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    This study introduces dynamic sparse connectivity patterns (dSCPs) to analyze brain functional connectivity (FC) in fMRI data. The new method reveals dynamic FC differences between children and young adults, improving brain network analysis.

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    Area of Science:

    • Neuroimaging
    • Computational Neuroscience
    • Developmental Neuroscience

    Background:

    • Functional connectivity (FC) from fMRI is crucial for understanding brain architecture in various states.
    • Existing methods often yield complex, high-dimensional data and assume stationarity, limiting interpretability.
    • Analyzing dynamic FC is essential for capturing evolving brain network interactions.

    Purpose of the Study:

    • To introduce a novel approach, dynamic sparse connectivity patterns (dSCPs), for improved estimation of functional connectivity.
    • To validate the dSCPs model using simulated data and real fMRI data from the Philadelphia Neurodevelopmental Cohort (PNC).
    • To investigate age-related differences in dynamic FC between children and young adults.

    Main Methods:

    • Developed dynamic sparse connectivity patterns (dSCPs) combining matrix factorization and time-varying fMRI data.
    • Validated the model's feasibility through simulated experiments.
    • Applied the dSCPs framework to analyze functional connectivity differences in the PNC dataset.

    Main Results:

    • The dSCPs model successfully analyzed dynamic FC and identified significant differences between children and young adults across four states.
    • Young adults showed reduced default mode network connectivity and altered visual system connectivity compared to children.
    • The model revealed temporal correlation patterns within subnetworks and suggested older individuals spend more time in connected states.

    Conclusions:

    • The proposed dSCPs method offers a valid approach for assessing dynamic functional connectivity in fMRI data.
    • This technique enhances the interpretability of brain network analysis, moving beyond static assumptions.
    • dSCPs can facilitate the study of dynamic brain networks across different developmental stages and health conditions.