Dynamic functional connectivity revealed by resting-state functional near-infrared spectroscopy |
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| Authors: | Zhen Li Hanli Liu Xuhong Liao Jingping Xu Wenli Liu Fenghua Tian Yong He Haijing Niu |
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| Institution: | 1State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875 China;2Center for Collaboration and Innovation in Brain and Learning Sciences, Beijing Normal University, Beijing, 100875 China;3Department of Bioengineering, the University of Texas at Arlington, Arlington, Texas, USA |
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| Abstract: | The brain is a complex network with time-varying functional connectivity (FC) and network organization. However, it remains largely unknown whether resting-state fNIRS measurements can be used to characterize dynamic characteristics of intrinsic brain organization. In this study, for the first time, we used the whole-cortical fNIRS time series and a sliding-window correlation approach to demonstrate that fNIRS measurement can be ultimately used to quantify the dynamic characteristics of resting-state brain connectivity. Our results reveal that the fNIRS-derived FC is time-varying, and the variability strength (Q) is correlated negatively with the time-averaged, static FC. Furthermore, the Q values also show significant differences in connectivity between different spatial locations (e.g., intrahemispheric and homotopic connections). The findings are reproducible across both sliding-window lengths and different brain scanning sessions, suggesting that the dynamic characteristics in fNIRS-derived cerebral functional correlation results from true cerebral fluctuation.OCIS codes: (170.2655) Functional monitoring and imaging, (170.5380) Physiology, (170.3880) Medical and biological imaging |
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