Estimation of static and dynamic functional connectivity in resting-state fMRI using zero-frequency resonator

Area of Science:

• Neuroimaging
• Computational Neuroscience
• Systems Neuroscience

Background:

• Resting-state functional magnetic resonance imaging (rs-fMRI) is vital for mapping brain functional organization.
• Understanding the spontaneous neuronal activity, reflected in Blood Oxygen Level-Dependent (BOLD) signals, remains challenging.
• Existing methods for extracting temporal events from rs-fMRI are sensitive to noise and may miss crucial information.

Purpose of the Study:

• To introduce a novel method for extracting event-related temporal onsets from rs-fMRI time courses.
• To utilize these extracted events for deriving reliable static and dynamic functional connectivity (FC).
• To assess the performance of the new method in characterizing resting-state brain networks.

Main Methods:

• Employed a zero-frequency resonator (ZFR) to identify transient BOLD events in rs-fMRI data.
• Derived static functional connectivity (FC) using the conditional rate (CR) of BOLD events relative to a seed.
• Computed static and dynamic FCs using high signal-to-noise ratio (SNR) segments identified around ZFR-detected events.

Main Results:

• The ZFR-based CR effectively distinguished coactivation and non-coactivation scores in static FC analysis.
• Coactivation pattern (CAP) analysis revealed stable resting-state networks with dominant long dwell times.
• High SNR segments driven by ZFR provided reliable and consistent static and dynamic FC estimates.

Conclusions:

• The ZFR-driven extraction of BOLD event onsets offers a robust approach for rs-fMRI analysis.
• This method enhances the reliability and consistency of both static and dynamic functional connectivity.
• The findings support the utility of ZFR for a deeper understanding of resting-state brain dynamics.