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We develop a graph node embedding Deep Neural Network that leverage on statistical outcome measure and graph structure given in the data. The objective is to identify regions of interests (ROIs) in the brain that are affected by topological changes of brain connectivity due to specific neurodegenerative diseases by enriching statistical group analysis. We tackle this problem by learning a latent space where statistical inference can be made more effectively. Our experiments on a large-scale Alzheimer's Disease dataset show promising result identifying ROIs that show statistically significant group differences separating even early and late Mild Cognitive Impairment (MCI) groups whose effect sizes are very subtle.
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Enriching Statistical Inferences on Brain Connectivity for Alzheimer's Disease Analysis Via Latent Space Graph Embedding
We develop a graph node embedding Deep Neural Network that leverage on statistical outcome measure and graph structure given in the data. The objective is to identify regions of interests (ROIs) in the brain that are affected by topological changes of brain connectivity due to specific neurodegenerative diseases by enriching statistical group analysis. We tackle this problem by learning a latent space where statistical inference can be made more effectively. Our experiments on a large-scale Alzheimer's Disease dataset show promising result identifying ROIs that show statistically significant group differences separating even early and late Mild Cognitive Impairment (MCI) groups whose effect sizes are very subtle.