Tracking the Brain State Transition Process of Dynamic Function Connectivity Based on Resting State fMRI
Joint Authors
Liu, Chang
Xue, Jie
Cheng, Xu
Zhan, Weiwei
Xiong, Xin
Wang, Bin
Source
Computational Intelligence and Neuroscience
Issue
Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-12, 12 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2019-10-07
Country of Publication
Egypt
No. of Pages
12
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Liu, Chang& Xue, Jie& Cheng, Xu& Zhan, Weiwei& Xiong, Xin& Wang, Bin. 2019. Tracking the Brain State Transition Process of Dynamic Function Connectivity Based on Resting State fMRI. Computational Intelligence and Neuroscience،Vol. 2019, no. 2019, pp.1-12.
https://search.emarefa.net/detail/BIM-1129646
Modern Language Association (MLA)
Liu, Chang…[et al.]. Tracking the Brain State Transition Process of Dynamic Function Connectivity Based on Resting State fMRI. Computational Intelligence and Neuroscience No. 2019 (2019), pp.1-12.
https://search.emarefa.net/detail/BIM-1129646
American Medical Association (AMA)
Liu, Chang& Xue, Jie& Cheng, Xu& Zhan, Weiwei& Xiong, Xin& Wang, Bin. Tracking the Brain State Transition Process of Dynamic Function Connectivity Based on Resting State fMRI. Computational Intelligence and Neuroscience. 2019. Vol. 2019, no. 2019, pp.1-12.
https://search.emarefa.net/detail/BIM-1129646
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1129646