4.7 Article

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

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NEUROIMAGE
卷 270, 期 -, 页码 -

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ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.neuroimage.2023.119944

关键词

Brain network identification; Functional connectivity; Spatiotemporal organization; Temporal synchronization; Tensor decomposition

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The human brain exhibits dynamic fluctuations in activity across space and time. By combining a temporal synchronization process (BrainSync) and a three-way tensor decomposition method (NASCAR), we can jointly analyze rs-fMRI data from multiple subjects without imposing unnatural constraints. The resulting interacting networks represent functionally coherent activity across the brain and can be clustered into distinct functional categories, forming a representative functional network atlas for a healthy population. This atlas can be used to explore group and individual differences in neurocognitive function, as demonstrated in the context of ADHD and IQ prediction.
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 typ-ically 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 synchroniza-tion 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 spatiotem-poral 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.

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