Multimodal MRI accurately identifies amyloid status in unbalanced cohorts in Alzheimer’s disease continuum

Amyloid-β (Aβ) plaques in conjunction with hyperphosphorylated tau proteins in the form of neurofibrillary tangles are the two neuropathological hallmarks of Alzheimer’s disease. It is well-known that the identification of individuals with Aβ positivity could enable early diagnosis. In this work, we aim at capturing the Aβ positivity status in an unbalanced cohort enclosing subjects at different disease stages, exploiting the underlying structural and connectivity disease-induced modulations as revealed by structural, functional, and diffusion MRI. Of note, due to the unbalanced cohort, the outcomes may be guided by those factors rather than amyloid accumulation. The partial views provided by each modality are integrated in the model, allowing to take full advantage of their complementarity in encoding the effects of the Aβ accumulation, leading to an accuracy of 0.762 ± 0.04. The specificity of the information brought by each modality is assessed by post hoc explainability analysis (guided backpropagation), highlighting the underlying structural and functional changes. Noteworthy, well-established biomarker key regions related to Aβ deposition could be identified by all modalities, including the hippocampus, thalamus, precuneus, and cingulate gyrus, witnessing in favor of the reliability of the method as well as its potential in shedding light on modality-specific possibly unknown Aβ deposition signatures.

In this work, we employed a multimodal MRI-based deep learning framework for the classification of unbalanced cohorts relying on the amyloid-β status in the Alzheimer’s disease continuum. To this end, structural, functional, and diffusion MRI data were used to feed a 3D-convolutional neural network and two different graph neural networks, respectively, reaching an accuracy of 0.762 ± 0.04. Post hoc explainability analysis was performed to extract the most relevant regions that led to the outcome, highlighting the involvement of different cortical and subcortical regions. This work provides evidence of the added value brought by exploiting different imaging modalities in decrypting the nature and extent of brain alterations in the amyloid-guided classification outcome.