Abstract #4803
Hypergraph learning-based convolutional neural network for classification of brain functional connectome
Junqi Wang1,2, Hailong Li1,2, Gang Qu3, Jonathan R Dillman1,2,4, Nehal A Parikh5,6, and Lili He1,2,4
1Department of Radiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States, 2Imaging Research Center, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States, 3Department of Biomedical Engineering, Tulane University, New Orleans, LA, United States, 4Department of Radiology, University of Cincinnati College of Medicine, Cincinnati, OH, United States, 5Center for Prevention of Neurodevelopmental Disorders, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States, 6Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, United States
Synopsis
The human brain is a highly interconnected network where local activation patterns are organized to cope with diverse environmental demands. We developed a hypergraph learning based convolutional neural network model to capture higher order relationships between brain regions and learn representative features for brain connectome classification. The model was applied to a large scale resting state fMRI cohort, containing hundreds of healthy developing adolescents, age 8 to 22. The proposed model is able to classify different age groups with a balanced accuracy of 86.8%.
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