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Brain functional networks extraction based on fMRI artifact removal: single subject and group approaches
Yuhui Du; Elena A Allen; Hao He; Sui Jing(隋婧); Vince D Calhoun
2014
Conference Name2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2014)
Conference Date2014/8/28-9/2
Conference PlaceChicago,USA
AbstractRecent works have shown that, even in resting state, functional networks undergo dynamic changes over short time. In this study, we describe an approach to assess the difference in default mode network (DMN) dynamics between healthy controls (HC) and schizophrenia patients (SZ) using resting-state functional magnetic resonance imaging. Firstly, dynamic DMN was computed using a sliding time window method. Then, stability of the dynamic DMN evaluated using the spectrum of time-varying functional connectivity was compared between HC and SZ. Subsequently, the overall functional connectivity pattern and dynamic graph measures were also investigated for both groups. Results show that dynamic DMN of HC had more stable and stronger functional connectivity than that of SZ. Regarding to dynamic graph measures, SZ had lower connectivity strength, clustering coefficient, global efficiency, and local efficiency than HC. The findings suggest that dynamic functional network analysis is a promising technique for understanding schizophrenia.
KeywordIndependent Component Analysis Resting-state Fmri Mri
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/20797
Collection脑网络组研究中心
AffiliationInstitute of Automation Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Yuhui Du,Elena A Allen,Hao He,et al. Brain functional networks extraction based on fMRI artifact removal: single subject and group approaches[C],2014.
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