  Alzheimer's disease (AD) is the most prevalent form of dementia with a
progressive decline in cognitive abilities. The AD continuum encompasses a
prodormal stage known as Mild Cognitive Impairment (MCI), where patients may
either progress to AD or remain stable. In this study, we leveraged structural
and functional MRI to investigate the disease-induced grey matter and
functional network connectivity changes. Moreover, considering AD's strong
genetic component, we introduce SNPs as a third channel. Given such diverse
inputs, missing one or more modalities is a typical concern of multimodal
methods. We hence propose a novel deep learning-based classification framework
where generative module employing Cycle GANs was adopted to impute missing data
within the latent space. Additionally, we adopted an Explainable AI method,
Integrated Gradients, to extract input features relevance, enhancing our
understanding of the learned representations. Two critical tasks were
addressed: AD detection and MCI conversion prediction. Experimental results
showed that our model was able to reach the SOA in the classification of CN/AD
reaching an average test accuracy of $0.926\pm0.02$. For the MCI task, we
achieved an average prediction accuracy of $0.711\pm0.01$ using the pre-trained
model for CN/AD. The interpretability analysis revealed significant grey matter
modulations in cortical and subcortical brain areas well known for their
association with AD. Moreover, impairments in sensory-motor and visual resting
state network connectivity along the disease continuum, as well as mutations in
SNPs defining biological processes linked to amyloid-beta and cholesterol
formation clearance and regulation, were identified as contributors to the
achieved performance. Overall, our integrative deep learning approach shows
promise for AD detection and MCI prediction, while shading light on important
biological insights.
