinced by the A/T/N
(amyloid/tau/neurodegeneration) framework, marks the initial stage. Thus, the
identification of individuals with A$\beta$ positivity could enable early
diagnosis and potentially lead to more effective interventions. Deep learning
methods relying mainly on amyloid PET images have been employed to this end.
However, PET imaging has some disadvantages, including the need of radiotracers
and expensive acquisitions. Hence, in this work, we propose a novel multimodal
approach that integrates information from structural, functional, and diffusion
MRI data to discriminate A$\beta$ status in the AD continuum. Our method
achieved an accuracy of $0.762\pm0.04$. Furthermore, a \textit{post-hoc}
explainability analysis (guided backpropagation) was performed to retrieve the
brain regions that most influenced the model predictions. This analysis
identified some key regions that were common across modalities, some of which
were well-established AD-discriminative biomarkers and related to A$\beta$
deposition, such as the hippocampus, thalamus, precuneus, and cingulate gyrus.
Hence, our study demonstrates the potential viability of MRI-based
characterization of A$\beta$ status, paving the way for further research in
this domain.
