ArticleComputational intelligence and neuroscience2019
Tracking the Brain State Transition Process of Dynamic Function Connectivity Based on Resting State fMRI.
Article in Computational intelligence and neuroscience, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed.
- Atypical Development of Functional Brain Networks in Neonates with Congenital Heart Disease.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2026Article
- A Statistical Characterization of Dynamic Brain Functional Connectivity.Human brain mapping · 2025Article
- Developmental and aging resting functional magnetic resonance imaging brain state adaptations in adolescents and adults: A large N (>47K) study.Human brain mapping · 2023Article
- Multiframe Evolving Dynamic Functional ConnectivityFrontiers in neuroscience · 2022Article
- Brain Mechanisms Supporting Flexible Cognition and Behavior in Adolescents With Autism Spectrum Disorder.Biological psychiatry · 2021Review
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6 authors.
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Abstract
BOLD-fMRI technology provides a good foundation for the research of human brain dynamic functional connectivity and brain state analysis. However, due to the complexity of brain function connectivity and the high dimensionality expression of brain dynamic attributions, more research studies are focusing on tracking the time-varying characteristics through the transition between different brain states. The transition process is considered to occur instantaneously at some special time point in the above research studies, whereas our work found the brain state transition may be completed in a time section gradually rather than instantaneously. In this paper, a brain state conversion rate model is constructed to observe the procedure of brain state transition trend at each time point, and the state change can be observed by the values of conversion rate. According to the results, the transition of status always lasts for a few time points, and a brain state network model with both steady state and transition state is presented. Network topological overlap coefficient is built to analyze the features of time-varying networks. With this method, some common regular patterns of time-varying characteristics can be observed strongly in healthy children but not in the autism children. This distinct can help us to distinguish children with autism from healthy children.
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