Learning dynamic graph embeddings for accurate detection of cognitive state changes in functional brain networks

• Published in: NeuroImage (Orlando, Fla.) Vol. 230; p. 117791
• Main Authors: Lin, Yi, Yang, Defu, Hou, Jia, Yan, Chenggang, Kim, Minjeong, Laurienti, Paul J, Wu, Guorong
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 15.04.2021; Elsevier Limited; Elsevier
• Subjects: Brain; Brain - diagnostic imaging; Brain - physiology; Brain mapping; Change detection; Cognition & reasoning; Cognition - physiology; Cognitive ability; Connectome - methods; Control theory; Functional brain networks; Functional magnetic resonance imaging; Graph learning; Humans; Learning; Learning - physiology; Magnetic Resonance Imaging - methods; Memory, Short-Term - physiology; Mental task performance; Nerve Net - diagnostic imaging; Nerve Net - physiology; Network topologies; Noise; Short term memory; Graph learning; Functional brain networks; Change detection

Mounting evidence shows that brain functions and cognitive states are dynamically changing even in the resting state rather than remaining at a single constant state. Due to the relatively small changes in BOLD (blood-oxygen-level-dependent) signals across tasks, it is difficult to detect the change...