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    Area of Science:

    • Neuroimaging
    • Physiological monitoring
    • Functional connectivity analysis

    Background:

    • The blood oxygen level dependent (BOLD) fMRI signal is affected by neuronal activity and physiological fluctuations (respiration, CO2, heart rate).
    • Resting-state functional connectivity estimates can be confounded by physiological noise.
    • Dynamic functional connectivity (DFC) varies over time, but the influence of physiological factors on DFC is not well understood.

    Purpose of the Study:

    • To investigate the impact of physiological signals on dynamic functional connectivity (DFC) in resting-state networks (RSNs).
    • To extend previous work on brain-heart interactions by employing a data-driven approach to analyze DFC.

    Main Methods:

    • Utilized low- and high-dimensional Independent Component Analysis (ICA) to data-drivenly extract RSNs.
    • Estimated DFC and network degree for RSNs.
    • Correlated DFC characteristics with simultaneously recorded physiological signals (respiration, CO2, heart rate).

    Main Results:

    • Physiological signals significantly modulate resting-state, fMRI-based DFC.
    • Confirmed the influence of physiological factors on the time-varying network degree of RSNs.
    • Identified brain-heart interactions within specific frequency bands.

    Conclusions:

    • Physiological signals play a crucial role in shaping dynamic functional connectivity in the resting brain.
    • The findings highlight the importance of accounting for physiological noise in fMRI studies of brain connectivity.
    • This research provides a more nuanced understanding of brain-heart interactions and their impact on brain network dynamics.