Pupil-fMRI correlation-based Explainable AI to classify Alzheimer’s Disease

Motivation: The pupil-fMRI correlation analysis reveals that erroneous pupillary light responses in AD mice are highly correlated to specific neuromodulatory systems. Goal(s): This study applied an explainable AI method with a pre-trained deep convolutional neural network to process pupil-fMRI interactive measurements of awake mice to verify AD biomarkers. Approach: Using the GradCAM method, we produced the saliency heatmap, which can be used to verify the underlying responsible functional nuclei for classification that could be impaired due to AD degeneration. Results: This study applied a novel GradCAM-based machine learning scheme to elucidate AD-specific pupillary responses based on impaired neuromodulatory dysfunction as a non-invasive AD biomarker. Impact: The GradCAM-based saliency map obtained with an XAI method could be used to verify the statistical differential maps of PLR-based fMRI correlation between AD and WT mice, providing a novel non-invasive AD bioimaging marker.