Dynamic functional connectivity revealed by resting-state functional near-infrared spectroscopy

Area of Science:

• Neuroscience
• Brain Imaging
• Functional Connectivity

Background:

• The brain exhibits complex, dynamic functional connectivity.
• Characterizing dynamic brain organization using resting-state functional near-infrared spectroscopy (fNIRS) is not well understood.

Purpose of the Study:

• To investigate if resting-state fNIRS can quantify dynamic characteristics of intrinsic brain organization.
• To demonstrate the utility of fNIRS in measuring time-varying functional connectivity.

Main Methods:

• Utilized whole-cortical fNIRS time series data.
• Employed a sliding-window correlation approach to analyze functional connectivity.
• Calculated variability strength (Q) of functional connectivity.

Main Results:

• Demonstrated that fNIRS measurements can quantify dynamic resting-state brain connectivity.
• fNIRS-derived functional connectivity is time-varying.
• Variability strength (Q) negatively correlated with static functional connectivity.
• Significant differences in Q values observed between intrahemispheric and homotopic connections.
• Findings were reproducible across different sliding-window lengths and scanning sessions.

Conclusions:

• Resting-state fNIRS is a viable tool for quantifying dynamic brain functional connectivity.
• The dynamic characteristics observed in fNIRS data reflect true cerebral fluctuations.
• This study opens avenues for exploring dynamic brain network organization with fNIRS.