P304 - Understanding Alzheimer disease’s structural connectivity through explainable AI

Achraf Essemlali, Etienne St-Onge, Jean Christophe Houde, Maxime Descoteaux, Pierre-Marc Jodoin

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In the following work, we use a modified version of deep BrainNet convolutional neural network (CNN) trained on the diffusion weighted MRI (DW-MRI) tractography connectomes of patients with Alzheimer’s Disease (AD) and Mild Cognitive Impairment (MCI) to better understand the structural connectomics of that disease. We show that with a relatively simple connectomic BrainNetCNN used to classify brain images and explainable AI techniques, one can underline brain regions and their connectivity involved in AD. Results reveal that the connected regions with high structural differences between groups are those also reported in previous AD literature. Our findings support that deep learning over structural connectomes is a powerful tool to leverage the complex structure within connectomes derived from diffusion MRI tractography. To our knowledge, our contribution is the first explainable AI work applied to structural analysis of a degenerative disease.
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Poster Session #6 - 13:30 - 15:00 UTC-4 (Wednesday)
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