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Abstract #0223

MLC-GCN: Multi-Level Connectomes Based GCN for AD Detection

Yinghua Fu1, Xinglin Zeng2, Jiarui Zhang3, John A Detre4, and Ze Wang5
1Diagnostic Radiology & Nuclear Medicine, Univerisity of Maryland, Baltimore, MD, United States, 2Department of Diagnostic Radiology & Nuclear Medicine, Univerisity of Maryland School of Medicine, Baltimore, MD, United States, 3Marriotts Ridge High School, Ellicott City, MD, United States, 4Neurology and Radiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, United States, 5University of Maryland School of Medicine, Baltimore, MD, United States

Synopsis

Keywords: Diagnosis/Prediction, Alzheimer's Disease

Motivation: Alzheimer's Disease (AD) is characterized by progressive cognitive impairments that are related to alterations in brain functional connectivity (FC).

Goal(s): to design a graph convolutional network (GCN) based classifier to differentiate AD from old cognitive normal controls.

Approach: We assessed the FC using Pearson correlation coefficient (CC) and cross entropy (CE) measure as association analysis and proposed a multi-level generated connectome (MLC) based GCN (MLC-GCN) containing a multi-graph generation block and a GCN prediction block to classify the fMRI data.

Results: Our method showed better performance than state-of-the-art GCN and rsfMRI based AD classifiers on two independent public medical datasets: ADNI and OASIS-3.

Impact: The MLC-GCN classifier significantly enhances Alzheimer’s disease detection by exploiting multi-level connectomes. The clinically meaningful classifier features suggest a potential of localizing disease-related nodes or regions, facilitating clinical diagnosis and future targeted interventions.

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Keywords