AUTHOR=Zhang Yujin , Zhu Chaozhe 

TITLE=Assessing Brain Networks by Resting-State Dynamic Functional Connectivity: An fNIRS-EEG Study

JOURNAL=Frontiers in Neuroscience

VOLUME=Volume 13 - 2019

YEAR=2020

URL=https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2019.01430

DOI=10.3389/fnins.2019.01430

ISSN=1662-453X

ABSTRACT=The coordination of brain activity between disparate neural populations is highly dynamic. Investigations of intrinsic brain organization by evaluating dynamic resting state functional connectivity (dRSFC) have attracted great attentions in recent years. However, there are few dRSFC studies based on functional near infrared spectroscopy (fNIRS) technique although it has some advantages to study the temporal evolution of brain function. Here, we recruited twenty young adults and measured their resting-state brain fluctuations in some parts of the frontal, parietal, temporal and occipital lobes using fNIRS-EEG simultaneous recording. Based on a sliding-window approach, we found the variability of dRSFC within ROI is significantly lower than the connections between ROIs, but obviously greater than the correlation between the channels with short S-D distance which mainly contain the physiological fluctuations in the superficial layers. Furthermore, based on a time-resolved k-means clustering analysis, the temporal evolutions of three dominant functional networks were extracted. These networks are roughly consistent between different subject subgroups and in varying sliding time window lengths from 20s to 30s and 60s. Between these three functional networks, there are an obviously time-varied and system-specific synchronous relationship. In addition, the oscillation of the frontal-parietal-temporal network shows significant correlation with the switching of one EEG microstate which is consistent with the previous fMRI-EEG study. All these evidence implies the functional significance of fNIRS-dRSFC, and demonstrate the feasibility of fNIRS to extract the dominant functional networks according to RSFC dynamics.