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    Area of Science:

    • Neuroscience
    • Computational Neuroscience

    Background:

    • Cross-frequency coupling integrates distributed neuronal oscillations.
    • Phase-amplitude coupling (PAC) links slow phase to fast amplitude, gaining research interest.
    • Current PAC methods lack multivariate analysis for multi-region, multi-frequency brain activity.

    Purpose of the Study:

    • To develop a multivariate method for quantifying phase-amplitude coupling across multiple brain regions and frequency bands.
    • To address limitations of existing PAC measures using multi-channel electroencephalography data.

    Main Methods:

    • Proposed a tensor-based approach using higher-order robust principal component analysis (HO-RPCA).
    • Applied the method to simulated and electroencephalography (EEG) data to identify response-evoked PAC.

    Main Results:

    • The multivariate PAC method demonstrated higher accuracy in capturing spatial and spectral dynamics compared to existing techniques.
    • HO-RPCA effectively filtered background PAC, highlighting event-related dynamics.

    Conclusions:

    • The proposed HO-RPCA based multivariate PAC analysis provides a more accurate quantification of brain network coordination.
    • This method offers valuable insights into spatially distributed brain networks across different frequency bands.