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    Area of Science:

    • Neuroscience
    • Network Science
    • Statistical Modeling

    Background:

    • Estimating dynamic brain connectivity in high-dimensional networks is challenging.
    • Existing methods struggle to simultaneously capture smooth and abrupt changes.
    • High-dimensional estimation often relies on suboptimal, ad-hoc approaches.

    Purpose of the Study:

    • To develop a robust method for estimating state-related changes in brain connectivity networks.
    • To overcome limitations of sliding-window and time-varying coefficient models.
    • To accurately identify change-points and dependencies in dynamic connectivity regimes.

    Main Methods:

    • Proposed a Markov-switching dynamic factor model.
    • Utilized a regime-switching vector autoregressive (SVAR) factor process.
    • Developed a three-step estimation: factor extraction (PCA), regime identification (SVAR), and metric construction.

    Main Results:

    • The proposed estimator significantly outperforms k-means clustering, reducing mean squared error by 60%.
    • Successfully identified modular organization in resting-state fMRI networks.
    • Revealed distinct large-scale directed connectivity patterns across different brain states.

    Conclusions:

    • The Markov-switching dynamic factor model provides a reliable and data-adaptive approach for analyzing dynamic brain connectivity.
    • The method accurately captures complex temporal dynamics and state transitions in fMRI data.
    • Offers new insights into brain network organization and state variations.