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

Area of Science:

• Neuroscience
• Cognitive Science
• Biomedical Engineering

Background:

• Brain activity coordination between neural populations is dynamic.
• Dynamic resting-state functional connectivity (dRSFC) is crucial for understanding intrinsic brain organization.
• Few studies utilize functional near-infrared spectroscopy (fNIRS) for dRSFC despite its advantages in temporal analysis.

Purpose of the Study:

• To investigate the feasibility of using fNIRS for dRSFC analysis.
• To extract dominant functional networks using fNIRS-EEG.
• To explore the temporal dynamics and relationships of these networks.

Main Methods:

• Simultaneous fNIRS-EEG recording in 20 young adults during resting-state.
• Sliding-window approach to calculate dRSFC.
• Time-resolved k-means clustering to identify dominant functional networks.

Main Results:

• dRSFC variability differs significantly between within-region, between-region, and short-distance channel connections.
• Three dominant functional networks were consistently extracted across subgroups and window lengths.
• Temporal dynamics of the frontal-parietal-temporal network correlated with EEG microstate switching.

Conclusions:

• fNIRS-based dRSFC analysis is functionally significant.
• fNIRS is a feasible tool for extracting dominant functional networks based on RSFC dynamics.
• Findings support the use of fNIRS for studying temporal brain function.