biostudies-literatureLi JNICHD NIH HHSNIBIB NIH HHSNINDS NIH HHSNational Institutes of Health119944https://www.ebi.ac.uk/biostudies/studies/S-EPMC10092006biostudies-literatureUnknown270The 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 NeuroImageIdentification of overlapping and interacting networks reveals intrinsic spatiotemporal organization of the human brain.PMC10092006K23 HD099309R01-NS074980R01-NS089212R01 NS074980R01-EB026299R01 NS089212K23-HD099309R01 EB026299Liu YLi JLeahy RMWisnowski JLfalseIdentification of overlapping and interacting networks reveals intrinsic spatiotemporal organization of the human brain.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 2023-01-01T00:00:00Z2023 Apr2025-04-19T13:29:32.177Z2025-04-19T13:29:32.177ZS-EPMC100920063680137110.1016/j.neuroimage.2023.119944