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• fMRI •
A review of methods and clinical applications for dynamic functional connectivity analysis based on resting-state functional magnetic resonance imaging
YUAN Yue-ming  ZHANG Li  ZHANG Zhi-guo 

DOI:10.12015/issn.1674-8034.2018.08.005.


[Abstract] Functional brain connectivity analysis based on resting-state functional magnetic resonance (fMRI) has been widely used in clinical medicine for studying neural mechanism and developing new diagnosis methods. Traditionally, functional connectivity is assumed to be static in time, but recently researchers have found that the dynamic characteristics of functional connectivity are functionally and clinically relevant for they can provide more information about the brain networks than static functional connectivity. Therefore, the study of dynamic functional connectivity (dFC) has attracted more and more interests. Many clinical studies have shown that dynamic functional connectivity analysis can provide a better proof for the pathologic exploration and auxiliary diagnosis of clinical diseases, but there are still many problems and limitations. This paper is aimed to systemically review existing methods for estimating dFC and extracting dFC features as well as the validating, the reliability and the statistical analysis of dFC analysis. Lastly, we introduced the applications of dFC analysis based on resting-state fMRI in diagnosis of clinical diseases.
[Keywords] Magnetic resonance imaging, functional；Dynamic functional connectivity；Brain network；Neuroimage decoding

YUAN Yue-ming School of Biomedical Engineering, Health Science Center, Shenzhen University, Guangdong Provincial Key Laboratory of Biomedical Measurements and Ultrasound Imaging, Shenzhen 518060, China

ZHANG Li School of Biomedical Engineering, Health Science Center, Shenzhen University, Guangdong Provincial Key Laboratory of Biomedical Measurements and Ultrasound Imaging, Shenzhen 518060, China

ZHANG Zhi-guo* School of Biomedical Engineering, Health Science Center, Shenzhen University, Guangdong Provincial Key Laboratory of Biomedical Measurements and Ultrasound Imaging, Shenzhen 518060, China

*Corresponding to: Zhang ZG, E-mail: zgzhang@szu.edu.cn

Conflicts of interest   None.

ACKNOWLEDGMENTS  This work was part of Science Technology and Innovation Commission of Shenzhen Municipality Technology Fund No. JCYJ20170818093322718 Shenzhen Peacock Plan No.KQTD2016053112051497
Received  2018-04-13
Accepted  2018-06-08
DOI: 10.12015/issn.1674-8034.2018.08.005
DOI:10.12015/issn.1674-8034.2018.08.005.

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