Reliability evaluation on weighted graph metrics of fNIRS brain networks | Zendy

Mengjing Wang | Zendy; Zhen Yuan | Zendy; Haijing Niu | Zendy

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Reliability evaluation on weighted graph metrics of fNIRS brain networks

Author(s) -

Mengjing Wang,

Zhen Yuan,

Haijing Niu

Publication year - 2019

Publication title -

quantitative imaging in medicine and surgery

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.766

H-Index - 21

eISSN - 2223-4292

pISSN - 2223-4306

DOI - 10.21037/qims.2019.05.08

Subject(s) - clustering coefficient , betweenness centrality , computer science , reliability (semiconductor) , intraclass correlation , average path length , graph , metrics , data mining , complex network , artificial intelligence , resting state fmri , cluster analysis , pattern recognition (psychology) , mathematics , statistics , theoretical computer science , shortest path problem , reproducibility , routing (electronic design automation) , psychology , neuroscience , computer network , static routing , routing protocol , power (physics) , quantum mechanics , physics , centrality , world wide web

Resting-state fNIRS (R-fNIRS) imaging data has proven to be a valuable technique to quantitatively characterize functional architectures of human brain network. However, whether the brain network metrics derived using weighted brain network model is test-retest (TRT) reliable remains largely unknown.

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