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Alzheimer's Disease (AD) is the world leading cause of dementia, aprogressively impairing condition leading to high hospitalization rates andmortality. To optimize the diagnostic process, numerous efforts have beendirected towards the development of deep learning approaches (DL) for theautomatic AD classification. However, their typical black box outline has ledto low trust and scarce usage within clinical frameworks. In this work, wepropose two state-of-the art DL models, trained respectively on structural MRI(ResNet18) and brain connectivity matrixes (BC-GCN-SE) derived from diffusiondata. The models were initially evaluated in terms of classification accuracy.Then, results were analyzed using an Explainable Artificial Intelligence (XAI)approach (Grad-CAM) to measure the level of interpretability of both models.The XAI assessment was conducted across 132 brain parcels, extracted from acombination of the Harvard-Oxford and AAL brain atlases, and compared towell-known pathological regions to measure adherence to domain knowledge.Results highlighted acceptable classification performance as compared to theexisting literature (ResNet18: TPRmedian = 0.817, TNRmedian = 0.816; BC-GCN-SE:TPRmedian = 0.703, TNRmedian = 0.738). As evaluated through a statistical test(p < 0.05) and ranking of the most relevant parcels (first 15%), Grad-CAMrevealed the involvement of target brain areas for both the ResNet18 andBC-GCN-SE models: the medial temporal lobe and the default mode network. Theobtained interpretabilities were not without limitations. Nevertheless, resultssuggested that combining different imaging modalities may result in increasedclassification performance and model reliability. This could potentially boostthe confidence laid in DL models and favor their wide applicability as aiddiagnostic tools.

Biomarker Investigation using Multiple Brain Measures from MRI through XAI in Alzheimer's Disease Classification