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
• ISSN: 1053-8119; 1095-9572; 1095-9572
• DOI: 10.1016/j.neuroimage.2021.117791

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 of cognitive status without requiring prior knowledge of the experimental design. To address this challenge, we present a dynamic graph learning approach to generate an ensemble of subject-specific dynamic graph embeddings, which allows us to use brain networks to disentangle cognitive events more accurately than using raw BOLD signals. The backbone of our method is essentially a representation learning process for projecting BOLD signals into a latent vertex-temporal domain with the greater biological underpinning of brain activities. Specifically, the learned representation domain is jointly formed by (1) a set of harmonic waves that govern the topology of whole-brain functional connectivities and (2) a set of Fourier bases that characterize the temporal dynamics of functional changes. In this regard, our dynamic graph embeddings provide a new methodology to investigate how these self-organized functional fluctuation patterns oscillate along with the evolving cognitive status. We have evaluated our proposed method on both simulated data and working memory task-based fMRI datasets, where our dynamic graph embeddings achieve higher accuracy in detecting multiple cognitive states than other state-of-the-art methods.