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    Area of Science:

    • Neuroscience
    • Brain Imaging
    • Data Science

    Background:

    • Multi-site neuroscience datasets enhance research but introduce site effects.
    • Dynamic functional network connectivity (dFNC) analysis relies on clustering temporal functional connectivity patterns.
    • Site effects can confound dFNC state identification in combined datasets.

    Purpose of the Study:

    • To investigate the impact of ComBat harmonization on dFNC states in mild traumatic brain injury (mTBI) data from two sites.
    • To introduce and utilize an 'Inclusivity' model for assessing site effect influence on dFNC clustering.

    Main Methods:

    • Applied ComBat harmonization to dFNC data from two mTBI studies.
    • Clustered dFNC states to identify recurring connectivity patterns.
    • Developed and employed an 'Inclusivity' model to quantify sample distribution across clusters concerning site effects.

    Main Results:

    • Site effects were found to significantly influence the formation and characteristics of dFNC states.
    • ComBat harmonization reduced site-specific biases in the dFNC data.
    • The 'Inclusivity' model effectively measured the impact of site effects on dFNC clustering before and after harmonization.

    Conclusions:

    • Data harmonization is crucial for mitigating site effects in multi-site dFNC studies.
    • The 'Inclusivity' model provides a novel metric for evaluating clustering robustness against site variations.
    • Findings support the use of harmonization techniques for reliable dFNC analysis in diverse datasets.