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    Investigating functional brain networks reveals that spatiotemporal resolution and network construction methods impact reliability. A novel topological data analysis framework shows the DISCOH2 method enhances network consistency across scales.

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    Area of Science:

    • Neuroscience
    • Data Science
    • Network Science

    Background:

    • Functional brain connectivity using fMRI is crucial but faces challenges.
    • The impact of spatiotemporal resolution and network construction on reliability is unclear.
    • Inconsistent findings across scales question the validity of functional network paradigms.

    Purpose of the Study:

    • To investigate the consistency of resting-state network structure.
    • To assess the influence of temporal sampling, spatial parcellation, and network construction methods.
    • To develop and apply a novel framework for network comparison.

    Main Methods:

    • Utilized topological data analysis, specifically persistent homology.
    • Developed a novel network comparison framework.
    • Applied methods to Human Connectome Project data.

    Main Results:

    • The DISCOH2 network construction method demonstrated superior performance.
    • Consistency of functional networks was characterized across various spatiotemporal scales.
    • The novel framework effectively evaluated network construction methods.

    Conclusions:

    • Spatiotemporal resolution and network construction significantly affect functional brain network reliability.
    • The developed topological framework provides robust tools for scale-dependent network analysis.
    • DISCOH2 offers a more reliable approach for constructing functional brain networks across resolutions.