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Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging
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Integrating Structural and Functional Connectivity for Dynamic fMRI Modeling via Graph Diffusion Autoregression.

Felix Schwock, Daniel Nordgren, Rachel M Iritani

    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

    We developed a new method called graph diffusion autoregressive (GDAR) modeling to analyze brain functional connectivity (FC). This approach improves reproducibility and captures dynamic brain signal flow, offering a more reliable imaging biomarker for neurological diseases.

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    Area of Science:

    • Neuroimaging
    • Computational Neuroscience
    • Biomarker Development

    Background:

    • Functional connectivity (FC) analysis using fMRI reveals brain organization but often assumes static connectivity and ignores structural influences.
    • Traditional FC methods using temporal cross-correlation have limitations in sensitivity, specificity, and reproducibility.
    • Existing FC biomarkers are restricted by their inability to capture dynamic spatiotemporal patterns and structural interactions.

    Purpose of the Study:

    • To introduce a novel approach, the graph diffusion autoregressive (GDAR) model, for analyzing brain functional connectivity.
    • To integrate structural connectivity data from diffusion MRI into FC analysis.
    • To capture dynamic, directional signal flow for a more comprehensive assessment of the brain's connectome.

    Main Methods:

    • Developed the graph diffusion autoregressive (GDAR) model.
    • Integrated structural connectivity data from diffusion MRI with functional MRI signals.
    • Analyzed dynamic, directional communication signals across brain regions.

    Main Results:

    • The GDAR model provides a reproducible measure of functional connectivity.
    • GDAR analysis differs fundamentally from traditional temporal cross-correlation methods.
    • GDAR demonstrated superior reproducibility compared to conventional fMRI analyses and sensitivity to age-related brain changes.

    Conclusions:

    • The graph diffusion autoregressive (GDAR) model offers a more comprehensive and reproducible assessment of brain functional connectivity.
    • GDAR captures dynamic and structural aspects of brain communication, overcoming limitations of traditional methods.
    • GDAR shows potential as a sensitive imaging biomarker for neurological diseases and age-related brain changes.