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    Area of Science:

    • Neuroscience
    • Cardiology
    • Biomedical Engineering

    Background:

    • Brain-Heart Interplay (BHI) research is complex due to time-varying dynamics and numerous variables.
    • Challenges include directionality, oscillatory dynamics, and scalp locations in BHI analysis.
    • The effectiveness of dimensionality reduction for capturing spatio-temporal BHI variability is unclear.

    Purpose of the Study:

    • To investigate the existence of synergistic Brain-Heart Interplay (BHI).
    • To explore the application of principal component analysis (PCA) for BHI analysis.
    • To determine if dimensionality reduction can effectively capture BHI spatio-temporal variability.

    Main Methods:

    • Utilized a principal component analysis (PCA)-based approach.
    • Analyzed a publicly available EEG-ECG dataset from healthy subjects at rest.
    • Examined BHI dimensions, considering directionality and oscillatory frequency.

    Main Results:

    • Confirmed the existence of principal components within BHI dimensions.
    • Identified distinct BHI characteristics based on directionality (brain-to-heart vs. heart-to-brain).
    • Observed unique patterns related to different oscillatory frequencies.

    Conclusions:

    • The PCA-based methodology effectively identifies principal components in BHI.
    • BHI exhibits distinct characteristics influenced by directionality and frequency.
    • This approach may yield novel biomarkers for neurological, psychiatric, and cardiovascular disorders.