Identification of overlapping and interacting networks reveals intrinsic spatiotemporal organization of the human brain

• Published in: NeuroImage (Orlando, Fla.) Vol. 270; p. 119944
• Main Authors: Li, Jian, Liu, Yijun, Wisnowski, Jessica L., Leahy, Richard M.
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 15.04.2023; Elsevier Limited; Elsevier
• Subjects: Attention deficit hyperactivity disorder; Brain; Brain - diagnostic imaging; Brain architecture; Brain mapping; Brain Mapping - methods; Brain network identification; Cognition; Datasets; Dictionaries; Functional connectivity; Functional magnetic resonance imaging; Humans; Magnetic Resonance Imaging - methods; Neural Pathways; Spatiotemporal organization; Synchronization; Temporal synchronization; Tensor decomposition; Functional connectivity; Tensor decomposition; Brain network identification; Temporal synchronization; Spatiotemporal organization
• ISSN: 1053-8119; 1095-9572
• DOI: 10.1016/j.neuroimage.2023.119944

•Spatially overlapping and temporally correlated brain networks can be reliably identified from resting state fMRI data using the NASCAR tensor decomposition method and Brainsync temporal synchronization.•These networks are highly reproducible across a large independent group of subjects.•Using these networks as a set of spatiotemporal bases, one can better predict neurological/psychological measures (e.g., ADHD scores) or personal traits (e.g., IQ). The human brain is a complex network that exhibits dynamic fluctuations in activity across space and time. Depending on the analysis method, canonical brain networks identified from resting-state fMRI (rs-fMRI) are typically constrained to be either orthogonal or statistically independent in their spatial and/or temporal domains. We avoid imposing these potentially unnatural constraints through the combination of a temporal synchronization process (“BrainSync”) and a three-way tensor decomposition method (“NASCAR”) to jointly analyze rs-fMRI data from multiple subjects. The resulting set of interacting networks comprises minimally constrained spatiotemporal distributions, each representing one component of functionally coherent activity across the brain. We show that these networks can be clustered into six distinct functional categories and naturally form a representative functional network atlas for a healthy population. This functional network atlas could help explore group and individual differences in neurocognitive function, as we demonstrate in the context of ADHD and IQ prediction.