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Graph Theoretic Analysis of Multilayer EEG Connectivity Networks.

Zoe Dittman, Tamanna T K Munia, Selin Aviyente

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |December 11, 2021
    PubMed
    Summary

    This study introduces a new multilayer brain network model to analyze functional connectivity across different brainwave frequencies. The novel approach reveals how brain regions interact dynamically across various frequency bands, offering deeper insights into brain function.

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    Area of Science:

    • Neuroscience
    • Complex Network Theory
    • Brain-Computer Interfaces

    Background:

    • Functional connectivity of the human brain has been extensively studied using complex network theory over the past two decades.
    • Traditional network models often focus on average activity within specific time windows and frequency bands, limiting their ability to capture dynamic changes.
    • Multilayer brain networks offer a more comprehensive view of neuronal connectivity by integrating information across different dimensions.

    Purpose of the Study:

    • To introduce a novel multilayer framework for analyzing functional brain connectivity across different frequency bands.
    • To develop new metrics for quantifying node centrality and importance within multi-frequency brain networks.
    • To apply this framework to electroencephalogram (EEG) data for a deeper understanding of brain activity during error monitoring.

    Main Methods:

    • Construction of multi-frequency functional connectivity networks from electroencephalogram (EEG) data.
    • Quantification of intra-layer edges using phase synchrony and inter-layer edges using phase-amplitude coupling.
    • Introduction and application of multilayer network metrics, including multilayer degree, participation coefficient, and clustering coefficient.

    Main Results:

    • The proposed multilayer network framework successfully captures dynamic functional connectivity across different frequency bands.
    • Novel multilayer metrics effectively quantify the centrality of brain regions and the importance of specific frequency bands.
    • The application to EEG data during error monitoring provides new insights into frequency-specific brain network interactions.

    Conclusions:

    • The multilayer brain network approach provides a more comprehensive understanding of functional brain connectivity than traditional methods.
    • This framework enables the analysis of dynamic interactions between different frequency bands, crucial for understanding complex cognitive processes.
    • The developed metrics offer valuable tools for neuroscience research and potentially for brain-computer interface applications.