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A Hodge-FAST Framework for High-Resolution Dynamic Functional Connectivity Analysis of Higher Order Interactions in

Om Roy, Yashar Moshfeghi, Jason Smith

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |December 3, 2025
    PubMed
    Summary

    This study introduces a new framework for analyzing dynamic functional connectivity in EEG signals, revealing higher-order interactions in Alzheimer's patients missed by traditional methods.

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    Area of Science:

    • Neuroscience
    • Signal Processing
    • Computational Biology

    Background:

    • Dynamic functional connectivity (DFC) analysis in EEG is crucial for understanding brain function.
    • Traditional methods often struggle with noise, sparsity, and capturing complex, higher-order interactions.
    • Existing approaches may not fully resolve transient connectivity patterns at multiple scales.

    Purpose of the Study:

    • To introduce a novel framework integrating Hodge decomposition and Filtered Average Short-Term (FAST) functional connectivity for EEG signal analysis.
    • To explore transient connectivity patterns at multiple scales using graph-based topology and simplicial analysis.
    • To capture higher-dimensional interactions at high temporal resolution in noisy EEG data.

    Main Methods:

    • Sparsifying temporal EEG data by retaining globally important connections.
    • Filtering instantaneous connectivity using global long-term stable correlations.
    • Decomposing the resulting tensor into orthogonal components to study signal flows over higher-order structures (e.g., triangles, loops).

    Main Results:

    • The novel framework successfully analyzes dynamic functional connectivity in EEG signals.
    • Significant temporal differences in higher-order interactions were identified in patients with Mild Cognitive Impairment (MCI) related to Alzheimer's disease.
    • These higher-order interactions were not implicated by pairwise analysis alone.

    Conclusions:

    • The integrated Hodge decomposition and FAST functional connectivity framework provides a powerful tool for analyzing complex brain dynamics in EEG.
    • This method enhances the ability to detect subtle, higher-dimensional interactions crucial for understanding neurological conditions like Alzheimer's disease.
    • The framework addresses limitations of existing methods by improving noise handling, sparsity management, and computational efficiency.