Abstract #4803

Hypergraph learning-based convolutional neural network for classification of brain functional connectome

Junqi Wang^1,2, Hailong Li^1,2, Gang Qu³, Jonathan R Dillman^1,2,4, Nehal A Parikh^5,6, and Lili He^1,2,4

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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