Multi-Hypergraph Learning-Based Brain Functional Connectivity Analysis in fMRI Data

• Published in: IEEE transactions on medical imaging Vol. 39; no. 5; pp. 1746 - 1758
• Main Authors: Xiao, Li, Wang, Junqi, Kassani, Peyman H., Zhang, Yipu, Bai, Yuntong, Stephen, Julia M., Wilson, Tony W., Calhoun, Vince D., Wang, Yu-Ping
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.05.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Brain; Brain - diagnostic imaging; Brain mapping; Classification; Cognitive ability; Cohort Studies; Connectivity analysis; Construction methods; Correlation; Data fusion; Feature extraction; functional connectivity; Functional magnetic resonance imaging; functional MRI; Graph theory; Graphs; Humans; hypergraph; Learning; learning ability; Learning systems; Magnetic Resonance Imaging; Methods; Neural networks; Neurodegenerative diseases; Neuroimaging; Similarity; similarity matrix; Sparse matrices; Time series analysis; Topology

Recently, a hypergraph constructed from functional magnetic resonance imaging (fMRI) was utilized to explore brain functional connectivity networks (FCNs) for the classification of neurodegenerative diseases. Each edge of a hypergraph (called hyperedge) can connect any number of brain regions-of-int...